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oseledets/ttpy | tt/core/tools.py | delta | def delta(n, d=None, center=0):
""" Create TT-vector for delta-function :math:`\\delta(x - x_0)`. """
if isinstance(n, six.integer_types):
n = [n]
if d is None:
n0 = _np.asanyarray(n, dtype=_np.int32)
else:
n0 = _np.array(n * d, dtype=_np.int32)
d = n0.size
if center < 0:
cind = [0] * d
else:
cind = []
for i in xrange(d):
cind.append(center % n0[i])
center //= n0[i]
if center > 0:
cind = [0] * d
cr = []
for i in xrange(d):
cur_core = _np.zeros((1, n0[i], 1))
cur_core[0, cind[i], 0] = 1
cr.append(cur_core)
return _vector.vector.from_list(cr) | python | def delta(n, d=None, center=0):
""" Create TT-vector for delta-function :math:`\\delta(x - x_0)`. """
if isinstance(n, six.integer_types):
n = [n]
if d is None:
n0 = _np.asanyarray(n, dtype=_np.int32)
else:
n0 = _np.array(n * d, dtype=_np.int32)
d = n0.size
if center < 0:
cind = [0] * d
else:
cind = []
for i in xrange(d):
cind.append(center % n0[i])
center //= n0[i]
if center > 0:
cind = [0] * d
cr = []
for i in xrange(d):
cur_core = _np.zeros((1, n0[i], 1))
cur_core[0, cind[i], 0] = 1
cr.append(cur_core)
return _vector.vector.from_list(cr) | Create TT-vector for delta-function :math:`\\delta(x - x_0)`. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/tools.py#L712-L736 |
oseledets/ttpy | tt/core/tools.py | stepfun | def stepfun(n, d=None, center=1, direction=1):
""" Create TT-vector for Heaviside step function :math:`\chi(x - x_0)`.
Heaviside step function is defined as
.. math::
\chi(x) = \\left\{ \\begin{array}{l} 1 \mbox{ when } x \ge 0, \\\\ 0 \mbox{ when } x < 0. \\end{array} \\right.
For negative value of ``direction`` :math:`\chi(x_0 - x)` is approximated. """
if isinstance(n, six.integer_types):
n = [n]
if d is None:
n0 = _np.asanyarray(n, dtype=_np.int32)
else:
n0 = _np.array(n * d, dtype=_np.int32)
d = n0.size
N = _np.prod(n0)
if center >= N and direction < 0 or center <= 0 and direction > 0:
return ones(n0)
if center <= 0 and direction < 0 or center >= N and direction > 0:
raise ValueError(
"Heaviside function with specified center and direction gives zero tensor!")
if direction > 0:
center = N - center
cind = []
for i in xrange(d):
cind.append(center % n0[i])
center //= n0[i]
def gen_notx(currcind, currn):
return [0.0] * (currn - currcind) + [1.0] * currcind
def gen_notx_rev(currcind, currn):
return [1.0] * currcind + [0.0] * (currn - currcind)
def gen_x(currcind, currn):
result = [0.0] * currn
result[currn - currcind - 1] = 1.0
return result
def gen_x_rev(currcind, currn):
result = [0.0] * currn
result[currcind] = 1.0
return result
if direction > 0:
x = gen_x
notx = gen_notx
else:
x = gen_x_rev
notx = gen_notx_rev
crs = []
prevrank = 1
for i in range(d)[::-1]:
break_further = max([0] + cind[:i])
nextrank = 2 if break_further else 1
one = [1] * n0[i]
cr = _np.zeros([nextrank, n0[i], prevrank], dtype=_np.float)
tempx = x(cind[i], n0[i])
tempnotx = notx(cind[i], n0[i])
# high-conditional magic
if not break_further:
if cind[i]:
if prevrank > 1:
cr[0, :, 0] = one
cr[0, :, 1] = tempnotx
else:
cr[0, :, 0] = tempnotx
else:
cr[0, :, 0] = one
else:
if prevrank > 1:
cr[0, :, 0] = one
if cind[i]:
cr[0, :, 1] = tempnotx
cr[1, :, 1] = tempx
else:
cr[1, :, 1] = tempx
else:
if cind[i]:
cr[0, :, 0] = tempnotx
cr[1, :, 0] = tempx
else:
nextrank = 1
cr = cr[:1, :, :]
cr[0, :, 0] = tempx
prevrank = nextrank
crs.append(cr)
return _vector.vector.from_list(crs[::-1]) | python | def stepfun(n, d=None, center=1, direction=1):
""" Create TT-vector for Heaviside step function :math:`\chi(x - x_0)`.
Heaviside step function is defined as
.. math::
\chi(x) = \\left\{ \\begin{array}{l} 1 \mbox{ when } x \ge 0, \\\\ 0 \mbox{ when } x < 0. \\end{array} \\right.
For negative value of ``direction`` :math:`\chi(x_0 - x)` is approximated. """
if isinstance(n, six.integer_types):
n = [n]
if d is None:
n0 = _np.asanyarray(n, dtype=_np.int32)
else:
n0 = _np.array(n * d, dtype=_np.int32)
d = n0.size
N = _np.prod(n0)
if center >= N and direction < 0 or center <= 0 and direction > 0:
return ones(n0)
if center <= 0 and direction < 0 or center >= N and direction > 0:
raise ValueError(
"Heaviside function with specified center and direction gives zero tensor!")
if direction > 0:
center = N - center
cind = []
for i in xrange(d):
cind.append(center % n0[i])
center //= n0[i]
def gen_notx(currcind, currn):
return [0.0] * (currn - currcind) + [1.0] * currcind
def gen_notx_rev(currcind, currn):
return [1.0] * currcind + [0.0] * (currn - currcind)
def gen_x(currcind, currn):
result = [0.0] * currn
result[currn - currcind - 1] = 1.0
return result
def gen_x_rev(currcind, currn):
result = [0.0] * currn
result[currcind] = 1.0
return result
if direction > 0:
x = gen_x
notx = gen_notx
else:
x = gen_x_rev
notx = gen_notx_rev
crs = []
prevrank = 1
for i in range(d)[::-1]:
break_further = max([0] + cind[:i])
nextrank = 2 if break_further else 1
one = [1] * n0[i]
cr = _np.zeros([nextrank, n0[i], prevrank], dtype=_np.float)
tempx = x(cind[i], n0[i])
tempnotx = notx(cind[i], n0[i])
# high-conditional magic
if not break_further:
if cind[i]:
if prevrank > 1:
cr[0, :, 0] = one
cr[0, :, 1] = tempnotx
else:
cr[0, :, 0] = tempnotx
else:
cr[0, :, 0] = one
else:
if prevrank > 1:
cr[0, :, 0] = one
if cind[i]:
cr[0, :, 1] = tempnotx
cr[1, :, 1] = tempx
else:
cr[1, :, 1] = tempx
else:
if cind[i]:
cr[0, :, 0] = tempnotx
cr[1, :, 0] = tempx
else:
nextrank = 1
cr = cr[:1, :, :]
cr[0, :, 0] = tempx
prevrank = nextrank
crs.append(cr)
return _vector.vector.from_list(crs[::-1]) | Create TT-vector for Heaviside step function :math:`\chi(x - x_0)`.
Heaviside step function is defined as
.. math::
\chi(x) = \\left\{ \\begin{array}{l} 1 \mbox{ when } x \ge 0, \\\\ 0 \mbox{ when } x < 0. \\end{array} \\right.
For negative value of ``direction`` :math:`\chi(x_0 - x)` is approximated. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/tools.py#L739-L831 |
oseledets/ttpy | tt/core/tools.py | unit | def unit(n, d=None, j=None, tt_instance=True):
''' Generates e_j _vector in tt.vector format
---------
Parameters:
n - modes (either integer or array)
d - dimensionality (integer)
j - position of 1 in full-format e_j (integer)
tt_instance - if True, returns tt.vector;
if False, returns tt cores as a list
'''
if isinstance(n, int):
if d is None:
d = 1
n = n * _np.ones(d, dtype=_np.int32)
else:
d = len(n)
if j is None:
j = 0
rv = []
j = _ind2sub(n, j)
for k in xrange(d):
rv.append(_np.zeros((1, n[k], 1)))
rv[-1][0, j[k], 0] = 1
if tt_instance:
rv = _vector.vector.from_list(rv)
return rv | python | def unit(n, d=None, j=None, tt_instance=True):
''' Generates e_j _vector in tt.vector format
---------
Parameters:
n - modes (either integer or array)
d - dimensionality (integer)
j - position of 1 in full-format e_j (integer)
tt_instance - if True, returns tt.vector;
if False, returns tt cores as a list
'''
if isinstance(n, int):
if d is None:
d = 1
n = n * _np.ones(d, dtype=_np.int32)
else:
d = len(n)
if j is None:
j = 0
rv = []
j = _ind2sub(n, j)
for k in xrange(d):
rv.append(_np.zeros((1, n[k], 1)))
rv[-1][0, j[k], 0] = 1
if tt_instance:
rv = _vector.vector.from_list(rv)
return rv | Generates e_j _vector in tt.vector format
---------
Parameters:
n - modes (either integer or array)
d - dimensionality (integer)
j - position of 1 in full-format e_j (integer)
tt_instance - if True, returns tt.vector;
if False, returns tt cores as a list | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/tools.py#L843-L870 |
oseledets/ttpy | tt/core/tools.py | IpaS | def IpaS(d, a, tt_instance=True):
'''A special bidiagonal _matrix in the QTT-format
M = IPAS(D, A)
Generates I+a*S_{-1} _matrix in the QTT-format:
1 0 0 0
a 1 0 0
0 a 1 0
0 0 a 1
Convenient for Crank-Nicolson and time gradient matrices
'''
if d == 1:
M = _np.array([[1, 0], [a, 1]]).reshape((1, 2, 2, 1), order='F')
else:
M = [None] * d
M[0] = _np.zeros((1, 2, 2, 2))
M[0][0, :, :, 0] = _np.array([[1, 0], [a, 1]])
M[0][0, :, :, 1] = _np.array([[0, a], [0, 0]])
for i in xrange(1, d - 1):
M[i] = _np.zeros((2, 2, 2, 2))
M[i][:, :, 0, 0] = _np.eye(2)
M[i][:, :, 1, 0] = _np.array([[0, 0], [1, 0]])
M[i][:, :, 1, 1] = _np.array([[0, 1], [0, 0]])
M[d - 1] = _np.zeros((2, 2, 2, 1))
M[d - 1][:, :, 0, 0] = _np.eye(2)
M[d - 1][:, :, 1, 0] = _np.array([[0, 0], [1, 0]])
if tt_instance:
M = _matrix.matrix.from_list(M)
return M | python | def IpaS(d, a, tt_instance=True):
'''A special bidiagonal _matrix in the QTT-format
M = IPAS(D, A)
Generates I+a*S_{-1} _matrix in the QTT-format:
1 0 0 0
a 1 0 0
0 a 1 0
0 0 a 1
Convenient for Crank-Nicolson and time gradient matrices
'''
if d == 1:
M = _np.array([[1, 0], [a, 1]]).reshape((1, 2, 2, 1), order='F')
else:
M = [None] * d
M[0] = _np.zeros((1, 2, 2, 2))
M[0][0, :, :, 0] = _np.array([[1, 0], [a, 1]])
M[0][0, :, :, 1] = _np.array([[0, a], [0, 0]])
for i in xrange(1, d - 1):
M[i] = _np.zeros((2, 2, 2, 2))
M[i][:, :, 0, 0] = _np.eye(2)
M[i][:, :, 1, 0] = _np.array([[0, 0], [1, 0]])
M[i][:, :, 1, 1] = _np.array([[0, 1], [0, 0]])
M[d - 1] = _np.zeros((2, 2, 2, 1))
M[d - 1][:, :, 0, 0] = _np.eye(2)
M[d - 1][:, :, 1, 0] = _np.array([[0, 0], [1, 0]])
if tt_instance:
M = _matrix.matrix.from_list(M)
return M | A special bidiagonal _matrix in the QTT-format
M = IPAS(D, A)
Generates I+a*S_{-1} _matrix in the QTT-format:
1 0 0 0
a 1 0 0
0 a 1 0
0 0 a 1
Convenient for Crank-Nicolson and time gradient matrices | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/tools.py#L873-L901 |
oseledets/ttpy | tt/core/tools.py | reshape | def reshape(tt_array, shape, eps=1e-14, rl=1, rr=1):
''' Reshape of the TT-vector
[TT1]=TT_RESHAPE(TT,SZ) reshapes TT-vector or TT-matrix into another
with mode sizes SZ, accuracy 1e-14
[TT1]=TT_RESHAPE(TT,SZ,EPS) reshapes TT-vector/matrix into another with
mode sizes SZ and accuracy EPS
[TT1]=TT_RESHAPE(TT,SZ,EPS, RL) reshapes TT-vector/matrix into another
with mode size SZ and left tail rank RL
[TT1]=TT_RESHAPE(TT,SZ,EPS, RL, RR) reshapes TT-vector/matrix into
another with mode size SZ and tail ranks RL*RR
Reshapes TT-vector/matrix into a new one, with dimensions specified by SZ.
If the i_nput is TT-matrix, SZ must have the sizes for both modes,
so it is a _matrix if sizes d2-by-2.
If the i_nput is TT-vector, SZ may be either a column or a row _vector.
'''
tt1 = _cp.deepcopy(tt_array)
sz = _cp.deepcopy(shape)
ismatrix = False
if isinstance(tt1, _matrix.matrix):
d1 = tt1.tt.d
d2 = sz.shape[0]
ismatrix = True
# The size should be [n,m] in R^{d x 2}
restn2_n = sz[:, 0]
restn2_m = sz[:, 1]
sz_n = _cp.copy(sz[:, 0])
sz_m = _cp.copy(sz[:, 1])
n1_n = tt1.n
n1_m = tt1.m
# We will split/convolve using the _vector form anyway
sz = _np.prod(sz, axis=1)
tt1 = tt1.tt
else:
d1 = tt1.d
d2 = len(sz)
# Recompute sz to include r0,rd,
# and the items of tt1
sz[0] = sz[0] * rl
sz[d2 - 1] = sz[d2 - 1] * rr
tt1.n[0] = tt1.n[0] * tt1.r[0]
tt1.n[d1 - 1] = tt1.n[d1 - 1] * tt1.r[d1]
if ismatrix: # in _matrix: 1st tail rank goes to the n-mode, last to the m-mode
restn2_n[0] = restn2_n[0] * rl
restn2_m[d2 - 1] = restn2_m[d2 - 1] * rr
n1_n[0] = n1_n[0] * tt1.r[0]
n1_m[d1 - 1] = n1_m[d1 - 1] * tt1.r[d1]
tt1.r[0] = 1
tt1.r[d1] = 1
n1 = tt1.n
assert _np.prod(n1) == _np.prod(sz), 'Reshape: incorrect sizes'
needQRs = False
if d2 > d1:
needQRs = True
if d2 <= d1:
i2 = 0
n2 = _cp.deepcopy(sz)
for i1 in range(d1):
if n2[i2] == 1:
i2 = i2 + 1
if i2 > d2:
break
if n2[i2] % n1[i1] == 0:
n2[i2] = n2[i2] // n1[i1]
else:
needQRs = True
break
r1 = tt1.r
tt1 = tt1.to_list(tt1)
if needQRs: # We have to split some cores -> perform QRs
for i in range(d1 - 1, 0, -1):
cr = tt1[i]
cr = _np.reshape(cr, (r1[i], n1[i] * r1[i + 1]), order='F')
[cr, rv] = _np.linalg.qr(cr.T) # Size n*r2, r1new - r1nwe,r1
cr0 = tt1[i - 1]
cr0 = _np.reshape(cr0, (r1[i - 1] * n1[i - 1], r1[i]), order='F')
cr0 = _np.dot(cr0, rv.T) # r0*n0, r1new
r1[i] = cr.shape[1]
cr0 = _np.reshape(cr0, (r1[i - 1], n1[i - 1], r1[i]), order='F')
cr = _np.reshape(cr.T, (r1[i], n1[i], r1[i + 1]), order='F')
tt1[i] = cr
tt1[i - 1] = cr0
r2 = _np.ones(d2 + 1, dtype=_np.int32)
i1 = 0 # Working index in tt1
i2 = 0 # Working index in tt2
core2 = _np.zeros((0))
curcr2 = 1
restn2 = sz
n2 = _np.ones(d2, dtype=_np.int32)
if ismatrix:
n2_n = _np.ones(d2, dtype=_np.int32)
n2_m = _np.ones(d2, dtype=_np.int32)
while i1 < d1:
curcr1 = tt1[i1]
if _gcd(restn2[i2], n1[i1]) == n1[i1]:
# The whole core1 fits to core2. Convolve it
if (i1 < d1 - 1) and (needQRs): # QR to the next core - for safety
curcr1 = _np.reshape(
curcr1, (r1[i1] * n1[i1], r1[i1 + 1]), order='F')
[curcr1, rv] = _np.linalg.qr(curcr1)
curcr12 = tt1[i1 + 1]
curcr12 = _np.reshape(
curcr12, (r1[i1 + 1], n1[i1 + 1] * r1[i1 + 2]), order='F')
curcr12 = _np.dot(rv, curcr12)
r1[i1 + 1] = curcr12.shape[0]
tt1[i1 + 1] = _np.reshape(curcr12,
(r1[i1 + 1],
n1[i1 + 1],
r1[i1 + 2]),
order='F')
# Actually merge is here
curcr1 = _np.reshape(
curcr1, (r1[i1], n1[i1] * r1[i1 + 1]), order='F')
curcr2 = _np.dot(curcr2, curcr1) # size r21*nold, dn*r22
if ismatrix: # Permute if we are working with tt_matrix
curcr2 = _np.reshape(curcr2, (r2[i2], n2_n[i2], n2_m[i2], n1_n[
i1], n1_m[i1], r1[i1 + 1]), order='F')
curcr2 = _np.transpose(curcr2, [0, 1, 3, 2, 4, 5])
# Update the "matrix" sizes
n2_n[i2] = n2_n[i2] * n1_n[i1]
n2_m[i2] = n2_m[i2] * n1_m[i1]
restn2_n[i2] = restn2_n[i2] // n1_n[i1]
restn2_m[i2] = restn2_m[i2] // n1_m[i1]
r2[i2 + 1] = r1[i1 + 1]
# Update the sizes of tt2
n2[i2] = n2[i2] * n1[i1]
restn2[i2] = restn2[i2] // n1[i1]
curcr2 = _np.reshape(
curcr2, (r2[i2] * n2[i2], r2[i2 + 1]), order='F')
i1 = i1 + 1 # current core1 is over
else:
if (_gcd(restn2[i2], n1[i1]) != 1) or (restn2[i2] == 1):
# There exists a nontrivial divisor, or a singleton requested
# Split it and convolve
n12 = _gcd(restn2[i2], n1[i1])
if ismatrix: # Permute before the truncation
# _matrix sizes we are able to split
n12_n = _gcd(restn2_n[i2], n1_n[i1])
n12_m = _gcd(restn2_m[i2], n1_m[i1])
curcr1 = _np.reshape(curcr1,
(r1[i1],
n12_n,
n1_n[i1] // n12_n,
n12_m,
n1_m[i1] // n12_m,
r1[i1 + 1]),
order='F')
curcr1 = _np.transpose(curcr1, [0, 1, 3, 2, 4, 5])
# Update the _matrix sizes of tt2 and tt1
n2_n[i2] = n2_n[i2] * n12_n
n2_m[i2] = n2_m[i2] * n12_m
restn2_n[i2] = restn2_n[i2] // n12_n
restn2_m[i2] = restn2_m[i2] // n12_m
n1_n[i1] = n1_n[i1] // n12_n
n1_m[i1] = n1_m[i1] // n12_m
curcr1 = _np.reshape(
curcr1, (r1[i1] * n12, (n1[i1] // n12) * r1[i1 + 1]), order='F')
[u, s, v] = _np.linalg.svd(curcr1, full_matrices=False)
r = _my_chop2(s, eps * _np.linalg.norm(s) / (d2 - 1) ** 0.5)
u = u[:, :r]
v = v.T
v = v[:, :r] * s[:r]
u = _np.reshape(u, (r1[i1], n12 * r), order='F')
# u is our admissible chunk, merge it to core2
curcr2 = _np.dot(curcr2, u) # size r21*nold, dn*r22
r2[i2 + 1] = r
# Update the sizes of tt2
n2[i2] = n2[i2] * n12
restn2[i2] = restn2[i2] // n12
curcr2 = _np.reshape(
curcr2, (r2[i2] * n2[i2], r2[i2 + 1]), order='F')
r1[i1] = r
# and tt1
n1[i1] = n1[i1] // n12
# keep v in tt1 for next operations
curcr1 = _np.reshape(
v.T, (r1[i1], n1[i1], r1[i1 + 1]), order='F')
tt1[i1] = curcr1
else:
# Bad case. We have to merge cores of tt1 until a common
# divisor appears
i1new = i1 + 1
curcr1 = _np.reshape(
curcr1, (r1[i1] * n1[i1], r1[i1 + 1]), order='F')
while (_gcd(restn2[i2], n1[i1]) == 1) and (i1new < d1):
cr1new = tt1[i1new]
cr1new = _np.reshape(
cr1new, (r1[i1new], n1[i1new] * r1[i1new + 1]), order='F')
# size r1(i1)*n1(i1), n1new*r1new
curcr1 = _np.dot(curcr1, cr1new)
if ismatrix: # Permutes and _matrix size updates
curcr1 = _np.reshape(curcr1, (r1[i1], n1_n[i1], n1_m[i1], n1_n[
i1new], n1_m[i1new], r1[i1new + 1]), order='F')
curcr1 = _np.transpose(curcr1, [0, 1, 3, 2, 4, 5])
n1_n[i1] = n1_n[i1] * n1_n[i1new]
n1_m[i1] = n1_m[i1] * n1_m[i1new]
n1[i1] = n1[i1] * n1[i1new]
curcr1 = _np.reshape(
curcr1, (r1[i1] * n1[i1], r1[i1new + 1]), order='F')
i1new = i1new + 1
# Inner cores merged => squeeze tt1 data
n1 = _np.concatenate((n1[:i1], n1[i1new:]))
r1 = _np.concatenate((r1[:i1], r1[i1new:]))
tt1[i] = _np.reshape(
curcr1, (r1[i1], n1[i1], r1[i1new]), order='F')
tt1 = tt1[:i1] + tt1[i1new:]
d1 = len(n1)
if (restn2[i2] == 1) and ((i1 >= d1) or ((i1 < d1) and (n1[i1] != 1))):
# The core of tt2 is finished
# The second condition prevents core2 from finishing until we
# squeeze all tailing singletons in tt1.
curcr2 = curcr2.flatten(order='F')
core2 = _np.concatenate((core2, curcr2))
i2 = i2 + 1
# Start new core2
curcr2 = 1
# If we have been asked for singletons - just add them
while (i2 < d2):
core2 = _np.concatenate((core2, _np.ones(1)))
r2[i2] = 1
i2 = i2 + 1
tt2 = ones(2, 1) # dummy tensor
tt2.d = d2
tt2.n = n2
tt2.r = r2
tt2.core = core2
tt2.ps = _np.int32(_np.cumsum(_np.concatenate((_np.ones(1), r2[:-1] * n2 * r2[1:]))))
tt2.n[0] = tt2.n[0] // rl
tt2.n[d2 - 1] = tt2.n[d2 - 1] // rr
tt2.r[0] = rl
tt2.r[d2] = rr
if ismatrix:
ttt = eye(1, 1) # dummy tt _matrix
ttt.n = sz_n
ttt.m = sz_m
ttt.tt = tt2
return ttt
else:
return tt2 | python | def reshape(tt_array, shape, eps=1e-14, rl=1, rr=1):
''' Reshape of the TT-vector
[TT1]=TT_RESHAPE(TT,SZ) reshapes TT-vector or TT-matrix into another
with mode sizes SZ, accuracy 1e-14
[TT1]=TT_RESHAPE(TT,SZ,EPS) reshapes TT-vector/matrix into another with
mode sizes SZ and accuracy EPS
[TT1]=TT_RESHAPE(TT,SZ,EPS, RL) reshapes TT-vector/matrix into another
with mode size SZ and left tail rank RL
[TT1]=TT_RESHAPE(TT,SZ,EPS, RL, RR) reshapes TT-vector/matrix into
another with mode size SZ and tail ranks RL*RR
Reshapes TT-vector/matrix into a new one, with dimensions specified by SZ.
If the i_nput is TT-matrix, SZ must have the sizes for both modes,
so it is a _matrix if sizes d2-by-2.
If the i_nput is TT-vector, SZ may be either a column or a row _vector.
'''
tt1 = _cp.deepcopy(tt_array)
sz = _cp.deepcopy(shape)
ismatrix = False
if isinstance(tt1, _matrix.matrix):
d1 = tt1.tt.d
d2 = sz.shape[0]
ismatrix = True
# The size should be [n,m] in R^{d x 2}
restn2_n = sz[:, 0]
restn2_m = sz[:, 1]
sz_n = _cp.copy(sz[:, 0])
sz_m = _cp.copy(sz[:, 1])
n1_n = tt1.n
n1_m = tt1.m
# We will split/convolve using the _vector form anyway
sz = _np.prod(sz, axis=1)
tt1 = tt1.tt
else:
d1 = tt1.d
d2 = len(sz)
# Recompute sz to include r0,rd,
# and the items of tt1
sz[0] = sz[0] * rl
sz[d2 - 1] = sz[d2 - 1] * rr
tt1.n[0] = tt1.n[0] * tt1.r[0]
tt1.n[d1 - 1] = tt1.n[d1 - 1] * tt1.r[d1]
if ismatrix: # in _matrix: 1st tail rank goes to the n-mode, last to the m-mode
restn2_n[0] = restn2_n[0] * rl
restn2_m[d2 - 1] = restn2_m[d2 - 1] * rr
n1_n[0] = n1_n[0] * tt1.r[0]
n1_m[d1 - 1] = n1_m[d1 - 1] * tt1.r[d1]
tt1.r[0] = 1
tt1.r[d1] = 1
n1 = tt1.n
assert _np.prod(n1) == _np.prod(sz), 'Reshape: incorrect sizes'
needQRs = False
if d2 > d1:
needQRs = True
if d2 <= d1:
i2 = 0
n2 = _cp.deepcopy(sz)
for i1 in range(d1):
if n2[i2] == 1:
i2 = i2 + 1
if i2 > d2:
break
if n2[i2] % n1[i1] == 0:
n2[i2] = n2[i2] // n1[i1]
else:
needQRs = True
break
r1 = tt1.r
tt1 = tt1.to_list(tt1)
if needQRs: # We have to split some cores -> perform QRs
for i in range(d1 - 1, 0, -1):
cr = tt1[i]
cr = _np.reshape(cr, (r1[i], n1[i] * r1[i + 1]), order='F')
[cr, rv] = _np.linalg.qr(cr.T) # Size n*r2, r1new - r1nwe,r1
cr0 = tt1[i - 1]
cr0 = _np.reshape(cr0, (r1[i - 1] * n1[i - 1], r1[i]), order='F')
cr0 = _np.dot(cr0, rv.T) # r0*n0, r1new
r1[i] = cr.shape[1]
cr0 = _np.reshape(cr0, (r1[i - 1], n1[i - 1], r1[i]), order='F')
cr = _np.reshape(cr.T, (r1[i], n1[i], r1[i + 1]), order='F')
tt1[i] = cr
tt1[i - 1] = cr0
r2 = _np.ones(d2 + 1, dtype=_np.int32)
i1 = 0 # Working index in tt1
i2 = 0 # Working index in tt2
core2 = _np.zeros((0))
curcr2 = 1
restn2 = sz
n2 = _np.ones(d2, dtype=_np.int32)
if ismatrix:
n2_n = _np.ones(d2, dtype=_np.int32)
n2_m = _np.ones(d2, dtype=_np.int32)
while i1 < d1:
curcr1 = tt1[i1]
if _gcd(restn2[i2], n1[i1]) == n1[i1]:
# The whole core1 fits to core2. Convolve it
if (i1 < d1 - 1) and (needQRs): # QR to the next core - for safety
curcr1 = _np.reshape(
curcr1, (r1[i1] * n1[i1], r1[i1 + 1]), order='F')
[curcr1, rv] = _np.linalg.qr(curcr1)
curcr12 = tt1[i1 + 1]
curcr12 = _np.reshape(
curcr12, (r1[i1 + 1], n1[i1 + 1] * r1[i1 + 2]), order='F')
curcr12 = _np.dot(rv, curcr12)
r1[i1 + 1] = curcr12.shape[0]
tt1[i1 + 1] = _np.reshape(curcr12,
(r1[i1 + 1],
n1[i1 + 1],
r1[i1 + 2]),
order='F')
# Actually merge is here
curcr1 = _np.reshape(
curcr1, (r1[i1], n1[i1] * r1[i1 + 1]), order='F')
curcr2 = _np.dot(curcr2, curcr1) # size r21*nold, dn*r22
if ismatrix: # Permute if we are working with tt_matrix
curcr2 = _np.reshape(curcr2, (r2[i2], n2_n[i2], n2_m[i2], n1_n[
i1], n1_m[i1], r1[i1 + 1]), order='F')
curcr2 = _np.transpose(curcr2, [0, 1, 3, 2, 4, 5])
# Update the "matrix" sizes
n2_n[i2] = n2_n[i2] * n1_n[i1]
n2_m[i2] = n2_m[i2] * n1_m[i1]
restn2_n[i2] = restn2_n[i2] // n1_n[i1]
restn2_m[i2] = restn2_m[i2] // n1_m[i1]
r2[i2 + 1] = r1[i1 + 1]
# Update the sizes of tt2
n2[i2] = n2[i2] * n1[i1]
restn2[i2] = restn2[i2] // n1[i1]
curcr2 = _np.reshape(
curcr2, (r2[i2] * n2[i2], r2[i2 + 1]), order='F')
i1 = i1 + 1 # current core1 is over
else:
if (_gcd(restn2[i2], n1[i1]) != 1) or (restn2[i2] == 1):
# There exists a nontrivial divisor, or a singleton requested
# Split it and convolve
n12 = _gcd(restn2[i2], n1[i1])
if ismatrix: # Permute before the truncation
# _matrix sizes we are able to split
n12_n = _gcd(restn2_n[i2], n1_n[i1])
n12_m = _gcd(restn2_m[i2], n1_m[i1])
curcr1 = _np.reshape(curcr1,
(r1[i1],
n12_n,
n1_n[i1] // n12_n,
n12_m,
n1_m[i1] // n12_m,
r1[i1 + 1]),
order='F')
curcr1 = _np.transpose(curcr1, [0, 1, 3, 2, 4, 5])
# Update the _matrix sizes of tt2 and tt1
n2_n[i2] = n2_n[i2] * n12_n
n2_m[i2] = n2_m[i2] * n12_m
restn2_n[i2] = restn2_n[i2] // n12_n
restn2_m[i2] = restn2_m[i2] // n12_m
n1_n[i1] = n1_n[i1] // n12_n
n1_m[i1] = n1_m[i1] // n12_m
curcr1 = _np.reshape(
curcr1, (r1[i1] * n12, (n1[i1] // n12) * r1[i1 + 1]), order='F')
[u, s, v] = _np.linalg.svd(curcr1, full_matrices=False)
r = _my_chop2(s, eps * _np.linalg.norm(s) / (d2 - 1) ** 0.5)
u = u[:, :r]
v = v.T
v = v[:, :r] * s[:r]
u = _np.reshape(u, (r1[i1], n12 * r), order='F')
# u is our admissible chunk, merge it to core2
curcr2 = _np.dot(curcr2, u) # size r21*nold, dn*r22
r2[i2 + 1] = r
# Update the sizes of tt2
n2[i2] = n2[i2] * n12
restn2[i2] = restn2[i2] // n12
curcr2 = _np.reshape(
curcr2, (r2[i2] * n2[i2], r2[i2 + 1]), order='F')
r1[i1] = r
# and tt1
n1[i1] = n1[i1] // n12
# keep v in tt1 for next operations
curcr1 = _np.reshape(
v.T, (r1[i1], n1[i1], r1[i1 + 1]), order='F')
tt1[i1] = curcr1
else:
# Bad case. We have to merge cores of tt1 until a common
# divisor appears
i1new = i1 + 1
curcr1 = _np.reshape(
curcr1, (r1[i1] * n1[i1], r1[i1 + 1]), order='F')
while (_gcd(restn2[i2], n1[i1]) == 1) and (i1new < d1):
cr1new = tt1[i1new]
cr1new = _np.reshape(
cr1new, (r1[i1new], n1[i1new] * r1[i1new + 1]), order='F')
# size r1(i1)*n1(i1), n1new*r1new
curcr1 = _np.dot(curcr1, cr1new)
if ismatrix: # Permutes and _matrix size updates
curcr1 = _np.reshape(curcr1, (r1[i1], n1_n[i1], n1_m[i1], n1_n[
i1new], n1_m[i1new], r1[i1new + 1]), order='F')
curcr1 = _np.transpose(curcr1, [0, 1, 3, 2, 4, 5])
n1_n[i1] = n1_n[i1] * n1_n[i1new]
n1_m[i1] = n1_m[i1] * n1_m[i1new]
n1[i1] = n1[i1] * n1[i1new]
curcr1 = _np.reshape(
curcr1, (r1[i1] * n1[i1], r1[i1new + 1]), order='F')
i1new = i1new + 1
# Inner cores merged => squeeze tt1 data
n1 = _np.concatenate((n1[:i1], n1[i1new:]))
r1 = _np.concatenate((r1[:i1], r1[i1new:]))
tt1[i] = _np.reshape(
curcr1, (r1[i1], n1[i1], r1[i1new]), order='F')
tt1 = tt1[:i1] + tt1[i1new:]
d1 = len(n1)
if (restn2[i2] == 1) and ((i1 >= d1) or ((i1 < d1) and (n1[i1] != 1))):
# The core of tt2 is finished
# The second condition prevents core2 from finishing until we
# squeeze all tailing singletons in tt1.
curcr2 = curcr2.flatten(order='F')
core2 = _np.concatenate((core2, curcr2))
i2 = i2 + 1
# Start new core2
curcr2 = 1
# If we have been asked for singletons - just add them
while (i2 < d2):
core2 = _np.concatenate((core2, _np.ones(1)))
r2[i2] = 1
i2 = i2 + 1
tt2 = ones(2, 1) # dummy tensor
tt2.d = d2
tt2.n = n2
tt2.r = r2
tt2.core = core2
tt2.ps = _np.int32(_np.cumsum(_np.concatenate((_np.ones(1), r2[:-1] * n2 * r2[1:]))))
tt2.n[0] = tt2.n[0] // rl
tt2.n[d2 - 1] = tt2.n[d2 - 1] // rr
tt2.r[0] = rl
tt2.r[d2] = rr
if ismatrix:
ttt = eye(1, 1) # dummy tt _matrix
ttt.n = sz_n
ttt.m = sz_m
ttt.tt = tt2
return ttt
else:
return tt2 | Reshape of the TT-vector
[TT1]=TT_RESHAPE(TT,SZ) reshapes TT-vector or TT-matrix into another
with mode sizes SZ, accuracy 1e-14
[TT1]=TT_RESHAPE(TT,SZ,EPS) reshapes TT-vector/matrix into another with
mode sizes SZ and accuracy EPS
[TT1]=TT_RESHAPE(TT,SZ,EPS, RL) reshapes TT-vector/matrix into another
with mode size SZ and left tail rank RL
[TT1]=TT_RESHAPE(TT,SZ,EPS, RL, RR) reshapes TT-vector/matrix into
another with mode size SZ and tail ranks RL*RR
Reshapes TT-vector/matrix into a new one, with dimensions specified by SZ.
If the i_nput is TT-matrix, SZ must have the sizes for both modes,
so it is a _matrix if sizes d2-by-2.
If the i_nput is TT-vector, SZ may be either a column or a row _vector. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/tools.py#L904-L1164 |
oseledets/ttpy | tt/core/tools.py | permute | def permute(x, order, eps=None, return_cores=False):
'''
Permute dimensions (python translation of original matlab code)
Y = permute(X, ORDER, EPS) permutes the dimensions of the TT-tensor X
according to ORDER, delivering a result at relative accuracy EPS. This
function is equivalent to
Y = tt_tensor(permute(reshape(full(X), X.n'),ORDER), EPS)
but avoids the conversion to the full format.
Simon Etter, Summer 2015
Seminar of Applied Mathematics, ETH Zurich
TT-Toolbox 2.2, 2009-2012
This is TT Toolbox, written by Ivan Oseledets et al. Institute of
Numerical Mathematics, Moscow, Russia webpage:
http://spring.inm.ras.ru/osel
For all questions, bugs and suggestions please mail
[email protected]
---------------------------
This code basically performs insertion sort on the TT dimensions:
for k = 2:d
Bubble the kth dimension to the right (according to ORDER) position in the first 1:k dimensions.
The current code could be optimised at the following places:
- Instead of initially orthogonalising with respect to the first two vertices,
orthogonalise directly with respect to the first inversion.
- When performing the SVD, check on which side of the current position the
next swap will occur and directly establish the appropriate orthogonality
(current implementation always assumes we move left).
Both changes reduce the number of QRs by at most O(d) and are therefore likely
to produce negligible speedup while rendering the code more complicated.
'''
def _reshape(tensor, shape):
return _np.reshape(tensor, shape, order='F')
# Parse input
if eps is None:
eps = _np.spacing(1)
cores = _vector.vector.to_list(x)
d = _cp.deepcopy(x.d)
n = _cp.deepcopy(x.n)
r = _cp.deepcopy(x.r)
idx = _np.empty(len(order))
idx[order] = _np.arange(len(order))
eps /= d ** 1.5
# ^Numerical evidence suggests that eps = eps/d may be sufficient, however I can only prove correctness
# for this choice of global-to-local conversion factor.
assert len(order) > d - 1, 'ORDER must have at least D elements for a D-dimensional tensor'
# RL-orthogonalise x
for kk in xrange(d - 1, 1, -1): ##########################################
new_shape = [r[kk], n[kk] * r[kk + 1]]
Q, R = _np.linalg.qr(_reshape(cores[kk], new_shape).T)
tr = min(new_shape)
cores[kk] = _reshape(Q.T, [tr, n[kk], r[kk + 1]])
tmp = _reshape(cores[kk - 1], [r[kk - 1] * n[kk - 1], r[kk]])
tmp = _np.dot(tmp, R.T)
cores[kk - 1] = _reshape(tmp, [r[kk - 1], n[kk - 1], tr])
r[kk] = tr
k = 0
while (True):
# Find next inversion
nk = k
while (nk < d - 1) and (idx[nk] < idx[nk + 1]):
nk += 1
if (nk == d - 1):
break
# Move orthogonal centre there
for kk in xrange(k, nk - 1): #############
new_shape = [r[kk] * n[kk], r[kk + 1]]
Q, R = _np.linalg.qr(_reshape(cores[kk], new_shape))
tr = min(new_shape)
new_shape = [r[kk], n[kk], tr]
cores[kk] = _reshape(Q, new_shape)
tmp = _reshape(cores[kk + 1], [r[kk + 1], n[kk + 1] * r[kk + 2]])
tmp = _np.dot(R, tmp)
cores[kk + 1] = _reshape(tmp, [tr, n[kk + 1], r[kk + 2]])
r[kk + 1] = tr
k = nk
# Swap dimensions
tmp = _reshape(cores[k], [r[k] * n[k], r[k + 1]])
tmp = _np.dot(tmp, _reshape(cores[k + 1], [r[k + 1], n[k + 1] * r[k + 2]]))
c = _reshape(tmp, [r[k], n[k], n[k + 1], r[k + 2]])
c = _np.transpose(c, [0, 2, 1, 3])
tmp = _reshape(c, [r[k] * n[k + 1], n[k] * r[k + 2]])
U, S, Vt = _np.linalg.svd(tmp, full_matrices=False)
r[k + 1] = max(_my_chop2(S, _np.linalg.norm(S) * eps), 1)
lenS = len(S)
tmp = U[:, :lenS] * S # multiplication by diagonal matrix
cores[k] = _reshape(tmp[:, :r[k + 1]], [r[k], n[k + 1], r[k + 1]])
cores[k + 1] = _reshape(Vt[:r[k + 1], :], [r[k + 1], n[k], r[k + 2]])
idx[[k, k + 1]] = idx[[k + 1, k]]
n[[k, k + 1]] = n[[k + 1, k]]
k = max(k - 1, 0) ##################
# Parse output
if return_cores:
return cores
return _vector.vector.from_list(cores) | python | def permute(x, order, eps=None, return_cores=False):
'''
Permute dimensions (python translation of original matlab code)
Y = permute(X, ORDER, EPS) permutes the dimensions of the TT-tensor X
according to ORDER, delivering a result at relative accuracy EPS. This
function is equivalent to
Y = tt_tensor(permute(reshape(full(X), X.n'),ORDER), EPS)
but avoids the conversion to the full format.
Simon Etter, Summer 2015
Seminar of Applied Mathematics, ETH Zurich
TT-Toolbox 2.2, 2009-2012
This is TT Toolbox, written by Ivan Oseledets et al. Institute of
Numerical Mathematics, Moscow, Russia webpage:
http://spring.inm.ras.ru/osel
For all questions, bugs and suggestions please mail
[email protected]
---------------------------
This code basically performs insertion sort on the TT dimensions:
for k = 2:d
Bubble the kth dimension to the right (according to ORDER) position in the first 1:k dimensions.
The current code could be optimised at the following places:
- Instead of initially orthogonalising with respect to the first two vertices,
orthogonalise directly with respect to the first inversion.
- When performing the SVD, check on which side of the current position the
next swap will occur and directly establish the appropriate orthogonality
(current implementation always assumes we move left).
Both changes reduce the number of QRs by at most O(d) and are therefore likely
to produce negligible speedup while rendering the code more complicated.
'''
def _reshape(tensor, shape):
return _np.reshape(tensor, shape, order='F')
# Parse input
if eps is None:
eps = _np.spacing(1)
cores = _vector.vector.to_list(x)
d = _cp.deepcopy(x.d)
n = _cp.deepcopy(x.n)
r = _cp.deepcopy(x.r)
idx = _np.empty(len(order))
idx[order] = _np.arange(len(order))
eps /= d ** 1.5
# ^Numerical evidence suggests that eps = eps/d may be sufficient, however I can only prove correctness
# for this choice of global-to-local conversion factor.
assert len(order) > d - 1, 'ORDER must have at least D elements for a D-dimensional tensor'
# RL-orthogonalise x
for kk in xrange(d - 1, 1, -1): ##########################################
new_shape = [r[kk], n[kk] * r[kk + 1]]
Q, R = _np.linalg.qr(_reshape(cores[kk], new_shape).T)
tr = min(new_shape)
cores[kk] = _reshape(Q.T, [tr, n[kk], r[kk + 1]])
tmp = _reshape(cores[kk - 1], [r[kk - 1] * n[kk - 1], r[kk]])
tmp = _np.dot(tmp, R.T)
cores[kk - 1] = _reshape(tmp, [r[kk - 1], n[kk - 1], tr])
r[kk] = tr
k = 0
while (True):
# Find next inversion
nk = k
while (nk < d - 1) and (idx[nk] < idx[nk + 1]):
nk += 1
if (nk == d - 1):
break
# Move orthogonal centre there
for kk in xrange(k, nk - 1): #############
new_shape = [r[kk] * n[kk], r[kk + 1]]
Q, R = _np.linalg.qr(_reshape(cores[kk], new_shape))
tr = min(new_shape)
new_shape = [r[kk], n[kk], tr]
cores[kk] = _reshape(Q, new_shape)
tmp = _reshape(cores[kk + 1], [r[kk + 1], n[kk + 1] * r[kk + 2]])
tmp = _np.dot(R, tmp)
cores[kk + 1] = _reshape(tmp, [tr, n[kk + 1], r[kk + 2]])
r[kk + 1] = tr
k = nk
# Swap dimensions
tmp = _reshape(cores[k], [r[k] * n[k], r[k + 1]])
tmp = _np.dot(tmp, _reshape(cores[k + 1], [r[k + 1], n[k + 1] * r[k + 2]]))
c = _reshape(tmp, [r[k], n[k], n[k + 1], r[k + 2]])
c = _np.transpose(c, [0, 2, 1, 3])
tmp = _reshape(c, [r[k] * n[k + 1], n[k] * r[k + 2]])
U, S, Vt = _np.linalg.svd(tmp, full_matrices=False)
r[k + 1] = max(_my_chop2(S, _np.linalg.norm(S) * eps), 1)
lenS = len(S)
tmp = U[:, :lenS] * S # multiplication by diagonal matrix
cores[k] = _reshape(tmp[:, :r[k + 1]], [r[k], n[k + 1], r[k + 1]])
cores[k + 1] = _reshape(Vt[:r[k + 1], :], [r[k + 1], n[k], r[k + 2]])
idx[[k, k + 1]] = idx[[k + 1, k]]
n[[k, k + 1]] = n[[k + 1, k]]
k = max(k - 1, 0) ##################
# Parse output
if return_cores:
return cores
return _vector.vector.from_list(cores) | Permute dimensions (python translation of original matlab code)
Y = permute(X, ORDER, EPS) permutes the dimensions of the TT-tensor X
according to ORDER, delivering a result at relative accuracy EPS. This
function is equivalent to
Y = tt_tensor(permute(reshape(full(X), X.n'),ORDER), EPS)
but avoids the conversion to the full format.
Simon Etter, Summer 2015
Seminar of Applied Mathematics, ETH Zurich
TT-Toolbox 2.2, 2009-2012
This is TT Toolbox, written by Ivan Oseledets et al. Institute of
Numerical Mathematics, Moscow, Russia webpage:
http://spring.inm.ras.ru/osel
For all questions, bugs and suggestions please mail
[email protected]
---------------------------
This code basically performs insertion sort on the TT dimensions:
for k = 2:d
Bubble the kth dimension to the right (according to ORDER) position in the first 1:k dimensions.
The current code could be optimised at the following places:
- Instead of initially orthogonalising with respect to the first two vertices,
orthogonalise directly with respect to the first inversion.
- When performing the SVD, check on which side of the current position the
next swap will occur and directly establish the appropriate orthogonality
(current implementation always assumes we move left).
Both changes reduce the number of QRs by at most O(d) and are therefore likely
to produce negligible speedup while rendering the code more complicated. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/tools.py#L1167-L1273 |
oseledets/ttpy | tt/optimize/tt_min.py | min_func | def min_func(fun, bounds_min, bounds_max, d=None, rmax=10,
n0=64, nswp=10, verb=True, smooth_fun=None):
"""Find (approximate) minimal value of the function on a d-dimensional grid."""
if d is None:
d = len(bounds_min)
a = np.asanyarray(bounds_min).copy()
b = np.asanyarray(bounds_max).copy()
else:
a = np.ones(d) * bounds_min
b = np.ones(d) * bounds_max
if smooth_fun is None:
smooth_fun = lambda p, lam: (math.pi / 2 - np.arctan(p - lam))
#smooth_fun = lambda p, lam: np.exp(-10*(p - lam))
# We do not need to store the cores, only the interfaces!
Rx = [[]] * (d + 1) # Python list for the interfaces
Rx[0] = np.ones((1, 1))
Rx[d] = np.ones((1, 1))
Jy = [np.empty(0, dtype=np.int)] * (d + 1)
ry = rmax * np.ones(d + 1, dtype=np.int)
ry[0] = 1
ry[d] = 1
n = n0 * np.ones(d, dtype=np.int)
fun_evals = 0
grid = [np.reshape(np.linspace(a[i], b[i], n[i]), (n[i], 1))
for i in xrange(d)]
for i in xrange(d - 1):
#cr1 = y[i]
ry[i + 1] = min(ry[i + 1], n[i] * ry[i])
cr1 = np.random.randn(ry[i], n[i], ry[i + 1])
cr1 = reshape(cr1, (ry[i] * n[i], ry[i + 1]))
q, r = np.linalg.qr(cr1)
ind = maxvol(q)
w1 = mkron(np.ones((n[i], 1), dtype=np.int), Jy[i])
w2 = mkron(grid[i], np.ones((ry[i], 1), dtype=np.int))
Jy[i + 1] = np.hstack((w1, w2))
Jy[i + 1] = reshape(Jy[i + 1], (ry[i] * n[i], -1))
Jy[i + 1] = Jy[i + 1][ind, :]
# Jy{i+1} = [kron(ones(n(i),1), Jy{i}), kron((1:n(i))', ones(ry(i),1))];
# Jy{i+1} = Jy{i+1}(ind,:);
swp = 0
dirn = -1
i = d - 1
lm = float('Inf')
while swp < nswp:
# Right-to-left sweep
# The idea: compute the current core; compute the function of it;
# Shift locally or globally? Local shift would be the first try
# Compute the current core
if np.size(Jy[i]) == 0:
w1 = np.zeros((ry[i] * n[i] * ry[i + 1], 0), dtype=np.int)
else:
w1 = mkron(np.ones((n[i] * ry[i + 1], 1), dtype=np.int), Jy[i])
w2 = mkron(mkron(np.ones((ry[i + 1], 1), dtype=np.int),
grid[i]), np.ones((ry[i], 1), dtype=np.int))
if np.size(Jy[i + 1]) == 0:
w3 = np.zeros((ry[i] * n[i] * ry[i + 1], 0), dtype=np.int)
else:
w3 = mkron(Jy[i + 1], np.ones((ry[i] * n[i], 1), dtype=np.int))
J = np.hstack((w1, w2, w3))
# Just add some random indices to J, which is rnr x d, need to make rn (r + r0) x add,
# i.e., just generate random r, random n and random multiindex
cry = fun(J)
fun_evals += cry.size
cry = reshape(cry, (ry[i], n[i], ry[i + 1]))
min_cur = np.min(cry.flatten("F"))
ind_cur = np.argmin(cry.flatten("F"))
if lm > min_cur:
lm = min_cur
x_full = J[ind_cur, :]
val = fun(x_full)
if verb:
print('New record:', val, 'Point:', x_full, 'fevals:', fun_evals)
cry = smooth_fun(cry, lm)
if (dirn < 0 and i > 0):
cry = reshape(cry, (ry[i], n[i] * ry[i + 1]))
cry = cry.T
#q, r = np.linalg.qr(cry)
u, s, v = mysvd(cry, full_matrices=False)
ry[i] = min(ry[i], rmax)
q = u[:, :ry[i]]
ind = rect_maxvol(q)[0] # maxvol(q)
ry[i] = ind.size
w1 = mkron(np.ones((ry[i + 1], 1), dtype=np.int), grid[i])
if np.size(Jy[i + 1]) == 0:
w2 = np.zeros((n[i] * ry[i + 1], 0), dtype=np.int)
else:
w2 = mkron(Jy[i + 1], np.ones((n[i], 1), dtype=np.int))
Jy[i] = np.hstack((w1, w2))
Jy[i] = reshape(Jy[i], (n[i] * ry[i + 1], -1))
Jy[i] = Jy[i][ind, :]
if (dirn > 0 and i < d - 1):
cry = reshape(cry, (ry[i] * n[i], ry[i + 1]))
q, r = np.linalg.qr(cry)
#ind = maxvol(q)
ind = rect_maxvol(q)[0]
ry[i + 1] = ind.size
w1 = mkron(np.ones((n[i], 1), dtype=np.int), Jy[i])
w2 = mkron(grid[i], np.ones((ry[i], 1), dtype=np.int))
Jy[i + 1] = np.hstack((w1, w2))
Jy[i + 1] = reshape(Jy[i + 1], (ry[i] * n[i], -1))
Jy[i + 1] = Jy[i + 1][ind, :]
i += dirn
if i == d or i == -1:
dirn = -dirn
i += dirn
swp = swp + 1
return val, x_full | python | def min_func(fun, bounds_min, bounds_max, d=None, rmax=10,
n0=64, nswp=10, verb=True, smooth_fun=None):
"""Find (approximate) minimal value of the function on a d-dimensional grid."""
if d is None:
d = len(bounds_min)
a = np.asanyarray(bounds_min).copy()
b = np.asanyarray(bounds_max).copy()
else:
a = np.ones(d) * bounds_min
b = np.ones(d) * bounds_max
if smooth_fun is None:
smooth_fun = lambda p, lam: (math.pi / 2 - np.arctan(p - lam))
#smooth_fun = lambda p, lam: np.exp(-10*(p - lam))
# We do not need to store the cores, only the interfaces!
Rx = [[]] * (d + 1) # Python list for the interfaces
Rx[0] = np.ones((1, 1))
Rx[d] = np.ones((1, 1))
Jy = [np.empty(0, dtype=np.int)] * (d + 1)
ry = rmax * np.ones(d + 1, dtype=np.int)
ry[0] = 1
ry[d] = 1
n = n0 * np.ones(d, dtype=np.int)
fun_evals = 0
grid = [np.reshape(np.linspace(a[i], b[i], n[i]), (n[i], 1))
for i in xrange(d)]
for i in xrange(d - 1):
#cr1 = y[i]
ry[i + 1] = min(ry[i + 1], n[i] * ry[i])
cr1 = np.random.randn(ry[i], n[i], ry[i + 1])
cr1 = reshape(cr1, (ry[i] * n[i], ry[i + 1]))
q, r = np.linalg.qr(cr1)
ind = maxvol(q)
w1 = mkron(np.ones((n[i], 1), dtype=np.int), Jy[i])
w2 = mkron(grid[i], np.ones((ry[i], 1), dtype=np.int))
Jy[i + 1] = np.hstack((w1, w2))
Jy[i + 1] = reshape(Jy[i + 1], (ry[i] * n[i], -1))
Jy[i + 1] = Jy[i + 1][ind, :]
# Jy{i+1} = [kron(ones(n(i),1), Jy{i}), kron((1:n(i))', ones(ry(i),1))];
# Jy{i+1} = Jy{i+1}(ind,:);
swp = 0
dirn = -1
i = d - 1
lm = float('Inf')
while swp < nswp:
# Right-to-left sweep
# The idea: compute the current core; compute the function of it;
# Shift locally or globally? Local shift would be the first try
# Compute the current core
if np.size(Jy[i]) == 0:
w1 = np.zeros((ry[i] * n[i] * ry[i + 1], 0), dtype=np.int)
else:
w1 = mkron(np.ones((n[i] * ry[i + 1], 1), dtype=np.int), Jy[i])
w2 = mkron(mkron(np.ones((ry[i + 1], 1), dtype=np.int),
grid[i]), np.ones((ry[i], 1), dtype=np.int))
if np.size(Jy[i + 1]) == 0:
w3 = np.zeros((ry[i] * n[i] * ry[i + 1], 0), dtype=np.int)
else:
w3 = mkron(Jy[i + 1], np.ones((ry[i] * n[i], 1), dtype=np.int))
J = np.hstack((w1, w2, w3))
# Just add some random indices to J, which is rnr x d, need to make rn (r + r0) x add,
# i.e., just generate random r, random n and random multiindex
cry = fun(J)
fun_evals += cry.size
cry = reshape(cry, (ry[i], n[i], ry[i + 1]))
min_cur = np.min(cry.flatten("F"))
ind_cur = np.argmin(cry.flatten("F"))
if lm > min_cur:
lm = min_cur
x_full = J[ind_cur, :]
val = fun(x_full)
if verb:
print('New record:', val, 'Point:', x_full, 'fevals:', fun_evals)
cry = smooth_fun(cry, lm)
if (dirn < 0 and i > 0):
cry = reshape(cry, (ry[i], n[i] * ry[i + 1]))
cry = cry.T
#q, r = np.linalg.qr(cry)
u, s, v = mysvd(cry, full_matrices=False)
ry[i] = min(ry[i], rmax)
q = u[:, :ry[i]]
ind = rect_maxvol(q)[0] # maxvol(q)
ry[i] = ind.size
w1 = mkron(np.ones((ry[i + 1], 1), dtype=np.int), grid[i])
if np.size(Jy[i + 1]) == 0:
w2 = np.zeros((n[i] * ry[i + 1], 0), dtype=np.int)
else:
w2 = mkron(Jy[i + 1], np.ones((n[i], 1), dtype=np.int))
Jy[i] = np.hstack((w1, w2))
Jy[i] = reshape(Jy[i], (n[i] * ry[i + 1], -1))
Jy[i] = Jy[i][ind, :]
if (dirn > 0 and i < d - 1):
cry = reshape(cry, (ry[i] * n[i], ry[i + 1]))
q, r = np.linalg.qr(cry)
#ind = maxvol(q)
ind = rect_maxvol(q)[0]
ry[i + 1] = ind.size
w1 = mkron(np.ones((n[i], 1), dtype=np.int), Jy[i])
w2 = mkron(grid[i], np.ones((ry[i], 1), dtype=np.int))
Jy[i + 1] = np.hstack((w1, w2))
Jy[i + 1] = reshape(Jy[i + 1], (ry[i] * n[i], -1))
Jy[i + 1] = Jy[i + 1][ind, :]
i += dirn
if i == d or i == -1:
dirn = -dirn
i += dirn
swp = swp + 1
return val, x_full | Find (approximate) minimal value of the function on a d-dimensional grid. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/optimize/tt_min.py#L29-L144 |
oseledets/ttpy | tt/optimize/tt_min.py | min_tens | def min_tens(tens, rmax=10, nswp=10, verb=True, smooth_fun=None):
"""Find (approximate) minimal element in a TT-tensor."""
if smooth_fun is None:
smooth_fun = lambda p, lam: (math.pi / 2 - np.arctan(p - lam))
d = tens.d
Rx = [[]] * (d + 1) # Python list for the interfaces
Rx[0] = np.ones((1, 1))
Rx[d] = np.ones((1, 1))
Jy = [np.empty(0, dtype=np.int)] * (d + 1)
ry = rmax * np.ones(d + 1, dtype=np.int)
ry[0] = 1
ry[d] = 1
n = tens.n
elements_seen = 0
phi_left = [np.empty(0)] * (d + 1)
phi_left[0] = np.array([1])
phi_right = [np.empty(0)] * (d + 1)
phi_right[d] = np.array([1])
cores = tt.tensor.to_list(tens)
# Fill initial multiindex J randomly.
grid = [np.reshape(range(n[i]), (n[i], 1)) for i in xrange(d)]
for i in xrange(d - 1):
ry[i + 1] = min(ry[i + 1], n[i] * ry[i])
ind = sorted(np.random.permutation(ry[i] * n[i])[0:ry[i + 1]])
w1 = mkron(np.ones((n[i], 1), dtype=np.int), Jy[i])
w2 = mkron(grid[i], np.ones((ry[i], 1), dtype=np.int))
Jy[i + 1] = np.hstack((w1, w2))
Jy[i + 1] = reshape(Jy[i + 1], (ry[i] * n[i], -1))
Jy[i + 1] = Jy[i + 1][ind, :]
phi_left[i + 1] = np.tensordot(phi_left[i], cores[i], 1)
phi_left[i + 1] = reshape(phi_left[i + 1], (ry[i] * n[i], -1))
phi_left[i + 1] = phi_left[i + 1][ind, :]
swp = 0
dirn = -1
i = d - 1
lm = float('Inf')
while swp < nswp:
# Right-to-left sweep
# The idea: compute the current core; compute the function of it;
# Shift locally or globally? Local shift would be the first try
# Compute the current core
if np.size(Jy[i]) == 0:
w1 = np.zeros((ry[i] * n[i] * ry[i + 1], 0), dtype=np.int)
else:
w1 = mkron(np.ones((n[i] * ry[i + 1], 1), dtype=np.int), Jy[i])
w2 = mkron(mkron(np.ones((ry[i + 1], 1), dtype=np.int),
grid[i]), np.ones((ry[i], 1), dtype=np.int))
if np.size(Jy[i + 1]) == 0:
w3 = np.zeros((ry[i] * n[i] * ry[i + 1], 0), dtype=np.int)
else:
w3 = mkron(Jy[i + 1], np.ones((ry[i] * n[i], 1), dtype=np.int))
J = np.hstack((w1, w2, w3))
phi_right[i] = np.tensordot(cores[i], phi_right[i + 1], 1)
phi_right[i] = reshape(phi_right[i], (-1, n[i] * ry[i + 1]))
cry = np.tensordot(
phi_left[i], np.tensordot(
cores[i], phi_right[
i + 1], 1), 1)
elements_seen += cry.size
cry = reshape(cry, (ry[i], n[i], ry[i + 1]))
min_cur = np.min(cry.flatten("F"))
ind_cur = np.argmin(cry.flatten("F"))
if lm > min_cur:
lm = min_cur
x_full = J[ind_cur, :]
val = tens[x_full]
if verb:
print('New record:', val, 'Point:', x_full, 'elements seen:', elements_seen)
cry = smooth_fun(cry, lm)
if dirn < 0 and i > 0:
cry = reshape(cry, (ry[i], n[i] * ry[i + 1]))
cry = cry.T
#q, r = np.linalg.qr(cry)
u, s, v = mysvd(cry, full_matrices=False)
ry[i] = min(ry[i], rmax)
q = u[:, :ry[i]]
ind = rect_maxvol(q)[0] # maxvol(q)
ry[i] = ind.size
w1 = mkron(np.ones((ry[i + 1], 1), dtype=np.int), grid[i])
if np.size(Jy[i + 1]) == 0:
w2 = np.zeros((n[i] * ry[i + 1], 0), dtype=np.int)
else:
w2 = mkron(Jy[i + 1], np.ones((n[i], 1), dtype=np.int))
Jy[i] = np.hstack((w1, w2))
Jy[i] = reshape(Jy[i], (n[i] * ry[i + 1], -1))
Jy[i] = Jy[i][ind, :]
phi_right[i] = np.tensordot(cores[i], phi_right[i + 1], 1)
phi_right[i] = reshape(phi_right[i], (-1, n[i] * ry[i + 1]))
phi_right[i] = phi_right[i][:, ind]
if dirn > 0 and i < d - 1:
cry = reshape(cry, (ry[i] * n[i], ry[i + 1]))
q, r = np.linalg.qr(cry)
#ind = maxvol(q)
ind = rect_maxvol(q)[0]
ry[i + 1] = ind.size
phi_left[i + 1] = np.tensordot(phi_left[i], cores[i], 1)
phi_left[i + 1] = reshape(phi_left[i + 1], (ry[i] * n[i], -1))
phi_left[i + 1] = phi_left[i + 1][ind, :]
w1 = mkron(np.ones((n[i], 1), dtype=np.int), Jy[i])
w2 = mkron(grid[i], np.ones((ry[i], 1), dtype=np.int))
Jy[i + 1] = np.hstack((w1, w2))
Jy[i + 1] = reshape(Jy[i + 1], (ry[i] * n[i], -1))
Jy[i + 1] = Jy[i + 1][ind, :]
i += dirn
if i == d or i == -1:
dirn = -dirn
i += dirn
swp = swp + 1
return val, x_full | python | def min_tens(tens, rmax=10, nswp=10, verb=True, smooth_fun=None):
"""Find (approximate) minimal element in a TT-tensor."""
if smooth_fun is None:
smooth_fun = lambda p, lam: (math.pi / 2 - np.arctan(p - lam))
d = tens.d
Rx = [[]] * (d + 1) # Python list for the interfaces
Rx[0] = np.ones((1, 1))
Rx[d] = np.ones((1, 1))
Jy = [np.empty(0, dtype=np.int)] * (d + 1)
ry = rmax * np.ones(d + 1, dtype=np.int)
ry[0] = 1
ry[d] = 1
n = tens.n
elements_seen = 0
phi_left = [np.empty(0)] * (d + 1)
phi_left[0] = np.array([1])
phi_right = [np.empty(0)] * (d + 1)
phi_right[d] = np.array([1])
cores = tt.tensor.to_list(tens)
# Fill initial multiindex J randomly.
grid = [np.reshape(range(n[i]), (n[i], 1)) for i in xrange(d)]
for i in xrange(d - 1):
ry[i + 1] = min(ry[i + 1], n[i] * ry[i])
ind = sorted(np.random.permutation(ry[i] * n[i])[0:ry[i + 1]])
w1 = mkron(np.ones((n[i], 1), dtype=np.int), Jy[i])
w2 = mkron(grid[i], np.ones((ry[i], 1), dtype=np.int))
Jy[i + 1] = np.hstack((w1, w2))
Jy[i + 1] = reshape(Jy[i + 1], (ry[i] * n[i], -1))
Jy[i + 1] = Jy[i + 1][ind, :]
phi_left[i + 1] = np.tensordot(phi_left[i], cores[i], 1)
phi_left[i + 1] = reshape(phi_left[i + 1], (ry[i] * n[i], -1))
phi_left[i + 1] = phi_left[i + 1][ind, :]
swp = 0
dirn = -1
i = d - 1
lm = float('Inf')
while swp < nswp:
# Right-to-left sweep
# The idea: compute the current core; compute the function of it;
# Shift locally or globally? Local shift would be the first try
# Compute the current core
if np.size(Jy[i]) == 0:
w1 = np.zeros((ry[i] * n[i] * ry[i + 1], 0), dtype=np.int)
else:
w1 = mkron(np.ones((n[i] * ry[i + 1], 1), dtype=np.int), Jy[i])
w2 = mkron(mkron(np.ones((ry[i + 1], 1), dtype=np.int),
grid[i]), np.ones((ry[i], 1), dtype=np.int))
if np.size(Jy[i + 1]) == 0:
w3 = np.zeros((ry[i] * n[i] * ry[i + 1], 0), dtype=np.int)
else:
w3 = mkron(Jy[i + 1], np.ones((ry[i] * n[i], 1), dtype=np.int))
J = np.hstack((w1, w2, w3))
phi_right[i] = np.tensordot(cores[i], phi_right[i + 1], 1)
phi_right[i] = reshape(phi_right[i], (-1, n[i] * ry[i + 1]))
cry = np.tensordot(
phi_left[i], np.tensordot(
cores[i], phi_right[
i + 1], 1), 1)
elements_seen += cry.size
cry = reshape(cry, (ry[i], n[i], ry[i + 1]))
min_cur = np.min(cry.flatten("F"))
ind_cur = np.argmin(cry.flatten("F"))
if lm > min_cur:
lm = min_cur
x_full = J[ind_cur, :]
val = tens[x_full]
if verb:
print('New record:', val, 'Point:', x_full, 'elements seen:', elements_seen)
cry = smooth_fun(cry, lm)
if dirn < 0 and i > 0:
cry = reshape(cry, (ry[i], n[i] * ry[i + 1]))
cry = cry.T
#q, r = np.linalg.qr(cry)
u, s, v = mysvd(cry, full_matrices=False)
ry[i] = min(ry[i], rmax)
q = u[:, :ry[i]]
ind = rect_maxvol(q)[0] # maxvol(q)
ry[i] = ind.size
w1 = mkron(np.ones((ry[i + 1], 1), dtype=np.int), grid[i])
if np.size(Jy[i + 1]) == 0:
w2 = np.zeros((n[i] * ry[i + 1], 0), dtype=np.int)
else:
w2 = mkron(Jy[i + 1], np.ones((n[i], 1), dtype=np.int))
Jy[i] = np.hstack((w1, w2))
Jy[i] = reshape(Jy[i], (n[i] * ry[i + 1], -1))
Jy[i] = Jy[i][ind, :]
phi_right[i] = np.tensordot(cores[i], phi_right[i + 1], 1)
phi_right[i] = reshape(phi_right[i], (-1, n[i] * ry[i + 1]))
phi_right[i] = phi_right[i][:, ind]
if dirn > 0 and i < d - 1:
cry = reshape(cry, (ry[i] * n[i], ry[i + 1]))
q, r = np.linalg.qr(cry)
#ind = maxvol(q)
ind = rect_maxvol(q)[0]
ry[i + 1] = ind.size
phi_left[i + 1] = np.tensordot(phi_left[i], cores[i], 1)
phi_left[i + 1] = reshape(phi_left[i + 1], (ry[i] * n[i], -1))
phi_left[i + 1] = phi_left[i + 1][ind, :]
w1 = mkron(np.ones((n[i], 1), dtype=np.int), Jy[i])
w2 = mkron(grid[i], np.ones((ry[i], 1), dtype=np.int))
Jy[i + 1] = np.hstack((w1, w2))
Jy[i + 1] = reshape(Jy[i + 1], (ry[i] * n[i], -1))
Jy[i + 1] = Jy[i + 1][ind, :]
i += dirn
if i == d or i == -1:
dirn = -dirn
i += dirn
swp = swp + 1
return val, x_full | Find (approximate) minimal element in a TT-tensor. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/optimize/tt_min.py#L147-L261 |
oseledets/ttpy | tt/riemannian/riemannian.py | cores_orthogonalization_step | def cores_orthogonalization_step(coresX, dim, left_to_right=True):
"""TT-Tensor X orthogonalization step.
The function can change the shape of some cores.
"""
cc = coresX[dim]
r1, n, r2 = cc.shape
if left_to_right:
# Left to right orthogonalization step.
assert(0 <= dim < len(coresX) - 1)
cc, rr = np.linalg.qr(reshape(cc, (-1, r2)))
r2 = cc.shape[1]
coresX[dim] = reshape(cc, (r1, n, r2))
coresX[dim+1] = np.tensordot(rr, coresX[dim+1], 1)
else:
# Right to left orthogonalization step.
assert(0 < dim < len(coresX))
cc, rr = np.linalg.qr(reshape(cc, (r1, -1)).T)
r1 = cc.shape[1]
coresX[dim] = reshape(cc.T, (r1, n, r2))
coresX[dim-1] = np.tensordot(coresX[dim-1], rr.T, 1)
return coresX | python | def cores_orthogonalization_step(coresX, dim, left_to_right=True):
"""TT-Tensor X orthogonalization step.
The function can change the shape of some cores.
"""
cc = coresX[dim]
r1, n, r2 = cc.shape
if left_to_right:
# Left to right orthogonalization step.
assert(0 <= dim < len(coresX) - 1)
cc, rr = np.linalg.qr(reshape(cc, (-1, r2)))
r2 = cc.shape[1]
coresX[dim] = reshape(cc, (r1, n, r2))
coresX[dim+1] = np.tensordot(rr, coresX[dim+1], 1)
else:
# Right to left orthogonalization step.
assert(0 < dim < len(coresX))
cc, rr = np.linalg.qr(reshape(cc, (r1, -1)).T)
r1 = cc.shape[1]
coresX[dim] = reshape(cc.T, (r1, n, r2))
coresX[dim-1] = np.tensordot(coresX[dim-1], rr.T, 1)
return coresX | TT-Tensor X orthogonalization step.
The function can change the shape of some cores. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L16-L37 |
oseledets/ttpy | tt/riemannian/riemannian.py | left | def left(X, i):
"""Compute the orthogonal matrix Q_{\leq i} as defined in [1]."""
if i < 0:
return np.ones([1, 1])
answ = np.ones([1, 1])
cores = tt.tensor.to_list(X)
for dim in xrange(i+1):
answ = np.tensordot(answ, cores[dim], 1)
answ = reshape(answ, (-1, X.r[i+1]))
return answ | python | def left(X, i):
"""Compute the orthogonal matrix Q_{\leq i} as defined in [1]."""
if i < 0:
return np.ones([1, 1])
answ = np.ones([1, 1])
cores = tt.tensor.to_list(X)
for dim in xrange(i+1):
answ = np.tensordot(answ, cores[dim], 1)
answ = reshape(answ, (-1, X.r[i+1]))
return answ | Compute the orthogonal matrix Q_{\leq i} as defined in [1]. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L42-L51 |
oseledets/ttpy | tt/riemannian/riemannian.py | right | def right(X, i):
"""Compute the orthogonal matrix Q_{\geq i} as defined in [1]."""
if i > X.d-1:
return np.ones([1, 1])
answ = np.ones([1, 1])
cores = tt.tensor.to_list(X)
for dim in xrange(X.d-1, i-1, -1):
answ = np.tensordot(cores[dim], answ, 1)
answ = reshape(answ, (X.r[i], -1))
return answ.T | python | def right(X, i):
"""Compute the orthogonal matrix Q_{\geq i} as defined in [1]."""
if i > X.d-1:
return np.ones([1, 1])
answ = np.ones([1, 1])
cores = tt.tensor.to_list(X)
for dim in xrange(X.d-1, i-1, -1):
answ = np.tensordot(cores[dim], answ, 1)
answ = reshape(answ, (X.r[i], -1))
return answ.T | Compute the orthogonal matrix Q_{\geq i} as defined in [1]. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L54-L63 |
oseledets/ttpy | tt/riemannian/riemannian.py | unfolding | def unfolding(tens, i):
"""Compute the i-th unfolding of a tensor."""
return reshape(tens.full(), (np.prod(tens.n[0:(i+1)]), -1)) | python | def unfolding(tens, i):
"""Compute the i-th unfolding of a tensor."""
return reshape(tens.full(), (np.prod(tens.n[0:(i+1)]), -1)) | Compute the i-th unfolding of a tensor. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L66-L68 |
oseledets/ttpy | tt/riemannian/riemannian.py | _update_lhs | def _update_lhs(lhs, xCore, zCore, new_lhs):
""" Function to be called from the project()"""
# TODO: Use intermediate variable to use 5 nested loops instead of 6.
r_old_x, n, r_x = xCore.shape
num_obj, r_old_z, n, r_z = zCore.shape
for idx in range(num_obj):
for val in range(n):
for alpha_old_z in range(r_old_z):
for alpha_z in range(r_z):
for alpha_old_x in range(r_old_x):
for alpha_x in range(r_x):
curr_value = lhs[idx, alpha_old_x, alpha_old_z]
curr_value *= xCore[alpha_old_x, val, alpha_x]
curr_value *= zCore[idx, alpha_old_z, val, alpha_z]
new_lhs[idx, alpha_x, alpha_z] += curr_value | python | def _update_lhs(lhs, xCore, zCore, new_lhs):
""" Function to be called from the project()"""
# TODO: Use intermediate variable to use 5 nested loops instead of 6.
r_old_x, n, r_x = xCore.shape
num_obj, r_old_z, n, r_z = zCore.shape
for idx in range(num_obj):
for val in range(n):
for alpha_old_z in range(r_old_z):
for alpha_z in range(r_z):
for alpha_old_x in range(r_old_x):
for alpha_x in range(r_x):
curr_value = lhs[idx, alpha_old_x, alpha_old_z]
curr_value *= xCore[alpha_old_x, val, alpha_x]
curr_value *= zCore[idx, alpha_old_z, val, alpha_z]
new_lhs[idx, alpha_x, alpha_z] += curr_value | Function to be called from the project() | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L75-L89 |
oseledets/ttpy | tt/riemannian/riemannian.py | _update_rhs | def _update_rhs(curr_rhs, xCore, zCore, new_rhs):
""" Function to be called from the project()"""
# TODO: Use intermediate variable to use 5 nested loops instead of 6.
r_x, n, r_old_x = xCore.shape
num_obj, r_z, n, r_old_z = zCore.shape
for idx in range(num_obj):
for val in range(n):
for alpha_old_z in range(r_old_z):
for alpha_z in range(r_z):
for alpha_old_x in range(r_old_x):
for alpha_x in range(r_x):
curr_value = curr_rhs[idx, alpha_old_z, alpha_old_x]
curr_value *= xCore[alpha_x, val, alpha_old_x]
curr_value *= zCore[idx, alpha_z, val, alpha_old_z]
new_rhs[idx, alpha_z, alpha_x] += curr_value | python | def _update_rhs(curr_rhs, xCore, zCore, new_rhs):
""" Function to be called from the project()"""
# TODO: Use intermediate variable to use 5 nested loops instead of 6.
r_x, n, r_old_x = xCore.shape
num_obj, r_z, n, r_old_z = zCore.shape
for idx in range(num_obj):
for val in range(n):
for alpha_old_z in range(r_old_z):
for alpha_z in range(r_z):
for alpha_old_x in range(r_old_x):
for alpha_x in range(r_x):
curr_value = curr_rhs[idx, alpha_old_z, alpha_old_x]
curr_value *= xCore[alpha_x, val, alpha_old_x]
curr_value *= zCore[idx, alpha_z, val, alpha_old_z]
new_rhs[idx, alpha_z, alpha_x] += curr_value | Function to be called from the project() | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L93-L107 |
oseledets/ttpy | tt/riemannian/riemannian.py | project | def project(X, Z, use_jit=False, debug=False):
""" Project tensor Z on the tangent space of tensor X.
X is a tensor in the TT format.
Z can be a tensor in the TT format or a list of tensors (in this case
the function computes projection of the sum off all tensors in the list:
project(X, Z) = P_X(\sum_i Z_i)
).
This function implements an algorithm from the paper [1], theorem 3.1.
The jit version of the code is much faster when projecting a lot of tensors
simultaneously (in other words Z is a list with many tensors).
Returns a tensor in the TT format with the TT-ranks equal 2 * rank(Z).
"""
zArr = None
if isinstance(Z, tt.vector):
zArr = [Z]
else:
zArr = Z
# Get rid of redundant ranks (they cause technical difficulties).
X = X.round(eps=0)
numDims, modeSize = X.d, X.n
coresX = tt.tensor.to_list(X)
coresZ = [None] * len(zArr)
for idx in xrange(len(zArr)):
assert(modeSize == zArr[idx].n).all()
coresZ[idx] = tt.tensor.to_list(zArr[idx])
if not use_jit and len(zArr) > 10:
print('Consider using use_jit=True option to speed up the projection '
'process.')
if use_jit:
for dim in xrange(numDims):
r1, n, r2 = coresZ[0][dim].shape
for idx in xrange(len(zArr)):
if (r1, n, r2) != coresZ[idx][dim].shape:
print('Warning: cannot use the jit version when not all '
'the ranks in the Z array are equal each other. '
'Switching to the non-jit version.')
use_jit = False
if use_jit:
zCoresDim = [None] * numDims
for dim in xrange(numDims):
r1, n, r2 = coresZ[0][dim].shape
zCoresDim[dim] = np.zeros([len(zArr), r1, n, r2])
for idx in xrange(len(zArr)):
if (r1, n, r2) != coresZ[idx][dim].shape:
print('Warning: cannot use the jit version when not all '
'the ranks in the Z array are equal each other. '
'Switching to the non-jit version.')
use_jit = False
zCoresDim[dim][idx, :, :, :] = coresZ[idx][dim]
# Initialize the cores of the projection_X(sum z[i]).
coresP = []
for dim in xrange(numDims):
r1 = 2 * X.r[dim]
r2 = 2 * X.r[dim+1]
if dim == 0:
r1 = 1
if dim == numDims - 1:
r2 = 1
coresP.append(np.zeros((r1, modeSize[dim], r2)))
# rhs[dim] is a len(zArr) x zArr[idx] x X.rank_dim.rank_dim ndarray.
# Right to left orthogonalization of X and preparation of the rhs vectors.
for dim in xrange(numDims-1, 0, -1):
# Right to left orthogonalization of the X cores.
coresX = cores_orthogonalization_step(coresX, dim, left_to_right=False)
r1, n, r2 = coresX[dim].shape
# Fill the right orthogonal part of the projection.
for value in xrange(modeSize[dim]):
coresP[dim][0:r1, value, 0:r2] = coresX[dim][:, value, :]
rhs = [None] * (numDims+1)
for dim in xrange(numDims):
rhs[dim] = np.zeros([len(zArr), zArr[idx].r[dim], coresX[dim].shape[0]])
rhs[numDims] = np.ones([len(zArr), 1, 1])
for dim in xrange(numDims-1, 0, -1):
_update_rhs(rhs[dim+1], coresX[dim], zCoresDim[dim], rhs[dim])
if debug:
assert(np.allclose(X.full(), tt.tensor.from_list(coresX).full()))
# lsh is a len(zArr) x X.rank_dim x zArr[idx].rank_dim ndarray.
lhs = np.ones([len(zArr), 1, 1])
# Left to right sweep.
for dim in xrange(numDims):
cc = coresX[dim].copy()
r1, n, r2 = cc.shape
if dim < numDims-1:
# Left to right orthogonalization.
cc = reshape(cc, (-1, r2))
cc, rr = np.linalg.qr(cc)
r2 = cc.shape[1]
# Warning: since ranks can change here, do not use X.r!
# Use coresX[dim].shape instead.
if debug:
# Need to do it before the move non orthogonal part rr to
# the coresX[dim+1].
rightQ = right(tt.tensor.from_list(coresX), dim+1)
coresX[dim] = reshape(cc, (r1, n, r2)).copy()
coresX[dim+1] = np.tensordot(rr, coresX[dim+1], 1)
new_lhs = np.zeros([len(zArr), r2, zArr[idx].r[dim+1]])
_update_lhs(lhs, coresX[dim], zCoresDim[dim], new_lhs)
# See the correspondic section in the non-jit version of this
# code for a less confusing implementation of
# the transformation below.
currPCore = np.einsum('ijk,iklm->ijlm', lhs, zCoresDim[dim])
currPCore = reshape(currPCore, (len(zArr), r1*n, -1))
currPCore -= np.einsum('ij,kjl->kil', cc, new_lhs)
currPCore = np.einsum('ijk,ikl', currPCore, rhs[dim+1])
currPCore = reshape(currPCore, (r1, modeSize[dim], r2))
if dim == 0:
coresP[dim][0:r1, :, 0:r2] += currPCore
else:
coresP[dim][r1:, :, 0:r2] += currPCore
if debug:
explicit_sum = np.zeros((r1, modeSize[dim], r2))
for idx in xrange(len(zArr)):
leftQm1 = left(tt.tensor.from_list(coresX), dim-1)
leftQ = left(tt.tensor.from_list(coresX), dim)
first = np.tensordot(leftQm1.T, unfolding(zArr[idx], dim-1), 1)
second = reshape(first, (-1, np.prod(modeSize[dim+1:])))
if dim < numDims-1:
explicit = second.dot(rightQ)
orth_cc = reshape(coresX[dim], (-1, coresX[dim].shape[2]))
explicit -= orth_cc.dot(leftQ.T.dot(unfolding(zArr[idx], dim)).dot(rightQ))
else:
explicit = second
explicit_sum += reshape(explicit, currPCore.shape)
assert(np.allclose(explicit_sum, currPCore))
lhs = new_lhs
if dim == 0:
coresP[dim][0:r1, :, r2:] = coresX[dim]
else:
coresP[dim][r1:, :, r2:] = coresX[dim]
if dim == numDims-1:
coresP[dim][r1:, :, 0:r2] += np.einsum('ijk,iklm->jlm', lhs, zCoresDim[dim])
if debug:
assert(np.allclose(X.full(), tt.tensor.from_list(coresX).full()))
return tt.tensor.from_list(coresP)
else:
# Non-jit version of the code.
# Initialize the cores of the projection_X(sum z[i]).
coresP = []
for dim in xrange(numDims):
r1 = 2 * X.r[dim]
r2 = 2 * X.r[dim+1]
if dim == 0:
r1 = 1
if dim == numDims - 1:
r2 = 1
coresP.append(np.zeros((r1, modeSize[dim], r2)))
# rhs[idx][dim] is an (Z.rank_dim * X.rank_dim) x 1 vector
rhs = [[0] * (numDims+1) for _ in xrange(len(zArr))]
for idx in xrange(len(zArr)):
rhs[idx][numDims] = np.ones([1, 1])
# Right to left sweep to orthogonalize the cores and prepare rhs.
for dim in xrange(numDims-1, 0, -1):
# Right to left orthogonalization of the X cores.
coresX = cores_orthogonalization_step(coresX, dim, left_to_right=False)
r1, n, r2 = coresX[dim].shape
# Fill the right orthogonal part of the projection.
coresP[dim][0:r1, :, 0:r2] = coresX[dim]
# Compute rhs.
for idx in xrange(len(zArr)):
coreProd = np.tensordot(coresZ[idx][dim], coresX[dim], axes=(1, 1))
coreProd = np.transpose(coreProd, (0, 2, 1, 3))
coreProd = reshape(coreProd, (zArr[idx].r[dim]*r1, zArr[idx].r[dim+1]*r2))
rhs[idx][dim] = np.dot(coreProd, rhs[idx][dim+1])
if debug:
assert(np.allclose(X.full(), tt.tensor.from_list(coresX).full()))
# lsh[idx] is an X.rank_dim x zArr[idx].rank_dim matrix.
lhs = [np.ones([1, 1]) for _ in xrange(len(zArr))]
# Left to right sweep.
for dim in xrange(numDims - 1):
if debug:
rightQ = right(tt.tensor.from_list(coresX), dim+1)
# Left to right orthogonalization of the X cores.
coresX = cores_orthogonalization_step(coresX, dim, left_to_right=True)
r1, n, r2 = coresX[dim].shape
cc = reshape(coresX[dim], (-1, r2))
for idx in xrange(len(zArr)):
currZCore = reshape(coresZ[idx][dim], (zArr[idx].r[dim], -1))
currPCore = np.dot(lhs[idx], currZCore)
# TODO: consider using np.einsum.
coreProd = np.tensordot(coresX[dim], coresZ[idx][dim], axes=(1, 1))
coreProd = np.transpose(coreProd, (0, 2, 1, 3))
coreProd = reshape(coreProd, (r1*zArr[idx].r[dim], r2*zArr[idx].r[dim+1]))
lhs[idx] = reshape(lhs[idx], (1, -1))
lhs[idx] = np.dot(lhs[idx], coreProd)
lhs[idx] = reshape(lhs[idx], (r2, zArr[idx].r[dim+1]))
currPCore = reshape(currPCore, (-1, zArr[idx].r[dim+1]))
currPCore -= np.dot(cc, lhs[idx])
rhs[idx][dim+1] = reshape(rhs[idx][dim+1], (zArr[idx].r[dim+1], r2))
currPCore = np.dot(currPCore, rhs[idx][dim+1])
currPCore = reshape(currPCore, (r1, modeSize[dim], r2))
if dim == 0:
coresP[dim][0:r1, :, 0:r2] += currPCore
else:
coresP[dim][r1:, :, 0:r2] += currPCore
if debug:
leftQm1 = left(tt.tensor.from_list(coresX), dim-1)
leftQ = left(tt.tensor.from_list(coresX), dim)
first = np.tensordot(leftQm1.T, unfolding(zArr[idx], dim-1), 1)
second = reshape(first, (-1, np.prod(modeSize[dim+1:])))
if dim < numDims-1:
explicit = second.dot(rightQ)
orth_cc = reshape(coresX[dim], (-1, coresX[dim].shape[2]))
explicit -= orth_cc.dot(leftQ.T.dot(unfolding(zArr[idx], dim)).dot(rightQ))
else:
explicit = second
explicit = reshape(explicit, currPCore.shape)
assert(np.allclose(explicit, currPCore))
if dim == 0:
coresP[dim][0:r1, :, r2:] = coresX[dim]
else:
coresP[dim][r1:, :, r2:] = coresX[dim]
for idx in xrange(len(zArr)):
r1, n, r2 = coresX[numDims-1].shape
currZCore = reshape(coresZ[idx][numDims-1], (zArr[idx].r[numDims-1], -1))
currPCore = np.dot(lhs[idx], currZCore)
currPCore = reshape(currPCore, (r1, n, r2))
coresP[numDims-1][r1:, :, 0:r2] += currPCore
if debug:
assert(np.allclose(X.full(), tt.tensor.from_list(coresX).full()))
return tt.tensor.from_list(coresP) | python | def project(X, Z, use_jit=False, debug=False):
""" Project tensor Z on the tangent space of tensor X.
X is a tensor in the TT format.
Z can be a tensor in the TT format or a list of tensors (in this case
the function computes projection of the sum off all tensors in the list:
project(X, Z) = P_X(\sum_i Z_i)
).
This function implements an algorithm from the paper [1], theorem 3.1.
The jit version of the code is much faster when projecting a lot of tensors
simultaneously (in other words Z is a list with many tensors).
Returns a tensor in the TT format with the TT-ranks equal 2 * rank(Z).
"""
zArr = None
if isinstance(Z, tt.vector):
zArr = [Z]
else:
zArr = Z
# Get rid of redundant ranks (they cause technical difficulties).
X = X.round(eps=0)
numDims, modeSize = X.d, X.n
coresX = tt.tensor.to_list(X)
coresZ = [None] * len(zArr)
for idx in xrange(len(zArr)):
assert(modeSize == zArr[idx].n).all()
coresZ[idx] = tt.tensor.to_list(zArr[idx])
if not use_jit and len(zArr) > 10:
print('Consider using use_jit=True option to speed up the projection '
'process.')
if use_jit:
for dim in xrange(numDims):
r1, n, r2 = coresZ[0][dim].shape
for idx in xrange(len(zArr)):
if (r1, n, r2) != coresZ[idx][dim].shape:
print('Warning: cannot use the jit version when not all '
'the ranks in the Z array are equal each other. '
'Switching to the non-jit version.')
use_jit = False
if use_jit:
zCoresDim = [None] * numDims
for dim in xrange(numDims):
r1, n, r2 = coresZ[0][dim].shape
zCoresDim[dim] = np.zeros([len(zArr), r1, n, r2])
for idx in xrange(len(zArr)):
if (r1, n, r2) != coresZ[idx][dim].shape:
print('Warning: cannot use the jit version when not all '
'the ranks in the Z array are equal each other. '
'Switching to the non-jit version.')
use_jit = False
zCoresDim[dim][idx, :, :, :] = coresZ[idx][dim]
# Initialize the cores of the projection_X(sum z[i]).
coresP = []
for dim in xrange(numDims):
r1 = 2 * X.r[dim]
r2 = 2 * X.r[dim+1]
if dim == 0:
r1 = 1
if dim == numDims - 1:
r2 = 1
coresP.append(np.zeros((r1, modeSize[dim], r2)))
# rhs[dim] is a len(zArr) x zArr[idx] x X.rank_dim.rank_dim ndarray.
# Right to left orthogonalization of X and preparation of the rhs vectors.
for dim in xrange(numDims-1, 0, -1):
# Right to left orthogonalization of the X cores.
coresX = cores_orthogonalization_step(coresX, dim, left_to_right=False)
r1, n, r2 = coresX[dim].shape
# Fill the right orthogonal part of the projection.
for value in xrange(modeSize[dim]):
coresP[dim][0:r1, value, 0:r2] = coresX[dim][:, value, :]
rhs = [None] * (numDims+1)
for dim in xrange(numDims):
rhs[dim] = np.zeros([len(zArr), zArr[idx].r[dim], coresX[dim].shape[0]])
rhs[numDims] = np.ones([len(zArr), 1, 1])
for dim in xrange(numDims-1, 0, -1):
_update_rhs(rhs[dim+1], coresX[dim], zCoresDim[dim], rhs[dim])
if debug:
assert(np.allclose(X.full(), tt.tensor.from_list(coresX).full()))
# lsh is a len(zArr) x X.rank_dim x zArr[idx].rank_dim ndarray.
lhs = np.ones([len(zArr), 1, 1])
# Left to right sweep.
for dim in xrange(numDims):
cc = coresX[dim].copy()
r1, n, r2 = cc.shape
if dim < numDims-1:
# Left to right orthogonalization.
cc = reshape(cc, (-1, r2))
cc, rr = np.linalg.qr(cc)
r2 = cc.shape[1]
# Warning: since ranks can change here, do not use X.r!
# Use coresX[dim].shape instead.
if debug:
# Need to do it before the move non orthogonal part rr to
# the coresX[dim+1].
rightQ = right(tt.tensor.from_list(coresX), dim+1)
coresX[dim] = reshape(cc, (r1, n, r2)).copy()
coresX[dim+1] = np.tensordot(rr, coresX[dim+1], 1)
new_lhs = np.zeros([len(zArr), r2, zArr[idx].r[dim+1]])
_update_lhs(lhs, coresX[dim], zCoresDim[dim], new_lhs)
# See the correspondic section in the non-jit version of this
# code for a less confusing implementation of
# the transformation below.
currPCore = np.einsum('ijk,iklm->ijlm', lhs, zCoresDim[dim])
currPCore = reshape(currPCore, (len(zArr), r1*n, -1))
currPCore -= np.einsum('ij,kjl->kil', cc, new_lhs)
currPCore = np.einsum('ijk,ikl', currPCore, rhs[dim+1])
currPCore = reshape(currPCore, (r1, modeSize[dim], r2))
if dim == 0:
coresP[dim][0:r1, :, 0:r2] += currPCore
else:
coresP[dim][r1:, :, 0:r2] += currPCore
if debug:
explicit_sum = np.zeros((r1, modeSize[dim], r2))
for idx in xrange(len(zArr)):
leftQm1 = left(tt.tensor.from_list(coresX), dim-1)
leftQ = left(tt.tensor.from_list(coresX), dim)
first = np.tensordot(leftQm1.T, unfolding(zArr[idx], dim-1), 1)
second = reshape(first, (-1, np.prod(modeSize[dim+1:])))
if dim < numDims-1:
explicit = second.dot(rightQ)
orth_cc = reshape(coresX[dim], (-1, coresX[dim].shape[2]))
explicit -= orth_cc.dot(leftQ.T.dot(unfolding(zArr[idx], dim)).dot(rightQ))
else:
explicit = second
explicit_sum += reshape(explicit, currPCore.shape)
assert(np.allclose(explicit_sum, currPCore))
lhs = new_lhs
if dim == 0:
coresP[dim][0:r1, :, r2:] = coresX[dim]
else:
coresP[dim][r1:, :, r2:] = coresX[dim]
if dim == numDims-1:
coresP[dim][r1:, :, 0:r2] += np.einsum('ijk,iklm->jlm', lhs, zCoresDim[dim])
if debug:
assert(np.allclose(X.full(), tt.tensor.from_list(coresX).full()))
return tt.tensor.from_list(coresP)
else:
# Non-jit version of the code.
# Initialize the cores of the projection_X(sum z[i]).
coresP = []
for dim in xrange(numDims):
r1 = 2 * X.r[dim]
r2 = 2 * X.r[dim+1]
if dim == 0:
r1 = 1
if dim == numDims - 1:
r2 = 1
coresP.append(np.zeros((r1, modeSize[dim], r2)))
# rhs[idx][dim] is an (Z.rank_dim * X.rank_dim) x 1 vector
rhs = [[0] * (numDims+1) for _ in xrange(len(zArr))]
for idx in xrange(len(zArr)):
rhs[idx][numDims] = np.ones([1, 1])
# Right to left sweep to orthogonalize the cores and prepare rhs.
for dim in xrange(numDims-1, 0, -1):
# Right to left orthogonalization of the X cores.
coresX = cores_orthogonalization_step(coresX, dim, left_to_right=False)
r1, n, r2 = coresX[dim].shape
# Fill the right orthogonal part of the projection.
coresP[dim][0:r1, :, 0:r2] = coresX[dim]
# Compute rhs.
for idx in xrange(len(zArr)):
coreProd = np.tensordot(coresZ[idx][dim], coresX[dim], axes=(1, 1))
coreProd = np.transpose(coreProd, (0, 2, 1, 3))
coreProd = reshape(coreProd, (zArr[idx].r[dim]*r1, zArr[idx].r[dim+1]*r2))
rhs[idx][dim] = np.dot(coreProd, rhs[idx][dim+1])
if debug:
assert(np.allclose(X.full(), tt.tensor.from_list(coresX).full()))
# lsh[idx] is an X.rank_dim x zArr[idx].rank_dim matrix.
lhs = [np.ones([1, 1]) for _ in xrange(len(zArr))]
# Left to right sweep.
for dim in xrange(numDims - 1):
if debug:
rightQ = right(tt.tensor.from_list(coresX), dim+1)
# Left to right orthogonalization of the X cores.
coresX = cores_orthogonalization_step(coresX, dim, left_to_right=True)
r1, n, r2 = coresX[dim].shape
cc = reshape(coresX[dim], (-1, r2))
for idx in xrange(len(zArr)):
currZCore = reshape(coresZ[idx][dim], (zArr[idx].r[dim], -1))
currPCore = np.dot(lhs[idx], currZCore)
# TODO: consider using np.einsum.
coreProd = np.tensordot(coresX[dim], coresZ[idx][dim], axes=(1, 1))
coreProd = np.transpose(coreProd, (0, 2, 1, 3))
coreProd = reshape(coreProd, (r1*zArr[idx].r[dim], r2*zArr[idx].r[dim+1]))
lhs[idx] = reshape(lhs[idx], (1, -1))
lhs[idx] = np.dot(lhs[idx], coreProd)
lhs[idx] = reshape(lhs[idx], (r2, zArr[idx].r[dim+1]))
currPCore = reshape(currPCore, (-1, zArr[idx].r[dim+1]))
currPCore -= np.dot(cc, lhs[idx])
rhs[idx][dim+1] = reshape(rhs[idx][dim+1], (zArr[idx].r[dim+1], r2))
currPCore = np.dot(currPCore, rhs[idx][dim+1])
currPCore = reshape(currPCore, (r1, modeSize[dim], r2))
if dim == 0:
coresP[dim][0:r1, :, 0:r2] += currPCore
else:
coresP[dim][r1:, :, 0:r2] += currPCore
if debug:
leftQm1 = left(tt.tensor.from_list(coresX), dim-1)
leftQ = left(tt.tensor.from_list(coresX), dim)
first = np.tensordot(leftQm1.T, unfolding(zArr[idx], dim-1), 1)
second = reshape(first, (-1, np.prod(modeSize[dim+1:])))
if dim < numDims-1:
explicit = second.dot(rightQ)
orth_cc = reshape(coresX[dim], (-1, coresX[dim].shape[2]))
explicit -= orth_cc.dot(leftQ.T.dot(unfolding(zArr[idx], dim)).dot(rightQ))
else:
explicit = second
explicit = reshape(explicit, currPCore.shape)
assert(np.allclose(explicit, currPCore))
if dim == 0:
coresP[dim][0:r1, :, r2:] = coresX[dim]
else:
coresP[dim][r1:, :, r2:] = coresX[dim]
for idx in xrange(len(zArr)):
r1, n, r2 = coresX[numDims-1].shape
currZCore = reshape(coresZ[idx][numDims-1], (zArr[idx].r[numDims-1], -1))
currPCore = np.dot(lhs[idx], currZCore)
currPCore = reshape(currPCore, (r1, n, r2))
coresP[numDims-1][r1:, :, 0:r2] += currPCore
if debug:
assert(np.allclose(X.full(), tt.tensor.from_list(coresX).full()))
return tt.tensor.from_list(coresP) | Project tensor Z on the tangent space of tensor X.
X is a tensor in the TT format.
Z can be a tensor in the TT format or a list of tensors (in this case
the function computes projection of the sum off all tensors in the list:
project(X, Z) = P_X(\sum_i Z_i)
).
This function implements an algorithm from the paper [1], theorem 3.1.
The jit version of the code is much faster when projecting a lot of tensors
simultaneously (in other words Z is a list with many tensors).
Returns a tensor in the TT format with the TT-ranks equal 2 * rank(Z). | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L110-L356 |
oseledets/ttpy | tt/riemannian/riemannian.py | projector_splitting_add | def projector_splitting_add(Y, delta, debug=False):
"""Compute Y + delta via the projector splitting scheme.
This function implements the projector splitting scheme (section 4.2 of [1]).
The result is a TT-tensor with the TT-ranks equal to the TT-ranks of Y."""
# Get rid of redundant ranks (they cause technical difficulties).
delta = delta.round(eps=0)
numDims = delta.d
assert(numDims == Y.d)
modeSize = delta.n
assert(modeSize == Y.n).all()
coresDelta = tt.tensor.to_list(delta)
coresY = tt.tensor.to_list(Y)
# rhs[dim] is an (delta.rank_dim * Y.rank_dim) x 1 vector
rhs = [None] * (numDims+1)
rhs[numDims] = np.ones([1, 1])
# Right to left sweep to orthogonalize the cores and prepare the rhs.
for dim in xrange(numDims-1, 0, -1):
# Right to left orthogonalization of the Y cores.
coresY = cores_orthogonalization_step(coresY, dim, left_to_right=False)
r1, n, r2 = coresY[dim].shape
# rhs computation.
coreProd = np.tensordot(coresDelta[dim], coresY[dim], axes=(1, 1))
coreProd = np.transpose(coreProd, (0, 2, 1, 3))
coreProd = reshape(coreProd, (delta.r[dim]*r1, delta.r[dim+1]*r2))
rhs[dim] = np.dot(coreProd, rhs[dim+1])
if debug:
assert(np.allclose(Y.full(), tt.tensor.from_list(coresY).full()))
# lsh is an Y.rank_dim x delta.rank_dim matrix.
lhs = np.ones([1, 1])
# s is an Y.rank_dim x Y.rank_dim matrix.
s = np.ones([1, 1])
# Left to right projector splitting sweep.
for dim in xrange(numDims):
# Y^+ (formula 4.10)
cc = coresDelta[dim].copy()
r1, n, r2 = coresY[dim].shape
cc = np.tensordot(lhs, cc, 1)
rhs[dim+1] = reshape(rhs[dim+1], (delta.r[dim+1], r2))
cc = reshape(cc, (-1, delta.r[dim+1]))
cc = np.dot(cc, rhs[dim+1])
if debug:
first = np.kron(np.eye(modeSize[dim]), left(tt.tensor.from_list(coresY), dim-1).T)
second = np.dot(first, unfolding(delta, dim))
explicit = np.dot(second, right(tt.tensor.from_list(coresY), dim+1))
assert(np.allclose(explicit, cc))
cc += reshape(np.tensordot(s, coresY[dim], 1), (-1, Y.r[dim+1]))
if dim < numDims-1:
cc, rr = np.linalg.qr(cc)
# TODO: do we need to use r1 = cc.shape[1] here????
cc = reshape(cc, coresY[dim].shape)
coresY[dim] = cc.copy()
if dim < numDims-1:
coreProd = np.tensordot(coresY[dim], coresDelta[dim], axes=(1, 1))
coreProd = np.transpose(coreProd, (0, 2, 1, 3))
coreProd = reshape(coreProd, (r1*delta.r[dim], r2*delta.r[dim+1]))
lhs = reshape(lhs, (1, -1))
lhs = np.dot(lhs, coreProd)
lhs = reshape(lhs, (r2, delta.r[dim+1]))
if dim < numDims-1:
# Y^- (formula 4.7)
s = rr - np.dot(lhs, rhs[dim+1])
if debug:
first = left(tt.tensor.from_list(coresY), dim).T
second = np.dot(first, unfolding(delta, dim))
explicit = np.dot(second, right(tt.tensor.from_list(coresY), dim+1))
assert(np.allclose(explicit, np.dot(lhs, rhs[dim+1])))
return tt.tensor.from_list(coresY) | python | def projector_splitting_add(Y, delta, debug=False):
"""Compute Y + delta via the projector splitting scheme.
This function implements the projector splitting scheme (section 4.2 of [1]).
The result is a TT-tensor with the TT-ranks equal to the TT-ranks of Y."""
# Get rid of redundant ranks (they cause technical difficulties).
delta = delta.round(eps=0)
numDims = delta.d
assert(numDims == Y.d)
modeSize = delta.n
assert(modeSize == Y.n).all()
coresDelta = tt.tensor.to_list(delta)
coresY = tt.tensor.to_list(Y)
# rhs[dim] is an (delta.rank_dim * Y.rank_dim) x 1 vector
rhs = [None] * (numDims+1)
rhs[numDims] = np.ones([1, 1])
# Right to left sweep to orthogonalize the cores and prepare the rhs.
for dim in xrange(numDims-1, 0, -1):
# Right to left orthogonalization of the Y cores.
coresY = cores_orthogonalization_step(coresY, dim, left_to_right=False)
r1, n, r2 = coresY[dim].shape
# rhs computation.
coreProd = np.tensordot(coresDelta[dim], coresY[dim], axes=(1, 1))
coreProd = np.transpose(coreProd, (0, 2, 1, 3))
coreProd = reshape(coreProd, (delta.r[dim]*r1, delta.r[dim+1]*r2))
rhs[dim] = np.dot(coreProd, rhs[dim+1])
if debug:
assert(np.allclose(Y.full(), tt.tensor.from_list(coresY).full()))
# lsh is an Y.rank_dim x delta.rank_dim matrix.
lhs = np.ones([1, 1])
# s is an Y.rank_dim x Y.rank_dim matrix.
s = np.ones([1, 1])
# Left to right projector splitting sweep.
for dim in xrange(numDims):
# Y^+ (formula 4.10)
cc = coresDelta[dim].copy()
r1, n, r2 = coresY[dim].shape
cc = np.tensordot(lhs, cc, 1)
rhs[dim+1] = reshape(rhs[dim+1], (delta.r[dim+1], r2))
cc = reshape(cc, (-1, delta.r[dim+1]))
cc = np.dot(cc, rhs[dim+1])
if debug:
first = np.kron(np.eye(modeSize[dim]), left(tt.tensor.from_list(coresY), dim-1).T)
second = np.dot(first, unfolding(delta, dim))
explicit = np.dot(second, right(tt.tensor.from_list(coresY), dim+1))
assert(np.allclose(explicit, cc))
cc += reshape(np.tensordot(s, coresY[dim], 1), (-1, Y.r[dim+1]))
if dim < numDims-1:
cc, rr = np.linalg.qr(cc)
# TODO: do we need to use r1 = cc.shape[1] here????
cc = reshape(cc, coresY[dim].shape)
coresY[dim] = cc.copy()
if dim < numDims-1:
coreProd = np.tensordot(coresY[dim], coresDelta[dim], axes=(1, 1))
coreProd = np.transpose(coreProd, (0, 2, 1, 3))
coreProd = reshape(coreProd, (r1*delta.r[dim], r2*delta.r[dim+1]))
lhs = reshape(lhs, (1, -1))
lhs = np.dot(lhs, coreProd)
lhs = reshape(lhs, (r2, delta.r[dim+1]))
if dim < numDims-1:
# Y^- (formula 4.7)
s = rr - np.dot(lhs, rhs[dim+1])
if debug:
first = left(tt.tensor.from_list(coresY), dim).T
second = np.dot(first, unfolding(delta, dim))
explicit = np.dot(second, right(tt.tensor.from_list(coresY), dim+1))
assert(np.allclose(explicit, np.dot(lhs, rhs[dim+1])))
return tt.tensor.from_list(coresY) | Compute Y + delta via the projector splitting scheme.
This function implements the projector splitting scheme (section 4.2 of [1]).
The result is a TT-tensor with the TT-ranks equal to the TT-ranks of Y. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L359-L432 |
oseledets/ttpy | tt/riemannian/riemannian.py | tt_qr | def tt_qr(X, left_to_right=True):
"""
Orthogonalizes a TT tensor from left to right or
from right to left.
:param: X - thensor to orthogonalise
:param: direction - direction. May be 'lr/LR' or 'rl/RL'
for left/right orthogonalization
:return: X_orth, R - orthogonal tensor and right (left)
upper (lower) triangular matrix
>>> import tt, numpy as np
>>> x = tt.rand(np.array([2, 3, 4, 5]), d=4)
>>> x_q, r = tt_qr(x, left_to_right=True)
>>> np.allclose((rm[0][0]*x_q).norm(), x.norm())
True
>>> x_u, l = tt_qr(x, left_to_right=False)
>>> np.allclose((l[0][0]*x_u).norm(), x.norm())
True
"""
# Get rid of redundant ranks (they cause technical difficulties).
X = X.round(eps=0)
numDims = X.d
coresX = tt.tensor.to_list(X)
if left_to_right:
# Left to right orthogonalization of the X cores.
for dim in xrange(0, numDims-1):
coresX = cores_orthogonalization_step(
coresX, dim, left_to_right=left_to_right)
last_core = coresX[numDims-1]
r1, n, r2 = last_core.shape
last_core, rr = np.linalg.qr(reshape(last_core, (-1, r2)))
coresX[numDims-1] = reshape(last_core, (r1, n, -1))
else:
# Right to left orthogonalization of X cores
for dim in xrange(numDims-1, 0, -1):
coresX = cores_orthogonalization_step(
coresX, dim, left_to_right=left_to_right)
last_core = coresX[0]
r1, n, r2 = last_core.shape
last_core, rr = np.linalg.qr(
np.transpose(reshape(last_core, (r1, -1)))
)
coresX[0] = reshape(
np.transpose(last_core),
(-1, n, r2))
rr = np.transpose(rr)
return tt.tensor.from_list(coresX), rr | python | def tt_qr(X, left_to_right=True):
"""
Orthogonalizes a TT tensor from left to right or
from right to left.
:param: X - thensor to orthogonalise
:param: direction - direction. May be 'lr/LR' or 'rl/RL'
for left/right orthogonalization
:return: X_orth, R - orthogonal tensor and right (left)
upper (lower) triangular matrix
>>> import tt, numpy as np
>>> x = tt.rand(np.array([2, 3, 4, 5]), d=4)
>>> x_q, r = tt_qr(x, left_to_right=True)
>>> np.allclose((rm[0][0]*x_q).norm(), x.norm())
True
>>> x_u, l = tt_qr(x, left_to_right=False)
>>> np.allclose((l[0][0]*x_u).norm(), x.norm())
True
"""
# Get rid of redundant ranks (they cause technical difficulties).
X = X.round(eps=0)
numDims = X.d
coresX = tt.tensor.to_list(X)
if left_to_right:
# Left to right orthogonalization of the X cores.
for dim in xrange(0, numDims-1):
coresX = cores_orthogonalization_step(
coresX, dim, left_to_right=left_to_right)
last_core = coresX[numDims-1]
r1, n, r2 = last_core.shape
last_core, rr = np.linalg.qr(reshape(last_core, (-1, r2)))
coresX[numDims-1] = reshape(last_core, (r1, n, -1))
else:
# Right to left orthogonalization of X cores
for dim in xrange(numDims-1, 0, -1):
coresX = cores_orthogonalization_step(
coresX, dim, left_to_right=left_to_right)
last_core = coresX[0]
r1, n, r2 = last_core.shape
last_core, rr = np.linalg.qr(
np.transpose(reshape(last_core, (r1, -1)))
)
coresX[0] = reshape(
np.transpose(last_core),
(-1, n, r2))
rr = np.transpose(rr)
return tt.tensor.from_list(coresX), rr | Orthogonalizes a TT tensor from left to right or
from right to left.
:param: X - thensor to orthogonalise
:param: direction - direction. May be 'lr/LR' or 'rl/RL'
for left/right orthogonalization
:return: X_orth, R - orthogonal tensor and right (left)
upper (lower) triangular matrix
>>> import tt, numpy as np
>>> x = tt.rand(np.array([2, 3, 4, 5]), d=4)
>>> x_q, r = tt_qr(x, left_to_right=True)
>>> np.allclose((rm[0][0]*x_q).norm(), x.norm())
True
>>> x_u, l = tt_qr(x, left_to_right=False)
>>> np.allclose((l[0][0]*x_u).norm(), x.norm())
True | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/riemannian/riemannian.py#L435-L483 |
oseledets/ttpy | tt/core/utils.py | ind2sub | def ind2sub(siz, idx):
'''
Translates full-format index into tt.vector one's.
----------
Parameters:
siz - tt.vector modes
idx - full-vector index
Note: not vectorized.
'''
n = len(siz)
subs = _np.empty((n))
k = _np.cumprod(siz[:-1])
k = _np.concatenate((_np.ones(1), k))
for i in xrange(n - 1, -1, -1):
subs[i] = _np.floor(idx / k[i])
idx = idx % k[i]
return subs.astype(_np.int32) | python | def ind2sub(siz, idx):
'''
Translates full-format index into tt.vector one's.
----------
Parameters:
siz - tt.vector modes
idx - full-vector index
Note: not vectorized.
'''
n = len(siz)
subs = _np.empty((n))
k = _np.cumprod(siz[:-1])
k = _np.concatenate((_np.ones(1), k))
for i in xrange(n - 1, -1, -1):
subs[i] = _np.floor(idx / k[i])
idx = idx % k[i]
return subs.astype(_np.int32) | Translates full-format index into tt.vector one's.
----------
Parameters:
siz - tt.vector modes
idx - full-vector index
Note: not vectorized. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/utils.py#L9-L25 |
oseledets/ttpy | tt/core/utils.py | gcd | def gcd(a, b):
'''Greatest common divider'''
f = _np.frompyfunc(_fractions.gcd, 2, 1)
return f(a, b) | python | def gcd(a, b):
'''Greatest common divider'''
f = _np.frompyfunc(_fractions.gcd, 2, 1)
return f(a, b) | Greatest common divider | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/utils.py#L27-L30 |
oseledets/ttpy | tt/ksl/ksl.py | ksl | def ksl(A, y0, tau, verb=1, scheme='symm', space=8, rmax=2000, use_normest=1):
""" Dynamical tensor-train approximation based on projector splitting
This function performs one step of dynamical tensor-train approximation
for the equation
.. math ::
\\frac{dy}{dt} = A y, \\quad y(0) = y_0
and outputs approximation for :math:`y(\\tau)`
:References:
1. Christian Lubich, Ivan Oseledets, and Bart Vandereycken.
Time integration of tensor trains. arXiv preprint 1407.2042, 2014.
http://arxiv.org/abs/1407.2042
2. Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator
for dynamical low-rank approximation. BIT, 54(1):171-188, 2014.
http://dx.doi.org/10.1007/s10543-013-0454-0
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial condition in the TT-format,
:type y0: tensor
:param tau: Timestep
:type tau: float
:param scheme: The integration scheme, possible values: 'symm' -- second order, 'first' -- first order
:type scheme: str
:param space: Maximal dimension of the Krylov space for the local EXPOKIT solver.
:type space: int
:param use_normest: Use matrix norm estimation instead of the true 1-norm in KSL procedure. 0 -use true norm, 1 - Higham norm estimator, 2 - fixed norm=1.0 (for testing purposes only)
:type use_normest: int, default: 1
:rtype: tensor
:Example:
>>> import tt
>>> import tt.ksl
>>> import numpy as np
>>> d = 8
>>> a = tt.qlaplace_dd([d, d, d])
>>> y0, ev = tt.eigb.eigb(a, tt.rand(2 , 24, 2), 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 1 eigenvalues with accuracy 1E-06
swp: 1 er = 1.1408 rmax:2
swp: 2 er = 190.01 rmax:2
swp: 3 er = 2.72582E-08 rmax:2
Total number of matvecs: 0
>>> y1 = tt.ksl.ksl(a, y0, 1e-2)
Solving a real-valued dynamical problem with tau=1E-02
>>> print tt.dot(y1, y0) / (y1.norm() * y0.norm()) - 1 #Eigenvectors should not change
0.0
"""
y0 = y0.round(1e-14) # This will fix ranks
# to be no more than maximal reasonable.
# Fortran part doesn't handle excessive ranks
ry = y0.r.copy()
if scheme is 'symm':
tp = 2
else:
tp = 1
usenrm = int(use_normest)
# Check for dtype
y = tt.vector()
if np.iscomplex(A.tt.core).any() or np.iscomplex(y0.core).any():
dyn_tt.dyn_tt.ztt_ksl(
y0.d,
A.n,
A.m,
A.tt.r,
A.tt.core + 0j,
y0.core + 0j,
ry,
tau,
rmax,
0,
10,
verb,
tp,
space,
usenrm
)
y.core = dyn_tt.dyn_tt.zresult_core.copy()
else:
A.tt.core = np.real(A.tt.core)
y0.core = np.real(y0.core)
dyn_tt.dyn_tt.tt_ksl(
y0.d,
A.n,
A.m,
A.tt.r,
A.tt.core,
y0.core,
ry,
tau,
rmax,
0,
10,
verb,
tp,
space,
usenrm
)
y.core = dyn_tt.dyn_tt.dresult_core.copy()
dyn_tt.dyn_tt.deallocate_result()
y.d = y0.d
y.n = A.n.copy()
y.r = ry
y.get_ps()
return y | python | def ksl(A, y0, tau, verb=1, scheme='symm', space=8, rmax=2000, use_normest=1):
""" Dynamical tensor-train approximation based on projector splitting
This function performs one step of dynamical tensor-train approximation
for the equation
.. math ::
\\frac{dy}{dt} = A y, \\quad y(0) = y_0
and outputs approximation for :math:`y(\\tau)`
:References:
1. Christian Lubich, Ivan Oseledets, and Bart Vandereycken.
Time integration of tensor trains. arXiv preprint 1407.2042, 2014.
http://arxiv.org/abs/1407.2042
2. Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator
for dynamical low-rank approximation. BIT, 54(1):171-188, 2014.
http://dx.doi.org/10.1007/s10543-013-0454-0
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial condition in the TT-format,
:type y0: tensor
:param tau: Timestep
:type tau: float
:param scheme: The integration scheme, possible values: 'symm' -- second order, 'first' -- first order
:type scheme: str
:param space: Maximal dimension of the Krylov space for the local EXPOKIT solver.
:type space: int
:param use_normest: Use matrix norm estimation instead of the true 1-norm in KSL procedure. 0 -use true norm, 1 - Higham norm estimator, 2 - fixed norm=1.0 (for testing purposes only)
:type use_normest: int, default: 1
:rtype: tensor
:Example:
>>> import tt
>>> import tt.ksl
>>> import numpy as np
>>> d = 8
>>> a = tt.qlaplace_dd([d, d, d])
>>> y0, ev = tt.eigb.eigb(a, tt.rand(2 , 24, 2), 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 1 eigenvalues with accuracy 1E-06
swp: 1 er = 1.1408 rmax:2
swp: 2 er = 190.01 rmax:2
swp: 3 er = 2.72582E-08 rmax:2
Total number of matvecs: 0
>>> y1 = tt.ksl.ksl(a, y0, 1e-2)
Solving a real-valued dynamical problem with tau=1E-02
>>> print tt.dot(y1, y0) / (y1.norm() * y0.norm()) - 1 #Eigenvectors should not change
0.0
"""
y0 = y0.round(1e-14) # This will fix ranks
# to be no more than maximal reasonable.
# Fortran part doesn't handle excessive ranks
ry = y0.r.copy()
if scheme is 'symm':
tp = 2
else:
tp = 1
usenrm = int(use_normest)
# Check for dtype
y = tt.vector()
if np.iscomplex(A.tt.core).any() or np.iscomplex(y0.core).any():
dyn_tt.dyn_tt.ztt_ksl(
y0.d,
A.n,
A.m,
A.tt.r,
A.tt.core + 0j,
y0.core + 0j,
ry,
tau,
rmax,
0,
10,
verb,
tp,
space,
usenrm
)
y.core = dyn_tt.dyn_tt.zresult_core.copy()
else:
A.tt.core = np.real(A.tt.core)
y0.core = np.real(y0.core)
dyn_tt.dyn_tt.tt_ksl(
y0.d,
A.n,
A.m,
A.tt.r,
A.tt.core,
y0.core,
ry,
tau,
rmax,
0,
10,
verb,
tp,
space,
usenrm
)
y.core = dyn_tt.dyn_tt.dresult_core.copy()
dyn_tt.dyn_tt.deallocate_result()
y.d = y0.d
y.n = A.n.copy()
y.r = ry
y.get_ps()
return y | Dynamical tensor-train approximation based on projector splitting
This function performs one step of dynamical tensor-train approximation
for the equation
.. math ::
\\frac{dy}{dt} = A y, \\quad y(0) = y_0
and outputs approximation for :math:`y(\\tau)`
:References:
1. Christian Lubich, Ivan Oseledets, and Bart Vandereycken.
Time integration of tensor trains. arXiv preprint 1407.2042, 2014.
http://arxiv.org/abs/1407.2042
2. Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator
for dynamical low-rank approximation. BIT, 54(1):171-188, 2014.
http://dx.doi.org/10.1007/s10543-013-0454-0
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial condition in the TT-format,
:type y0: tensor
:param tau: Timestep
:type tau: float
:param scheme: The integration scheme, possible values: 'symm' -- second order, 'first' -- first order
:type scheme: str
:param space: Maximal dimension of the Krylov space for the local EXPOKIT solver.
:type space: int
:param use_normest: Use matrix norm estimation instead of the true 1-norm in KSL procedure. 0 -use true norm, 1 - Higham norm estimator, 2 - fixed norm=1.0 (for testing purposes only)
:type use_normest: int, default: 1
:rtype: tensor
:Example:
>>> import tt
>>> import tt.ksl
>>> import numpy as np
>>> d = 8
>>> a = tt.qlaplace_dd([d, d, d])
>>> y0, ev = tt.eigb.eigb(a, tt.rand(2 , 24, 2), 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 1 eigenvalues with accuracy 1E-06
swp: 1 er = 1.1408 rmax:2
swp: 2 er = 190.01 rmax:2
swp: 3 er = 2.72582E-08 rmax:2
Total number of matvecs: 0
>>> y1 = tt.ksl.ksl(a, y0, 1e-2)
Solving a real-valued dynamical problem with tau=1E-02
>>> print tt.dot(y1, y0) / (y1.norm() * y0.norm()) - 1 #Eigenvectors should not change
0.0 | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/ksl/ksl.py#L7-L123 |
oseledets/ttpy | tt/ksl/ksl.py | diag_ksl | def diag_ksl(A, y0, tau, verb=1, scheme='symm', space=8, rmax=2000):
""" Dynamical tensor-train approximation based on projector splitting
This function performs one step of dynamical tensor-train approximation with diagonal matrix, i.e. it solves the equation
for the equation
.. math ::
\\frac{dy}{dt} = V y, \\quad y(0) = y_0
and outputs approximation for :math:`y(\\tau)`
:References:
1. Christian Lubich, Ivan Oseledets, and Bart Vandereycken.
Time integration of tensor trains. arXiv preprint 1407.2042, 2014.
http://arxiv.org/abs/1407.2042
2. Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator
for dynamical low-rank approximation. BIT, 54(1):171-188, 2014.
http://dx.doi.org/10.1007/s10543-013-0454-0
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial condition in the TT-format,
:type y0: tensor
:param tau: Timestep
:type tau: float
:param scheme: The integration scheme, possible values: 'symm' -- second order, 'first' -- first order
:type scheme: str
:param space: Maximal dimension of the Krylov space for the local EXPOKIT solver.
:type space: int
:rtype: tensor
:Example:
>>> import tt
>>> import tt.ksl
>>> import numpy as np
>>> d = 8
>>> a = tt.qlaplace_dd([d, d, d])
>>> y0, ev = tt.eigb.eigb(a, tt.rand(2 , 24, 2), 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 1 eigenvalues with accuracy 1E-06
swp: 1 er = 1.1408 rmax:2
swp: 2 er = 190.01 rmax:2
swp: 3 er = 2.72582E-08 rmax:2
Total number of matvecs: 0
>>> y1 = tt.ksl.ksl(a, y0, 1e-2)
Solving a real-valued dynamical problem with tau=1E-02
>>> print tt.dot(y1, y0) / (y1.norm() * y0.norm()) - 1 #Eigenvectors should not change
0.0
"""
y0 = y0.round(1e-14) # This will fix ranks
# to be no more than maximal reasonable.
# Fortran part doesn't handle excessive ranks
ry = y0.r.copy()
if scheme is 'symm':
tp = 2
else:
tp = 1
# Check for dtype
y = tt.vector()
if np.iscomplex(A.core).any() or np.iscomplex(y0.core).any():
dyn_tt.dyn_diag_tt.ztt_diag_ksl(
y0.d,
A.n,
A.r,
A.core + 0j,
y0.core + 0j,
ry,
tau,
rmax,
0,
10,
verb,
tp,
space)
y.core = dyn_tt.dyn_diag_tt.zresult_core.copy()
else:
A.core = np.real(A.core)
y0.core = np.real(y0.core)
dyn_tt.dyn_diag_tt.dtt_diag_ksl(
y0.d,
A.n,
A.r,
A.core,
y0.core,
ry,
tau,
rmax,
0,
10,
verb,
tp,
space)
y.core = dyn_tt.dyn_diag_tt.dresult_core.copy()
dyn_tt.dyn_diag_tt.deallocate_result()
y.d = y0.d
y.n = A.n.copy()
y.r = ry
y.get_ps()
return y | python | def diag_ksl(A, y0, tau, verb=1, scheme='symm', space=8, rmax=2000):
""" Dynamical tensor-train approximation based on projector splitting
This function performs one step of dynamical tensor-train approximation with diagonal matrix, i.e. it solves the equation
for the equation
.. math ::
\\frac{dy}{dt} = V y, \\quad y(0) = y_0
and outputs approximation for :math:`y(\\tau)`
:References:
1. Christian Lubich, Ivan Oseledets, and Bart Vandereycken.
Time integration of tensor trains. arXiv preprint 1407.2042, 2014.
http://arxiv.org/abs/1407.2042
2. Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator
for dynamical low-rank approximation. BIT, 54(1):171-188, 2014.
http://dx.doi.org/10.1007/s10543-013-0454-0
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial condition in the TT-format,
:type y0: tensor
:param tau: Timestep
:type tau: float
:param scheme: The integration scheme, possible values: 'symm' -- second order, 'first' -- first order
:type scheme: str
:param space: Maximal dimension of the Krylov space for the local EXPOKIT solver.
:type space: int
:rtype: tensor
:Example:
>>> import tt
>>> import tt.ksl
>>> import numpy as np
>>> d = 8
>>> a = tt.qlaplace_dd([d, d, d])
>>> y0, ev = tt.eigb.eigb(a, tt.rand(2 , 24, 2), 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 1 eigenvalues with accuracy 1E-06
swp: 1 er = 1.1408 rmax:2
swp: 2 er = 190.01 rmax:2
swp: 3 er = 2.72582E-08 rmax:2
Total number of matvecs: 0
>>> y1 = tt.ksl.ksl(a, y0, 1e-2)
Solving a real-valued dynamical problem with tau=1E-02
>>> print tt.dot(y1, y0) / (y1.norm() * y0.norm()) - 1 #Eigenvectors should not change
0.0
"""
y0 = y0.round(1e-14) # This will fix ranks
# to be no more than maximal reasonable.
# Fortran part doesn't handle excessive ranks
ry = y0.r.copy()
if scheme is 'symm':
tp = 2
else:
tp = 1
# Check for dtype
y = tt.vector()
if np.iscomplex(A.core).any() or np.iscomplex(y0.core).any():
dyn_tt.dyn_diag_tt.ztt_diag_ksl(
y0.d,
A.n,
A.r,
A.core + 0j,
y0.core + 0j,
ry,
tau,
rmax,
0,
10,
verb,
tp,
space)
y.core = dyn_tt.dyn_diag_tt.zresult_core.copy()
else:
A.core = np.real(A.core)
y0.core = np.real(y0.core)
dyn_tt.dyn_diag_tt.dtt_diag_ksl(
y0.d,
A.n,
A.r,
A.core,
y0.core,
ry,
tau,
rmax,
0,
10,
verb,
tp,
space)
y.core = dyn_tt.dyn_diag_tt.dresult_core.copy()
dyn_tt.dyn_diag_tt.deallocate_result()
y.d = y0.d
y.n = A.n.copy()
y.r = ry
y.get_ps()
return y | Dynamical tensor-train approximation based on projector splitting
This function performs one step of dynamical tensor-train approximation with diagonal matrix, i.e. it solves the equation
for the equation
.. math ::
\\frac{dy}{dt} = V y, \\quad y(0) = y_0
and outputs approximation for :math:`y(\\tau)`
:References:
1. Christian Lubich, Ivan Oseledets, and Bart Vandereycken.
Time integration of tensor trains. arXiv preprint 1407.2042, 2014.
http://arxiv.org/abs/1407.2042
2. Christian Lubich and Ivan V. Oseledets. A projector-splitting integrator
for dynamical low-rank approximation. BIT, 54(1):171-188, 2014.
http://dx.doi.org/10.1007/s10543-013-0454-0
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial condition in the TT-format,
:type y0: tensor
:param tau: Timestep
:type tau: float
:param scheme: The integration scheme, possible values: 'symm' -- second order, 'first' -- first order
:type scheme: str
:param space: Maximal dimension of the Krylov space for the local EXPOKIT solver.
:type space: int
:rtype: tensor
:Example:
>>> import tt
>>> import tt.ksl
>>> import numpy as np
>>> d = 8
>>> a = tt.qlaplace_dd([d, d, d])
>>> y0, ev = tt.eigb.eigb(a, tt.rand(2 , 24, 2), 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 1 eigenvalues with accuracy 1E-06
swp: 1 er = 1.1408 rmax:2
swp: 2 er = 190.01 rmax:2
swp: 3 er = 2.72582E-08 rmax:2
Total number of matvecs: 0
>>> y1 = tt.ksl.ksl(a, y0, 1e-2)
Solving a real-valued dynamical problem with tau=1E-02
>>> print tt.dot(y1, y0) / (y1.norm() * y0.norm()) - 1 #Eigenvectors should not change
0.0 | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/ksl/ksl.py#L126-L231 |
oseledets/ttpy | tt/amen/__init__.py | amen_solve | def amen_solve(A, f, x0, eps, kickrank=4, nswp=20, local_prec='n',
local_iters=2, local_restart=40, trunc_norm=1, max_full_size=50, verb=1):
""" Approximate linear system solution in the tensor-train (TT) format
using Alternating minimal energy (AMEN approach)
:References: Sergey Dolgov, Dmitry. Savostyanov
Paper 1: http://arxiv.org/abs/1301.6068
Paper 2: http://arxiv.org/abs/1304.1222
:param A: Matrix in the TT-format
:type A: matrix
:param f: Right-hand side in the TT-format
:type f: tensor
:param x0: TT-tensor of initial guess.
:type x0: tensor
:param eps: Accuracy.
:type eps: float
:param kickrank: compression rank of the residual Z, i.e. enrichment size [4]
:param nswp: maximal number of sweeps [50]
:param local_prec: local preconditioner: '' (no prec.), 'ljacobi', 'cjacobi', 'rjacobi' ['']
:param local_iters: dimension of local gmres [40]
:param local_restart: dimension of local gmres [40]
:param trunc_norm: truncate in either Frob. ('fro'), or residual norm ('residual') ['residual']
:param max_full_size: maximal size of the local matrix for the full solver [50]
:param verb: 0 -- no info output, 1 -- print info output
:Example:
>>> import tt
>>> import tt.amen #Needed, not imported automatically
>>> a = tt.qlaplace_dd([8, 8, 8]) #3D-Laplacian
>>> rhs = tt.ones(2, 3 * 8) #Right-hand side of all ones
>>> x = tt.amen.amen_solve(a, rhs, rhs, 1e-8)
amen_solve: swp=1, max_dx= 9.766E-01, max_res= 3.269E+00, max_rank=5
amen_solve: swp=2, max_dx= 4.293E-01, max_res= 8.335E+00, max_rank=9
amen_solve: swp=3, max_dx= 1.135E-01, max_res= 5.341E+00, max_rank=13
amen_solve: swp=4, max_dx= 9.032E-03, max_res= 5.908E-01, max_rank=17
amen_solve: swp=5, max_dx= 9.500E-04, max_res= 7.636E-02, max_rank=21
amen_solve: swp=6, max_dx= 4.002E-05, max_res= 5.573E-03, max_rank=25
amen_solve: swp=7, max_dx= 4.949E-06, max_res= 8.418E-04, max_rank=29
amen_solve: swp=8, max_dx= 9.618E-07, max_res= 2.599E-04, max_rank=33
amen_solve: swp=9, max_dx= 2.792E-07, max_res= 6.336E-05, max_rank=37
amen_solve: swp=10, max_dx= 4.730E-08, max_res= 1.663E-05, max_rank=41
amen_solve: swp=11, max_dx= 1.508E-08, max_res= 5.463E-06, max_rank=45
amen_solve: swp=12, max_dx= 3.771E-09, max_res= 1.847E-06, max_rank=49
amen_solve: swp=13, max_dx= 7.797E-10, max_res= 6.203E-07, max_rank=53
amen_solve: swp=14, max_dx= 1.747E-10, max_res= 2.058E-07, max_rank=57
amen_solve: swp=15, max_dx= 8.150E-11, max_res= 8.555E-08, max_rank=61
amen_solve: swp=16, max_dx= 2.399E-11, max_res= 4.215E-08, max_rank=65
amen_solve: swp=17, max_dx= 7.871E-12, max_res= 1.341E-08, max_rank=69
amen_solve: swp=18, max_dx= 3.053E-12, max_res= 6.982E-09, max_rank=73
>>> print (tt.matvec(a, x) - rhs).norm() / rhs.norm()
5.5152374305127345e-09
"""
m = A.m.copy()
rx0 = x0.r.copy()
psx0 = x0.ps.copy()
if A.is_complex or f.is_complex:
amen_f90.amen_f90.ztt_amen_wrapper(f.d, A.n, m,
A.tt.r, A.tt.ps, A.tt.core,
f.r, f.ps, f.core,
rx0, psx0, x0.core,
eps, kickrank, nswp, local_iters, local_restart, trunc_norm, max_full_size, verb, local_prec)
else:
if x0.is_complex:
x0 = x0.real()
rx0 = x0.r.copy()
psx0 = x0.ps.copy()
amen_f90.amen_f90.dtt_amen_wrapper(f.d, A.n, m,
A.tt.r, A.tt.ps, A.tt.core,
f.r, f.ps, f.core,
rx0, psx0, x0.core,
eps, kickrank, nswp, local_iters, local_restart, trunc_norm, max_full_size, verb, local_prec)
x = tt.tensor()
x.d = f.d
x.n = m.copy()
x.r = rx0
if A.is_complex or f.is_complex:
x.core = amen_f90.amen_f90.zcore.copy()
else:
x.core = amen_f90.amen_f90.core.copy()
amen_f90.amen_f90.deallocate_result()
x.get_ps()
return tt.vector.from_list(tt.tensor.to_list(x)) | python | def amen_solve(A, f, x0, eps, kickrank=4, nswp=20, local_prec='n',
local_iters=2, local_restart=40, trunc_norm=1, max_full_size=50, verb=1):
""" Approximate linear system solution in the tensor-train (TT) format
using Alternating minimal energy (AMEN approach)
:References: Sergey Dolgov, Dmitry. Savostyanov
Paper 1: http://arxiv.org/abs/1301.6068
Paper 2: http://arxiv.org/abs/1304.1222
:param A: Matrix in the TT-format
:type A: matrix
:param f: Right-hand side in the TT-format
:type f: tensor
:param x0: TT-tensor of initial guess.
:type x0: tensor
:param eps: Accuracy.
:type eps: float
:param kickrank: compression rank of the residual Z, i.e. enrichment size [4]
:param nswp: maximal number of sweeps [50]
:param local_prec: local preconditioner: '' (no prec.), 'ljacobi', 'cjacobi', 'rjacobi' ['']
:param local_iters: dimension of local gmres [40]
:param local_restart: dimension of local gmres [40]
:param trunc_norm: truncate in either Frob. ('fro'), or residual norm ('residual') ['residual']
:param max_full_size: maximal size of the local matrix for the full solver [50]
:param verb: 0 -- no info output, 1 -- print info output
:Example:
>>> import tt
>>> import tt.amen #Needed, not imported automatically
>>> a = tt.qlaplace_dd([8, 8, 8]) #3D-Laplacian
>>> rhs = tt.ones(2, 3 * 8) #Right-hand side of all ones
>>> x = tt.amen.amen_solve(a, rhs, rhs, 1e-8)
amen_solve: swp=1, max_dx= 9.766E-01, max_res= 3.269E+00, max_rank=5
amen_solve: swp=2, max_dx= 4.293E-01, max_res= 8.335E+00, max_rank=9
amen_solve: swp=3, max_dx= 1.135E-01, max_res= 5.341E+00, max_rank=13
amen_solve: swp=4, max_dx= 9.032E-03, max_res= 5.908E-01, max_rank=17
amen_solve: swp=5, max_dx= 9.500E-04, max_res= 7.636E-02, max_rank=21
amen_solve: swp=6, max_dx= 4.002E-05, max_res= 5.573E-03, max_rank=25
amen_solve: swp=7, max_dx= 4.949E-06, max_res= 8.418E-04, max_rank=29
amen_solve: swp=8, max_dx= 9.618E-07, max_res= 2.599E-04, max_rank=33
amen_solve: swp=9, max_dx= 2.792E-07, max_res= 6.336E-05, max_rank=37
amen_solve: swp=10, max_dx= 4.730E-08, max_res= 1.663E-05, max_rank=41
amen_solve: swp=11, max_dx= 1.508E-08, max_res= 5.463E-06, max_rank=45
amen_solve: swp=12, max_dx= 3.771E-09, max_res= 1.847E-06, max_rank=49
amen_solve: swp=13, max_dx= 7.797E-10, max_res= 6.203E-07, max_rank=53
amen_solve: swp=14, max_dx= 1.747E-10, max_res= 2.058E-07, max_rank=57
amen_solve: swp=15, max_dx= 8.150E-11, max_res= 8.555E-08, max_rank=61
amen_solve: swp=16, max_dx= 2.399E-11, max_res= 4.215E-08, max_rank=65
amen_solve: swp=17, max_dx= 7.871E-12, max_res= 1.341E-08, max_rank=69
amen_solve: swp=18, max_dx= 3.053E-12, max_res= 6.982E-09, max_rank=73
>>> print (tt.matvec(a, x) - rhs).norm() / rhs.norm()
5.5152374305127345e-09
"""
m = A.m.copy()
rx0 = x0.r.copy()
psx0 = x0.ps.copy()
if A.is_complex or f.is_complex:
amen_f90.amen_f90.ztt_amen_wrapper(f.d, A.n, m,
A.tt.r, A.tt.ps, A.tt.core,
f.r, f.ps, f.core,
rx0, psx0, x0.core,
eps, kickrank, nswp, local_iters, local_restart, trunc_norm, max_full_size, verb, local_prec)
else:
if x0.is_complex:
x0 = x0.real()
rx0 = x0.r.copy()
psx0 = x0.ps.copy()
amen_f90.amen_f90.dtt_amen_wrapper(f.d, A.n, m,
A.tt.r, A.tt.ps, A.tt.core,
f.r, f.ps, f.core,
rx0, psx0, x0.core,
eps, kickrank, nswp, local_iters, local_restart, trunc_norm, max_full_size, verb, local_prec)
x = tt.tensor()
x.d = f.d
x.n = m.copy()
x.r = rx0
if A.is_complex or f.is_complex:
x.core = amen_f90.amen_f90.zcore.copy()
else:
x.core = amen_f90.amen_f90.core.copy()
amen_f90.amen_f90.deallocate_result()
x.get_ps()
return tt.vector.from_list(tt.tensor.to_list(x)) | Approximate linear system solution in the tensor-train (TT) format
using Alternating minimal energy (AMEN approach)
:References: Sergey Dolgov, Dmitry. Savostyanov
Paper 1: http://arxiv.org/abs/1301.6068
Paper 2: http://arxiv.org/abs/1304.1222
:param A: Matrix in the TT-format
:type A: matrix
:param f: Right-hand side in the TT-format
:type f: tensor
:param x0: TT-tensor of initial guess.
:type x0: tensor
:param eps: Accuracy.
:type eps: float
:param kickrank: compression rank of the residual Z, i.e. enrichment size [4]
:param nswp: maximal number of sweeps [50]
:param local_prec: local preconditioner: '' (no prec.), 'ljacobi', 'cjacobi', 'rjacobi' ['']
:param local_iters: dimension of local gmres [40]
:param local_restart: dimension of local gmres [40]
:param trunc_norm: truncate in either Frob. ('fro'), or residual norm ('residual') ['residual']
:param max_full_size: maximal size of the local matrix for the full solver [50]
:param verb: 0 -- no info output, 1 -- print info output
:Example:
>>> import tt
>>> import tt.amen #Needed, not imported automatically
>>> a = tt.qlaplace_dd([8, 8, 8]) #3D-Laplacian
>>> rhs = tt.ones(2, 3 * 8) #Right-hand side of all ones
>>> x = tt.amen.amen_solve(a, rhs, rhs, 1e-8)
amen_solve: swp=1, max_dx= 9.766E-01, max_res= 3.269E+00, max_rank=5
amen_solve: swp=2, max_dx= 4.293E-01, max_res= 8.335E+00, max_rank=9
amen_solve: swp=3, max_dx= 1.135E-01, max_res= 5.341E+00, max_rank=13
amen_solve: swp=4, max_dx= 9.032E-03, max_res= 5.908E-01, max_rank=17
amen_solve: swp=5, max_dx= 9.500E-04, max_res= 7.636E-02, max_rank=21
amen_solve: swp=6, max_dx= 4.002E-05, max_res= 5.573E-03, max_rank=25
amen_solve: swp=7, max_dx= 4.949E-06, max_res= 8.418E-04, max_rank=29
amen_solve: swp=8, max_dx= 9.618E-07, max_res= 2.599E-04, max_rank=33
amen_solve: swp=9, max_dx= 2.792E-07, max_res= 6.336E-05, max_rank=37
amen_solve: swp=10, max_dx= 4.730E-08, max_res= 1.663E-05, max_rank=41
amen_solve: swp=11, max_dx= 1.508E-08, max_res= 5.463E-06, max_rank=45
amen_solve: swp=12, max_dx= 3.771E-09, max_res= 1.847E-06, max_rank=49
amen_solve: swp=13, max_dx= 7.797E-10, max_res= 6.203E-07, max_rank=53
amen_solve: swp=14, max_dx= 1.747E-10, max_res= 2.058E-07, max_rank=57
amen_solve: swp=15, max_dx= 8.150E-11, max_res= 8.555E-08, max_rank=61
amen_solve: swp=16, max_dx= 2.399E-11, max_res= 4.215E-08, max_rank=65
amen_solve: swp=17, max_dx= 7.871E-12, max_res= 1.341E-08, max_rank=69
amen_solve: swp=18, max_dx= 3.053E-12, max_res= 6.982E-09, max_rank=73
>>> print (tt.matvec(a, x) - rhs).norm() / rhs.norm()
5.5152374305127345e-09 | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/amen/__init__.py#L7-L92 |
oseledets/ttpy | tt/core/matrix.py | matrix.T | def T(self):
"""Transposed TT-matrix"""
mycrs = matrix.to_list(self)
trans_crs = []
for cr in mycrs:
trans_crs.append(_np.transpose(cr, [0, 2, 1, 3]))
return matrix.from_list(trans_crs) | python | def T(self):
"""Transposed TT-matrix"""
mycrs = matrix.to_list(self)
trans_crs = []
for cr in mycrs:
trans_crs.append(_np.transpose(cr, [0, 2, 1, 3]))
return matrix.from_list(trans_crs) | Transposed TT-matrix | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/matrix.py#L126-L132 |
oseledets/ttpy | tt/core/matrix.py | matrix.real | def real(self):
"""Return real part of a matrix."""
return matrix(self.tt.real(), n=self.n, m=self.m) | python | def real(self):
"""Return real part of a matrix."""
return matrix(self.tt.real(), n=self.n, m=self.m) | Return real part of a matrix. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/matrix.py#L134-L136 |
oseledets/ttpy | tt/core/matrix.py | matrix.imag | def imag(self):
"""Return imaginary part of a matrix."""
return matrix(self.tt.imag(), n=self.n, m=self.m) | python | def imag(self):
"""Return imaginary part of a matrix."""
return matrix(self.tt.imag(), n=self.n, m=self.m) | Return imaginary part of a matrix. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/matrix.py#L138-L140 |
oseledets/ttpy | tt/core/matrix.py | matrix.c2r | def c2r(self):
"""Get real matrix from complex one suitable for solving complex linear system with real solver.
For matrix :math:`M(i_1,j_1,\\ldots,i_d,j_d) = \\Re M + i\\Im M` returns (d+1)-dimensional matrix
:math:`\\tilde{M}(i_1,j_1,\\ldots,i_d,j_d,i_{d+1},j_{d+1})` of form
:math:`\\begin{bmatrix}\\Re M & -\\Im M \\\\ \\Im M & \\Re M \\end{bmatrix}`. This function
is useful for solving complex linear system :math:`\\mathcal{A}X = B` with real solver by
transforming it into
.. math::
\\begin{bmatrix}\\Re\\mathcal{A} & -\\Im\\mathcal{A} \\\\
\\Im\\mathcal{A} & \\Re\\mathcal{A} \\end{bmatrix}
\\begin{bmatrix}\\Re X \\\\ \\Im X\\end{bmatrix} =
\\begin{bmatrix}\\Re B \\\\ \\Im B\\end{bmatrix}.
"""
return matrix(a=self.tt.__complex_op('M'), n=_np.concatenate(
(self.n, [2])), m=_np.concatenate((self.m, [2]))) | python | def c2r(self):
"""Get real matrix from complex one suitable for solving complex linear system with real solver.
For matrix :math:`M(i_1,j_1,\\ldots,i_d,j_d) = \\Re M + i\\Im M` returns (d+1)-dimensional matrix
:math:`\\tilde{M}(i_1,j_1,\\ldots,i_d,j_d,i_{d+1},j_{d+1})` of form
:math:`\\begin{bmatrix}\\Re M & -\\Im M \\\\ \\Im M & \\Re M \\end{bmatrix}`. This function
is useful for solving complex linear system :math:`\\mathcal{A}X = B` with real solver by
transforming it into
.. math::
\\begin{bmatrix}\\Re\\mathcal{A} & -\\Im\\mathcal{A} \\\\
\\Im\\mathcal{A} & \\Re\\mathcal{A} \\end{bmatrix}
\\begin{bmatrix}\\Re X \\\\ \\Im X\\end{bmatrix} =
\\begin{bmatrix}\\Re B \\\\ \\Im B\\end{bmatrix}.
"""
return matrix(a=self.tt.__complex_op('M'), n=_np.concatenate(
(self.n, [2])), m=_np.concatenate((self.m, [2]))) | Get real matrix from complex one suitable for solving complex linear system with real solver.
For matrix :math:`M(i_1,j_1,\\ldots,i_d,j_d) = \\Re M + i\\Im M` returns (d+1)-dimensional matrix
:math:`\\tilde{M}(i_1,j_1,\\ldots,i_d,j_d,i_{d+1},j_{d+1})` of form
:math:`\\begin{bmatrix}\\Re M & -\\Im M \\\\ \\Im M & \\Re M \\end{bmatrix}`. This function
is useful for solving complex linear system :math:`\\mathcal{A}X = B` with real solver by
transforming it into
.. math::
\\begin{bmatrix}\\Re\\mathcal{A} & -\\Im\\mathcal{A} \\\\
\\Im\\mathcal{A} & \\Re\\mathcal{A} \\end{bmatrix}
\\begin{bmatrix}\\Re X \\\\ \\Im X\\end{bmatrix} =
\\begin{bmatrix}\\Re B \\\\ \\Im B\\end{bmatrix}. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/matrix.py#L142-L159 |
oseledets/ttpy | tt/core/matrix.py | matrix.round | def round(self, eps=1e-14, rmax=100000):
""" Computes an approximation to a
TT-matrix in with accuracy EPS
"""
c = matrix()
c.tt = self.tt.round(eps, rmax)
c.n = self.n.copy()
c.m = self.m.copy()
return c | python | def round(self, eps=1e-14, rmax=100000):
""" Computes an approximation to a
TT-matrix in with accuracy EPS
"""
c = matrix()
c.tt = self.tt.round(eps, rmax)
c.n = self.n.copy()
c.m = self.m.copy()
return c | Computes an approximation to a
TT-matrix in with accuracy EPS | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/matrix.py#L317-L325 |
oseledets/ttpy | tt/core/matrix.py | matrix.copy | def copy(self):
""" Creates a copy of the TT-matrix """
c = matrix()
c.tt = self.tt.copy()
c.n = self.n.copy()
c.m = self.m.copy()
return c | python | def copy(self):
""" Creates a copy of the TT-matrix """
c = matrix()
c.tt = self.tt.copy()
c.n = self.n.copy()
c.m = self.m.copy()
return c | Creates a copy of the TT-matrix | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/matrix.py#L327-L333 |
oseledets/ttpy | tt/core/matrix.py | matrix.full | def full(self):
""" Transforms a TT-matrix into a full matrix"""
N = self.n.prod()
M = self.m.prod()
a = self.tt.full()
d = self.tt.d
sz = _np.vstack((self.n, self.m)).flatten('F')
a = a.reshape(sz, order='F')
# Design a permutation
prm = _np.arange(2 * d)
prm = prm.reshape((d, 2), order='F')
prm = prm.transpose()
prm = prm.flatten('F')
# Get the inverse permutation
iprm = [0] * (2 * d)
for i in xrange(2 * d):
iprm[prm[i]] = i
a = a.transpose(iprm).reshape(N, M, order='F')
a = a.reshape(N, M)
return a | python | def full(self):
""" Transforms a TT-matrix into a full matrix"""
N = self.n.prod()
M = self.m.prod()
a = self.tt.full()
d = self.tt.d
sz = _np.vstack((self.n, self.m)).flatten('F')
a = a.reshape(sz, order='F')
# Design a permutation
prm = _np.arange(2 * d)
prm = prm.reshape((d, 2), order='F')
prm = prm.transpose()
prm = prm.flatten('F')
# Get the inverse permutation
iprm = [0] * (2 * d)
for i in xrange(2 * d):
iprm[prm[i]] = i
a = a.transpose(iprm).reshape(N, M, order='F')
a = a.reshape(N, M)
return a | Transforms a TT-matrix into a full matrix | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/matrix.py#L355-L374 |
oseledets/ttpy | tt/amen/amen_mv.py | _svdgram | def _svdgram(A, tol=None, tol2=1e-7):
''' Highly experimental acceleration of SVD/QR using Gram matrix.
Use with caution for m>>n only!
function [u,s,r]=_svdgram(A,[tol])
u is the left singular factor of A,
s is the singular values (vector!),
r has the meaning of diag(s)*v'.
if tol is given, performs the truncation with Fro-threshold.
'''
R2 = _np.dot(_tconj(A), A)
[u, s, vt] = _np.linalg.svd(R2, full_matrices=False)
u = _np.dot(A, _tconj(vt))
s = (u**2).sum(axis=0)
s = s ** 0.5
if tol is not None:
p = _my_chop2(s, _np.linalg.norm(s) * tol)
u = u[:, :p]
s = s[:p]
vt = vt[:p, :]
tmp = _spdiags(1. / s, 0, len(s), len(s)).todense()
tmp = _np.array(tmp)
u = _np.dot(u, tmp)
r = _np.dot(_np.diag(s), vt)
# Run chol for reortogonalization.
# It will stop if the matrix will be singular.
# Fortunately, it means rank truncation with eps in our business.
if (s[0] / s[-1] > 1. / tol2):
p = 1
while (p > 0):
R2 = _np.dot(_tconj(u), u)
#[u_r2, s_r2, vt_r2] = _np.linalg.svd(R2) # in matlab [R, p] = chol(a) - here is a *dirty* patch
#p = s_r2[s_r2 > eps].size
#R2 = R2[:p, :p]
R = _cholesky(R2, lower=True)
if (p > 0):
u = u[:, :p]
s = s[:p]
r = r[:p, :]
iR = _np.linalg.inv(R)
u = _np.dot(u, iR)
r = _np.dot(R, r)
return u, s, r | python | def _svdgram(A, tol=None, tol2=1e-7):
''' Highly experimental acceleration of SVD/QR using Gram matrix.
Use with caution for m>>n only!
function [u,s,r]=_svdgram(A,[tol])
u is the left singular factor of A,
s is the singular values (vector!),
r has the meaning of diag(s)*v'.
if tol is given, performs the truncation with Fro-threshold.
'''
R2 = _np.dot(_tconj(A), A)
[u, s, vt] = _np.linalg.svd(R2, full_matrices=False)
u = _np.dot(A, _tconj(vt))
s = (u**2).sum(axis=0)
s = s ** 0.5
if tol is not None:
p = _my_chop2(s, _np.linalg.norm(s) * tol)
u = u[:, :p]
s = s[:p]
vt = vt[:p, :]
tmp = _spdiags(1. / s, 0, len(s), len(s)).todense()
tmp = _np.array(tmp)
u = _np.dot(u, tmp)
r = _np.dot(_np.diag(s), vt)
# Run chol for reortogonalization.
# It will stop if the matrix will be singular.
# Fortunately, it means rank truncation with eps in our business.
if (s[0] / s[-1] > 1. / tol2):
p = 1
while (p > 0):
R2 = _np.dot(_tconj(u), u)
#[u_r2, s_r2, vt_r2] = _np.linalg.svd(R2) # in matlab [R, p] = chol(a) - here is a *dirty* patch
#p = s_r2[s_r2 > eps].size
#R2 = R2[:p, :p]
R = _cholesky(R2, lower=True)
if (p > 0):
u = u[:, :p]
s = s[:p]
r = r[:p, :]
iR = _np.linalg.inv(R)
u = _np.dot(u, iR)
r = _np.dot(R, r)
return u, s, r | Highly experimental acceleration of SVD/QR using Gram matrix.
Use with caution for m>>n only!
function [u,s,r]=_svdgram(A,[tol])
u is the left singular factor of A,
s is the singular values (vector!),
r has the meaning of diag(s)*v'.
if tol is given, performs the truncation with Fro-threshold. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/amen/amen_mv.py#L29-L74 |
oseledets/ttpy | tt/amen/amen_mv.py | amen_mv | def amen_mv(A, x, tol, y=None, z=None, nswp=20, kickrank=4,
kickrank2=0, verb=True, init_qr=True, renorm='direct', fkick=False):
'''
Approximate the matrix-by-vector via the AMEn iteration
[y,z]=amen_mv(A, x, tol, varargin)
Attempts to approximate the y = A*x
with accuracy TOL using the AMEn+ALS iteration.
Matrix A has to be given in the TT-format, right-hand side x should be
given in the TT-format also.
Options are provided in form
'PropertyName1',PropertyValue1,'PropertyName2',PropertyValue2 and so
on. The parameters are set to default (in brackets in the following)
The list of option names and default values are:
o y0 - initial approximation to Ax [rand rank-2]
o nswp - maximal number of sweeps [20]
o verb - verbosity level, 0-silent, 1-sweep info, 2-block info [1]
o kickrank - compression rank of the error,
i.e. enrichment size [3]
o init_qr - perform QR of the input (save some time in ts, etc) [true]
o renorm - Orthog. and truncation methods: direct (svd,qr) or gram
(apply svd to the gram matrix, faster for m>>n) [direct]
o fkick - Perform solution enrichment during forward sweeps [false]
(rather questionable yet; false makes error higher, but "better
structured": it does not explode in e.g. subsequent matvecs)
o z0 - initial approximation to the error Ax-y [rand rank-kickrank]
********
For description of adaptive ALS please see
Sergey V. Dolgov, Dmitry V. Savostyanov,
Alternating minimal energy methods for linear systems in higher dimensions.
Part I: SPD systems, http://arxiv.org/abs/1301.6068,
Part II: Faster algorithm and application to nonsymmetric systems, http://arxiv.org/abs/1304.1222
Use {sergey.v.dolgov, dmitry.savostyanov}@gmail.com for feedback
********
'''
if renorm is 'gram':
print("Not implemented yet. Renorm is switched to 'direct'")
renorm = 'direct'
if isinstance(x, _tt.vector):
d = x.d
m = x.n
rx = x.r
x = _tt.vector.to_list(x)
vectype = 1 # tt_tensor
elif isinstance(x, list):
d = len(x)
m = _np.zeros(d)
rx = _np.ones(d + 1, dtype=_np.int32)
for i in xrange(d):
[_, m[i], rx[i + 1]] = x[i].shape
vectype = 0 # cell
else:
raise Exception('x: use tt.tensor or list of cores as numpy.arrays')
if isinstance(A, _tt.matrix):
n = A.n
ra = A.tt.r
A = _tt.matrix.to_list(A)
# prepare A for fast ALS-mv
for i in xrange(d):
A[i] = _reshape(A[i], (ra[i] * n[i], m[i] * ra[i + 1]))
atype = 1 # tt_matrix
# Alternative: A is a cell of cell: sparse canonical format
elif isinstance(A, list):
n = _np.zeros(d)
for i in xrange(d):
n[i] = A[i][0].shape[0]
ra = len(A[0])
atype = 0 # cell
else:
raise Exception('A: use tt.matrix or list of cores as numpy.arrays')
if y is None:
y = _tt.rand(n, d, 2)
y = _tt.vector.to_list(y)
else:
if isinstance(y, _tt.vector):
y = _tt.vector.to_list(y)
ry = _np.ones(d + 1, dtype=_np.int32)
for i in range(d):
ry[i + 1] = y[i].shape[2]
if (kickrank + kickrank2 > 0):
if z is None:
z = _tt.rand(n, d, kickrank + kickrank2)
rz = z.r
z = _tt.vector.to_list(z)
else:
if isinstance(z, _tt.vector):
z = _tt.vector.to_list(z)
rz = _np.ones(d + 1, dtype=_np.int32)
for i in range(d):
rz[i + 1] = z[i].shape[2]
phizax = [None] * (d + 1) # cell(d+1,1);
if (atype == 1):
phizax[0] = _np.ones((1, 1, 1)) # 1
phizax[d] = _np.ones((1, 1, 1)) # 1
else:
phizax[0] = _np.ones((1, ra)) # 33
phizax[d] = _np.ones((1, ra))
phizy = [None] * (d + 1)
phizy[0] = _np.ones((1)) # , 1))
phizy[d] = _np.ones((1)) # , 1))
phiyax = [None] * (d + 1)
if (atype == 1):
phiyax[0] = _np.ones((1, 1, 1)) # 1
phiyax[d] = _np.ones((1, 1, 1)) # 1
else:
phiyax[0] = _np.ones((1, ra)) # 3
phiyax[d] = _np.ones((1, ra))
nrms = _np.ones(d)
# Initial ort
for i in range(d - 1):
if init_qr:
cr = _reshape(y[i], (ry[i] * n[i], ry[i + 1]))
if (renorm is 'gram') and (ry[i] * n[i] > 5 * ry[i + 1]):
[cr, s, R] = _svdgram(cr)
else:
[cr, R] = _np.linalg.qr(cr)
nrmr = _np.linalg.norm(R) # , 'fro')
if (nrmr > 0):
R = R / nrmr
cr2 = _reshape(y[i + 1], (ry[i + 1], n[i + 1] * ry[i + 2]))
cr2 = _np.dot(R, cr2)
ry[i + 1] = cr.shape[1]
y[i] = _reshape(cr, (ry[i], n[i], ry[i + 1]))
y[i + 1] = _reshape(cr2, (ry[i + 1], n[i + 1], ry[i + 2]))
[phiyax[i + 1], nrms[i]
] = _compute_next_Phi(phiyax[i], y[i], x[i], 'lr', A[i])
if (kickrank + kickrank2 > 0):
cr = _reshape(z[i], (rz[i] * n[i], rz[i + 1]))
if (renorm == 'gram') and (rz[i] * n[i] > 5 * rz[i + 1]):
[cr, s, R] = _svdgram(cr)
else:
[cr, R] = _np.linalg.qr(cr)
nrmr = _np.linalg.norm(R) # , 'fro')
if (nrmr > 0):
R = R / nrmr
cr2 = _reshape(z[i + 1], (rz[i + 1], n[i + 1] * rz[i + 2]))
cr2 = _np.dot(R, cr2)
rz[i + 1] = cr.shape[1]
z[i] = _reshape(cr, (rz[i], n[i], rz[i + 1]))
z[i + 1] = _reshape(cr2, (rz[i + 1], n[i + 1], rz[i + 2]))
phizax[
i +
1] = _compute_next_Phi(
phizax[i],
z[i],
x[i],
'lr',
A[i],
nrms[i],
return_norm=False)
phizy[
i +
1] = _compute_next_Phi(
phizy[i],
z[i],
y[i],
'lr',
return_norm=False)
i = d - 1
direct = -1
swp = 1
max_dx = 0
while swp <= nswp:
# Project the MatVec generating vector
crx = _reshape(x[i], (rx[i] * m[i] * rx[i + 1], 1))
cry = _bfun3(phiyax[i], A[i], phiyax[i + 1], crx)
nrms[i] = _np.linalg.norm(cry) # , 'fro')
# The main goal is to keep y[i] of norm 1
if (nrms[i] > 0):
cry = cry / nrms[i]
else:
nrms[i] = 1
y[i] = _reshape(y[i], (ry[i] * n[i] * ry[i + 1], 1))
dx = _np.linalg.norm(cry - y[i])
max_dx = max(max_dx, dx)
# Truncation and enrichment
if ((direct > 0) and (i < d - 1)): # ?? i<d
cry = _reshape(cry, (ry[i] * n[i], ry[i + 1]))
if (renorm == 'gram'):
[u, s, v] = _svdgram(cry, tol / d**0.5)
v = v.T
r = u.shape[1]
else:
[u, s, vt] = _np.linalg.svd(cry, full_matrices=False)
#s = diag(s)
r = _my_chop2(s, tol * _np.linalg.norm(s) / d**0.5)
u = u[:, :r]
# ????? s - matrix or vector
v = _np.dot(_tconj(vt[:r, :]), _np.diag(s[:r]))
# Prepare enrichment, if needed
if (kickrank + kickrank2 > 0):
cry = _np.dot(u, v.T)
cry = _reshape(cry, (ry[i] * n[i], ry[i + 1]))
# For updating z
crz = _bfun3(phizax[i], A[i], phizax[i + 1], crx)
crz = _reshape(crz, (rz[i] * n[i], rz[i + 1]))
ys = _np.dot(cry, phizy[i + 1])
yz = _reshape(ys, (ry[i], n[i] * rz[i + 1]))
yz = _np.dot(phizy[i], yz)
yz = _reshape(yz, (rz[i] * n[i], rz[i + 1]))
crz = crz / nrms[i] - yz
nrmz = _np.linalg.norm(crz) # , 'fro')
if (kickrank2 > 0):
[crz, _, _] = _np.linalg.svd(crz, full_matrices=False)
crz = crz[:, : min(crz.shape[1], kickrank)]
crz = _np.hstack(
(crz, _np.random.randn(
rz[i] * n[i], kickrank2)))
# For adding into solution
if fkick:
crs = _bfun3(phiyax[i], A[i], phizax[i + 1], crx)
crs = _reshape(crs, (ry[i] * n[i], rz[i + 1]))
crs = crs / nrms[i] - ys
u = _np.hstack((u, crs))
if (renorm == 'gram') and (
ry[i] * n[i] > 5 * (ry[i + 1] + rz[i + 1])):
[u, s, R] = _svdgram(u)
else:
[u, R] = _np.linalg.qr(u)
v = _np.hstack((v, _np.zeros((ry[i + 1], rz[i + 1]))))
v = _np.dot(v, R.T)
r = u.shape[1]
y[i] = _reshape(u, (ry[i], n[i], r))
cr2 = _reshape(y[i + 1], (ry[i + 1], n[i + 1] * ry[i + 2]))
v = _reshape(v, (ry[i + 1], r))
cr2 = _np.dot(v.T, cr2)
y[i + 1] = _reshape(cr2, (r, n[i + 1], ry[i + 2]))
ry[i + 1] = r
[phiyax[i + 1], nrms[i]
] = _compute_next_Phi(phiyax[i], y[i], x[i], 'lr', A[i])
if (kickrank + kickrank2 > 0):
if (renorm == 'gram') and (rz[i] * n[i] > 5 * rz[i + 1]):
[crz, s, R] = _svdgram(crz)
else:
[crz, R] = _np.linalg.qr(crz)
rz[i + 1] = crz.shape[1]
z[i] = _reshape(crz, (rz[i], n[i], rz[i + 1]))
# z[i+1] will be recomputed from scratch in the next step
phizax[
i +
1] = _compute_next_Phi(
phizax[i],
z[i],
x[i],
'lr',
A[i],
nrms[i],
return_norm=False)
phizy[
i +
1] = _compute_next_Phi(
phizy[i],
z[i],
y[i],
'lr',
return_norm=False)
elif ((direct < 0) and (i > 0)):
cry = _reshape(cry, (ry[i], n[i] * ry[i + 1]))
if (renorm == 'gram'):
[v, s, u] = _svdgram(cry.T, tol / d**0.5)
u = u.T
r = v.shape[1]
else:
#[v, s, u] = _np.linalg.svd(cry.T, full_matrices=False)
[u, s, vt] = _np.linalg.svd(cry, full_matrices=False)
#s = diag(s);
r = _my_chop2(s, tol * _np.linalg.norm(s) / d**0.5)
v = _tconj(vt[:r, :])
#v = vt[:r, :]
#v = _np.dot(v[:, :r], _np.diag(s[:r]))
u = _np.dot(u[:, :r], _np.diag(s[:r])) # ??????????????????
# Prepare enrichment, if needed
if (kickrank + kickrank2 > 0):
cry = _np.dot(u, v.T) # .T)
cry = _reshape(cry, (ry[i], n[i] * ry[i + 1]))
# For updating z
crz = _bfun3(phizax[i], A[i], phizax[i + 1], crx)
crz = _reshape(crz, (rz[i], n[i] * rz[i + 1]))
ys = _np.dot(phizy[i], cry)
yz = _reshape(ys, (rz[i] * n[i], ry[i + 1]))
yz = _np.dot(yz, phizy[i + 1])
yz = _reshape(yz, (rz[i], n[i] * rz[i + 1]))
crz = crz / nrms[i] - yz
nrmz = _np.linalg.norm(crz) # , 'fro')
if (kickrank2 > 0):
[_, _, crz] = _np.linalg.svd(crz, full_matrices=False)
crz = crz[:, : min(crz.shape[1], kickrank)]
crz = _tconj(crz)
crz = _np.vstack(
(crz, _np.random.randn(kickrank2, n[i] * rz[i + 1])))
# For adding into solution
crs = _bfun3(phizax[i], A[i], phiyax[i + 1], crx)
crs = _reshape(crs, (rz[i], n[i] * ry[i + 1]))
crs = crs / nrms[i] - ys
v = _np.hstack((v, crs.T)) # .T
#v = v.T
if (renorm == 'gram') and (
n[i] * ry[i + 1] > 5 * (ry[i] + rz[i])):
[v, s, R] = _svdgram(v)
else:
[v, R] = _np.linalg.qr(v)
u = _np.hstack((u, _np.zeros((ry[i], rz[i]))))
u = _np.dot(u, R.T)
r = v.shape[1]
cr2 = _reshape(y[i - 1], (ry[i - 1] * n[i - 1], ry[i]))
cr2 = _np.dot(cr2, u)
y[i - 1] = _reshape(cr2, (ry[i - 1], n[i - 1], r))
y[i] = _reshape(v.T, (r, n[i], ry[i + 1]))
ry[i] = r
[phiyax[i], nrms[i]] = _compute_next_Phi(
phiyax[i + 1], y[i], x[i], 'rl', A[i])
if (kickrank + kickrank2 > 0):
if (renorm == 'gram') and (n[i] * rz[i + 1] > 5 * rz[i]):
[crz, s, R] = _svdgram(crz.T)
else:
[crz, R] = _np.linalg.qr(crz.T)
rz[i] = crz.shape[1]
z[i] = _reshape(crz.T, (rz[i], n[i], rz[i + 1]))
# don't update z[i-1], it will be recomputed from scratch
phizax[i] = _compute_next_Phi(
phizax[
i + 1],
z[i],
x[i],
'rl',
A[i],
nrms[i],
return_norm=False)
phizy[i] = _compute_next_Phi(
phizy[i + 1], z[i], y[i], 'rl', return_norm=False)
if (verb > 1):
print('amen-mv: swp=[%d,%d], dx=%.3e, r=%d, |y|=%.3e, |z|=%.3e' % (swp, i, dx, r, _np.linalg.norm(cry), nrmz))
# Stopping or reversing
if ((direct > 0) and (i == d - 1)) or ((direct < 0) and (i == 0)):
if (verb > 0):
print('amen-mv: swp=%d{%d}, max_dx=%.3e, max_r=%d' % (swp, (1 - direct) // 2, max_dx, max(ry)))
if ((max_dx < tol) or (swp == nswp)) and (direct > 0):
break
else:
# We are at the terminal block
y[i] = _reshape(cry, (ry[i], n[i], ry[i + 1]))
if (direct > 0):
swp = swp + 1
max_dx = 0
direct = -direct
else:
i = i + direct
# if (direct>0)
y[d - 1] = _reshape(cry, (ry[d - 1], n[d - 1], ry[d]))
# else
# y{1} = reshape(cry, ry(1), n(1), ry(2));
# end;
# Distribute norms equally...
nrms = _np.exp(sum(_np.log(nrms)) / d)
# ... and plug them into y
for i in xrange(d):
y[i] = _np.dot(y[i], nrms)
if (vectype == 1):
y = _tt.vector.from_list(y)
if kickrank == 0:
z = None
else:
z = _tt.vector.from_list(z)
return y, z | python | def amen_mv(A, x, tol, y=None, z=None, nswp=20, kickrank=4,
kickrank2=0, verb=True, init_qr=True, renorm='direct', fkick=False):
'''
Approximate the matrix-by-vector via the AMEn iteration
[y,z]=amen_mv(A, x, tol, varargin)
Attempts to approximate the y = A*x
with accuracy TOL using the AMEn+ALS iteration.
Matrix A has to be given in the TT-format, right-hand side x should be
given in the TT-format also.
Options are provided in form
'PropertyName1',PropertyValue1,'PropertyName2',PropertyValue2 and so
on. The parameters are set to default (in brackets in the following)
The list of option names and default values are:
o y0 - initial approximation to Ax [rand rank-2]
o nswp - maximal number of sweeps [20]
o verb - verbosity level, 0-silent, 1-sweep info, 2-block info [1]
o kickrank - compression rank of the error,
i.e. enrichment size [3]
o init_qr - perform QR of the input (save some time in ts, etc) [true]
o renorm - Orthog. and truncation methods: direct (svd,qr) or gram
(apply svd to the gram matrix, faster for m>>n) [direct]
o fkick - Perform solution enrichment during forward sweeps [false]
(rather questionable yet; false makes error higher, but "better
structured": it does not explode in e.g. subsequent matvecs)
o z0 - initial approximation to the error Ax-y [rand rank-kickrank]
********
For description of adaptive ALS please see
Sergey V. Dolgov, Dmitry V. Savostyanov,
Alternating minimal energy methods for linear systems in higher dimensions.
Part I: SPD systems, http://arxiv.org/abs/1301.6068,
Part II: Faster algorithm and application to nonsymmetric systems, http://arxiv.org/abs/1304.1222
Use {sergey.v.dolgov, dmitry.savostyanov}@gmail.com for feedback
********
'''
if renorm is 'gram':
print("Not implemented yet. Renorm is switched to 'direct'")
renorm = 'direct'
if isinstance(x, _tt.vector):
d = x.d
m = x.n
rx = x.r
x = _tt.vector.to_list(x)
vectype = 1 # tt_tensor
elif isinstance(x, list):
d = len(x)
m = _np.zeros(d)
rx = _np.ones(d + 1, dtype=_np.int32)
for i in xrange(d):
[_, m[i], rx[i + 1]] = x[i].shape
vectype = 0 # cell
else:
raise Exception('x: use tt.tensor or list of cores as numpy.arrays')
if isinstance(A, _tt.matrix):
n = A.n
ra = A.tt.r
A = _tt.matrix.to_list(A)
# prepare A for fast ALS-mv
for i in xrange(d):
A[i] = _reshape(A[i], (ra[i] * n[i], m[i] * ra[i + 1]))
atype = 1 # tt_matrix
# Alternative: A is a cell of cell: sparse canonical format
elif isinstance(A, list):
n = _np.zeros(d)
for i in xrange(d):
n[i] = A[i][0].shape[0]
ra = len(A[0])
atype = 0 # cell
else:
raise Exception('A: use tt.matrix or list of cores as numpy.arrays')
if y is None:
y = _tt.rand(n, d, 2)
y = _tt.vector.to_list(y)
else:
if isinstance(y, _tt.vector):
y = _tt.vector.to_list(y)
ry = _np.ones(d + 1, dtype=_np.int32)
for i in range(d):
ry[i + 1] = y[i].shape[2]
if (kickrank + kickrank2 > 0):
if z is None:
z = _tt.rand(n, d, kickrank + kickrank2)
rz = z.r
z = _tt.vector.to_list(z)
else:
if isinstance(z, _tt.vector):
z = _tt.vector.to_list(z)
rz = _np.ones(d + 1, dtype=_np.int32)
for i in range(d):
rz[i + 1] = z[i].shape[2]
phizax = [None] * (d + 1) # cell(d+1,1);
if (atype == 1):
phizax[0] = _np.ones((1, 1, 1)) # 1
phizax[d] = _np.ones((1, 1, 1)) # 1
else:
phizax[0] = _np.ones((1, ra)) # 33
phizax[d] = _np.ones((1, ra))
phizy = [None] * (d + 1)
phizy[0] = _np.ones((1)) # , 1))
phizy[d] = _np.ones((1)) # , 1))
phiyax = [None] * (d + 1)
if (atype == 1):
phiyax[0] = _np.ones((1, 1, 1)) # 1
phiyax[d] = _np.ones((1, 1, 1)) # 1
else:
phiyax[0] = _np.ones((1, ra)) # 3
phiyax[d] = _np.ones((1, ra))
nrms = _np.ones(d)
# Initial ort
for i in range(d - 1):
if init_qr:
cr = _reshape(y[i], (ry[i] * n[i], ry[i + 1]))
if (renorm is 'gram') and (ry[i] * n[i] > 5 * ry[i + 1]):
[cr, s, R] = _svdgram(cr)
else:
[cr, R] = _np.linalg.qr(cr)
nrmr = _np.linalg.norm(R) # , 'fro')
if (nrmr > 0):
R = R / nrmr
cr2 = _reshape(y[i + 1], (ry[i + 1], n[i + 1] * ry[i + 2]))
cr2 = _np.dot(R, cr2)
ry[i + 1] = cr.shape[1]
y[i] = _reshape(cr, (ry[i], n[i], ry[i + 1]))
y[i + 1] = _reshape(cr2, (ry[i + 1], n[i + 1], ry[i + 2]))
[phiyax[i + 1], nrms[i]
] = _compute_next_Phi(phiyax[i], y[i], x[i], 'lr', A[i])
if (kickrank + kickrank2 > 0):
cr = _reshape(z[i], (rz[i] * n[i], rz[i + 1]))
if (renorm == 'gram') and (rz[i] * n[i] > 5 * rz[i + 1]):
[cr, s, R] = _svdgram(cr)
else:
[cr, R] = _np.linalg.qr(cr)
nrmr = _np.linalg.norm(R) # , 'fro')
if (nrmr > 0):
R = R / nrmr
cr2 = _reshape(z[i + 1], (rz[i + 1], n[i + 1] * rz[i + 2]))
cr2 = _np.dot(R, cr2)
rz[i + 1] = cr.shape[1]
z[i] = _reshape(cr, (rz[i], n[i], rz[i + 1]))
z[i + 1] = _reshape(cr2, (rz[i + 1], n[i + 1], rz[i + 2]))
phizax[
i +
1] = _compute_next_Phi(
phizax[i],
z[i],
x[i],
'lr',
A[i],
nrms[i],
return_norm=False)
phizy[
i +
1] = _compute_next_Phi(
phizy[i],
z[i],
y[i],
'lr',
return_norm=False)
i = d - 1
direct = -1
swp = 1
max_dx = 0
while swp <= nswp:
# Project the MatVec generating vector
crx = _reshape(x[i], (rx[i] * m[i] * rx[i + 1], 1))
cry = _bfun3(phiyax[i], A[i], phiyax[i + 1], crx)
nrms[i] = _np.linalg.norm(cry) # , 'fro')
# The main goal is to keep y[i] of norm 1
if (nrms[i] > 0):
cry = cry / nrms[i]
else:
nrms[i] = 1
y[i] = _reshape(y[i], (ry[i] * n[i] * ry[i + 1], 1))
dx = _np.linalg.norm(cry - y[i])
max_dx = max(max_dx, dx)
# Truncation and enrichment
if ((direct > 0) and (i < d - 1)): # ?? i<d
cry = _reshape(cry, (ry[i] * n[i], ry[i + 1]))
if (renorm == 'gram'):
[u, s, v] = _svdgram(cry, tol / d**0.5)
v = v.T
r = u.shape[1]
else:
[u, s, vt] = _np.linalg.svd(cry, full_matrices=False)
#s = diag(s)
r = _my_chop2(s, tol * _np.linalg.norm(s) / d**0.5)
u = u[:, :r]
# ????? s - matrix or vector
v = _np.dot(_tconj(vt[:r, :]), _np.diag(s[:r]))
# Prepare enrichment, if needed
if (kickrank + kickrank2 > 0):
cry = _np.dot(u, v.T)
cry = _reshape(cry, (ry[i] * n[i], ry[i + 1]))
# For updating z
crz = _bfun3(phizax[i], A[i], phizax[i + 1], crx)
crz = _reshape(crz, (rz[i] * n[i], rz[i + 1]))
ys = _np.dot(cry, phizy[i + 1])
yz = _reshape(ys, (ry[i], n[i] * rz[i + 1]))
yz = _np.dot(phizy[i], yz)
yz = _reshape(yz, (rz[i] * n[i], rz[i + 1]))
crz = crz / nrms[i] - yz
nrmz = _np.linalg.norm(crz) # , 'fro')
if (kickrank2 > 0):
[crz, _, _] = _np.linalg.svd(crz, full_matrices=False)
crz = crz[:, : min(crz.shape[1], kickrank)]
crz = _np.hstack(
(crz, _np.random.randn(
rz[i] * n[i], kickrank2)))
# For adding into solution
if fkick:
crs = _bfun3(phiyax[i], A[i], phizax[i + 1], crx)
crs = _reshape(crs, (ry[i] * n[i], rz[i + 1]))
crs = crs / nrms[i] - ys
u = _np.hstack((u, crs))
if (renorm == 'gram') and (
ry[i] * n[i] > 5 * (ry[i + 1] + rz[i + 1])):
[u, s, R] = _svdgram(u)
else:
[u, R] = _np.linalg.qr(u)
v = _np.hstack((v, _np.zeros((ry[i + 1], rz[i + 1]))))
v = _np.dot(v, R.T)
r = u.shape[1]
y[i] = _reshape(u, (ry[i], n[i], r))
cr2 = _reshape(y[i + 1], (ry[i + 1], n[i + 1] * ry[i + 2]))
v = _reshape(v, (ry[i + 1], r))
cr2 = _np.dot(v.T, cr2)
y[i + 1] = _reshape(cr2, (r, n[i + 1], ry[i + 2]))
ry[i + 1] = r
[phiyax[i + 1], nrms[i]
] = _compute_next_Phi(phiyax[i], y[i], x[i], 'lr', A[i])
if (kickrank + kickrank2 > 0):
if (renorm == 'gram') and (rz[i] * n[i] > 5 * rz[i + 1]):
[crz, s, R] = _svdgram(crz)
else:
[crz, R] = _np.linalg.qr(crz)
rz[i + 1] = crz.shape[1]
z[i] = _reshape(crz, (rz[i], n[i], rz[i + 1]))
# z[i+1] will be recomputed from scratch in the next step
phizax[
i +
1] = _compute_next_Phi(
phizax[i],
z[i],
x[i],
'lr',
A[i],
nrms[i],
return_norm=False)
phizy[
i +
1] = _compute_next_Phi(
phizy[i],
z[i],
y[i],
'lr',
return_norm=False)
elif ((direct < 0) and (i > 0)):
cry = _reshape(cry, (ry[i], n[i] * ry[i + 1]))
if (renorm == 'gram'):
[v, s, u] = _svdgram(cry.T, tol / d**0.5)
u = u.T
r = v.shape[1]
else:
#[v, s, u] = _np.linalg.svd(cry.T, full_matrices=False)
[u, s, vt] = _np.linalg.svd(cry, full_matrices=False)
#s = diag(s);
r = _my_chop2(s, tol * _np.linalg.norm(s) / d**0.5)
v = _tconj(vt[:r, :])
#v = vt[:r, :]
#v = _np.dot(v[:, :r], _np.diag(s[:r]))
u = _np.dot(u[:, :r], _np.diag(s[:r])) # ??????????????????
# Prepare enrichment, if needed
if (kickrank + kickrank2 > 0):
cry = _np.dot(u, v.T) # .T)
cry = _reshape(cry, (ry[i], n[i] * ry[i + 1]))
# For updating z
crz = _bfun3(phizax[i], A[i], phizax[i + 1], crx)
crz = _reshape(crz, (rz[i], n[i] * rz[i + 1]))
ys = _np.dot(phizy[i], cry)
yz = _reshape(ys, (rz[i] * n[i], ry[i + 1]))
yz = _np.dot(yz, phizy[i + 1])
yz = _reshape(yz, (rz[i], n[i] * rz[i + 1]))
crz = crz / nrms[i] - yz
nrmz = _np.linalg.norm(crz) # , 'fro')
if (kickrank2 > 0):
[_, _, crz] = _np.linalg.svd(crz, full_matrices=False)
crz = crz[:, : min(crz.shape[1], kickrank)]
crz = _tconj(crz)
crz = _np.vstack(
(crz, _np.random.randn(kickrank2, n[i] * rz[i + 1])))
# For adding into solution
crs = _bfun3(phizax[i], A[i], phiyax[i + 1], crx)
crs = _reshape(crs, (rz[i], n[i] * ry[i + 1]))
crs = crs / nrms[i] - ys
v = _np.hstack((v, crs.T)) # .T
#v = v.T
if (renorm == 'gram') and (
n[i] * ry[i + 1] > 5 * (ry[i] + rz[i])):
[v, s, R] = _svdgram(v)
else:
[v, R] = _np.linalg.qr(v)
u = _np.hstack((u, _np.zeros((ry[i], rz[i]))))
u = _np.dot(u, R.T)
r = v.shape[1]
cr2 = _reshape(y[i - 1], (ry[i - 1] * n[i - 1], ry[i]))
cr2 = _np.dot(cr2, u)
y[i - 1] = _reshape(cr2, (ry[i - 1], n[i - 1], r))
y[i] = _reshape(v.T, (r, n[i], ry[i + 1]))
ry[i] = r
[phiyax[i], nrms[i]] = _compute_next_Phi(
phiyax[i + 1], y[i], x[i], 'rl', A[i])
if (kickrank + kickrank2 > 0):
if (renorm == 'gram') and (n[i] * rz[i + 1] > 5 * rz[i]):
[crz, s, R] = _svdgram(crz.T)
else:
[crz, R] = _np.linalg.qr(crz.T)
rz[i] = crz.shape[1]
z[i] = _reshape(crz.T, (rz[i], n[i], rz[i + 1]))
# don't update z[i-1], it will be recomputed from scratch
phizax[i] = _compute_next_Phi(
phizax[
i + 1],
z[i],
x[i],
'rl',
A[i],
nrms[i],
return_norm=False)
phizy[i] = _compute_next_Phi(
phizy[i + 1], z[i], y[i], 'rl', return_norm=False)
if (verb > 1):
print('amen-mv: swp=[%d,%d], dx=%.3e, r=%d, |y|=%.3e, |z|=%.3e' % (swp, i, dx, r, _np.linalg.norm(cry), nrmz))
# Stopping or reversing
if ((direct > 0) and (i == d - 1)) or ((direct < 0) and (i == 0)):
if (verb > 0):
print('amen-mv: swp=%d{%d}, max_dx=%.3e, max_r=%d' % (swp, (1 - direct) // 2, max_dx, max(ry)))
if ((max_dx < tol) or (swp == nswp)) and (direct > 0):
break
else:
# We are at the terminal block
y[i] = _reshape(cry, (ry[i], n[i], ry[i + 1]))
if (direct > 0):
swp = swp + 1
max_dx = 0
direct = -direct
else:
i = i + direct
# if (direct>0)
y[d - 1] = _reshape(cry, (ry[d - 1], n[d - 1], ry[d]))
# else
# y{1} = reshape(cry, ry(1), n(1), ry(2));
# end;
# Distribute norms equally...
nrms = _np.exp(sum(_np.log(nrms)) / d)
# ... and plug them into y
for i in xrange(d):
y[i] = _np.dot(y[i], nrms)
if (vectype == 1):
y = _tt.vector.from_list(y)
if kickrank == 0:
z = None
else:
z = _tt.vector.from_list(z)
return y, z | Approximate the matrix-by-vector via the AMEn iteration
[y,z]=amen_mv(A, x, tol, varargin)
Attempts to approximate the y = A*x
with accuracy TOL using the AMEn+ALS iteration.
Matrix A has to be given in the TT-format, right-hand side x should be
given in the TT-format also.
Options are provided in form
'PropertyName1',PropertyValue1,'PropertyName2',PropertyValue2 and so
on. The parameters are set to default (in brackets in the following)
The list of option names and default values are:
o y0 - initial approximation to Ax [rand rank-2]
o nswp - maximal number of sweeps [20]
o verb - verbosity level, 0-silent, 1-sweep info, 2-block info [1]
o kickrank - compression rank of the error,
i.e. enrichment size [3]
o init_qr - perform QR of the input (save some time in ts, etc) [true]
o renorm - Orthog. and truncation methods: direct (svd,qr) or gram
(apply svd to the gram matrix, faster for m>>n) [direct]
o fkick - Perform solution enrichment during forward sweeps [false]
(rather questionable yet; false makes error higher, but "better
structured": it does not explode in e.g. subsequent matvecs)
o z0 - initial approximation to the error Ax-y [rand rank-kickrank]
********
For description of adaptive ALS please see
Sergey V. Dolgov, Dmitry V. Savostyanov,
Alternating minimal energy methods for linear systems in higher dimensions.
Part I: SPD systems, http://arxiv.org/abs/1301.6068,
Part II: Faster algorithm and application to nonsymmetric systems, http://arxiv.org/abs/1304.1222
Use {sergey.v.dolgov, dmitry.savostyanov}@gmail.com for feedback
******** | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/amen/amen_mv.py#L77-L477 |
oseledets/ttpy | tt/amen/amen_mv.py | _compute_next_Phi | def _compute_next_Phi(Phi_prev, x, y, direction, A=None,
extnrm=None, return_norm=True):
'''
Performs the recurrent Phi (or Psi) matrix computation
Phi = Phi_prev * (x'Ay).
If direction is 'lr', computes Psi
if direction is 'rl', computes Phi
A can be empty, then only x'y is computed.
Phi1: rx1, ry1, ra1, or {rx1, ry1}_ra, or rx1, ry1
Phi2: ry2, ra2, rx2, or {ry2, rx2}_ra, or ry2, rx2
'''
[rx1, n, rx2] = x.shape
[ry1, m, ry2] = y.shape
if A is not None:
if isinstance(A, list): # ?????????????????????????????????
# A is a canonical block
ra = len(A)
else:
# Just full format
[ra1, ra2] = A.shape
ra1 = ra1 // n
ra2 = ra2 // m
# ?????????????????????????????????????
else:
[ra1, ra2] = [1, 1]
if isinstance(Phi_prev, list):
Phi = [None] * ra
if return_norm:
nrm = 0
if (direction == 'lr'):
# lr: Phi1
x = _reshape(x, (rx1, n * rx2))
y = _reshape(y, (ry1 * m, ry2))
for i in xrange(ra):
Phi[i] = _np.dot(_tconj(x), Phi_prev[i])
Phi[i] = _reshape(Phi[i], (n, rx2 * ry1))
Phi[i] = Phi[i].T
Phi[i] = _np.dot(Phi[i], A[i])
Phi[i] = _reshape(Phi[i], (rx2, ry1 * m))
Phi[i] = _np.dot(Phi[i], y)
if return_norm:
nrm = max(nrm, _np.linalg.norm(Phi[i])) # , 'fro'))
else:
# rl: Phi2
y = _reshape(y, (ry1, m * ry2))
x = _reshape(x, (rx1 * n, rx2))
for i in xrange(ra):
Phi[i] = _np.dot(Phi_prev[i], x.T)
Phi[i] = _reshape(Phi[i], (ry2 * rx1, n))
Phi[i] = _np.dot(Phi[i], A[i])
Phi[i] = Phi[i].T
Phi[i] = _reshape(Phi[i], (m * ry2, rx1))
Phi[i] = _np.dot(y, Phi[i])
if return_norm:
nrm = max(nrm, _np.linalg.norm(Phi[i])) # , 'fro'))
if return_norm:
# Extract the scale to prevent overload
if (nrm > 0):
for i in xrange(ra):
Phi[i] = Phi[i] / nrm
else:
nrm = 1
elif extnrm is not None:
# Override the normalization
for i in xrange(ra):
Phi[i] = Phi[i] / extnrm
else:
if (direction == 'lr'):
# lr: Phi1
x = _reshape(x, (rx1, n * rx2))
Phi = _reshape(Phi_prev, (rx1, ry1 * ra1))
Phi = _np.dot(_tconj(x), Phi)
if A is not None:
Phi = _reshape(Phi, (n * rx2 * ry1, ra1))
Phi = Phi.T
Phi = _reshape(Phi, (ra1 * n, rx2 * ry1))
Phi = _np.dot(A.T, Phi)
Phi = _reshape(Phi, (m, ra2 * rx2 * ry1))
else:
Phi = _reshape(Phi, (n, rx2 * ry1))
Phi = Phi.T
Phi = _reshape(Phi, (ra2 * rx2, ry1 * m))
y = _reshape(y, (ry1 * m, ry2))
Phi = _np.dot(Phi, y)
if A is not None:
Phi = _reshape(Phi, (ra2, rx2 * ry2))
Phi = Phi.T
Phi = _reshape(Phi, (rx2, ry2, ra2))
else:
Phi = _reshape(Phi, (rx2, ry2))
else:
# rl: Phi2
y = _reshape(y, (ry1 * m, ry2))
Phi = _reshape(Phi_prev, (ry2, ra2 * rx2))
Phi = _np.dot(y, Phi)
if A is not None:
Phi = _reshape(Phi, (ry1, m * ra2 * rx2))
Phi = Phi.T
Phi = _reshape(Phi, (m * ra2, rx2 * ry1))
Phi = _np.dot(A, Phi)
Phi = _reshape(Phi, (ra1 * n * rx2, ry1))
Phi = Phi.T
Phi = _reshape(Phi, (ry1 * ra1, n * rx2))
x = _reshape(x, (rx1, n * rx2))
Phi = _np.dot(Phi, _tconj(x))
if A is not None:
Phi = _reshape(Phi, (ry1, ra1, rx1))
else:
Phi = _reshape(Phi, (ry1, rx1))
if return_norm:
# Extract the scale to prevent overload
nrm = _np.linalg.norm(Phi) # , 'fro')
if (nrm > 0):
Phi = Phi / nrm
else:
nrm = 1
elif extnrm is not None:
# Override the normalization by the external one
Phi = Phi / extnrm
if return_norm:
return Phi, nrm
else:
return Phi | python | def _compute_next_Phi(Phi_prev, x, y, direction, A=None,
extnrm=None, return_norm=True):
'''
Performs the recurrent Phi (or Psi) matrix computation
Phi = Phi_prev * (x'Ay).
If direction is 'lr', computes Psi
if direction is 'rl', computes Phi
A can be empty, then only x'y is computed.
Phi1: rx1, ry1, ra1, or {rx1, ry1}_ra, or rx1, ry1
Phi2: ry2, ra2, rx2, or {ry2, rx2}_ra, or ry2, rx2
'''
[rx1, n, rx2] = x.shape
[ry1, m, ry2] = y.shape
if A is not None:
if isinstance(A, list): # ?????????????????????????????????
# A is a canonical block
ra = len(A)
else:
# Just full format
[ra1, ra2] = A.shape
ra1 = ra1 // n
ra2 = ra2 // m
# ?????????????????????????????????????
else:
[ra1, ra2] = [1, 1]
if isinstance(Phi_prev, list):
Phi = [None] * ra
if return_norm:
nrm = 0
if (direction == 'lr'):
# lr: Phi1
x = _reshape(x, (rx1, n * rx2))
y = _reshape(y, (ry1 * m, ry2))
for i in xrange(ra):
Phi[i] = _np.dot(_tconj(x), Phi_prev[i])
Phi[i] = _reshape(Phi[i], (n, rx2 * ry1))
Phi[i] = Phi[i].T
Phi[i] = _np.dot(Phi[i], A[i])
Phi[i] = _reshape(Phi[i], (rx2, ry1 * m))
Phi[i] = _np.dot(Phi[i], y)
if return_norm:
nrm = max(nrm, _np.linalg.norm(Phi[i])) # , 'fro'))
else:
# rl: Phi2
y = _reshape(y, (ry1, m * ry2))
x = _reshape(x, (rx1 * n, rx2))
for i in xrange(ra):
Phi[i] = _np.dot(Phi_prev[i], x.T)
Phi[i] = _reshape(Phi[i], (ry2 * rx1, n))
Phi[i] = _np.dot(Phi[i], A[i])
Phi[i] = Phi[i].T
Phi[i] = _reshape(Phi[i], (m * ry2, rx1))
Phi[i] = _np.dot(y, Phi[i])
if return_norm:
nrm = max(nrm, _np.linalg.norm(Phi[i])) # , 'fro'))
if return_norm:
# Extract the scale to prevent overload
if (nrm > 0):
for i in xrange(ra):
Phi[i] = Phi[i] / nrm
else:
nrm = 1
elif extnrm is not None:
# Override the normalization
for i in xrange(ra):
Phi[i] = Phi[i] / extnrm
else:
if (direction == 'lr'):
# lr: Phi1
x = _reshape(x, (rx1, n * rx2))
Phi = _reshape(Phi_prev, (rx1, ry1 * ra1))
Phi = _np.dot(_tconj(x), Phi)
if A is not None:
Phi = _reshape(Phi, (n * rx2 * ry1, ra1))
Phi = Phi.T
Phi = _reshape(Phi, (ra1 * n, rx2 * ry1))
Phi = _np.dot(A.T, Phi)
Phi = _reshape(Phi, (m, ra2 * rx2 * ry1))
else:
Phi = _reshape(Phi, (n, rx2 * ry1))
Phi = Phi.T
Phi = _reshape(Phi, (ra2 * rx2, ry1 * m))
y = _reshape(y, (ry1 * m, ry2))
Phi = _np.dot(Phi, y)
if A is not None:
Phi = _reshape(Phi, (ra2, rx2 * ry2))
Phi = Phi.T
Phi = _reshape(Phi, (rx2, ry2, ra2))
else:
Phi = _reshape(Phi, (rx2, ry2))
else:
# rl: Phi2
y = _reshape(y, (ry1 * m, ry2))
Phi = _reshape(Phi_prev, (ry2, ra2 * rx2))
Phi = _np.dot(y, Phi)
if A is not None:
Phi = _reshape(Phi, (ry1, m * ra2 * rx2))
Phi = Phi.T
Phi = _reshape(Phi, (m * ra2, rx2 * ry1))
Phi = _np.dot(A, Phi)
Phi = _reshape(Phi, (ra1 * n * rx2, ry1))
Phi = Phi.T
Phi = _reshape(Phi, (ry1 * ra1, n * rx2))
x = _reshape(x, (rx1, n * rx2))
Phi = _np.dot(Phi, _tconj(x))
if A is not None:
Phi = _reshape(Phi, (ry1, ra1, rx1))
else:
Phi = _reshape(Phi, (ry1, rx1))
if return_norm:
# Extract the scale to prevent overload
nrm = _np.linalg.norm(Phi) # , 'fro')
if (nrm > 0):
Phi = Phi / nrm
else:
nrm = 1
elif extnrm is not None:
# Override the normalization by the external one
Phi = Phi / extnrm
if return_norm:
return Phi, nrm
else:
return Phi | Performs the recurrent Phi (or Psi) matrix computation
Phi = Phi_prev * (x'Ay).
If direction is 'lr', computes Psi
if direction is 'rl', computes Phi
A can be empty, then only x'y is computed.
Phi1: rx1, ry1, ra1, or {rx1, ry1}_ra, or rx1, ry1
Phi2: ry2, ra2, rx2, or {ry2, rx2}_ra, or ry2, rx2 | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/amen/amen_mv.py#L480-L608 |
oseledets/ttpy | tt/multifuncrs.py | multifuncrs | def multifuncrs(X, funs, eps=1E-6,
nswp=10,
kickrank=5,
y0=None,
rmax=999999, # TODO:infinity \
kicktype='amr-two', \
pcatype='svd', \
trunctype='fro', \
d2=1, \
do_qr=False, \
verb=1):
"""Cross approximation of a (vector-)function of several TT-tensors.
:param X: tuple of TT-tensors
:param funs: multivariate function
:param eps: accuracy
"""
dtype = np.float64
if len([x for x in X if x.is_complex]) > 0:
dtype = np.complex128
y = y0
wasrand = False
nx = len(X)
d = X[0].d
n = X[0].n
rx = np.transpose(np.array([ttx.r for ttx in X]))
#crx = [tt.tensor.to_list(ttx) for x in X]
#crx = zip(*crx)
crx = np.transpose(np.array([tt.tensor.to_list(ttx)
for ttx in X], dtype=np.object))
crx = np.empty((nx, d), dtype=np.object)
i = 0
for ttx in X:
v = tt.tensor.to_list(ttx)
j = 0
for w in v:
crx[i, j] = w
j = j + 1
i = i + 1
crx = crx.T
if y is None:
ry = d2 * np.ones((d + 1,), dtype=np.int32)
ry[0] = 1
y = tt.rand(n, d, ry)
wasrand = True
ry = y.r
cry = tt.tensor.to_list(y)
Ry = np.zeros((d + 1, ), dtype=np.object)
Ry[0] = np.array([[1.0]], dtype=dtype)
Ry[d] = np.array([[1.0]], dtype=dtype)
Rx = np.zeros((d + 1, nx), dtype=np.object)
Rx[0, :] = np.ones(nx, dtype=dtype)
Rx[d, :] = np.ones(nx, dtype=dtype)
block_order = [+d, -d]
# orth
for i in range(0, d - 1):
cr = cry[i]
cr = reshape(cr, (ry[i] * n[i], ry[i + 1]))
cr, rv = np.linalg.qr(cr)
cr2 = cry[i + 1]
cr2 = reshape(cr2, (ry[i + 1], n[i + 1] * ry[i + 2]))
cr2 = np.dot(rv, cr2) # matrix multiplication
ry[i + 1] = cr.shape[1]
cr = reshape(cr, (ry[i], n[i], ry[i + 1]))
cry[i + 1] = reshape(cr2, (ry[i + 1], n[i + 1], ry[i + 2]))
cry[i] = cr
Ry[i + 1] = np.dot(Ry[i], reshape(cr, (ry[i], n[i] * ry[i + 1])))
Ry[i + 1] = reshape(Ry[i + 1], (ry[i] * n[i], ry[i + 1]))
curind = []
if wasrand:
# EVERY DAY I'M SHUFFLIN'
curind = np.random.permutation(n[i] * ry[i])[:ry[i + 1]]
else:
curind = maxvol(Ry[i + 1])
Ry[i + 1] = Ry[i + 1][curind, :]
for j in range(0, nx):
try:
Rx[i + 1, j] = reshape(crx[i, j],
(rx[i, j], n[i] * rx[i + 1, j]))
except:
pass
Rx[i + 1, j] = np.dot(Rx[i, j], Rx[i + 1, j])
Rx[i + 1, j] = reshape(Rx[i + 1, j], (ry[i] * n[i], rx[i + 1, j]))
Rx[i + 1, j] = Rx[i + 1, j][curind, :]
d2 = ry[d]
ry[d] = 1
cry[d - 1] = np.transpose(cry[d - 1], [2, 0, 1]) # permute
last_sweep = False
swp = 1
dy = np.zeros((d, ))
max_dy = 0
cur_order = copy.copy(block_order)
order_index = 1
i = d - 1
# can't use 'dir' identifier in python
dirn = int(math.copysign(1, cur_order[order_index]))
# DMRG sweeps
while swp <= nswp or dirn > 0:
oldy = reshape(cry[i], (d2 * ry[i] * n[i] * ry[i + 1],))
if not last_sweep:
# compute the X superblocks
curbl = np.zeros((ry[i] * n[i] * ry[i + 1], nx), dtype=dtype)
for j in range(0, nx):
cr = reshape(crx[i, j], (rx[i, j], n[i] * rx[i + 1, j]))
cr = np.dot(Rx[i, j], cr)
cr = reshape(cr, (ry[i] * n[i], rx[i + 1, j]))
cr = np.dot(cr, Rx[i + 1, j])
curbl[:, j] = cr.flatten('F')
# call the function
newy = funs(curbl)
# multiply with inverted Ry
newy = reshape(newy, (ry[i], n[i] * ry[i + 1] * d2))
newy = np.linalg.solve(Ry[i], newy) # y = R \ y
newy = reshape(newy, (ry[i] * n[i] * ry[i + 1], d2))
newy = reshape(np.transpose(newy), (d2 * ry[i] * n[i], ry[i + 1]))
newy = np.transpose(np.linalg.solve(
np.transpose(Ry[i + 1]), np.transpose(newy))) # y=y/R
newy = reshape(newy, (d2 * ry[i] * n[i] * ry[i + 1],))
else:
newy = oldy
dy[i] = np.linalg.norm(newy - oldy) / np.linalg.norm(newy)
max_dy = max(max_dy, dy[i])
# truncation
if dirn > 0: # left-to-right
newy = reshape(newy, (d2, ry[i] * n[i] * ry[i + 1]))
newy = reshape(np.transpose(newy), (ry[i] * n[i], ry[i + 1] * d2))
else:
newy = reshape(newy, (d2 * ry[i], n[i] * ry[i + 1]))
r = 0 # defines a variable in global scope
if kickrank >= 0:
u, s, v = np.linalg.svd(newy, full_matrices=False)
v = np.conj(np.transpose(v))
if trunctype == "fro" or last_sweep:
r = my_chop2(s, eps / math.sqrt(d) * np.linalg.norm(s))
else:
# truncate taking into account the (r+1) overhead in the cross
# (T.S.: what?)
cums = abs(s * np.arange(2, len(s) + 2)) ** 2
cums = np.cumsum(cums[::-1])[::-1]
cums = cums / cums[0]
ff = [i for i in range(len(cums)) if cums[i] < eps ** 2 / d]
if len(ff) == 0:
r = len(s)
else:
r = np.amin(ff)
r = min(r, rmax, len(s))
else:
if dirn > 0:
u, v = np.linalg.qr(newy)
v = np.conj(np.transpose(v))
r = u.shape[1]
s = np.ones((r, ))
else:
v, u = np.linalg.qr(np.transpose(newy))
v = np.conj(v)
u = np.transpose(u)
r = u.shape[1]
s = np.ones((r, ))
if verb > 1:
print('=multifuncrs= block %d{%d}, dy: %3.3e, r: %d' % (i, dirn, dy[i], r))
# kicks and interfaces
if dirn > 0 and i < d - 1:
u = u[:, :r]
v = np.dot(v[:, :r], np.diag(s[:r]))
# kick
radd = 0
rv = 1
if not last_sweep and kickrank > 0:
uk = None
if kicktype == 'amr-two':
# AMR(two)-like kick.
# compute the X superblocks
ind2 = np.unique(np.random.randint(
0, ry[i + 2] * n[i + 1], ry[i + 1]))
#ind2 = np.unique(np.floor(np.random.rand(ry[i + 1]) * (ry[i + 2] * n[i + 1])))
rkick = len(ind2)
curbl = np.zeros((ry[i] * n[i] * rkick, nx), dtype=dtype)
for j in range(nx):
cr1 = reshape(
crx[i, j], (rx[i, j], n[i] * rx[i + 1, j]))
cr1 = np.dot(Rx[i, j], cr1)
cr1 = reshape(cr1, (ry[i] * n[i], rx[i + 1, j]))
cr2 = reshape(
crx[i + 1, j], (rx[i + 1, j] * n[i + 1], rx[i + 2, j]))
cr2 = np.dot(cr2, Rx[i + 2, j])
cr2 = reshape(
cr2, (rx[i + 1, j], n[i + 1] * ry[i + 2]))
cr2 = cr2[:, ind2]
curbl[:, j] = reshape(
np.dot(cr1, cr2), (ry[i] * n[i] * rkick,))
# call the function
uk = funs(curbl)
uk = reshape(uk, (ry[i], n[i] * rkick * d2))
uk = np.linalg.solve(Ry[i], uk)
uk = reshape(uk, (ry[i] * n[i], rkick * d2))
if pcatype == 'svd':
uk, sk, vk = np.linalg.svd(uk, full_matrices=False)
vk = np.conj(np.transpose(vk))
uk = uk[:, :min(kickrank, uk.shape[1])]
else:
# uk = uchol(np.transpose(uk), kickrank + 1) # TODO
uk = uk[:, :max(uk.shape[1] - kickrank + 1, 1):-1]
else:
uk = np.random.rand(ry[i] * n[i], kickrank)
u, rv = np.linalg.qr(np.concatenate((u, uk), axis=1))
radd = uk.shape[1]
v = np.concatenate(
(v, np.zeros((ry[i + 1] * d2, radd), dtype=dtype)), axis=1)
v = np.dot(rv, np.conj(np.transpose(v)))
r = u.shape[1]
cr2 = cry[i + 1]
cr2 = reshape(cr2, (ry[i + 1], n[i + 1] * ry[i + 2]))
v = reshape(v, (r * ry[i + 1], d2))
v = reshape(np.transpose(v), (d2 * r, ry[i + 1]))
v = np.dot(v, cr2)
ry[i + 1] = r
u = reshape(u, (ry[i], n[i], r))
v = reshape(v, (d2, r, n[i + 1], ry[i + 2]))
cry[i] = u
cry[i + 1] = v
Ry[i + 1] = np.dot(Ry[i], reshape(u, (ry[i], n[i] * ry[i + 1])))
Ry[i + 1] = reshape(Ry[i + 1], (ry[i] * n[i], ry[i + 1]))
curind = maxvol(Ry[i + 1])
Ry[i + 1] = Ry[i + 1][curind, :]
for j in range(nx):
Rx[i + 1, j] = reshape(crx[i, j],
(rx[i, j], n[i] * rx[i + 1, j]))
Rx[i + 1, j] = np.dot(Rx[i, j], Rx[i + 1, j])
Rx[i + 1, j] = reshape(Rx[i + 1, j],
(ry[i] * n[i], rx[i + 1, j]))
Rx[i + 1, j] = Rx[i + 1, j][curind, :]
elif dirn < 0 and i > 0:
u = np.dot(u[:, :r], np.diag(s[:r]))
v = np.conj(v[:, :r])
radd = 0
rv = 1
if not last_sweep and kickrank > 0:
if kicktype == 'amr-two':
# compute the X superblocks
ind2 = np.unique(np.random.randint(
0, ry[i - 1] * n[i - 1], ry[i]))
rkick = len(ind2)
curbl = np.zeros(
(rkick * n[i] * ry[i + 1], nx), dtype=dtype)
for j in range(nx):
cr1 = reshape(
crx[i, j], (rx[i, j] * n[i], rx[i + 1, j]))
cr1 = np.dot(cr1, Rx[i + 1, j])
cr1 = reshape(cr1, (rx[i, j], n[i] * ry[i + 1]))
cr2 = reshape(
crx[i - 1, j], (rx[i - 1, j], n[i - 1] * rx[i, j]))
cr2 = np.dot(Rx[i - 1, j], cr2)
cr2 = reshape(cr2, (ry[i - 1] * n[i - 1], rx[i, j]))
cr2 = cr2[ind2, :]
curbl[:, j] = reshape(
np.dot(cr2, cr1), (rkick * n[i] * ry[i + 1],))
# calling the function
uk = funs(curbl)
uk = reshape(uk, (rkick * n[i] * ry[i + 1], d2))
uk = reshape(np.transpose(
uk), (d2 * rkick * n[i], ry[i + 1]))
uk = np.transpose(np.linalg.solve(
np.transpose(Ry[i + 1]), np.transpose(uk)))
uk = reshape(uk, (d2 * rkick, n[i] * ry[i + 1]))
if pcatype == 'svd':
vk, sk, uk = np.linalg.svd(uk, full_matrices=False)
uk = np.conj(np.transpose(uk))
# TODO: refactor
uk = uk[:, :min(kickrank, uk.shape[1])]
else:
# uk = uchol(uk, kickrank + 1) # TODO
uk = uk[:, :max(uk.shape[1] - kickrank + 1, 1):-1]
else:
uk = np.random.rand(n[i] * ry[i + 1], kickrank)
v, rv = np.linalg.qr(np.concatenate((v, uk), axis=1))
radd = uk.shape[1]
u = np.concatenate(
(u, np.zeros((d2 * ry[i], radd), dtype=dtype)), axis=1)
u = np.dot(u, np.transpose(rv))
r = v.shape[1]
cr2 = cry[i - 1]
cr2 = reshape(cr2, (ry[i - 1] * n[i - 1], ry[i]))
u = reshape(u, (d2, ry[i] * r))
u = reshape(np.transpose(u), (ry[i], r * d2))
u = np.dot(cr2, u)
u = reshape(u, (ry[i - 1] * n[i - 1] * r, d2))
u = reshape(np.transpose(u), (d2, ry[i - 1], n[i - 1], r))
v = reshape(np.transpose(v), (r, n[i], ry[i + 1]))
ry[i] = r
cry[i - 1] = u
cry[i] = v
Ry[i] = np.dot(reshape(v, (ry[i] * n[i], ry[i + 1])), Ry[i + 1])
Ry[i] = reshape(Ry[i], (ry[i], n[i] * ry[i + 1]))
curind = maxvol(np.transpose(Ry[i]))
Ry[i] = Ry[i][:, curind]
for j in range(nx):
Rx[i, j] = reshape(crx[i, j], (rx[i, j] * n[i], rx[i + 1, j]))
Rx[i, j] = np.dot(Rx[i, j], Rx[i + 1, j])
Rx[i, j] = reshape(Rx[i, j], (rx[i, j], n[i] * ry[i + 1]))
Rx[i, j] = Rx[i, j][:, curind]
elif dirn > 0 and i == d - 1:
newy = np.dot(np.dot(u[:, :r], np.diag(s[:r])),
np.conj(np.transpose(v[:, :r])))
newy = reshape(newy, (ry[i] * n[i] * ry[i + 1], d2))
cry[i] = reshape(np.transpose(newy), (d2, ry[i], n[i], ry[i + 1]))
elif dirn < 0 and i == 0:
newy = np.dot(np.dot(u[:, :r], np.diag(s[:r])),
np.conj(np.transpose(v[:, :r])))
newy = reshape(newy, (d2, ry[i], n[i], ry[i + 1]))
cry[i] = newy
i = i + dirn
cur_order[order_index] = cur_order[order_index] - dirn
if cur_order[order_index] == 0:
order_index = order_index + 1
if verb > 0:
print('=multifuncrs= sweep %d{%d}, max_dy: %3.3e, erank: %g' % (swp, order_index, max_dy,
math.sqrt(np.dot(ry[:d], n * ry[1:]) / np.sum(n))))
if last_sweep:
break
if max_dy < eps and dirn < 0:
last_sweep = True
kickrank = 0
if order_index >= len(cur_order):
cur_order = copy.copy(block_order)
order_index = 0
if last_sweep:
cur_order = [d - 1]
max_dy = 0
swp = swp + 1
dirn = int(math.copysign(1, cur_order[order_index]))
i = i + dirn
cry[d - 1] = np.transpose(cry[d - 1][:, :, :, 0], [1, 2, 0])
y = tt.tensor.from_list(cry)
return y | python | def multifuncrs(X, funs, eps=1E-6,
nswp=10,
kickrank=5,
y0=None,
rmax=999999, # TODO:infinity \
kicktype='amr-two', \
pcatype='svd', \
trunctype='fro', \
d2=1, \
do_qr=False, \
verb=1):
"""Cross approximation of a (vector-)function of several TT-tensors.
:param X: tuple of TT-tensors
:param funs: multivariate function
:param eps: accuracy
"""
dtype = np.float64
if len([x for x in X if x.is_complex]) > 0:
dtype = np.complex128
y = y0
wasrand = False
nx = len(X)
d = X[0].d
n = X[0].n
rx = np.transpose(np.array([ttx.r for ttx in X]))
#crx = [tt.tensor.to_list(ttx) for x in X]
#crx = zip(*crx)
crx = np.transpose(np.array([tt.tensor.to_list(ttx)
for ttx in X], dtype=np.object))
crx = np.empty((nx, d), dtype=np.object)
i = 0
for ttx in X:
v = tt.tensor.to_list(ttx)
j = 0
for w in v:
crx[i, j] = w
j = j + 1
i = i + 1
crx = crx.T
if y is None:
ry = d2 * np.ones((d + 1,), dtype=np.int32)
ry[0] = 1
y = tt.rand(n, d, ry)
wasrand = True
ry = y.r
cry = tt.tensor.to_list(y)
Ry = np.zeros((d + 1, ), dtype=np.object)
Ry[0] = np.array([[1.0]], dtype=dtype)
Ry[d] = np.array([[1.0]], dtype=dtype)
Rx = np.zeros((d + 1, nx), dtype=np.object)
Rx[0, :] = np.ones(nx, dtype=dtype)
Rx[d, :] = np.ones(nx, dtype=dtype)
block_order = [+d, -d]
# orth
for i in range(0, d - 1):
cr = cry[i]
cr = reshape(cr, (ry[i] * n[i], ry[i + 1]))
cr, rv = np.linalg.qr(cr)
cr2 = cry[i + 1]
cr2 = reshape(cr2, (ry[i + 1], n[i + 1] * ry[i + 2]))
cr2 = np.dot(rv, cr2) # matrix multiplication
ry[i + 1] = cr.shape[1]
cr = reshape(cr, (ry[i], n[i], ry[i + 1]))
cry[i + 1] = reshape(cr2, (ry[i + 1], n[i + 1], ry[i + 2]))
cry[i] = cr
Ry[i + 1] = np.dot(Ry[i], reshape(cr, (ry[i], n[i] * ry[i + 1])))
Ry[i + 1] = reshape(Ry[i + 1], (ry[i] * n[i], ry[i + 1]))
curind = []
if wasrand:
# EVERY DAY I'M SHUFFLIN'
curind = np.random.permutation(n[i] * ry[i])[:ry[i + 1]]
else:
curind = maxvol(Ry[i + 1])
Ry[i + 1] = Ry[i + 1][curind, :]
for j in range(0, nx):
try:
Rx[i + 1, j] = reshape(crx[i, j],
(rx[i, j], n[i] * rx[i + 1, j]))
except:
pass
Rx[i + 1, j] = np.dot(Rx[i, j], Rx[i + 1, j])
Rx[i + 1, j] = reshape(Rx[i + 1, j], (ry[i] * n[i], rx[i + 1, j]))
Rx[i + 1, j] = Rx[i + 1, j][curind, :]
d2 = ry[d]
ry[d] = 1
cry[d - 1] = np.transpose(cry[d - 1], [2, 0, 1]) # permute
last_sweep = False
swp = 1
dy = np.zeros((d, ))
max_dy = 0
cur_order = copy.copy(block_order)
order_index = 1
i = d - 1
# can't use 'dir' identifier in python
dirn = int(math.copysign(1, cur_order[order_index]))
# DMRG sweeps
while swp <= nswp or dirn > 0:
oldy = reshape(cry[i], (d2 * ry[i] * n[i] * ry[i + 1],))
if not last_sweep:
# compute the X superblocks
curbl = np.zeros((ry[i] * n[i] * ry[i + 1], nx), dtype=dtype)
for j in range(0, nx):
cr = reshape(crx[i, j], (rx[i, j], n[i] * rx[i + 1, j]))
cr = np.dot(Rx[i, j], cr)
cr = reshape(cr, (ry[i] * n[i], rx[i + 1, j]))
cr = np.dot(cr, Rx[i + 1, j])
curbl[:, j] = cr.flatten('F')
# call the function
newy = funs(curbl)
# multiply with inverted Ry
newy = reshape(newy, (ry[i], n[i] * ry[i + 1] * d2))
newy = np.linalg.solve(Ry[i], newy) # y = R \ y
newy = reshape(newy, (ry[i] * n[i] * ry[i + 1], d2))
newy = reshape(np.transpose(newy), (d2 * ry[i] * n[i], ry[i + 1]))
newy = np.transpose(np.linalg.solve(
np.transpose(Ry[i + 1]), np.transpose(newy))) # y=y/R
newy = reshape(newy, (d2 * ry[i] * n[i] * ry[i + 1],))
else:
newy = oldy
dy[i] = np.linalg.norm(newy - oldy) / np.linalg.norm(newy)
max_dy = max(max_dy, dy[i])
# truncation
if dirn > 0: # left-to-right
newy = reshape(newy, (d2, ry[i] * n[i] * ry[i + 1]))
newy = reshape(np.transpose(newy), (ry[i] * n[i], ry[i + 1] * d2))
else:
newy = reshape(newy, (d2 * ry[i], n[i] * ry[i + 1]))
r = 0 # defines a variable in global scope
if kickrank >= 0:
u, s, v = np.linalg.svd(newy, full_matrices=False)
v = np.conj(np.transpose(v))
if trunctype == "fro" or last_sweep:
r = my_chop2(s, eps / math.sqrt(d) * np.linalg.norm(s))
else:
# truncate taking into account the (r+1) overhead in the cross
# (T.S.: what?)
cums = abs(s * np.arange(2, len(s) + 2)) ** 2
cums = np.cumsum(cums[::-1])[::-1]
cums = cums / cums[0]
ff = [i for i in range(len(cums)) if cums[i] < eps ** 2 / d]
if len(ff) == 0:
r = len(s)
else:
r = np.amin(ff)
r = min(r, rmax, len(s))
else:
if dirn > 0:
u, v = np.linalg.qr(newy)
v = np.conj(np.transpose(v))
r = u.shape[1]
s = np.ones((r, ))
else:
v, u = np.linalg.qr(np.transpose(newy))
v = np.conj(v)
u = np.transpose(u)
r = u.shape[1]
s = np.ones((r, ))
if verb > 1:
print('=multifuncrs= block %d{%d}, dy: %3.3e, r: %d' % (i, dirn, dy[i], r))
# kicks and interfaces
if dirn > 0 and i < d - 1:
u = u[:, :r]
v = np.dot(v[:, :r], np.diag(s[:r]))
# kick
radd = 0
rv = 1
if not last_sweep and kickrank > 0:
uk = None
if kicktype == 'amr-two':
# AMR(two)-like kick.
# compute the X superblocks
ind2 = np.unique(np.random.randint(
0, ry[i + 2] * n[i + 1], ry[i + 1]))
#ind2 = np.unique(np.floor(np.random.rand(ry[i + 1]) * (ry[i + 2] * n[i + 1])))
rkick = len(ind2)
curbl = np.zeros((ry[i] * n[i] * rkick, nx), dtype=dtype)
for j in range(nx):
cr1 = reshape(
crx[i, j], (rx[i, j], n[i] * rx[i + 1, j]))
cr1 = np.dot(Rx[i, j], cr1)
cr1 = reshape(cr1, (ry[i] * n[i], rx[i + 1, j]))
cr2 = reshape(
crx[i + 1, j], (rx[i + 1, j] * n[i + 1], rx[i + 2, j]))
cr2 = np.dot(cr2, Rx[i + 2, j])
cr2 = reshape(
cr2, (rx[i + 1, j], n[i + 1] * ry[i + 2]))
cr2 = cr2[:, ind2]
curbl[:, j] = reshape(
np.dot(cr1, cr2), (ry[i] * n[i] * rkick,))
# call the function
uk = funs(curbl)
uk = reshape(uk, (ry[i], n[i] * rkick * d2))
uk = np.linalg.solve(Ry[i], uk)
uk = reshape(uk, (ry[i] * n[i], rkick * d2))
if pcatype == 'svd':
uk, sk, vk = np.linalg.svd(uk, full_matrices=False)
vk = np.conj(np.transpose(vk))
uk = uk[:, :min(kickrank, uk.shape[1])]
else:
# uk = uchol(np.transpose(uk), kickrank + 1) # TODO
uk = uk[:, :max(uk.shape[1] - kickrank + 1, 1):-1]
else:
uk = np.random.rand(ry[i] * n[i], kickrank)
u, rv = np.linalg.qr(np.concatenate((u, uk), axis=1))
radd = uk.shape[1]
v = np.concatenate(
(v, np.zeros((ry[i + 1] * d2, radd), dtype=dtype)), axis=1)
v = np.dot(rv, np.conj(np.transpose(v)))
r = u.shape[1]
cr2 = cry[i + 1]
cr2 = reshape(cr2, (ry[i + 1], n[i + 1] * ry[i + 2]))
v = reshape(v, (r * ry[i + 1], d2))
v = reshape(np.transpose(v), (d2 * r, ry[i + 1]))
v = np.dot(v, cr2)
ry[i + 1] = r
u = reshape(u, (ry[i], n[i], r))
v = reshape(v, (d2, r, n[i + 1], ry[i + 2]))
cry[i] = u
cry[i + 1] = v
Ry[i + 1] = np.dot(Ry[i], reshape(u, (ry[i], n[i] * ry[i + 1])))
Ry[i + 1] = reshape(Ry[i + 1], (ry[i] * n[i], ry[i + 1]))
curind = maxvol(Ry[i + 1])
Ry[i + 1] = Ry[i + 1][curind, :]
for j in range(nx):
Rx[i + 1, j] = reshape(crx[i, j],
(rx[i, j], n[i] * rx[i + 1, j]))
Rx[i + 1, j] = np.dot(Rx[i, j], Rx[i + 1, j])
Rx[i + 1, j] = reshape(Rx[i + 1, j],
(ry[i] * n[i], rx[i + 1, j]))
Rx[i + 1, j] = Rx[i + 1, j][curind, :]
elif dirn < 0 and i > 0:
u = np.dot(u[:, :r], np.diag(s[:r]))
v = np.conj(v[:, :r])
radd = 0
rv = 1
if not last_sweep and kickrank > 0:
if kicktype == 'amr-two':
# compute the X superblocks
ind2 = np.unique(np.random.randint(
0, ry[i - 1] * n[i - 1], ry[i]))
rkick = len(ind2)
curbl = np.zeros(
(rkick * n[i] * ry[i + 1], nx), dtype=dtype)
for j in range(nx):
cr1 = reshape(
crx[i, j], (rx[i, j] * n[i], rx[i + 1, j]))
cr1 = np.dot(cr1, Rx[i + 1, j])
cr1 = reshape(cr1, (rx[i, j], n[i] * ry[i + 1]))
cr2 = reshape(
crx[i - 1, j], (rx[i - 1, j], n[i - 1] * rx[i, j]))
cr2 = np.dot(Rx[i - 1, j], cr2)
cr2 = reshape(cr2, (ry[i - 1] * n[i - 1], rx[i, j]))
cr2 = cr2[ind2, :]
curbl[:, j] = reshape(
np.dot(cr2, cr1), (rkick * n[i] * ry[i + 1],))
# calling the function
uk = funs(curbl)
uk = reshape(uk, (rkick * n[i] * ry[i + 1], d2))
uk = reshape(np.transpose(
uk), (d2 * rkick * n[i], ry[i + 1]))
uk = np.transpose(np.linalg.solve(
np.transpose(Ry[i + 1]), np.transpose(uk)))
uk = reshape(uk, (d2 * rkick, n[i] * ry[i + 1]))
if pcatype == 'svd':
vk, sk, uk = np.linalg.svd(uk, full_matrices=False)
uk = np.conj(np.transpose(uk))
# TODO: refactor
uk = uk[:, :min(kickrank, uk.shape[1])]
else:
# uk = uchol(uk, kickrank + 1) # TODO
uk = uk[:, :max(uk.shape[1] - kickrank + 1, 1):-1]
else:
uk = np.random.rand(n[i] * ry[i + 1], kickrank)
v, rv = np.linalg.qr(np.concatenate((v, uk), axis=1))
radd = uk.shape[1]
u = np.concatenate(
(u, np.zeros((d2 * ry[i], radd), dtype=dtype)), axis=1)
u = np.dot(u, np.transpose(rv))
r = v.shape[1]
cr2 = cry[i - 1]
cr2 = reshape(cr2, (ry[i - 1] * n[i - 1], ry[i]))
u = reshape(u, (d2, ry[i] * r))
u = reshape(np.transpose(u), (ry[i], r * d2))
u = np.dot(cr2, u)
u = reshape(u, (ry[i - 1] * n[i - 1] * r, d2))
u = reshape(np.transpose(u), (d2, ry[i - 1], n[i - 1], r))
v = reshape(np.transpose(v), (r, n[i], ry[i + 1]))
ry[i] = r
cry[i - 1] = u
cry[i] = v
Ry[i] = np.dot(reshape(v, (ry[i] * n[i], ry[i + 1])), Ry[i + 1])
Ry[i] = reshape(Ry[i], (ry[i], n[i] * ry[i + 1]))
curind = maxvol(np.transpose(Ry[i]))
Ry[i] = Ry[i][:, curind]
for j in range(nx):
Rx[i, j] = reshape(crx[i, j], (rx[i, j] * n[i], rx[i + 1, j]))
Rx[i, j] = np.dot(Rx[i, j], Rx[i + 1, j])
Rx[i, j] = reshape(Rx[i, j], (rx[i, j], n[i] * ry[i + 1]))
Rx[i, j] = Rx[i, j][:, curind]
elif dirn > 0 and i == d - 1:
newy = np.dot(np.dot(u[:, :r], np.diag(s[:r])),
np.conj(np.transpose(v[:, :r])))
newy = reshape(newy, (ry[i] * n[i] * ry[i + 1], d2))
cry[i] = reshape(np.transpose(newy), (d2, ry[i], n[i], ry[i + 1]))
elif dirn < 0 and i == 0:
newy = np.dot(np.dot(u[:, :r], np.diag(s[:r])),
np.conj(np.transpose(v[:, :r])))
newy = reshape(newy, (d2, ry[i], n[i], ry[i + 1]))
cry[i] = newy
i = i + dirn
cur_order[order_index] = cur_order[order_index] - dirn
if cur_order[order_index] == 0:
order_index = order_index + 1
if verb > 0:
print('=multifuncrs= sweep %d{%d}, max_dy: %3.3e, erank: %g' % (swp, order_index, max_dy,
math.sqrt(np.dot(ry[:d], n * ry[1:]) / np.sum(n))))
if last_sweep:
break
if max_dy < eps and dirn < 0:
last_sweep = True
kickrank = 0
if order_index >= len(cur_order):
cur_order = copy.copy(block_order)
order_index = 0
if last_sweep:
cur_order = [d - 1]
max_dy = 0
swp = swp + 1
dirn = int(math.copysign(1, cur_order[order_index]))
i = i + dirn
cry[d - 1] = np.transpose(cry[d - 1][:, :, :, 0], [1, 2, 0])
y = tt.tensor.from_list(cry)
return y | Cross approximation of a (vector-)function of several TT-tensors.
:param X: tuple of TT-tensors
:param funs: multivariate function
:param eps: accuracy | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/multifuncrs.py#L54-L425 |
oseledets/ttpy | tt/core/vector.py | vector.from_list | def from_list(a, order='F'):
"""Generate TT-vectorr object from given TT cores.
:param a: List of TT cores.
:type a: list
:returns: vector -- TT-vector constructed from the given cores.
"""
d = len(a) # Number of cores
res = vector()
n = _np.zeros(d, dtype=_np.int32)
r = _np.zeros(d+1, dtype=_np.int32)
cr = _np.array([])
for i in xrange(d):
cr = _np.concatenate((cr, a[i].flatten(order)))
r[i] = a[i].shape[0]
r[i+1] = a[i].shape[2]
n[i] = a[i].shape[1]
res.d = d
res.n = n
res.r = r
res.core = cr
res.get_ps()
return res | python | def from_list(a, order='F'):
"""Generate TT-vectorr object from given TT cores.
:param a: List of TT cores.
:type a: list
:returns: vector -- TT-vector constructed from the given cores.
"""
d = len(a) # Number of cores
res = vector()
n = _np.zeros(d, dtype=_np.int32)
r = _np.zeros(d+1, dtype=_np.int32)
cr = _np.array([])
for i in xrange(d):
cr = _np.concatenate((cr, a[i].flatten(order)))
r[i] = a[i].shape[0]
r[i+1] = a[i].shape[2]
n[i] = a[i].shape[1]
res.d = d
res.n = n
res.r = r
res.core = cr
res.get_ps()
return res | Generate TT-vectorr object from given TT cores.
:param a: List of TT cores.
:type a: list
:returns: vector -- TT-vector constructed from the given cores. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/vector.py#L82-L105 |
oseledets/ttpy | tt/core/vector.py | vector.erank | def erank(self):
""" Effective rank of the TT-vector """
r = self.r
n = self.n
d = self.d
if d <= 1:
er = 0e0
else:
sz = _np.dot(n * r[0:d], r[1:])
if sz == 0:
er = 0e0
else:
b = r[0] * n[0] + n[d - 1] * r[d]
if d is 2:
er = sz * 1.0 / b
else:
a = _np.sum(n[1:d - 1])
er = (_np.sqrt(b * b + 4 * a * sz) - b) / (2 * a)
return er | python | def erank(self):
""" Effective rank of the TT-vector """
r = self.r
n = self.n
d = self.d
if d <= 1:
er = 0e0
else:
sz = _np.dot(n * r[0:d], r[1:])
if sz == 0:
er = 0e0
else:
b = r[0] * n[0] + n[d - 1] * r[d]
if d is 2:
er = sz * 1.0 / b
else:
a = _np.sum(n[1:d - 1])
er = (_np.sqrt(b * b + 4 * a * sz) - b) / (2 * a)
return er | Effective rank of the TT-vector | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/vector.py#L127-L145 |
oseledets/ttpy | tt/core/vector.py | vector.r2c | def r2c(self):
"""Get complex vector.from real one made by ``tensor.c2r()``.
For tensor :math:`\\tilde{X}(i_1,\\ldots,i_d,i_{d+1})` returns complex tensor
.. math::
X(i_1,\\ldots,i_d) = \\tilde{X}(i_1,\\ldots,i_d,0) + i\\tilde{X}(i_1,\\ldots,i_d,1).
>>> a = tt.rand(2,10,5) + 1j * tt.rand(2,10,5)
>>> (a.c2r().r2c() - a).norm() / a.norm()
7.310562016615692e-16
"""
tmp = self.copy()
newcore = _np.array(tmp.core, dtype=_np.complex)
cr = newcore[tmp.ps[-2] - 1:tmp.ps[-1] - 1]
cr = cr.reshape((tmp.r[-2], tmp.n[-1], tmp.r[-1]), order='F')
cr[:, 1, :] *= 1j
newcore[tmp.ps[-2] - 1:tmp.ps[-1] - 1] = cr.flatten('F')
tmp.core = newcore
return sum(tmp, axis=tmp.d - 1) | python | def r2c(self):
"""Get complex vector.from real one made by ``tensor.c2r()``.
For tensor :math:`\\tilde{X}(i_1,\\ldots,i_d,i_{d+1})` returns complex tensor
.. math::
X(i_1,\\ldots,i_d) = \\tilde{X}(i_1,\\ldots,i_d,0) + i\\tilde{X}(i_1,\\ldots,i_d,1).
>>> a = tt.rand(2,10,5) + 1j * tt.rand(2,10,5)
>>> (a.c2r().r2c() - a).norm() / a.norm()
7.310562016615692e-16
"""
tmp = self.copy()
newcore = _np.array(tmp.core, dtype=_np.complex)
cr = newcore[tmp.ps[-2] - 1:tmp.ps[-1] - 1]
cr = cr.reshape((tmp.r[-2], tmp.n[-1], tmp.r[-1]), order='F')
cr[:, 1, :] *= 1j
newcore[tmp.ps[-2] - 1:tmp.ps[-1] - 1] = cr.flatten('F')
tmp.core = newcore
return sum(tmp, axis=tmp.d - 1) | Get complex vector.from real one made by ``tensor.c2r()``.
For tensor :math:`\\tilde{X}(i_1,\\ldots,i_d,i_{d+1})` returns complex tensor
.. math::
X(i_1,\\ldots,i_d) = \\tilde{X}(i_1,\\ldots,i_d,0) + i\\tilde{X}(i_1,\\ldots,i_d,1).
>>> a = tt.rand(2,10,5) + 1j * tt.rand(2,10,5)
>>> (a.c2r().r2c() - a).norm() / a.norm()
7.310562016615692e-16 | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/vector.py#L282-L302 |
oseledets/ttpy | tt/core/vector.py | vector.full | def full(self, asvector=False):
"""Returns full array (uncompressed).
.. warning::
TT compression allows to keep in memory tensors much larger than ones PC can handle in
raw format. Therefore this function is quite unsafe; use it at your own risk.
:returns: numpy.ndarray -- full tensor.
"""
# Generate correct size vector
sz = self.n.copy()
if self.r[0] > 1:
sz = _np.concatenate(([self.r[0]], sz))
if self.r[self.d] > 1:
sz = _np.concatenate(([self.r[self.d]], sz))
if (_np.iscomplex(self.core).any()):
a = _tt_f90.tt_f90.ztt_to_full(
self.n, self.r, self.ps, self.core, _np.prod(sz))
else:
a = _tt_f90.tt_f90.dtt_to_full(
self.n, self.r, self.ps, _np.real(
self.core), _np.prod(sz))
a = a.reshape(sz, order='F')
if asvector:
a=a.flatten(order='F')
return a | python | def full(self, asvector=False):
"""Returns full array (uncompressed).
.. warning::
TT compression allows to keep in memory tensors much larger than ones PC can handle in
raw format. Therefore this function is quite unsafe; use it at your own risk.
:returns: numpy.ndarray -- full tensor.
"""
# Generate correct size vector
sz = self.n.copy()
if self.r[0] > 1:
sz = _np.concatenate(([self.r[0]], sz))
if self.r[self.d] > 1:
sz = _np.concatenate(([self.r[self.d]], sz))
if (_np.iscomplex(self.core).any()):
a = _tt_f90.tt_f90.ztt_to_full(
self.n, self.r, self.ps, self.core, _np.prod(sz))
else:
a = _tt_f90.tt_f90.dtt_to_full(
self.n, self.r, self.ps, _np.real(
self.core), _np.prod(sz))
a = a.reshape(sz, order='F')
if asvector:
a=a.flatten(order='F')
return a | Returns full array (uncompressed).
.. warning::
TT compression allows to keep in memory tensors much larger than ones PC can handle in
raw format. Therefore this function is quite unsafe; use it at your own risk.
:returns: numpy.ndarray -- full tensor. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/vector.py#L326-L352 |
oseledets/ttpy | tt/core/vector.py | vector.round | def round(self, eps=1e-14, rmax=1000000):
"""Applies TT rounding procedure to the TT-vector and **returns rounded tensor**.
:param eps: Rounding accuracy.
:type eps: float
:param rmax: Maximal rank
:type rmax: int
:returns: tensor -- rounded TT-vector.
Usage example:
>>> a = tt.ones(2, 10)
>>> b = a + a
>>> print b.r
array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1], dtype=int32)
>>> b = b.round(1E-14)
>>> print b.r
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)
"""
c = vector()
c.n = _np.copy(self.n)
c.d = self.d
c.r = _np.copy(self.r)
c.ps = _np.copy(self.ps)
if (_np.iscomplex(self.core).any()):
_tt_f90.tt_f90.ztt_compr2(c.n, c.r, c.ps, self.core, eps, rmax)
c.core = _tt_f90.tt_f90.zcore.copy()
else:
_tt_f90.tt_f90.dtt_compr2(c.n, c.r, c.ps, self.core, eps, rmax)
c.core = _tt_f90.tt_f90.core.copy()
_tt_f90.tt_f90.tt_dealloc()
return c | python | def round(self, eps=1e-14, rmax=1000000):
"""Applies TT rounding procedure to the TT-vector and **returns rounded tensor**.
:param eps: Rounding accuracy.
:type eps: float
:param rmax: Maximal rank
:type rmax: int
:returns: tensor -- rounded TT-vector.
Usage example:
>>> a = tt.ones(2, 10)
>>> b = a + a
>>> print b.r
array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1], dtype=int32)
>>> b = b.round(1E-14)
>>> print b.r
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32)
"""
c = vector()
c.n = _np.copy(self.n)
c.d = self.d
c.r = _np.copy(self.r)
c.ps = _np.copy(self.ps)
if (_np.iscomplex(self.core).any()):
_tt_f90.tt_f90.ztt_compr2(c.n, c.r, c.ps, self.core, eps, rmax)
c.core = _tt_f90.tt_f90.zcore.copy()
else:
_tt_f90.tt_f90.dtt_compr2(c.n, c.r, c.ps, self.core, eps, rmax)
c.core = _tt_f90.tt_f90.core.copy()
_tt_f90.tt_f90.tt_dealloc()
return c | Applies TT rounding procedure to the TT-vector and **returns rounded tensor**.
:param eps: Rounding accuracy.
:type eps: float
:param rmax: Maximal rank
:type rmax: int
:returns: tensor -- rounded TT-vector.
Usage example:
>>> a = tt.ones(2, 10)
>>> b = a + a
>>> print b.r
array([1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1], dtype=int32)
>>> b = b.round(1E-14)
>>> print b.r
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=int32) | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/vector.py#L381-L413 |
oseledets/ttpy | tt/core/vector.py | vector.rmean | def rmean(self):
""" Calculates the mean rank of a TT-vector."""
if not _np.all(self.n):
return 0
# Solving quadratic equation ar^2 + br + c = 0;
a = _np.sum(self.n[1:-1])
b = self.n[0] + self.n[-1]
c = - _np.sum(self.n * self.r[1:] * self.r[:-1])
D = b ** 2 - 4 * a * c
r = 0.5 * (-b + _np.sqrt(D)) / a
return r | python | def rmean(self):
""" Calculates the mean rank of a TT-vector."""
if not _np.all(self.n):
return 0
# Solving quadratic equation ar^2 + br + c = 0;
a = _np.sum(self.n[1:-1])
b = self.n[0] + self.n[-1]
c = - _np.sum(self.n * self.r[1:] * self.r[:-1])
D = b ** 2 - 4 * a * c
r = 0.5 * (-b + _np.sqrt(D)) / a
return r | Calculates the mean rank of a TT-vector. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/vector.py#L568-L578 |
oseledets/ttpy | tt/core/vector.py | vector.qtt_fft1 | def qtt_fft1(self,tol,inverse=False, bitReverse=True):
""" Compute 1D (inverse) discrete Fourier Transform in the QTT format.
:param tol: error tolerance.
:type tol: float
:param inverse: whether do an inverse FFT or not.
:type inverse: Boolean
:param bitReverse: whether do the bit reversion or not. If this function is used as a subroutine for multi-dimensional qtt-fft, this option
need to be set False.
:type bitReverse: Boolean.
:returns: QTT-vector of FFT coefficients.
This is a python translation of the Matlab function "qtt_fft1" in Ivan Oseledets' project TT-Toolbox(https://github.com/oseledets/TT-Toolbox)
See S. Dolgov, B. Khoromskij, D. Savostyanov,
Superfast Fourier transform using QTT approximation,
J. Fourier Anal. Appl., 18(5), 2012.
"""
d = self.d
r = self.r.copy()
y = self.to_list(self)
if inverse:
twiddle =-1+1.22e-16j # exp(pi*1j)
else:
twiddle =-1-1.22e-16j # exp(-pi*1j)
for i in range(d-1, 0, -1):
r1= y[i].shape[0] # head r
r2= y[i].shape[2] # tail r
crd2 = _np.zeros((r1, 2, r2), order='F', dtype=complex)
# last block +-
crd2[:,0,:]= (y[i][:,0,:] + y[i][:,1,:])/_np.sqrt(2)
crd2[:,1,:]= (y[i][:,0,:] - y[i][:,1,:])/_np.sqrt(2)
# last block twiddles
y[i]= _np.zeros((r1*2, 2, r2),order='F',dtype=complex)
y[i][0:r1, 0, 0:r2]= crd2[:,0,:]
y[i][r1:r1*2, 1, 0:r2]= crd2[:,1,:]
#1..i-1 block twiddles and qr
rv=1;
for j in range(0, i):
cr=y[j]
r1= cr.shape[0] # head r
r2= cr.shape[2] # tail r
if j==0:
r[j]=r1
r[j+1] = r2*2
y[j] = _np.zeros((r[j], 2, r[j+1]),order='F',dtype=complex)
y[j][0:r1, :, 0:r2] = cr
y[j][0:r1, 0, r2 :r[j+1]] = cr[:,0,:]
y[j][0:r1, 1, r2 :r[j+1]] = twiddle**(1.0/(2**(i-j)))*cr[:,1,:]
else:
r[j]=r1*2
r[j+1] = r2*2
y[j] = _np.zeros((r[j], 2, r[j+1]),order='F',dtype=complex)
y[j][0:r1, :, 0:r2] = cr
y[j][r1:r[j], 0, r2 :r[j+1]] = cr[:,0,:]
y[j][r1:r[j], 1, r2 :r[j+1]] = twiddle**(1.0/(2**(i-j)))*cr[:,1,:]
y[j] = _np.reshape(y[j],( r[j], 2*r[j+1]),order='F')
y[j] = _np.dot(rv,y[j])
r[j] = y[j].shape[0]
y[j] = _np.reshape(y[j],( 2*r[j], r[j+1]),order='F')
y[j], rv = _np.linalg.qr(y[j])
y[j] = _np.reshape(y[j], (r[j], 2, rv.shape[0]),order='F')
y[i] = _np.reshape(y[i], (r[i], 2*r[i+1]),order='F')
y[i] = _np.dot(rv,y[i])
r[i] = rv.shape[0]
# backward svd
for j in range(i, 0,-1):
u,s,v = _np.linalg.svd(y[j], full_matrices=False)
rnew = my_chop2(s, _np.linalg.norm(s)*tol/_np.sqrt(i))
u=_np.dot(u[:, 0:rnew], _np.diag(s[0:rnew]))
v= v[0:rnew, :]
y[j] = _np.reshape(v, (rnew, 2, r[j+1]),order='F' )
y[j-1] = _np.reshape(y[j-1], (r[j-1]*2,r[j] ),order='F' )
y[j-1] = _np.dot(y[j-1], u)
r[j] = rnew
y[j-1] = _np.reshape(y[j-1], (r[j-1],r[j]*2 ),order='F' )
y[0] = _np.reshape(y[0], (r[0],2, r[1]), order='F' )
# FFT on the first block
y[0]=_np.transpose(y[0],(1,0,2))
y[0]=_np.reshape(y[0],(2, r[0]*r[1]),order='F')
y[0]= _np.dot( _np.array([[1,1],[1,-1]]), y[0])/_np.sqrt(2)
y[0]=_np.reshape(y[0],(2, r[0], r[1]),order='F')
y[0]=_np.transpose(y[0],(1,0,2))
if bitReverse:
# Reverse the train
y2=[None]*d
for i in range(d):
y2[d-i-1]= _np.transpose(y[i],(2,1,0))
y=self.from_list(y2)
else: # for multi-dimensional qtt_fft
y=self.from_list(y)
return y | python | def qtt_fft1(self,tol,inverse=False, bitReverse=True):
""" Compute 1D (inverse) discrete Fourier Transform in the QTT format.
:param tol: error tolerance.
:type tol: float
:param inverse: whether do an inverse FFT or not.
:type inverse: Boolean
:param bitReverse: whether do the bit reversion or not. If this function is used as a subroutine for multi-dimensional qtt-fft, this option
need to be set False.
:type bitReverse: Boolean.
:returns: QTT-vector of FFT coefficients.
This is a python translation of the Matlab function "qtt_fft1" in Ivan Oseledets' project TT-Toolbox(https://github.com/oseledets/TT-Toolbox)
See S. Dolgov, B. Khoromskij, D. Savostyanov,
Superfast Fourier transform using QTT approximation,
J. Fourier Anal. Appl., 18(5), 2012.
"""
d = self.d
r = self.r.copy()
y = self.to_list(self)
if inverse:
twiddle =-1+1.22e-16j # exp(pi*1j)
else:
twiddle =-1-1.22e-16j # exp(-pi*1j)
for i in range(d-1, 0, -1):
r1= y[i].shape[0] # head r
r2= y[i].shape[2] # tail r
crd2 = _np.zeros((r1, 2, r2), order='F', dtype=complex)
# last block +-
crd2[:,0,:]= (y[i][:,0,:] + y[i][:,1,:])/_np.sqrt(2)
crd2[:,1,:]= (y[i][:,0,:] - y[i][:,1,:])/_np.sqrt(2)
# last block twiddles
y[i]= _np.zeros((r1*2, 2, r2),order='F',dtype=complex)
y[i][0:r1, 0, 0:r2]= crd2[:,0,:]
y[i][r1:r1*2, 1, 0:r2]= crd2[:,1,:]
#1..i-1 block twiddles and qr
rv=1;
for j in range(0, i):
cr=y[j]
r1= cr.shape[0] # head r
r2= cr.shape[2] # tail r
if j==0:
r[j]=r1
r[j+1] = r2*2
y[j] = _np.zeros((r[j], 2, r[j+1]),order='F',dtype=complex)
y[j][0:r1, :, 0:r2] = cr
y[j][0:r1, 0, r2 :r[j+1]] = cr[:,0,:]
y[j][0:r1, 1, r2 :r[j+1]] = twiddle**(1.0/(2**(i-j)))*cr[:,1,:]
else:
r[j]=r1*2
r[j+1] = r2*2
y[j] = _np.zeros((r[j], 2, r[j+1]),order='F',dtype=complex)
y[j][0:r1, :, 0:r2] = cr
y[j][r1:r[j], 0, r2 :r[j+1]] = cr[:,0,:]
y[j][r1:r[j], 1, r2 :r[j+1]] = twiddle**(1.0/(2**(i-j)))*cr[:,1,:]
y[j] = _np.reshape(y[j],( r[j], 2*r[j+1]),order='F')
y[j] = _np.dot(rv,y[j])
r[j] = y[j].shape[0]
y[j] = _np.reshape(y[j],( 2*r[j], r[j+1]),order='F')
y[j], rv = _np.linalg.qr(y[j])
y[j] = _np.reshape(y[j], (r[j], 2, rv.shape[0]),order='F')
y[i] = _np.reshape(y[i], (r[i], 2*r[i+1]),order='F')
y[i] = _np.dot(rv,y[i])
r[i] = rv.shape[0]
# backward svd
for j in range(i, 0,-1):
u,s,v = _np.linalg.svd(y[j], full_matrices=False)
rnew = my_chop2(s, _np.linalg.norm(s)*tol/_np.sqrt(i))
u=_np.dot(u[:, 0:rnew], _np.diag(s[0:rnew]))
v= v[0:rnew, :]
y[j] = _np.reshape(v, (rnew, 2, r[j+1]),order='F' )
y[j-1] = _np.reshape(y[j-1], (r[j-1]*2,r[j] ),order='F' )
y[j-1] = _np.dot(y[j-1], u)
r[j] = rnew
y[j-1] = _np.reshape(y[j-1], (r[j-1],r[j]*2 ),order='F' )
y[0] = _np.reshape(y[0], (r[0],2, r[1]), order='F' )
# FFT on the first block
y[0]=_np.transpose(y[0],(1,0,2))
y[0]=_np.reshape(y[0],(2, r[0]*r[1]),order='F')
y[0]= _np.dot( _np.array([[1,1],[1,-1]]), y[0])/_np.sqrt(2)
y[0]=_np.reshape(y[0],(2, r[0], r[1]),order='F')
y[0]=_np.transpose(y[0],(1,0,2))
if bitReverse:
# Reverse the train
y2=[None]*d
for i in range(d):
y2[d-i-1]= _np.transpose(y[i],(2,1,0))
y=self.from_list(y2)
else: # for multi-dimensional qtt_fft
y=self.from_list(y)
return y | Compute 1D (inverse) discrete Fourier Transform in the QTT format.
:param tol: error tolerance.
:type tol: float
:param inverse: whether do an inverse FFT or not.
:type inverse: Boolean
:param bitReverse: whether do the bit reversion or not. If this function is used as a subroutine for multi-dimensional qtt-fft, this option
need to be set False.
:type bitReverse: Boolean.
:returns: QTT-vector of FFT coefficients.
This is a python translation of the Matlab function "qtt_fft1" in Ivan Oseledets' project TT-Toolbox(https://github.com/oseledets/TT-Toolbox)
See S. Dolgov, B. Khoromskij, D. Savostyanov,
Superfast Fourier transform using QTT approximation,
J. Fourier Anal. Appl., 18(5), 2012. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/core/vector.py#L580-L687 |
oseledets/ttpy | tt/eigb/eigb.py | eigb | def eigb(A, y0, eps, rmax=150, nswp=20, max_full_size=1000, verb=1):
""" Approximate computation of minimal eigenvalues in tensor train format
This function uses alternating least-squares algorithm for the computation of several
minimal eigenvalues. If you want maximal eigenvalues, just send -A to the function.
:Reference:
S. V. Dolgov, B. N. Khoromskij, I. V. Oseledets, and D. V. Savostyanov.
Computation of extreme eigenvalues in higher dimensions using block tensor train format. Computer Phys. Comm.,
185(4):1207-1216, 2014. http://dx.doi.org/10.1016/j.cpc.2013.12.017
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial guess in the block TT-format, r(d+1) is the number of eigenvalues sought
:type y0: tensor
:param eps: Accuracy required
:type eps: float
:param rmax: Maximal rank
:type rmax: int
:param kickrank: Addition rank, the larger the more robus the method,
:type kickrank: int
:rtype: A tuple (ev, tensor), where ev is a list of eigenvalues, tensor is an approximation to eigenvectors.
:Example:
>>> import tt
>>> import tt.eigb
>>> d = 8; f = 3
>>> r = [8] * (d * f + 1); r[d * f] = 8; r[0] = 1
>>> x = tt.rand(n, d * f, r)
>>> a = tt.qlaplace_dd([8, 8, 8])
>>> sol, ev = tt.eigb.eigb(a, x, 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 8 eigenvalues with accuracy 1E-06
swp: 1 er = 35.93 rmax:19
swp: 2 er = 4.51015E-04 rmax:18
swp: 3 er = 1.87584E-12 rmax:17
Total number of matvecs: 0
>>> print ev
[ 0.00044828 0.00089654 0.00089654 0.00089654 0.0013448 0.0013448
0.0013448 0.00164356]
"""
ry = y0.r.copy()
lam = tt_eigb.tt_block_eig.tt_eigb(y0.d, A.n, A.m, A.tt.r, A.tt.core, y0.core, ry, eps,
rmax, ry[y0.d], 0, nswp, max_full_size, verb)
y = tensor()
y.d = y0.d
y.n = A.n.copy()
y.r = ry
y.core = tt_eigb.tt_block_eig.result_core.copy()
tt_eigb.tt_block_eig.deallocate_result()
y.get_ps()
return y, lam | python | def eigb(A, y0, eps, rmax=150, nswp=20, max_full_size=1000, verb=1):
""" Approximate computation of minimal eigenvalues in tensor train format
This function uses alternating least-squares algorithm for the computation of several
minimal eigenvalues. If you want maximal eigenvalues, just send -A to the function.
:Reference:
S. V. Dolgov, B. N. Khoromskij, I. V. Oseledets, and D. V. Savostyanov.
Computation of extreme eigenvalues in higher dimensions using block tensor train format. Computer Phys. Comm.,
185(4):1207-1216, 2014. http://dx.doi.org/10.1016/j.cpc.2013.12.017
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial guess in the block TT-format, r(d+1) is the number of eigenvalues sought
:type y0: tensor
:param eps: Accuracy required
:type eps: float
:param rmax: Maximal rank
:type rmax: int
:param kickrank: Addition rank, the larger the more robus the method,
:type kickrank: int
:rtype: A tuple (ev, tensor), where ev is a list of eigenvalues, tensor is an approximation to eigenvectors.
:Example:
>>> import tt
>>> import tt.eigb
>>> d = 8; f = 3
>>> r = [8] * (d * f + 1); r[d * f] = 8; r[0] = 1
>>> x = tt.rand(n, d * f, r)
>>> a = tt.qlaplace_dd([8, 8, 8])
>>> sol, ev = tt.eigb.eigb(a, x, 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 8 eigenvalues with accuracy 1E-06
swp: 1 er = 35.93 rmax:19
swp: 2 er = 4.51015E-04 rmax:18
swp: 3 er = 1.87584E-12 rmax:17
Total number of matvecs: 0
>>> print ev
[ 0.00044828 0.00089654 0.00089654 0.00089654 0.0013448 0.0013448
0.0013448 0.00164356]
"""
ry = y0.r.copy()
lam = tt_eigb.tt_block_eig.tt_eigb(y0.d, A.n, A.m, A.tt.r, A.tt.core, y0.core, ry, eps,
rmax, ry[y0.d], 0, nswp, max_full_size, verb)
y = tensor()
y.d = y0.d
y.n = A.n.copy()
y.r = ry
y.core = tt_eigb.tt_block_eig.result_core.copy()
tt_eigb.tt_block_eig.deallocate_result()
y.get_ps()
return y, lam | Approximate computation of minimal eigenvalues in tensor train format
This function uses alternating least-squares algorithm for the computation of several
minimal eigenvalues. If you want maximal eigenvalues, just send -A to the function.
:Reference:
S. V. Dolgov, B. N. Khoromskij, I. V. Oseledets, and D. V. Savostyanov.
Computation of extreme eigenvalues in higher dimensions using block tensor train format. Computer Phys. Comm.,
185(4):1207-1216, 2014. http://dx.doi.org/10.1016/j.cpc.2013.12.017
:param A: Matrix in the TT-format
:type A: matrix
:param y0: Initial guess in the block TT-format, r(d+1) is the number of eigenvalues sought
:type y0: tensor
:param eps: Accuracy required
:type eps: float
:param rmax: Maximal rank
:type rmax: int
:param kickrank: Addition rank, the larger the more robus the method,
:type kickrank: int
:rtype: A tuple (ev, tensor), where ev is a list of eigenvalues, tensor is an approximation to eigenvectors.
:Example:
>>> import tt
>>> import tt.eigb
>>> d = 8; f = 3
>>> r = [8] * (d * f + 1); r[d * f] = 8; r[0] = 1
>>> x = tt.rand(n, d * f, r)
>>> a = tt.qlaplace_dd([8, 8, 8])
>>> sol, ev = tt.eigb.eigb(a, x, 1e-6, verb=0)
Solving a block eigenvalue problem
Looking for 8 eigenvalues with accuracy 1E-06
swp: 1 er = 35.93 rmax:19
swp: 2 er = 4.51015E-04 rmax:18
swp: 3 er = 1.87584E-12 rmax:17
Total number of matvecs: 0
>>> print ev
[ 0.00044828 0.00089654 0.00089654 0.00089654 0.0013448 0.0013448
0.0013448 0.00164356] | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/eigb/eigb.py#L7-L65 |
oseledets/ttpy | tt/solvers.py | GMRES | def GMRES(A, u_0, b, eps=1e-6, maxit=100, m=20, _iteration=0, callback=None, verbose=0):
"""
Flexible TT GMRES
:param A: matvec(x[, eps])
:param u_0: initial vector
:param b: answer
:param maxit: max number of iterations
:param eps: required accuracy
:param m: number of iteration without restart
:param _iteration: iteration counter
:param callback:
:param verbose: to print debug info or not
:return: answer, residual
>>> from tt import GMRES
>>> def matvec(x, eps):
>>> return tt.matvec(S, x).round(eps)
>>> answer, res = GMRES(matvec, u_0, b, eps=1e-8)
"""
maxitexceeded = False
converged = False
if verbose:
print('GMRES(m=%d, _iteration=%d, maxit=%d)' % (m, _iteration, maxit))
v = np.ones((m + 1), dtype=object) * np.nan
R = np.ones((m, m)) * np.nan
g = np.zeros(m)
s = np.ones(m) * np.nan
c = np.ones(m) * np.nan
v[0] = b - A(u_0, eps=eps)
v[0] = v[0].round(eps)
resnorm = v[0].norm()
curr_beta = resnorm
bnorm = b.norm()
wlen = resnorm
q = m
for j in range(m):
_iteration += 1
delta = eps / (curr_beta / resnorm)
if verbose:
print("it = %d delta = " % _iteration, delta)
v[j] *= 1.0 / wlen
v[j + 1] = A(v[j], eps=delta)
for i in range(j + 1):
R[i, j] = tt.dot(v[j + 1], v[i])
v[j + 1] = v[j + 1] - R[i, j] * v[i]
v[j + 1] = v[j + 1].round(delta)
wlen = v[j + 1].norm()
for i in range(j):
r1 = R[i, j]
r2 = R[i + 1, j]
R[i, j] = c[i] * r1 - s[i] * r2
R[i + 1, j] = c[i] * r2 + s[i] * r1
denom = np.hypot(wlen, R[j, j])
s[j] = wlen / denom
c[j] = -R[j, j] / denom
R[j, j] = -denom
g[j] = c[j] * curr_beta
curr_beta *= s[j]
if verbose:
print("it = {}, ||r|| = {}".format(_iteration, curr_beta / bnorm))
converged = (curr_beta / bnorm) < eps or (curr_beta / resnorm) < eps
maxitexceeded = _iteration >= maxit
if converged or maxitexceeded:
q = j + 1
break
y = la.solve_triangular(R[:q, :q], g[:q], check_finite=False)
for idx in range(q):
u_0 += v[idx] * y[idx]
u_0 = u_0.round(eps)
if callback is not None:
callback(u_0)
if converged or maxitexceeded:
return u_0, resnorm / bnorm
return GMRES(A, u_0, b, eps, maxit, m, _iteration, callback=callback, verbose=verbose) | python | def GMRES(A, u_0, b, eps=1e-6, maxit=100, m=20, _iteration=0, callback=None, verbose=0):
"""
Flexible TT GMRES
:param A: matvec(x[, eps])
:param u_0: initial vector
:param b: answer
:param maxit: max number of iterations
:param eps: required accuracy
:param m: number of iteration without restart
:param _iteration: iteration counter
:param callback:
:param verbose: to print debug info or not
:return: answer, residual
>>> from tt import GMRES
>>> def matvec(x, eps):
>>> return tt.matvec(S, x).round(eps)
>>> answer, res = GMRES(matvec, u_0, b, eps=1e-8)
"""
maxitexceeded = False
converged = False
if verbose:
print('GMRES(m=%d, _iteration=%d, maxit=%d)' % (m, _iteration, maxit))
v = np.ones((m + 1), dtype=object) * np.nan
R = np.ones((m, m)) * np.nan
g = np.zeros(m)
s = np.ones(m) * np.nan
c = np.ones(m) * np.nan
v[0] = b - A(u_0, eps=eps)
v[0] = v[0].round(eps)
resnorm = v[0].norm()
curr_beta = resnorm
bnorm = b.norm()
wlen = resnorm
q = m
for j in range(m):
_iteration += 1
delta = eps / (curr_beta / resnorm)
if verbose:
print("it = %d delta = " % _iteration, delta)
v[j] *= 1.0 / wlen
v[j + 1] = A(v[j], eps=delta)
for i in range(j + 1):
R[i, j] = tt.dot(v[j + 1], v[i])
v[j + 1] = v[j + 1] - R[i, j] * v[i]
v[j + 1] = v[j + 1].round(delta)
wlen = v[j + 1].norm()
for i in range(j):
r1 = R[i, j]
r2 = R[i + 1, j]
R[i, j] = c[i] * r1 - s[i] * r2
R[i + 1, j] = c[i] * r2 + s[i] * r1
denom = np.hypot(wlen, R[j, j])
s[j] = wlen / denom
c[j] = -R[j, j] / denom
R[j, j] = -denom
g[j] = c[j] * curr_beta
curr_beta *= s[j]
if verbose:
print("it = {}, ||r|| = {}".format(_iteration, curr_beta / bnorm))
converged = (curr_beta / bnorm) < eps or (curr_beta / resnorm) < eps
maxitexceeded = _iteration >= maxit
if converged or maxitexceeded:
q = j + 1
break
y = la.solve_triangular(R[:q, :q], g[:q], check_finite=False)
for idx in range(q):
u_0 += v[idx] * y[idx]
u_0 = u_0.round(eps)
if callback is not None:
callback(u_0)
if converged or maxitexceeded:
return u_0, resnorm / bnorm
return GMRES(A, u_0, b, eps, maxit, m, _iteration, callback=callback, verbose=verbose) | Flexible TT GMRES
:param A: matvec(x[, eps])
:param u_0: initial vector
:param b: answer
:param maxit: max number of iterations
:param eps: required accuracy
:param m: number of iteration without restart
:param _iteration: iteration counter
:param callback:
:param verbose: to print debug info or not
:return: answer, residual
>>> from tt import GMRES
>>> def matvec(x, eps):
>>> return tt.matvec(S, x).round(eps)
>>> answer, res = GMRES(matvec, u_0, b, eps=1e-8) | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/solvers.py#L10-L95 |
oseledets/ttpy | tt/completion/als.py | getRow | def getRow(leftU, rightV, jVec):
'''
Compute X_{\geq \mu}^T \otimes X_{leq \mu}
X_{\geq \mu} = V_{\mu+1}(j_{\mu}) \ldots V_{d} (j_{d}) [left interface matrix]
X_{\leq \mu} = U_{1} (j_{1}) \ldots U_{\mu-1}(j_{\mu-1}) [right interface matrix]
Parameters:
:list of numpy.arrays: leftU
left-orthogonal cores from 1 to \mu-1
:list of numpy.arrays: rightV
right-orthogonal cores from \mu+1 to d
:list, tuple, np.array: jVec
indices for each dimension n[k]
Returns:
:numpy.array: result
Kronecker product between left and right interface
matrices. Left matrix is transposed.
'''
jLeft = None
jRight = None
if len(leftU) > 0:
jLeft = jVec[:len(leftU)]
if len(rightV) > 0:
jRight = jVec[-len(rightV):]
multU = np.ones([1,1])
for k in xrange(len(leftU)):
multU = np.dot(multU, leftU[k][:, jLeft[k], :])
multV= np.ones([1,1])
for k in xrange(len(rightV)-1, -1, -1):
multV = np.dot(rightV[k][:, jRight[k], :], multV)
result = np.kron(multV.T, multU)
return result | python | def getRow(leftU, rightV, jVec):
'''
Compute X_{\geq \mu}^T \otimes X_{leq \mu}
X_{\geq \mu} = V_{\mu+1}(j_{\mu}) \ldots V_{d} (j_{d}) [left interface matrix]
X_{\leq \mu} = U_{1} (j_{1}) \ldots U_{\mu-1}(j_{\mu-1}) [right interface matrix]
Parameters:
:list of numpy.arrays: leftU
left-orthogonal cores from 1 to \mu-1
:list of numpy.arrays: rightV
right-orthogonal cores from \mu+1 to d
:list, tuple, np.array: jVec
indices for each dimension n[k]
Returns:
:numpy.array: result
Kronecker product between left and right interface
matrices. Left matrix is transposed.
'''
jLeft = None
jRight = None
if len(leftU) > 0:
jLeft = jVec[:len(leftU)]
if len(rightV) > 0:
jRight = jVec[-len(rightV):]
multU = np.ones([1,1])
for k in xrange(len(leftU)):
multU = np.dot(multU, leftU[k][:, jLeft[k], :])
multV= np.ones([1,1])
for k in xrange(len(rightV)-1, -1, -1):
multV = np.dot(rightV[k][:, jRight[k], :], multV)
result = np.kron(multV.T, multU)
return result | Compute X_{\geq \mu}^T \otimes X_{leq \mu}
X_{\geq \mu} = V_{\mu+1}(j_{\mu}) \ldots V_{d} (j_{d}) [left interface matrix]
X_{\leq \mu} = U_{1} (j_{1}) \ldots U_{\mu-1}(j_{\mu-1}) [right interface matrix]
Parameters:
:list of numpy.arrays: leftU
left-orthogonal cores from 1 to \mu-1
:list of numpy.arrays: rightV
right-orthogonal cores from \mu+1 to d
:list, tuple, np.array: jVec
indices for each dimension n[k]
Returns:
:numpy.array: result
Kronecker product between left and right interface
matrices. Left matrix is transposed. | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/completion/als.py#L23-L56 |
oseledets/ttpy | tt/completion/als.py | orthLRFull | def orthLRFull(coreList, mu, splitResult = True):
'''
Orthogonalize list of TT-cores.
Parameters:
:list: coreList
list of TT-cores (stored as numpy arrays)
:int: mu
separating index for left and right orthogonalization.
Output cores will be left-orthogonal for dimensions from 1 to \mu-1
and right-orthogonal for dimensions from \mu+1 to d
:boolean: splitResult = True
Controls whether outut should be splitted into left-, non-, right-orthogonal
parts or not.
Returns:
:list: resultU
left-orthogonal cores with indices from 1 to \mu-1
:np.array: W
\mu-th core
:list: reultV
right-orthogonal cores with indices from \mu+1 to d
OR
:list: resultU + [W] + resultV
concatenated list of cores
'''
d = len(coreList)
assert (mu >= 0) and (mu <= d)
resultU = []
for k in xrange(mu):
core = coreList[k].copy()
if k > 0:
core = np.einsum('ijk,li->ljk', core, R)
[r1, n, r2] = core.shape
if (k < mu-1):
core = reshape(core, [r1*n, r2])
Q, R = np.linalg.qr(core)
rnew = Q.shape[1]
core = reshape(Q, [r1, n, rnew])
resultU = resultU + [core]
if mu > 0:
W = core.copy()
resultV = []
for k in xrange(d-1, mu, -1):
core = coreList[k].copy()
if (k < d-1):
core = np.einsum('ijk,lk->ijl', core, R)
[r1, n, r2] = core.shape
if (k > mu+1):
core = reshape(core, [r1, n*r2])
Q, R = np.linalg.qr(core.T)
rnew = Q.shape[1]
core = reshape(Q.T, [rnew, n, r2])
resultV = [core] + resultV
if mu < d-1:
if mu > 0:
W = np.einsum('ijk,lk->ijl', W, R)
else:
W = np.einsum('ijk,lk->ijl', coreList[0], R)
if splitResult:
return resultU, W, resultV
return resultU + [W] + resultV | python | def orthLRFull(coreList, mu, splitResult = True):
'''
Orthogonalize list of TT-cores.
Parameters:
:list: coreList
list of TT-cores (stored as numpy arrays)
:int: mu
separating index for left and right orthogonalization.
Output cores will be left-orthogonal for dimensions from 1 to \mu-1
and right-orthogonal for dimensions from \mu+1 to d
:boolean: splitResult = True
Controls whether outut should be splitted into left-, non-, right-orthogonal
parts or not.
Returns:
:list: resultU
left-orthogonal cores with indices from 1 to \mu-1
:np.array: W
\mu-th core
:list: reultV
right-orthogonal cores with indices from \mu+1 to d
OR
:list: resultU + [W] + resultV
concatenated list of cores
'''
d = len(coreList)
assert (mu >= 0) and (mu <= d)
resultU = []
for k in xrange(mu):
core = coreList[k].copy()
if k > 0:
core = np.einsum('ijk,li->ljk', core, R)
[r1, n, r2] = core.shape
if (k < mu-1):
core = reshape(core, [r1*n, r2])
Q, R = np.linalg.qr(core)
rnew = Q.shape[1]
core = reshape(Q, [r1, n, rnew])
resultU = resultU + [core]
if mu > 0:
W = core.copy()
resultV = []
for k in xrange(d-1, mu, -1):
core = coreList[k].copy()
if (k < d-1):
core = np.einsum('ijk,lk->ijl', core, R)
[r1, n, r2] = core.shape
if (k > mu+1):
core = reshape(core, [r1, n*r2])
Q, R = np.linalg.qr(core.T)
rnew = Q.shape[1]
core = reshape(Q.T, [rnew, n, r2])
resultV = [core] + resultV
if mu < d-1:
if mu > 0:
W = np.einsum('ijk,lk->ijl', W, R)
else:
W = np.einsum('ijk,lk->ijl', coreList[0], R)
if splitResult:
return resultU, W, resultV
return resultU + [W] + resultV | Orthogonalize list of TT-cores.
Parameters:
:list: coreList
list of TT-cores (stored as numpy arrays)
:int: mu
separating index for left and right orthogonalization.
Output cores will be left-orthogonal for dimensions from 1 to \mu-1
and right-orthogonal for dimensions from \mu+1 to d
:boolean: splitResult = True
Controls whether outut should be splitted into left-, non-, right-orthogonal
parts or not.
Returns:
:list: resultU
left-orthogonal cores with indices from 1 to \mu-1
:np.array: W
\mu-th core
:list: reultV
right-orthogonal cores with indices from \mu+1 to d
OR
:list: resultU + [W] + resultV
concatenated list of cores | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/completion/als.py#L58-L119 |
oseledets/ttpy | tt/completion/als.py | computeFunctional | def computeFunctional(x, cooP):
'''
Compute value of functional J(X) = ||PX - PA||^2_F,
where P is projector into index subspace of known elements,
X is our approximation,
A is original tensor.
Parameters:
:tt.vector: x
current approximation [X]
:dict: cooP
dictionary with two records
- 'indices': numpy.array of P x d shape,
contains index subspace of P known elements;
each string is an index of one element.
- 'values': numpy array of size P,
contains P known values.
Returns:
:float: result
value of functional
'''
indices = cooP['indices']
values = cooP['values']
[P, d] = indices.shape
assert P == len(values)
result = 0
for p in xrange(P):
index = tuple(indices[p, :])
result += (x[index] - values[p])**2
result *= 0.5
return result | python | def computeFunctional(x, cooP):
'''
Compute value of functional J(X) = ||PX - PA||^2_F,
where P is projector into index subspace of known elements,
X is our approximation,
A is original tensor.
Parameters:
:tt.vector: x
current approximation [X]
:dict: cooP
dictionary with two records
- 'indices': numpy.array of P x d shape,
contains index subspace of P known elements;
each string is an index of one element.
- 'values': numpy array of size P,
contains P known values.
Returns:
:float: result
value of functional
'''
indices = cooP['indices']
values = cooP['values']
[P, d] = indices.shape
assert P == len(values)
result = 0
for p in xrange(P):
index = tuple(indices[p, :])
result += (x[index] - values[p])**2
result *= 0.5
return result | Compute value of functional J(X) = ||PX - PA||^2_F,
where P is projector into index subspace of known elements,
X is our approximation,
A is original tensor.
Parameters:
:tt.vector: x
current approximation [X]
:dict: cooP
dictionary with two records
- 'indices': numpy.array of P x d shape,
contains index subspace of P known elements;
each string is an index of one element.
- 'values': numpy array of size P,
contains P known values.
Returns:
:float: result
value of functional | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/completion/als.py#L121-L154 |
oseledets/ttpy | tt/completion/als.py | ttSparseALS | def ttSparseALS(cooP, shape, x0=None, ttRank=1, tol=1e-5, maxnsweeps=20, verbose=True, alpha=1e-2):
'''
TT completion via Alternating Least Squares algorithm.
Parameters:
:dict: cooP
dictionary with two records
- 'indices': numpy.array of P x d shape,
contains index subspace of P known elements;
each string is an index of one element.
- 'values': numpy array of size P,
contains P known values.
:list, numpy.array: shape
full-format shape of tensor to be completed [dimensions]
:tt.vector: x0 = None
initial approximation of completed tensor
If it is specified, parameters 'shape' and 'ttRank' will be ignored
:int, numpy.array: ttRank = 1
assumed rank of completed tensor
:float: tol = 1e-5
tolerance for functional value
:int: maxnsweeps = 20
maximal number of sweeps [sequential optimization of all d cores
in right or left direction]
:boolean: verbose = True
switcher of messages from function
:float: alpha: = 1e-2
regularizer of least squares problem for each slice of current TT core.
[rcond parameter for np.linalg.lstsq]
Returns:
:tt.vector: xNew
completed TT vector
:list: fit
list of functional values at each sweep
'''
indices = cooP['indices']
values = cooP['values']
[P, d] = indices.shape
assert P == len(values)
timeVal = time.clock()
if x0 is None:
x = tt.rand(shape, r = ttRank)
x = x.round(0.)
x = (1./x.norm())*x
else:
x = copy.deepcopy(x0)
assert d == x.d
# TODO: also check if cooP indices are aligned with shape
normP = np.linalg.norm(values)
values /= normP
fitList = []
sweepTimeList = []
initTime = time.clock() - timeVal
timeVal = time.clock()
coreList = tt.vector.to_list(x)
#coreList = orthLRFull(coreList, mu = d, splitResult = False)
# orthTime = time.clock() - timeVal
if verbose:
print("Initialization time: %.3f seconds (proc.time)" % (initTime))
# print "Orthogonalizing time: %.3f seconds (proc.time)" % (orthTime)
for sweep in xrange(maxnsweeps):
sweepStart = time.clock()
# list left + right
[kStart, kEnd, kStep] = [0, d, 1]
# select direction of sweep
'''
if sweep % 2 == 0: # left to rigth
[kStart, kEnd, kStep] = [0, d, 1]
else: # right to left
[kStart, kEnd, kStep] = [d-1, -1, -1]
'''
# fix k-th core to update
for k in xrange(kStart, kEnd, kStep):
[r1, n, r2] = coreList[k].shape
core = np.zeros([r1, n, r2])
leftU = []
rightV = []
if k > 0:
leftU = coreList[:k]
if k < d-1:
rightV = coreList[k+1:]
for i in xrange(n):
thetaI = np.where(indices[:, k] == i)[0]
if len(thetaI) > 0:
A = np.zeros([len(thetaI), r1*r2])
for j in xrange(len(thetaI)):
tmp = getRow(leftU, rightV, indices[thetaI[j], :])
A[j:j+1, :] += tmp # .flatten(order = 'F')
vecCoreSlice, _, _, _ = np.linalg.lstsq(A, values[thetaI])#, rcond = alpha)
# 0.5*np.linalg.norm(np.dot(A, vecCoreSlice) - values[thetaI])**2.
core[:, i, :] += reshape(vecCoreSlice, [r1, r2]) ####
'''
if k < (d-1):
core = reshape(core, [r1*n, r2])
Q, R = np.linalg.qr(core)
rnew = Q.shape[1]
core = reshape(Q, [r1, n, rnew])
coreList[k+1] = np.einsum('ijk,li->ljk', coreList[k+1], R)
'''
coreList[k] = core.copy()
'''
else:
if (k > 0):
core = reshape(core, [r1, n*r2])
Q, R = np.linalg.qr(core.T)
rnew = Q.shape[1]
core = reshape(Q.T, [rnew, n, r2])
coreList[k-1] = np.einsum('ijk,lk->ijl', coreList[k-1], R)
'''
xNew = tt.vector.from_list(coreList)
fit = computeFunctional(xNew, cooP)
fitList.append(fit)
if fit < tol:
break
if sweep > 0:
if abs(fit - fitList[-2]) < tol:
break
sweepTimeList.append(time.clock() - sweepStart)
if verbose:
print("sweep %d/%d\t fit value: %.5e\t time: %.3f seconds (proc.time)" % (sweep+1, maxnsweeps, fit, sweepTimeList[-1]))
if verbose:
print("Total sweep time: %.3f seconds (proc.time)\t Total time: %.3f seconds (proc.time)" % (sum(sweepTimeList), sum(sweepTimeList) + initTime))# + orthTime)
info = {'fit': fitList, 'initTime': initTime, 'sweepTime': sweepTimeList} # 'orthTime': orthTime,
xNew *= normP
values *= normP
return xNew, info | python | def ttSparseALS(cooP, shape, x0=None, ttRank=1, tol=1e-5, maxnsweeps=20, verbose=True, alpha=1e-2):
'''
TT completion via Alternating Least Squares algorithm.
Parameters:
:dict: cooP
dictionary with two records
- 'indices': numpy.array of P x d shape,
contains index subspace of P known elements;
each string is an index of one element.
- 'values': numpy array of size P,
contains P known values.
:list, numpy.array: shape
full-format shape of tensor to be completed [dimensions]
:tt.vector: x0 = None
initial approximation of completed tensor
If it is specified, parameters 'shape' and 'ttRank' will be ignored
:int, numpy.array: ttRank = 1
assumed rank of completed tensor
:float: tol = 1e-5
tolerance for functional value
:int: maxnsweeps = 20
maximal number of sweeps [sequential optimization of all d cores
in right or left direction]
:boolean: verbose = True
switcher of messages from function
:float: alpha: = 1e-2
regularizer of least squares problem for each slice of current TT core.
[rcond parameter for np.linalg.lstsq]
Returns:
:tt.vector: xNew
completed TT vector
:list: fit
list of functional values at each sweep
'''
indices = cooP['indices']
values = cooP['values']
[P, d] = indices.shape
assert P == len(values)
timeVal = time.clock()
if x0 is None:
x = tt.rand(shape, r = ttRank)
x = x.round(0.)
x = (1./x.norm())*x
else:
x = copy.deepcopy(x0)
assert d == x.d
# TODO: also check if cooP indices are aligned with shape
normP = np.linalg.norm(values)
values /= normP
fitList = []
sweepTimeList = []
initTime = time.clock() - timeVal
timeVal = time.clock()
coreList = tt.vector.to_list(x)
#coreList = orthLRFull(coreList, mu = d, splitResult = False)
# orthTime = time.clock() - timeVal
if verbose:
print("Initialization time: %.3f seconds (proc.time)" % (initTime))
# print "Orthogonalizing time: %.3f seconds (proc.time)" % (orthTime)
for sweep in xrange(maxnsweeps):
sweepStart = time.clock()
# list left + right
[kStart, kEnd, kStep] = [0, d, 1]
# select direction of sweep
'''
if sweep % 2 == 0: # left to rigth
[kStart, kEnd, kStep] = [0, d, 1]
else: # right to left
[kStart, kEnd, kStep] = [d-1, -1, -1]
'''
# fix k-th core to update
for k in xrange(kStart, kEnd, kStep):
[r1, n, r2] = coreList[k].shape
core = np.zeros([r1, n, r2])
leftU = []
rightV = []
if k > 0:
leftU = coreList[:k]
if k < d-1:
rightV = coreList[k+1:]
for i in xrange(n):
thetaI = np.where(indices[:, k] == i)[0]
if len(thetaI) > 0:
A = np.zeros([len(thetaI), r1*r2])
for j in xrange(len(thetaI)):
tmp = getRow(leftU, rightV, indices[thetaI[j], :])
A[j:j+1, :] += tmp # .flatten(order = 'F')
vecCoreSlice, _, _, _ = np.linalg.lstsq(A, values[thetaI])#, rcond = alpha)
# 0.5*np.linalg.norm(np.dot(A, vecCoreSlice) - values[thetaI])**2.
core[:, i, :] += reshape(vecCoreSlice, [r1, r2]) ####
'''
if k < (d-1):
core = reshape(core, [r1*n, r2])
Q, R = np.linalg.qr(core)
rnew = Q.shape[1]
core = reshape(Q, [r1, n, rnew])
coreList[k+1] = np.einsum('ijk,li->ljk', coreList[k+1], R)
'''
coreList[k] = core.copy()
'''
else:
if (k > 0):
core = reshape(core, [r1, n*r2])
Q, R = np.linalg.qr(core.T)
rnew = Q.shape[1]
core = reshape(Q.T, [rnew, n, r2])
coreList[k-1] = np.einsum('ijk,lk->ijl', coreList[k-1], R)
'''
xNew = tt.vector.from_list(coreList)
fit = computeFunctional(xNew, cooP)
fitList.append(fit)
if fit < tol:
break
if sweep > 0:
if abs(fit - fitList[-2]) < tol:
break
sweepTimeList.append(time.clock() - sweepStart)
if verbose:
print("sweep %d/%d\t fit value: %.5e\t time: %.3f seconds (proc.time)" % (sweep+1, maxnsweeps, fit, sweepTimeList[-1]))
if verbose:
print("Total sweep time: %.3f seconds (proc.time)\t Total time: %.3f seconds (proc.time)" % (sum(sweepTimeList), sum(sweepTimeList) + initTime))# + orthTime)
info = {'fit': fitList, 'initTime': initTime, 'sweepTime': sweepTimeList} # 'orthTime': orthTime,
xNew *= normP
values *= normP
return xNew, info | TT completion via Alternating Least Squares algorithm.
Parameters:
:dict: cooP
dictionary with two records
- 'indices': numpy.array of P x d shape,
contains index subspace of P known elements;
each string is an index of one element.
- 'values': numpy array of size P,
contains P known values.
:list, numpy.array: shape
full-format shape of tensor to be completed [dimensions]
:tt.vector: x0 = None
initial approximation of completed tensor
If it is specified, parameters 'shape' and 'ttRank' will be ignored
:int, numpy.array: ttRank = 1
assumed rank of completed tensor
:float: tol = 1e-5
tolerance for functional value
:int: maxnsweeps = 20
maximal number of sweeps [sequential optimization of all d cores
in right or left direction]
:boolean: verbose = True
switcher of messages from function
:float: alpha: = 1e-2
regularizer of least squares problem for each slice of current TT core.
[rcond parameter for np.linalg.lstsq]
Returns:
:tt.vector: xNew
completed TT vector
:list: fit
list of functional values at each sweep | https://github.com/oseledets/ttpy/blob/b440f6299a6338de4aea67f3d839d613f4ef1374/tt/completion/als.py#L157-L290 |
Infinidat/infi.systray | src/infi/systray/traybar.py | SysTrayIcon.update | def update(self, icon=None, hover_text=None):
""" update icon image and/or hover text """
if icon:
self._icon = icon
self._load_icon()
if hover_text:
self._hover_text = hover_text
self._refresh_icon() | python | def update(self, icon=None, hover_text=None):
""" update icon image and/or hover text """
if icon:
self._icon = icon
self._load_icon()
if hover_text:
self._hover_text = hover_text
self._refresh_icon() | update icon image and/or hover text | https://github.com/Infinidat/infi.systray/blob/209a6f8e5313122b35fdd2774aeceeaf9ce7c16d/src/infi/systray/traybar.py#L125-L132 |
Infinidat/infi.systray | src/infi/systray/win32_adapter.py | encode_for_locale | def encode_for_locale(s):
"""
Encode text items for system locale. If encoding fails, fall back to ASCII.
"""
try:
return s.encode(LOCALE_ENCODING, 'ignore')
except (AttributeError, UnicodeDecodeError):
return s.decode('ascii', 'ignore').encode(LOCALE_ENCODING) | python | def encode_for_locale(s):
"""
Encode text items for system locale. If encoding fails, fall back to ASCII.
"""
try:
return s.encode(LOCALE_ENCODING, 'ignore')
except (AttributeError, UnicodeDecodeError):
return s.decode('ascii', 'ignore').encode(LOCALE_ENCODING) | Encode text items for system locale. If encoding fails, fall back to ASCII. | https://github.com/Infinidat/infi.systray/blob/209a6f8e5313122b35fdd2774aeceeaf9ce7c16d/src/infi/systray/win32_adapter.py#L86-L93 |
pbrady/fastcache | fastcache/__init__.py | lru_cache | def lru_cache(maxsize=128, typed=False, state=None, unhashable='error'):
"""Least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and
the cache can grow without bound.
If *typed* is True, arguments of different types will be cached
separately. For example, f(3.0) and f(3) will be treated as distinct
calls with distinct results.
If *state* is a list or dict, the items will be incorporated into
argument hash.
The result of calling the cached function with unhashable (mutable)
arguments depends on the value of *unhashable*:
If *unhashable* is 'error', a TypeError will be raised.
If *unhashable* is 'warning', a UserWarning will be raised, and
the wrapped function will be called with the supplied arguments.
A miss will be recorded in the cache statistics.
If *unhashable* is 'ignore', the wrapped function will be called
with the supplied arguments. A miss will will be recorded in
the cache statistics.
View the cache statistics named tuple (hits, misses, maxsize, currsize)
with f.cache_info(). Clear the cache and statistics with
f.cache_clear(). Access the underlying function with f.__wrapped__.
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
def func_wrapper(func):
_cached_func = clru_cache(maxsize, typed, state, unhashable)(func)
def wrapper(*args, **kwargs):
return _cached_func(*args, **kwargs)
wrapper.__wrapped__ = func
wrapper.cache_info = _cached_func.cache_info
wrapper.cache_clear = _cached_func.cache_clear
return update_wrapper(wrapper,func)
return func_wrapper | python | def lru_cache(maxsize=128, typed=False, state=None, unhashable='error'):
"""Least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and
the cache can grow without bound.
If *typed* is True, arguments of different types will be cached
separately. For example, f(3.0) and f(3) will be treated as distinct
calls with distinct results.
If *state* is a list or dict, the items will be incorporated into
argument hash.
The result of calling the cached function with unhashable (mutable)
arguments depends on the value of *unhashable*:
If *unhashable* is 'error', a TypeError will be raised.
If *unhashable* is 'warning', a UserWarning will be raised, and
the wrapped function will be called with the supplied arguments.
A miss will be recorded in the cache statistics.
If *unhashable* is 'ignore', the wrapped function will be called
with the supplied arguments. A miss will will be recorded in
the cache statistics.
View the cache statistics named tuple (hits, misses, maxsize, currsize)
with f.cache_info(). Clear the cache and statistics with
f.cache_clear(). Access the underlying function with f.__wrapped__.
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used
"""
def func_wrapper(func):
_cached_func = clru_cache(maxsize, typed, state, unhashable)(func)
def wrapper(*args, **kwargs):
return _cached_func(*args, **kwargs)
wrapper.__wrapped__ = func
wrapper.cache_info = _cached_func.cache_info
wrapper.cache_clear = _cached_func.cache_clear
return update_wrapper(wrapper,func)
return func_wrapper | Least-recently-used cache decorator.
If *maxsize* is set to None, the LRU features are disabled and
the cache can grow without bound.
If *typed* is True, arguments of different types will be cached
separately. For example, f(3.0) and f(3) will be treated as distinct
calls with distinct results.
If *state* is a list or dict, the items will be incorporated into
argument hash.
The result of calling the cached function with unhashable (mutable)
arguments depends on the value of *unhashable*:
If *unhashable* is 'error', a TypeError will be raised.
If *unhashable* is 'warning', a UserWarning will be raised, and
the wrapped function will be called with the supplied arguments.
A miss will be recorded in the cache statistics.
If *unhashable* is 'ignore', the wrapped function will be called
with the supplied arguments. A miss will will be recorded in
the cache statistics.
View the cache statistics named tuple (hits, misses, maxsize, currsize)
with f.cache_info(). Clear the cache and statistics with
f.cache_clear(). Access the underlying function with f.__wrapped__.
See: http://en.wikipedia.org/wiki/Cache_algorithms#Least_Recently_Used | https://github.com/pbrady/fastcache/blob/c216def5d29808585123562b56a9a083ea337cad/fastcache/__init__.py#L30-L75 |
pbrady/fastcache | scripts/threadsafety.py | fib | def fib(n):
"""Terrible Fibonacci number generator."""
v = n.value
return v if v < 2 else fib2(PythonInt(v-1)) + fib(PythonInt(v-2)) | python | def fib(n):
"""Terrible Fibonacci number generator."""
v = n.value
return v if v < 2 else fib2(PythonInt(v-1)) + fib(PythonInt(v-2)) | Terrible Fibonacci number generator. | https://github.com/pbrady/fastcache/blob/c216def5d29808585123562b56a9a083ea337cad/scripts/threadsafety.py#L43-L46 |
pbrady/fastcache | scripts/threadsafety.py | run_fib_with_clear | def run_fib_with_clear(r):
""" Run Fibonacci generator r times. """
for i in range(r):
if randint(RAND_MIN, RAND_MAX) == RAND_MIN:
fib.cache_clear()
fib2.cache_clear()
res = fib(PythonInt(FIB))
if RESULT != res:
raise ValueError("Expected %d, Got %d" % (RESULT, res)) | python | def run_fib_with_clear(r):
""" Run Fibonacci generator r times. """
for i in range(r):
if randint(RAND_MIN, RAND_MAX) == RAND_MIN:
fib.cache_clear()
fib2.cache_clear()
res = fib(PythonInt(FIB))
if RESULT != res:
raise ValueError("Expected %d, Got %d" % (RESULT, res)) | Run Fibonacci generator r times. | https://github.com/pbrady/fastcache/blob/c216def5d29808585123562b56a9a083ea337cad/scripts/threadsafety.py#L59-L67 |
pbrady/fastcache | scripts/threadsafety.py | run_fib_with_stats | def run_fib_with_stats(r):
""" Run Fibonacci generator r times. """
for i in range(r):
res = fib(PythonInt(FIB))
if RESULT != res:
raise ValueError("Expected %d, Got %d" % (RESULT, res)) | python | def run_fib_with_stats(r):
""" Run Fibonacci generator r times. """
for i in range(r):
res = fib(PythonInt(FIB))
if RESULT != res:
raise ValueError("Expected %d, Got %d" % (RESULT, res)) | Run Fibonacci generator r times. | https://github.com/pbrady/fastcache/blob/c216def5d29808585123562b56a9a083ea337cad/scripts/threadsafety.py#L69-L74 |
FraBle/python-sutime | sutime/sutime.py | SUTime.parse | def parse(self, input_str, reference_date=""):
"""Parses datetime information out of string input.
It invokes the SUTimeWrapper.annotate() function in Java.
Args:
input_str: The input as string that has to be parsed.
reference_date: Optional reference data for SUTime.
Returns:
A list of dicts with the result from the SUTimeWrapper.annotate()
call.
"""
if not jpype.isThreadAttachedToJVM():
jpype.attachThreadToJVM()
if reference_date:
return json.loads(self._sutime.annotate(input_str, reference_date))
return json.loads(self._sutime.annotate(input_str)) | python | def parse(self, input_str, reference_date=""):
"""Parses datetime information out of string input.
It invokes the SUTimeWrapper.annotate() function in Java.
Args:
input_str: The input as string that has to be parsed.
reference_date: Optional reference data for SUTime.
Returns:
A list of dicts with the result from the SUTimeWrapper.annotate()
call.
"""
if not jpype.isThreadAttachedToJVM():
jpype.attachThreadToJVM()
if reference_date:
return json.loads(self._sutime.annotate(input_str, reference_date))
return json.loads(self._sutime.annotate(input_str)) | Parses datetime information out of string input.
It invokes the SUTimeWrapper.annotate() function in Java.
Args:
input_str: The input as string that has to be parsed.
reference_date: Optional reference data for SUTime.
Returns:
A list of dicts with the result from the SUTimeWrapper.annotate()
call. | https://github.com/FraBle/python-sutime/blob/2b36828cd1d215c472572d253cd6c56271d71169/sutime/sutime.py#L143-L160 |
aichaos/rivescript-python | rivescript/brain.py | Brain.format_message | def format_message(self, msg, botreply=False):
"""Format a user's message for safe processing.
This runs substitutions on the message and strips out any remaining
symbols (depending on UTF-8 mode).
:param str msg: The user's message.
:param bool botreply: Whether this formatting is being done for the
bot's last reply (e.g. in a ``%Previous`` command).
:return str: The formatted message.
"""
# Make sure the string is Unicode for Python 2.
if sys.version_info[0] < 3 and isinstance(msg, str):
msg = msg.decode()
# Lowercase it.
msg = msg.lower()
# Run substitutions on it.
msg = self.substitute(msg, "sub")
# In UTF-8 mode, only strip metacharacters and HTML brackets
# (to protect from obvious XSS attacks).
if self.utf8:
msg = re.sub(RE.utf8_meta, '', msg)
msg = re.sub(self.master.unicode_punctuation, '', msg)
# For the bot's reply, also strip common punctuation.
if botreply:
msg = re.sub(RE.utf8_punct, '', msg)
else:
# For everything else, strip all non-alphanumerics.
msg = utils.strip_nasties(msg)
msg = msg.strip() # Strip leading and trailing white space
msg = RE.ws.sub(" ",msg) # Replace the multiple whitespaces by single whitespace
return msg | python | def format_message(self, msg, botreply=False):
"""Format a user's message for safe processing.
This runs substitutions on the message and strips out any remaining
symbols (depending on UTF-8 mode).
:param str msg: The user's message.
:param bool botreply: Whether this formatting is being done for the
bot's last reply (e.g. in a ``%Previous`` command).
:return str: The formatted message.
"""
# Make sure the string is Unicode for Python 2.
if sys.version_info[0] < 3 and isinstance(msg, str):
msg = msg.decode()
# Lowercase it.
msg = msg.lower()
# Run substitutions on it.
msg = self.substitute(msg, "sub")
# In UTF-8 mode, only strip metacharacters and HTML brackets
# (to protect from obvious XSS attacks).
if self.utf8:
msg = re.sub(RE.utf8_meta, '', msg)
msg = re.sub(self.master.unicode_punctuation, '', msg)
# For the bot's reply, also strip common punctuation.
if botreply:
msg = re.sub(RE.utf8_punct, '', msg)
else:
# For everything else, strip all non-alphanumerics.
msg = utils.strip_nasties(msg)
msg = msg.strip() # Strip leading and trailing white space
msg = RE.ws.sub(" ",msg) # Replace the multiple whitespaces by single whitespace
return msg | Format a user's message for safe processing.
This runs substitutions on the message and strips out any remaining
symbols (depending on UTF-8 mode).
:param str msg: The user's message.
:param bool botreply: Whether this formatting is being done for the
bot's last reply (e.g. in a ``%Previous`` command).
:return str: The formatted message. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/brain.py#L104-L141 |
aichaos/rivescript-python | rivescript/brain.py | Brain._getreply | def _getreply(self, user, msg, context='normal', step=0, ignore_object_errors=True):
"""The internal reply getter function.
DO NOT CALL THIS YOURSELF.
:param str user: The user ID as passed to ``reply()``.
:param str msg: The formatted user message.
:param str context: The reply context, one of ``begin`` or ``normal``.
:param int step: The recursion depth counter.
:param bool ignore_object_errors: Whether to ignore errors from within
Python object macros and not raise an ``ObjectError`` exception.
:return str: The reply output.
"""
# Needed to sort replies?
if 'topics' not in self.master._sorted:
raise RepliesNotSortedError("You must call sort_replies() once you are done loading RiveScript documents")
# Initialize the user's profile?
topic = self.master.get_uservar(user, "topic")
if topic in [None, "undefined"]:
topic = "random"
self.master.set_uservar(user, "topic", topic)
# Collect data on the user.
stars = []
thatstars = [] # For %Previous's.
reply = ''
# Avoid letting them fall into a missing topic.
if topic not in self.master._topics:
self.warn("User " + user + " was in an empty topic named '" + topic + "'")
topic = "random"
self.master.set_uservar(user, "topic", topic)
# Avoid deep recursion.
if step > self.master._depth:
raise DeepRecursionError
# Are we in the BEGIN statement?
if context == 'begin':
topic = '__begin__'
# Initialize this user's history.
history = self.master.get_uservar(user, "__history__")
if type(history) is not dict or "input" not in history or "reply" not in history:
history = self.default_history()
self.master.set_uservar(user, "__history__", history)
# More topic sanity checking.
if topic not in self.master._topics:
# This was handled before, which would mean topic=random and
# it doesn't exist. Serious issue!
raise NoDefaultRandomTopicError("no default topic 'random' was found")
# Create a pointer for the matched data when we find it.
matched = None
matchedTrigger = None
foundMatch = False
# See if there were any %Previous's in this topic, or any topic related
# to it. This should only be done the first time -- not during a
# recursive redirection. This is because in a redirection, "lastreply"
# is still gonna be the same as it was the first time, causing an
# infinite loop!
if step == 0:
allTopics = [topic]
if topic in self.master._includes or topic in self.master._lineage:
# Get all the topics!
allTopics = inherit_utils.get_topic_tree(self.master, topic)
# Scan them all!
for top in allTopics:
self.say("Checking topic " + top + " for any %Previous's.")
if top in self.master._sorted["thats"]:
self.say("There is a %Previous in this topic!")
# Do we have history yet?
lastReply = history["reply"][0]
# Format the bot's last reply the same way as the human's.
lastReply = self.format_message(lastReply, botreply=True)
self.say("lastReply: " + lastReply)
# See if it's a match.
for trig in self.master._sorted["thats"][top]:
pattern = trig[1]["previous"]
botside = self.reply_regexp(user, pattern)
self.say("Try to match lastReply ({}) to {} ({})".format(lastReply, pattern, repr(botside)))
# Match??
match = re.match(botside, lastReply)
if match:
# Huzzah! See if OUR message is right too.
self.say("Bot side matched!")
thatstars = match.groups()
# Compare the triggers to the user's message.
user_side = trig[1]
subtrig = self.reply_regexp(user, user_side["trigger"])
self.say("Now try to match " + msg + " to " + user_side["trigger"])
match = re.match(subtrig, msg)
if match:
self.say("Found a match!")
matched = trig[1]
matchedTrigger = user_side["trigger"]
foundMatch = True
# Get the stars!
stars = match.groups()
break
# Break if we found a match.
if foundMatch:
break
# Break if we found a match.
if foundMatch:
break
# Search their topic for a match to their trigger.
if not foundMatch:
for trig in self.master._sorted["topics"][topic]:
pattern = trig[0]
# Process the triggers.
regexp = self.reply_regexp(user, pattern)
self.say("Try to match %r against %r (%r)" % (msg, pattern, regexp.pattern))
# Python's regular expression engine is slow. Try a verbatim
# match if this is an atomic trigger.
isAtomic = utils.is_atomic(pattern)
isMatch = False
if isAtomic:
# Only look for exact matches, no sense running atomic triggers
# through the regexp engine.
if msg == pattern:
isMatch = True
else:
# Non-atomic triggers always need the regexp.
match = re.match(regexp, msg)
if match:
# The regexp matched!
isMatch = True
# Collect the stars.
stars = match.groups()
if isMatch:
self.say("Found a match!")
matched = trig[1]
foundMatch = True
matchedTrigger = pattern
break
# Store what trigger they matched on. If their matched trigger is None,
# this will be too, which is great.
self.master.set_uservar(user, "__lastmatch__", matchedTrigger)
if matched:
for nil in [1]:
# See if there are any hard redirects.
if matched["redirect"]:
self.say("Redirecting us to " + matched["redirect"])
redirect = self.process_tags(user, msg, matched["redirect"], stars, thatstars, step,
ignore_object_errors)
redirect = redirect.lower()
self.say("Pretend user said: " + redirect)
reply = self._getreply(user, redirect, step=(step + 1), ignore_object_errors=ignore_object_errors)
break
# Check the conditionals.
for con in matched["condition"]:
halves = re.split(RE.cond_split, con)
if halves and len(halves) == 2:
condition = re.match(RE.cond_parse, halves[0])
if condition:
left = condition.group(1)
eq = condition.group(2)
right = condition.group(3)
potreply = halves[1]
self.say("Left: " + left + "; eq: " + eq + "; right: " + right + " => " + potreply)
# Process tags all around.
left = self.process_tags(user, msg, left, stars, thatstars, step, ignore_object_errors)
right = self.process_tags(user, msg, right, stars, thatstars, step, ignore_object_errors)
# Defaults?
if len(left) == 0:
left = 'undefined'
if len(right) == 0:
right = 'undefined'
self.say("Check if " + left + " " + eq + " " + right)
# Validate it.
passed = False
if eq == 'eq' or eq == '==':
if left == right:
passed = True
elif eq == 'ne' or eq == '!=' or eq == '<>':
if left != right:
passed = True
else:
# Gasp, dealing with numbers here...
try:
left, right = int(left), int(right)
if eq == '<':
if left < right:
passed = True
elif eq == '<=':
if left <= right:
passed = True
elif eq == '>':
if left > right:
passed = True
elif eq == '>=':
if left >= right:
passed = True
except:
self.warn("Failed to evaluate numeric condition!")
# How truthful?
if passed:
reply = potreply
break
# Have our reply yet?
if len(reply) > 0:
break
# Process weights in the replies.
bucket = []
for text in matched["reply"]:
weight = 1
match = re.search(RE.weight, text)
if match:
weight = int(match.group(1))
if weight <= 0:
self.warn("Can't have a weight <= 0!")
weight = 1
for i in range(0, weight):
bucket.append(text)
# Get a random reply.
reply = utils.random_choice(bucket)
break
# Still no reply?
if not foundMatch:
raise NoMatchError
elif len(reply) == 0:
raise NoReplyError
self.say("Reply: " + reply)
# Process tags for the BEGIN block.
if context == "begin":
# BEGIN blocks can only set topics and uservars. The rest happen
# later!
reTopic = re.findall(RE.topic_tag, reply)
for match in reTopic:
self.say("Setting user's topic to " + match)
self.master.set_uservar(user, "topic", match)
reply = reply.replace('{{topic={match}}}'.format(match=match), '')
reSet = re.findall(RE.set_tag, reply)
for match in reSet:
self.say("Set uservar " + str(match[0]) + "=" + str(match[1]))
self.master.set_uservar(user, match[0], match[1])
reply = reply.replace('<set {key}={value}>'.format(key=match[0], value=match[1]), '')
else:
# Process more tags if not in BEGIN.
reply = self.process_tags(user, msg, reply, stars, thatstars, step, ignore_object_errors)
return reply | python | def _getreply(self, user, msg, context='normal', step=0, ignore_object_errors=True):
"""The internal reply getter function.
DO NOT CALL THIS YOURSELF.
:param str user: The user ID as passed to ``reply()``.
:param str msg: The formatted user message.
:param str context: The reply context, one of ``begin`` or ``normal``.
:param int step: The recursion depth counter.
:param bool ignore_object_errors: Whether to ignore errors from within
Python object macros and not raise an ``ObjectError`` exception.
:return str: The reply output.
"""
# Needed to sort replies?
if 'topics' not in self.master._sorted:
raise RepliesNotSortedError("You must call sort_replies() once you are done loading RiveScript documents")
# Initialize the user's profile?
topic = self.master.get_uservar(user, "topic")
if topic in [None, "undefined"]:
topic = "random"
self.master.set_uservar(user, "topic", topic)
# Collect data on the user.
stars = []
thatstars = [] # For %Previous's.
reply = ''
# Avoid letting them fall into a missing topic.
if topic not in self.master._topics:
self.warn("User " + user + " was in an empty topic named '" + topic + "'")
topic = "random"
self.master.set_uservar(user, "topic", topic)
# Avoid deep recursion.
if step > self.master._depth:
raise DeepRecursionError
# Are we in the BEGIN statement?
if context == 'begin':
topic = '__begin__'
# Initialize this user's history.
history = self.master.get_uservar(user, "__history__")
if type(history) is not dict or "input" not in history or "reply" not in history:
history = self.default_history()
self.master.set_uservar(user, "__history__", history)
# More topic sanity checking.
if topic not in self.master._topics:
# This was handled before, which would mean topic=random and
# it doesn't exist. Serious issue!
raise NoDefaultRandomTopicError("no default topic 'random' was found")
# Create a pointer for the matched data when we find it.
matched = None
matchedTrigger = None
foundMatch = False
# See if there were any %Previous's in this topic, or any topic related
# to it. This should only be done the first time -- not during a
# recursive redirection. This is because in a redirection, "lastreply"
# is still gonna be the same as it was the first time, causing an
# infinite loop!
if step == 0:
allTopics = [topic]
if topic in self.master._includes or topic in self.master._lineage:
# Get all the topics!
allTopics = inherit_utils.get_topic_tree(self.master, topic)
# Scan them all!
for top in allTopics:
self.say("Checking topic " + top + " for any %Previous's.")
if top in self.master._sorted["thats"]:
self.say("There is a %Previous in this topic!")
# Do we have history yet?
lastReply = history["reply"][0]
# Format the bot's last reply the same way as the human's.
lastReply = self.format_message(lastReply, botreply=True)
self.say("lastReply: " + lastReply)
# See if it's a match.
for trig in self.master._sorted["thats"][top]:
pattern = trig[1]["previous"]
botside = self.reply_regexp(user, pattern)
self.say("Try to match lastReply ({}) to {} ({})".format(lastReply, pattern, repr(botside)))
# Match??
match = re.match(botside, lastReply)
if match:
# Huzzah! See if OUR message is right too.
self.say("Bot side matched!")
thatstars = match.groups()
# Compare the triggers to the user's message.
user_side = trig[1]
subtrig = self.reply_regexp(user, user_side["trigger"])
self.say("Now try to match " + msg + " to " + user_side["trigger"])
match = re.match(subtrig, msg)
if match:
self.say("Found a match!")
matched = trig[1]
matchedTrigger = user_side["trigger"]
foundMatch = True
# Get the stars!
stars = match.groups()
break
# Break if we found a match.
if foundMatch:
break
# Break if we found a match.
if foundMatch:
break
# Search their topic for a match to their trigger.
if not foundMatch:
for trig in self.master._sorted["topics"][topic]:
pattern = trig[0]
# Process the triggers.
regexp = self.reply_regexp(user, pattern)
self.say("Try to match %r against %r (%r)" % (msg, pattern, regexp.pattern))
# Python's regular expression engine is slow. Try a verbatim
# match if this is an atomic trigger.
isAtomic = utils.is_atomic(pattern)
isMatch = False
if isAtomic:
# Only look for exact matches, no sense running atomic triggers
# through the regexp engine.
if msg == pattern:
isMatch = True
else:
# Non-atomic triggers always need the regexp.
match = re.match(regexp, msg)
if match:
# The regexp matched!
isMatch = True
# Collect the stars.
stars = match.groups()
if isMatch:
self.say("Found a match!")
matched = trig[1]
foundMatch = True
matchedTrigger = pattern
break
# Store what trigger they matched on. If their matched trigger is None,
# this will be too, which is great.
self.master.set_uservar(user, "__lastmatch__", matchedTrigger)
if matched:
for nil in [1]:
# See if there are any hard redirects.
if matched["redirect"]:
self.say("Redirecting us to " + matched["redirect"])
redirect = self.process_tags(user, msg, matched["redirect"], stars, thatstars, step,
ignore_object_errors)
redirect = redirect.lower()
self.say("Pretend user said: " + redirect)
reply = self._getreply(user, redirect, step=(step + 1), ignore_object_errors=ignore_object_errors)
break
# Check the conditionals.
for con in matched["condition"]:
halves = re.split(RE.cond_split, con)
if halves and len(halves) == 2:
condition = re.match(RE.cond_parse, halves[0])
if condition:
left = condition.group(1)
eq = condition.group(2)
right = condition.group(3)
potreply = halves[1]
self.say("Left: " + left + "; eq: " + eq + "; right: " + right + " => " + potreply)
# Process tags all around.
left = self.process_tags(user, msg, left, stars, thatstars, step, ignore_object_errors)
right = self.process_tags(user, msg, right, stars, thatstars, step, ignore_object_errors)
# Defaults?
if len(left) == 0:
left = 'undefined'
if len(right) == 0:
right = 'undefined'
self.say("Check if " + left + " " + eq + " " + right)
# Validate it.
passed = False
if eq == 'eq' or eq == '==':
if left == right:
passed = True
elif eq == 'ne' or eq == '!=' or eq == '<>':
if left != right:
passed = True
else:
# Gasp, dealing with numbers here...
try:
left, right = int(left), int(right)
if eq == '<':
if left < right:
passed = True
elif eq == '<=':
if left <= right:
passed = True
elif eq == '>':
if left > right:
passed = True
elif eq == '>=':
if left >= right:
passed = True
except:
self.warn("Failed to evaluate numeric condition!")
# How truthful?
if passed:
reply = potreply
break
# Have our reply yet?
if len(reply) > 0:
break
# Process weights in the replies.
bucket = []
for text in matched["reply"]:
weight = 1
match = re.search(RE.weight, text)
if match:
weight = int(match.group(1))
if weight <= 0:
self.warn("Can't have a weight <= 0!")
weight = 1
for i in range(0, weight):
bucket.append(text)
# Get a random reply.
reply = utils.random_choice(bucket)
break
# Still no reply?
if not foundMatch:
raise NoMatchError
elif len(reply) == 0:
raise NoReplyError
self.say("Reply: " + reply)
# Process tags for the BEGIN block.
if context == "begin":
# BEGIN blocks can only set topics and uservars. The rest happen
# later!
reTopic = re.findall(RE.topic_tag, reply)
for match in reTopic:
self.say("Setting user's topic to " + match)
self.master.set_uservar(user, "topic", match)
reply = reply.replace('{{topic={match}}}'.format(match=match), '')
reSet = re.findall(RE.set_tag, reply)
for match in reSet:
self.say("Set uservar " + str(match[0]) + "=" + str(match[1]))
self.master.set_uservar(user, match[0], match[1])
reply = reply.replace('<set {key}={value}>'.format(key=match[0], value=match[1]), '')
else:
# Process more tags if not in BEGIN.
reply = self.process_tags(user, msg, reply, stars, thatstars, step, ignore_object_errors)
return reply | The internal reply getter function.
DO NOT CALL THIS YOURSELF.
:param str user: The user ID as passed to ``reply()``.
:param str msg: The formatted user message.
:param str context: The reply context, one of ``begin`` or ``normal``.
:param int step: The recursion depth counter.
:param bool ignore_object_errors: Whether to ignore errors from within
Python object macros and not raise an ``ObjectError`` exception.
:return str: The reply output. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/brain.py#L143-L419 |
aichaos/rivescript-python | rivescript/brain.py | Brain.reply_regexp | def reply_regexp(self, user, regexp):
"""Prepares a trigger for the regular expression engine.
:param str user: The user ID invoking a reply.
:param str regexp: The original trigger text to be turned into a regexp.
:return regexp: The final regexp object."""
if regexp in self.master._regexc["trigger"]:
# Already compiled this one!
return self.master._regexc["trigger"][regexp]
# If the trigger is simply '*' then the * there needs to become (.*?)
# to match the blank string too.
regexp = re.sub(RE.zero_star, r'<zerowidthstar>', regexp)
# Filter in arrays.
arrays = re.findall(RE.array, regexp)
for array in arrays:
rep = ''
if array in self.master._array:
rep = r'(?:' + '|'.join(self.expand_array(array)) + ')'
regexp = re.sub(r'\@' + re.escape(array) + r'\b', rep, regexp)
# Simple replacements.
regexp = regexp.replace('*', '(.+?)') # Convert * into (.+?)
regexp = regexp.replace('#', '(\d+?)') # Convert # into (\d+?)
regexp = regexp.replace('_', '(\w+?)') # Convert _ into (\w+?)
regexp = re.sub(RE.weight, '', regexp) # Remove {weight} tags, allow spaces before the bracket
regexp = regexp.replace('<zerowidthstar>', r'(.*?)')
# Optionals.
optionals = re.findall(RE.optionals, regexp)
for match in optionals:
parts = match.split("|")
new = []
for p in parts:
p = r'(?:\\s|\\b)+{}(?:\\s|\\b)+'.format(p.strip())
new.append(p)
# If this optional had a star or anything in it, make it
# non-matching.
pipes = '|'.join(new)
pipes = pipes.replace(r'(.+?)', r'(?:.+?)')
pipes = pipes.replace(r'(\d+?)', r'(?:\d+?)')
pipes = pipes.replace(r'([A-Za-z]+?)', r'(?:[A-Za-z]+?)')
regexp = re.sub(r'\s*\[' + re.escape(match) + '\]\s*',
'(?:' + pipes + r'|(?:\\s|\\b))', regexp)
# _ wildcards can't match numbers!
regexp = re.sub(RE.literal_w, r'[^\\s\\d]', regexp)
# Filter in bot variables.
bvars = re.findall(RE.bot_tag, regexp)
for var in bvars:
rep = ''
if var in self.master._var:
rep = self.format_message(self.master._var[var])
regexp = regexp.replace('<bot {var}>'.format(var=var), rep)
# Filter in user variables.
uvars = re.findall(RE.get_tag, regexp)
for var in uvars:
rep = ''
value = self.master.get_uservar(user, var)
if value not in [None, "undefined"]:
rep = utils.strip_nasties(value)
regexp = regexp.replace('<get {var}>'.format(var=var), rep)
# Filter in <input> and <reply> tags. This is a slow process, so only
# do it if we have to!
if '<input' in regexp or '<reply' in regexp:
history = self.master.get_uservar(user, "__history__")
for type in ['input', 'reply']:
tags = re.findall(r'<' + type + r'([0-9])>', regexp)
for index in tags:
rep = self.format_message(history[type][int(index) - 1])
regexp = regexp.replace('<{type}{index}>'.format(type=type, index=index), rep)
regexp = regexp.replace('<{type}>'.format(type=type),
self.format_message(history[type][0]))
# TODO: the Perl version doesn't do just <input>/<reply> in trigs!
if self.utf8:
return re.compile(r'^' + regexp.lower() + r'$', re.UNICODE)
else:
return re.compile(r'^' + regexp.lower() + r'$') | python | def reply_regexp(self, user, regexp):
"""Prepares a trigger for the regular expression engine.
:param str user: The user ID invoking a reply.
:param str regexp: The original trigger text to be turned into a regexp.
:return regexp: The final regexp object."""
if regexp in self.master._regexc["trigger"]:
# Already compiled this one!
return self.master._regexc["trigger"][regexp]
# If the trigger is simply '*' then the * there needs to become (.*?)
# to match the blank string too.
regexp = re.sub(RE.zero_star, r'<zerowidthstar>', regexp)
# Filter in arrays.
arrays = re.findall(RE.array, regexp)
for array in arrays:
rep = ''
if array in self.master._array:
rep = r'(?:' + '|'.join(self.expand_array(array)) + ')'
regexp = re.sub(r'\@' + re.escape(array) + r'\b', rep, regexp)
# Simple replacements.
regexp = regexp.replace('*', '(.+?)') # Convert * into (.+?)
regexp = regexp.replace('#', '(\d+?)') # Convert # into (\d+?)
regexp = regexp.replace('_', '(\w+?)') # Convert _ into (\w+?)
regexp = re.sub(RE.weight, '', regexp) # Remove {weight} tags, allow spaces before the bracket
regexp = regexp.replace('<zerowidthstar>', r'(.*?)')
# Optionals.
optionals = re.findall(RE.optionals, regexp)
for match in optionals:
parts = match.split("|")
new = []
for p in parts:
p = r'(?:\\s|\\b)+{}(?:\\s|\\b)+'.format(p.strip())
new.append(p)
# If this optional had a star or anything in it, make it
# non-matching.
pipes = '|'.join(new)
pipes = pipes.replace(r'(.+?)', r'(?:.+?)')
pipes = pipes.replace(r'(\d+?)', r'(?:\d+?)')
pipes = pipes.replace(r'([A-Za-z]+?)', r'(?:[A-Za-z]+?)')
regexp = re.sub(r'\s*\[' + re.escape(match) + '\]\s*',
'(?:' + pipes + r'|(?:\\s|\\b))', regexp)
# _ wildcards can't match numbers!
regexp = re.sub(RE.literal_w, r'[^\\s\\d]', regexp)
# Filter in bot variables.
bvars = re.findall(RE.bot_tag, regexp)
for var in bvars:
rep = ''
if var in self.master._var:
rep = self.format_message(self.master._var[var])
regexp = regexp.replace('<bot {var}>'.format(var=var), rep)
# Filter in user variables.
uvars = re.findall(RE.get_tag, regexp)
for var in uvars:
rep = ''
value = self.master.get_uservar(user, var)
if value not in [None, "undefined"]:
rep = utils.strip_nasties(value)
regexp = regexp.replace('<get {var}>'.format(var=var), rep)
# Filter in <input> and <reply> tags. This is a slow process, so only
# do it if we have to!
if '<input' in regexp or '<reply' in regexp:
history = self.master.get_uservar(user, "__history__")
for type in ['input', 'reply']:
tags = re.findall(r'<' + type + r'([0-9])>', regexp)
for index in tags:
rep = self.format_message(history[type][int(index) - 1])
regexp = regexp.replace('<{type}{index}>'.format(type=type, index=index), rep)
regexp = regexp.replace('<{type}>'.format(type=type),
self.format_message(history[type][0]))
# TODO: the Perl version doesn't do just <input>/<reply> in trigs!
if self.utf8:
return re.compile(r'^' + regexp.lower() + r'$', re.UNICODE)
else:
return re.compile(r'^' + regexp.lower() + r'$') | Prepares a trigger for the regular expression engine.
:param str user: The user ID invoking a reply.
:param str regexp: The original trigger text to be turned into a regexp.
:return regexp: The final regexp object. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/brain.py#L421-L507 |
aichaos/rivescript-python | rivescript/brain.py | Brain.do_expand_array | def do_expand_array(self, array_name, depth=0):
"""Do recurrent array expansion, returning a set of keywords.
Exception is thrown when there are cyclical dependencies between
arrays or if the ``@array`` name references an undefined array.
:param str array_name: The name of the array to expand.
:param int depth: The recursion depth counter.
:return set: The final set of array entries.
"""
if depth > self.master._depth:
raise Exception("deep recursion detected")
if not array_name in self.master._array:
raise Exception("array '%s' not defined" % (array_name))
ret = list(self.master._array[array_name])
for array in self.master._array[array_name]:
if array.startswith('@'):
ret.remove(array)
expanded = self.do_expand_array(array[1:], depth+1)
ret.extend(expanded)
return set(ret) | python | def do_expand_array(self, array_name, depth=0):
"""Do recurrent array expansion, returning a set of keywords.
Exception is thrown when there are cyclical dependencies between
arrays or if the ``@array`` name references an undefined array.
:param str array_name: The name of the array to expand.
:param int depth: The recursion depth counter.
:return set: The final set of array entries.
"""
if depth > self.master._depth:
raise Exception("deep recursion detected")
if not array_name in self.master._array:
raise Exception("array '%s' not defined" % (array_name))
ret = list(self.master._array[array_name])
for array in self.master._array[array_name]:
if array.startswith('@'):
ret.remove(array)
expanded = self.do_expand_array(array[1:], depth+1)
ret.extend(expanded)
return set(ret) | Do recurrent array expansion, returning a set of keywords.
Exception is thrown when there are cyclical dependencies between
arrays or if the ``@array`` name references an undefined array.
:param str array_name: The name of the array to expand.
:param int depth: The recursion depth counter.
:return set: The final set of array entries. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/brain.py#L509-L531 |
aichaos/rivescript-python | rivescript/brain.py | Brain.expand_array | def expand_array(self, array_name):
"""Expand variables and return a set of keywords.
:param str array_name: The name of the array to expand.
:return list: The final array contents.
Warning is issued when exceptions occur."""
ret = self.master._array[array_name] if array_name in self.master._array else []
try:
ret = self.do_expand_array(array_name)
except Exception as e:
self.warn("Error expanding array '%s': %s" % (array_name, str(e)))
return ret | python | def expand_array(self, array_name):
"""Expand variables and return a set of keywords.
:param str array_name: The name of the array to expand.
:return list: The final array contents.
Warning is issued when exceptions occur."""
ret = self.master._array[array_name] if array_name in self.master._array else []
try:
ret = self.do_expand_array(array_name)
except Exception as e:
self.warn("Error expanding array '%s': %s" % (array_name, str(e)))
return ret | Expand variables and return a set of keywords.
:param str array_name: The name of the array to expand.
:return list: The final array contents.
Warning is issued when exceptions occur. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/brain.py#L533-L546 |
aichaos/rivescript-python | rivescript/brain.py | Brain.process_tags | def process_tags(self, user, msg, reply, st=[], bst=[], depth=0, ignore_object_errors=True):
"""Post process tags in a message.
:param str user: The user ID.
:param str msg: The user's formatted message.
:param str reply: The raw RiveScript reply for the message.
:param []str st: The array of ``<star>`` matches from the trigger.
:param []str bst: The array of ``<botstar>`` matches from a
``%Previous`` command.
:param int depth: The recursion depth counter.
:param bool ignore_object_errors: Whether to ignore errors in Python
object macros instead of raising an ``ObjectError`` exception.
:return str: The final reply after tags have been processed.
"""
stars = ['']
stars.extend(st)
botstars = ['']
botstars.extend(bst)
if len(stars) == 1:
stars.append("undefined")
if len(botstars) == 1:
botstars.append("undefined")
matcher = re.findall(RE.reply_array, reply)
for match in matcher:
name = match
if name in self.master._array:
result = "{random}" + "|".join(self.master._array[name]) + "{/random}"
else:
result = "\x00@" + name + "\x00"
reply = reply.replace("(@"+name+")", result)
reply = re.sub(RE.ph_array, r'(@\1)', reply)
# Tag shortcuts.
reply = reply.replace('<person>', '{person}<star>{/person}')
reply = reply.replace('<@>', '{@<star>}')
reply = reply.replace('<formal>', '{formal}<star>{/formal}')
reply = reply.replace('<sentence>', '{sentence}<star>{/sentence}')
reply = reply.replace('<uppercase>', '{uppercase}<star>{/uppercase}')
reply = reply.replace('<lowercase>', '{lowercase}<star>{/lowercase}')
# Weight and <star> tags.
reply = re.sub(RE.weight, '', reply) # Leftover {weight}s
if len(stars) > 0:
reply = reply.replace('<star>', text_type(stars[1]))
reStars = re.findall(RE.star_tags, reply)
for match in reStars:
if int(match) < len(stars):
reply = reply.replace('<star{match}>'.format(match=match), text_type(stars[int(match)]))
if len(botstars) > 0:
reply = reply.replace('<botstar>', botstars[1])
reStars = re.findall(RE.botstars, reply)
for match in reStars:
if int(match) < len(botstars):
reply = reply.replace('<botstar{match}>'.format(match=match), text_type(botstars[int(match)]))
# <input> and <reply>
history = self.master.get_uservar(user, "__history__")
if type(history) is not dict:
history = self.default_history()
reply = reply.replace('<input>', history['input'][0])
reply = reply.replace('<reply>', history['reply'][0])
reInput = re.findall(RE.input_tags, reply)
for match in reInput:
reply = reply.replace('<input{match}>'.format(match=match),
history['input'][int(match) - 1])
reReply = re.findall(RE.reply_tags, reply)
for match in reReply:
reply = reply.replace('<reply{match}>'.format(match=match),
history['reply'][int(match) - 1])
# <id> and escape codes.
reply = reply.replace('<id>', user)
reply = reply.replace('\\s', ' ')
reply = reply.replace('\\n', "\n")
reply = reply.replace('\\#', '#')
# Random bits.
reRandom = re.findall(RE.random_tags, reply)
for match in reRandom:
output = ''
if '|' in match:
output = utils.random_choice(match.split('|'))
else:
output = utils.random_choice(match.split(' '))
reply = reply.replace('{{random}}{match}{{/random}}'.format(match=match), output, 1) # Replace 1st match
# Person Substitutions and String Formatting.
for item in ['person', 'formal', 'sentence', 'uppercase', 'lowercase']:
matcher = re.findall(r'\{' + item + r'\}(.+?)\{/' + item + r'\}', reply)
for match in matcher:
output = None
if item == 'person':
# Person substitutions.
output = self.substitute(match, "person")
else:
output = utils.string_format(match, item)
reply = reply.replace('{{{item}}}{match}{{/{item}}}'.format(item=item, match=match), output)
# Handle all variable-related tags with an iterative regex approach,
# to allow for nesting of tags in arbitrary ways (think <set a=<get b>>)
# Dummy out the <call> tags first, because we don't handle them right
# here.
reply = reply.replace("<call>", "{__call__}")
reply = reply.replace("</call>", "{/__call__}")
while True:
# This regex will match a <tag> which contains no other tag inside
# it, i.e. in the case of <set a=<get b>> it will match <get b> but
# not the <set> tag, on the first pass. The second pass will get the
# <set> tag, and so on.
match = re.search(RE.tag_search, reply)
if not match: break # No remaining tags!
match = match.group(1)
parts = match.split(" ", 1)
tag = parts[0].lower()
data = parts[1] if len(parts) > 1 else ""
insert = "" # Result of the tag evaluation
# Handle the tags.
if tag == "bot" or tag == "env":
# <bot> and <env> tags are similar.
target = self.master._var if tag == "bot" else self.master._global
if "=" in data:
# Setting a bot/env variable.
parts = data.split("=")
self.say("Set " + tag + " variable " + text_type(parts[0]) + "=" + text_type(parts[1]))
target[parts[0]] = parts[1]
else:
# Getting a bot/env variable.
insert = target.get(data, "undefined")
elif tag == "set":
# <set> user vars.
parts = data.split("=")
self.say("Set uservar " + text_type(parts[0]) + "=" + text_type(parts[1]))
self.master.set_uservar(user, parts[0], parts[1])
elif tag in ["add", "sub", "mult", "div"]:
# Math operator tags.
parts = data.split("=")
var = parts[0]
value = parts[1]
curv = self.master.get_uservar(user, var)
# Sanity check the value.
try:
value = int(value)
if curv in [None, "undefined"]:
# Initialize it.
curv = 0
except:
insert = "[ERR: Math can't '{}' non-numeric value '{}']".format(tag, value)
# Attempt the operation.
try:
orig = int(curv)
new = 0
if tag == "add":
new = orig + value
elif tag == "sub":
new = orig - value
elif tag == "mult":
new = orig * value
elif tag == "div":
new = orig // value
self.master.set_uservar(user, var, new)
except:
insert = "[ERR: Math couldn't '{}' to value '{}']".format(tag, curv)
elif tag == "get":
insert = self.master.get_uservar(user, data)
else:
# Unrecognized tag.
insert = "\x00{}\x01".format(match)
reply = reply.replace("<{}>".format(match), text_type(insert))
# Restore unrecognized tags.
reply = reply.replace("\x00", "<").replace("\x01", ">")
# Streaming code. DEPRECATED!
if '{!' in reply:
self._warn("Use of the {!...} tag is deprecated and not supported here.")
# Topic setter.
reTopic = re.findall(RE.topic_tag, reply)
for match in reTopic:
self.say("Setting user's topic to " + match)
self.master.set_uservar(user, "topic", match)
reply = reply.replace('{{topic={match}}}'.format(match=match), '')
# Inline redirecter.
reRedir = re.findall(RE.redir_tag, reply)
for match in reRedir:
self.say("Redirect to " + match)
at = match.strip()
subreply = self._getreply(user, at, step=(depth + 1))
reply = reply.replace('{{@{match}}}'.format(match=match), subreply)
# Object caller.
reply = reply.replace("{__call__}", "<call>")
reply = reply.replace("{/__call__}", "</call>")
reCall = re.findall(r'<call>(.+?)</call>', reply)
for match in reCall:
parts = re.split(RE.ws, match)
output = ''
obj = parts[0]
args = []
if len(parts) > 1:
args = parts[1:]
# Do we know this object?
if obj in self.master._objlangs:
# We do, but do we have a handler for that language?
lang = self.master._objlangs[obj]
if lang in self.master._handlers:
# We do.
try:
output = self.master._handlers[lang].call(self.master, obj, user, args)
except python.PythonObjectError as e:
self.warn(str(e))
if not ignore_object_errors:
raise ObjectError(str(e))
output = RS_ERR_OBJECT
else:
if not ignore_object_errors:
raise ObjectError(RS_ERR_OBJECT_HANDLER)
output = RS_ERR_OBJECT_HANDLER
else:
if not ignore_object_errors:
raise ObjectError(RS_ERR_OBJECT_MISSING)
output = RS_ERR_OBJECT_MISSING
reply = reply.replace('<call>{match}</call>'.format(match=match), output)
return reply | python | def process_tags(self, user, msg, reply, st=[], bst=[], depth=0, ignore_object_errors=True):
"""Post process tags in a message.
:param str user: The user ID.
:param str msg: The user's formatted message.
:param str reply: The raw RiveScript reply for the message.
:param []str st: The array of ``<star>`` matches from the trigger.
:param []str bst: The array of ``<botstar>`` matches from a
``%Previous`` command.
:param int depth: The recursion depth counter.
:param bool ignore_object_errors: Whether to ignore errors in Python
object macros instead of raising an ``ObjectError`` exception.
:return str: The final reply after tags have been processed.
"""
stars = ['']
stars.extend(st)
botstars = ['']
botstars.extend(bst)
if len(stars) == 1:
stars.append("undefined")
if len(botstars) == 1:
botstars.append("undefined")
matcher = re.findall(RE.reply_array, reply)
for match in matcher:
name = match
if name in self.master._array:
result = "{random}" + "|".join(self.master._array[name]) + "{/random}"
else:
result = "\x00@" + name + "\x00"
reply = reply.replace("(@"+name+")", result)
reply = re.sub(RE.ph_array, r'(@\1)', reply)
# Tag shortcuts.
reply = reply.replace('<person>', '{person}<star>{/person}')
reply = reply.replace('<@>', '{@<star>}')
reply = reply.replace('<formal>', '{formal}<star>{/formal}')
reply = reply.replace('<sentence>', '{sentence}<star>{/sentence}')
reply = reply.replace('<uppercase>', '{uppercase}<star>{/uppercase}')
reply = reply.replace('<lowercase>', '{lowercase}<star>{/lowercase}')
# Weight and <star> tags.
reply = re.sub(RE.weight, '', reply) # Leftover {weight}s
if len(stars) > 0:
reply = reply.replace('<star>', text_type(stars[1]))
reStars = re.findall(RE.star_tags, reply)
for match in reStars:
if int(match) < len(stars):
reply = reply.replace('<star{match}>'.format(match=match), text_type(stars[int(match)]))
if len(botstars) > 0:
reply = reply.replace('<botstar>', botstars[1])
reStars = re.findall(RE.botstars, reply)
for match in reStars:
if int(match) < len(botstars):
reply = reply.replace('<botstar{match}>'.format(match=match), text_type(botstars[int(match)]))
# <input> and <reply>
history = self.master.get_uservar(user, "__history__")
if type(history) is not dict:
history = self.default_history()
reply = reply.replace('<input>', history['input'][0])
reply = reply.replace('<reply>', history['reply'][0])
reInput = re.findall(RE.input_tags, reply)
for match in reInput:
reply = reply.replace('<input{match}>'.format(match=match),
history['input'][int(match) - 1])
reReply = re.findall(RE.reply_tags, reply)
for match in reReply:
reply = reply.replace('<reply{match}>'.format(match=match),
history['reply'][int(match) - 1])
# <id> and escape codes.
reply = reply.replace('<id>', user)
reply = reply.replace('\\s', ' ')
reply = reply.replace('\\n', "\n")
reply = reply.replace('\\#', '#')
# Random bits.
reRandom = re.findall(RE.random_tags, reply)
for match in reRandom:
output = ''
if '|' in match:
output = utils.random_choice(match.split('|'))
else:
output = utils.random_choice(match.split(' '))
reply = reply.replace('{{random}}{match}{{/random}}'.format(match=match), output, 1) # Replace 1st match
# Person Substitutions and String Formatting.
for item in ['person', 'formal', 'sentence', 'uppercase', 'lowercase']:
matcher = re.findall(r'\{' + item + r'\}(.+?)\{/' + item + r'\}', reply)
for match in matcher:
output = None
if item == 'person':
# Person substitutions.
output = self.substitute(match, "person")
else:
output = utils.string_format(match, item)
reply = reply.replace('{{{item}}}{match}{{/{item}}}'.format(item=item, match=match), output)
# Handle all variable-related tags with an iterative regex approach,
# to allow for nesting of tags in arbitrary ways (think <set a=<get b>>)
# Dummy out the <call> tags first, because we don't handle them right
# here.
reply = reply.replace("<call>", "{__call__}")
reply = reply.replace("</call>", "{/__call__}")
while True:
# This regex will match a <tag> which contains no other tag inside
# it, i.e. in the case of <set a=<get b>> it will match <get b> but
# not the <set> tag, on the first pass. The second pass will get the
# <set> tag, and so on.
match = re.search(RE.tag_search, reply)
if not match: break # No remaining tags!
match = match.group(1)
parts = match.split(" ", 1)
tag = parts[0].lower()
data = parts[1] if len(parts) > 1 else ""
insert = "" # Result of the tag evaluation
# Handle the tags.
if tag == "bot" or tag == "env":
# <bot> and <env> tags are similar.
target = self.master._var if tag == "bot" else self.master._global
if "=" in data:
# Setting a bot/env variable.
parts = data.split("=")
self.say("Set " + tag + " variable " + text_type(parts[0]) + "=" + text_type(parts[1]))
target[parts[0]] = parts[1]
else:
# Getting a bot/env variable.
insert = target.get(data, "undefined")
elif tag == "set":
# <set> user vars.
parts = data.split("=")
self.say("Set uservar " + text_type(parts[0]) + "=" + text_type(parts[1]))
self.master.set_uservar(user, parts[0], parts[1])
elif tag in ["add", "sub", "mult", "div"]:
# Math operator tags.
parts = data.split("=")
var = parts[0]
value = parts[1]
curv = self.master.get_uservar(user, var)
# Sanity check the value.
try:
value = int(value)
if curv in [None, "undefined"]:
# Initialize it.
curv = 0
except:
insert = "[ERR: Math can't '{}' non-numeric value '{}']".format(tag, value)
# Attempt the operation.
try:
orig = int(curv)
new = 0
if tag == "add":
new = orig + value
elif tag == "sub":
new = orig - value
elif tag == "mult":
new = orig * value
elif tag == "div":
new = orig // value
self.master.set_uservar(user, var, new)
except:
insert = "[ERR: Math couldn't '{}' to value '{}']".format(tag, curv)
elif tag == "get":
insert = self.master.get_uservar(user, data)
else:
# Unrecognized tag.
insert = "\x00{}\x01".format(match)
reply = reply.replace("<{}>".format(match), text_type(insert))
# Restore unrecognized tags.
reply = reply.replace("\x00", "<").replace("\x01", ">")
# Streaming code. DEPRECATED!
if '{!' in reply:
self._warn("Use of the {!...} tag is deprecated and not supported here.")
# Topic setter.
reTopic = re.findall(RE.topic_tag, reply)
for match in reTopic:
self.say("Setting user's topic to " + match)
self.master.set_uservar(user, "topic", match)
reply = reply.replace('{{topic={match}}}'.format(match=match), '')
# Inline redirecter.
reRedir = re.findall(RE.redir_tag, reply)
for match in reRedir:
self.say("Redirect to " + match)
at = match.strip()
subreply = self._getreply(user, at, step=(depth + 1))
reply = reply.replace('{{@{match}}}'.format(match=match), subreply)
# Object caller.
reply = reply.replace("{__call__}", "<call>")
reply = reply.replace("{/__call__}", "</call>")
reCall = re.findall(r'<call>(.+?)</call>', reply)
for match in reCall:
parts = re.split(RE.ws, match)
output = ''
obj = parts[0]
args = []
if len(parts) > 1:
args = parts[1:]
# Do we know this object?
if obj in self.master._objlangs:
# We do, but do we have a handler for that language?
lang = self.master._objlangs[obj]
if lang in self.master._handlers:
# We do.
try:
output = self.master._handlers[lang].call(self.master, obj, user, args)
except python.PythonObjectError as e:
self.warn(str(e))
if not ignore_object_errors:
raise ObjectError(str(e))
output = RS_ERR_OBJECT
else:
if not ignore_object_errors:
raise ObjectError(RS_ERR_OBJECT_HANDLER)
output = RS_ERR_OBJECT_HANDLER
else:
if not ignore_object_errors:
raise ObjectError(RS_ERR_OBJECT_MISSING)
output = RS_ERR_OBJECT_MISSING
reply = reply.replace('<call>{match}</call>'.format(match=match), output)
return reply | Post process tags in a message.
:param str user: The user ID.
:param str msg: The user's formatted message.
:param str reply: The raw RiveScript reply for the message.
:param []str st: The array of ``<star>`` matches from the trigger.
:param []str bst: The array of ``<botstar>`` matches from a
``%Previous`` command.
:param int depth: The recursion depth counter.
:param bool ignore_object_errors: Whether to ignore errors in Python
object macros instead of raising an ``ObjectError`` exception.
:return str: The final reply after tags have been processed. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/brain.py#L548-L782 |
aichaos/rivescript-python | rivescript/brain.py | Brain.substitute | def substitute(self, msg, kind):
"""Run a kind of substitution on a message.
:param str msg: The message to run substitutions against.
:param str kind: The kind of substitution to run,
one of ``subs`` or ``person``.
"""
# Safety checking.
if 'lists' not in self.master._sorted:
raise RepliesNotSortedError("You must call sort_replies() once you are done loading RiveScript documents")
if kind not in self.master._sorted["lists"]:
raise RepliesNotSortedError("You must call sort_replies() once you are done loading RiveScript documents")
# Get the substitution map.
subs = None
if kind == 'sub':
subs = self.master._sub
else:
subs = self.master._person
# Make placeholders each time we substitute something.
ph = []
i = 0
for pattern in self.master._sorted["lists"][kind]:
result = subs[pattern]
# Make a placeholder.
ph.append(result)
placeholder = "\x00%d\x00" % i
i += 1
cache = self.master._regexc[kind][pattern]
msg = re.sub(cache["sub1"], placeholder, msg)
msg = re.sub(cache["sub2"], placeholder + r'\1', msg)
msg = re.sub(cache["sub3"], r'\1' + placeholder + r'\2', msg)
msg = re.sub(cache["sub4"], r'\1' + placeholder, msg)
placeholders = re.findall(RE.placeholder, msg)
for match in placeholders:
i = int(match)
result = ph[i]
msg = msg.replace('\x00' + match + '\x00', result)
# Strip & return.
return msg.strip() | python | def substitute(self, msg, kind):
"""Run a kind of substitution on a message.
:param str msg: The message to run substitutions against.
:param str kind: The kind of substitution to run,
one of ``subs`` or ``person``.
"""
# Safety checking.
if 'lists' not in self.master._sorted:
raise RepliesNotSortedError("You must call sort_replies() once you are done loading RiveScript documents")
if kind not in self.master._sorted["lists"]:
raise RepliesNotSortedError("You must call sort_replies() once you are done loading RiveScript documents")
# Get the substitution map.
subs = None
if kind == 'sub':
subs = self.master._sub
else:
subs = self.master._person
# Make placeholders each time we substitute something.
ph = []
i = 0
for pattern in self.master._sorted["lists"][kind]:
result = subs[pattern]
# Make a placeholder.
ph.append(result)
placeholder = "\x00%d\x00" % i
i += 1
cache = self.master._regexc[kind][pattern]
msg = re.sub(cache["sub1"], placeholder, msg)
msg = re.sub(cache["sub2"], placeholder + r'\1', msg)
msg = re.sub(cache["sub3"], r'\1' + placeholder + r'\2', msg)
msg = re.sub(cache["sub4"], r'\1' + placeholder, msg)
placeholders = re.findall(RE.placeholder, msg)
for match in placeholders:
i = int(match)
result = ph[i]
msg = msg.replace('\x00' + match + '\x00', result)
# Strip & return.
return msg.strip() | Run a kind of substitution on a message.
:param str msg: The message to run substitutions against.
:param str kind: The kind of substitution to run,
one of ``subs`` or ``person``. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/brain.py#L784-L830 |
aichaos/rivescript-python | rivescript/python.py | PyRiveObjects.load | def load(self, name, code):
"""Prepare a Python code object given by the RiveScript interpreter.
:param str name: The name of the Python object macro.
:param []str code: The Python source code for the object macro.
"""
# We need to make a dynamic Python method.
source = "def RSOBJ(rs, args):\n"
for line in code:
source = source + "\t" + line + "\n"
source += "self._objects[name] = RSOBJ\n"
try:
exec(source)
# self._objects[name] = RSOBJ
except Exception as e:
print("Failed to load code from object", name)
print("The error given was: ", e) | python | def load(self, name, code):
"""Prepare a Python code object given by the RiveScript interpreter.
:param str name: The name of the Python object macro.
:param []str code: The Python source code for the object macro.
"""
# We need to make a dynamic Python method.
source = "def RSOBJ(rs, args):\n"
for line in code:
source = source + "\t" + line + "\n"
source += "self._objects[name] = RSOBJ\n"
try:
exec(source)
# self._objects[name] = RSOBJ
except Exception as e:
print("Failed to load code from object", name)
print("The error given was: ", e) | Prepare a Python code object given by the RiveScript interpreter.
:param str name: The name of the Python object macro.
:param []str code: The Python source code for the object macro. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/python.py#L41-L59 |
aichaos/rivescript-python | rivescript/python.py | PyRiveObjects.call | def call(self, rs, name, user, fields):
"""Invoke a previously loaded object.
:param RiveScript rs: the parent RiveScript instance.
:param str name: The name of the object macro to be called.
:param str user: The user ID invoking the object macro.
:param []str fields: Array of words sent as the object's arguments.
:return str: The output of the object macro.
"""
# Call the dynamic method.
if name not in self._objects:
return '[ERR: Object Not Found]'
func = self._objects[name]
reply = ''
try:
reply = func(rs, fields)
if reply is None:
reply = ''
except Exception as e:
raise PythonObjectError("Error executing Python object: " + str(e))
return text_type(reply) | python | def call(self, rs, name, user, fields):
"""Invoke a previously loaded object.
:param RiveScript rs: the parent RiveScript instance.
:param str name: The name of the object macro to be called.
:param str user: The user ID invoking the object macro.
:param []str fields: Array of words sent as the object's arguments.
:return str: The output of the object macro.
"""
# Call the dynamic method.
if name not in self._objects:
return '[ERR: Object Not Found]'
func = self._objects[name]
reply = ''
try:
reply = func(rs, fields)
if reply is None:
reply = ''
except Exception as e:
raise PythonObjectError("Error executing Python object: " + str(e))
return text_type(reply) | Invoke a previously loaded object.
:param RiveScript rs: the parent RiveScript instance.
:param str name: The name of the object macro to be called.
:param str user: The user ID invoking the object macro.
:param []str fields: Array of words sent as the object's arguments.
:return str: The output of the object macro. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/python.py#L61-L82 |
aichaos/rivescript-python | rivescript/inheritance.py | get_topic_triggers | def get_topic_triggers(rs, topic, thats, depth=0, inheritance=0, inherited=False):
"""Recursively scan a topic and return a list of all triggers.
Arguments:
rs (RiveScript): A reference to the parent RiveScript instance.
topic (str): The original topic name.
thats (bool): Are we getting triggers for 'previous' replies?
depth (int): Recursion step counter.
inheritance (int): The inheritance level counter, for topics that
inherit other topics.
inherited (bool): Whether the current topic is inherited by others.
Returns:
[]str: List of all triggers found.
"""
# Break if we're in too deep.
if depth > rs._depth:
rs._warn("Deep recursion while scanning topic inheritance")
# Keep in mind here that there is a difference between 'includes' and
# 'inherits' -- topics that inherit other topics are able to OVERRIDE
# triggers that appear in the inherited topic. This means that if the top
# topic has a trigger of simply '*', then NO triggers are capable of
# matching in ANY inherited topic, because even though * has the lowest
# priority, it has an automatic priority over all inherited topics.
#
# The getTopicTriggers method takes this into account. All topics that
# inherit other topics will have their triggers prefixed with a fictional
# {inherits} tag, which would start at {inherits=0} and increment if this
# topic has other inheriting topics. So we can use this tag to make sure
# topics that inherit things will have their triggers always be on top of
# the stack, from inherits=0 to inherits=n.
# Important info about the depth vs inheritance params to this function:
# depth increments by 1 each time this function recursively calls itrs.
# inheritance increments by 1 only when this topic inherits another
# topic.
#
# This way, '> topic alpha includes beta inherits gamma' will have this
# effect:
# alpha and beta's triggers are combined together into one matching
# pool, and then those triggers have higher matching priority than
# gamma's.
#
# The inherited option is True if this is a recursive call, from a topic
# that inherits other topics. This forces the {inherits} tag to be added
# to the triggers. This only applies when the top topic 'includes'
# another topic.
rs._say("\tCollecting trigger list for topic " + topic + "(depth="
+ str(depth) + "; inheritance=" + str(inheritance) + "; "
+ "inherited=" + str(inherited) + ")")
# topic: the name of the topic
# depth: starts at 0 and ++'s with each recursion
# Topic doesn't exist?
if not topic in rs._topics:
rs._warn("Inherited or included topic {} doesn't exist or has no triggers".format(
topic
))
return []
# Collect an array of triggers to return.
triggers = []
# Get those that exist in this topic directly.
inThisTopic = []
if not thats:
# The non-that structure is {topic}->[array of triggers]
if topic in rs._topics:
for trigger in rs._topics[topic]:
inThisTopic.append([ trigger["trigger"], trigger ])
else:
# The 'that' structure is: {topic}->{cur trig}->{prev trig}->{trig info}
if topic in rs._thats.keys():
for curtrig in rs._thats[topic].keys():
for previous, pointer in rs._thats[topic][curtrig].items():
inThisTopic.append([ pointer["trigger"], pointer ])
# Does this topic include others?
if topic in rs._includes:
# Check every included topic.
for includes in rs._includes[topic]:
rs._say("\t\tTopic " + topic + " includes " + includes)
triggers.extend(get_topic_triggers(rs, includes, thats, (depth + 1), inheritance, True))
# Does this topic inherit others?
if topic in rs._lineage:
# Check every inherited topic.
for inherits in rs._lineage[topic]:
rs._say("\t\tTopic " + topic + " inherits " + inherits)
triggers.extend(get_topic_triggers(rs, inherits, thats, (depth + 1), (inheritance + 1), False))
# Collect the triggers for *this* topic. If this topic inherits any
# other topics, it means that this topic's triggers have higher
# priority than those in any inherited topics. Enforce this with an
# {inherits} tag.
if topic in rs._lineage or inherited:
for trigger in inThisTopic:
rs._say("\t\tPrefixing trigger with {inherits=" + str(inheritance) + "}" + trigger[0])
triggers.append(["{inherits=" + str(inheritance) + "}" + trigger[0], trigger[1]])
else:
triggers.extend(inThisTopic)
return triggers | python | def get_topic_triggers(rs, topic, thats, depth=0, inheritance=0, inherited=False):
"""Recursively scan a topic and return a list of all triggers.
Arguments:
rs (RiveScript): A reference to the parent RiveScript instance.
topic (str): The original topic name.
thats (bool): Are we getting triggers for 'previous' replies?
depth (int): Recursion step counter.
inheritance (int): The inheritance level counter, for topics that
inherit other topics.
inherited (bool): Whether the current topic is inherited by others.
Returns:
[]str: List of all triggers found.
"""
# Break if we're in too deep.
if depth > rs._depth:
rs._warn("Deep recursion while scanning topic inheritance")
# Keep in mind here that there is a difference between 'includes' and
# 'inherits' -- topics that inherit other topics are able to OVERRIDE
# triggers that appear in the inherited topic. This means that if the top
# topic has a trigger of simply '*', then NO triggers are capable of
# matching in ANY inherited topic, because even though * has the lowest
# priority, it has an automatic priority over all inherited topics.
#
# The getTopicTriggers method takes this into account. All topics that
# inherit other topics will have their triggers prefixed with a fictional
# {inherits} tag, which would start at {inherits=0} and increment if this
# topic has other inheriting topics. So we can use this tag to make sure
# topics that inherit things will have their triggers always be on top of
# the stack, from inherits=0 to inherits=n.
# Important info about the depth vs inheritance params to this function:
# depth increments by 1 each time this function recursively calls itrs.
# inheritance increments by 1 only when this topic inherits another
# topic.
#
# This way, '> topic alpha includes beta inherits gamma' will have this
# effect:
# alpha and beta's triggers are combined together into one matching
# pool, and then those triggers have higher matching priority than
# gamma's.
#
# The inherited option is True if this is a recursive call, from a topic
# that inherits other topics. This forces the {inherits} tag to be added
# to the triggers. This only applies when the top topic 'includes'
# another topic.
rs._say("\tCollecting trigger list for topic " + topic + "(depth="
+ str(depth) + "; inheritance=" + str(inheritance) + "; "
+ "inherited=" + str(inherited) + ")")
# topic: the name of the topic
# depth: starts at 0 and ++'s with each recursion
# Topic doesn't exist?
if not topic in rs._topics:
rs._warn("Inherited or included topic {} doesn't exist or has no triggers".format(
topic
))
return []
# Collect an array of triggers to return.
triggers = []
# Get those that exist in this topic directly.
inThisTopic = []
if not thats:
# The non-that structure is {topic}->[array of triggers]
if topic in rs._topics:
for trigger in rs._topics[topic]:
inThisTopic.append([ trigger["trigger"], trigger ])
else:
# The 'that' structure is: {topic}->{cur trig}->{prev trig}->{trig info}
if topic in rs._thats.keys():
for curtrig in rs._thats[topic].keys():
for previous, pointer in rs._thats[topic][curtrig].items():
inThisTopic.append([ pointer["trigger"], pointer ])
# Does this topic include others?
if topic in rs._includes:
# Check every included topic.
for includes in rs._includes[topic]:
rs._say("\t\tTopic " + topic + " includes " + includes)
triggers.extend(get_topic_triggers(rs, includes, thats, (depth + 1), inheritance, True))
# Does this topic inherit others?
if topic in rs._lineage:
# Check every inherited topic.
for inherits in rs._lineage[topic]:
rs._say("\t\tTopic " + topic + " inherits " + inherits)
triggers.extend(get_topic_triggers(rs, inherits, thats, (depth + 1), (inheritance + 1), False))
# Collect the triggers for *this* topic. If this topic inherits any
# other topics, it means that this topic's triggers have higher
# priority than those in any inherited topics. Enforce this with an
# {inherits} tag.
if topic in rs._lineage or inherited:
for trigger in inThisTopic:
rs._say("\t\tPrefixing trigger with {inherits=" + str(inheritance) + "}" + trigger[0])
triggers.append(["{inherits=" + str(inheritance) + "}" + trigger[0], trigger[1]])
else:
triggers.extend(inThisTopic)
return triggers | Recursively scan a topic and return a list of all triggers.
Arguments:
rs (RiveScript): A reference to the parent RiveScript instance.
topic (str): The original topic name.
thats (bool): Are we getting triggers for 'previous' replies?
depth (int): Recursion step counter.
inheritance (int): The inheritance level counter, for topics that
inherit other topics.
inherited (bool): Whether the current topic is inherited by others.
Returns:
[]str: List of all triggers found. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/inheritance.py#L8-L113 |
aichaos/rivescript-python | rivescript/inheritance.py | get_topic_tree | def get_topic_tree(rs, topic, depth=0):
"""Given one topic, get the list of all included/inherited topics.
:param str topic: The topic to start the search at.
:param int depth: The recursion depth counter.
:return []str: Array of topics.
"""
# Break if we're in too deep.
if depth > rs._depth:
rs._warn("Deep recursion while scanning topic trees!")
return []
# Collect an array of all topics.
topics = [topic]
# Does this topic include others?
if topic in rs._includes:
# Try each of these.
for includes in sorted(rs._includes[topic]):
topics.extend(get_topic_tree(rs, includes, depth + 1))
# Does this topic inherit others?
if topic in rs._lineage:
# Try each of these.
for inherits in sorted(rs._lineage[topic]):
topics.extend(get_topic_tree(rs, inherits, depth + 1))
return topics | python | def get_topic_tree(rs, topic, depth=0):
"""Given one topic, get the list of all included/inherited topics.
:param str topic: The topic to start the search at.
:param int depth: The recursion depth counter.
:return []str: Array of topics.
"""
# Break if we're in too deep.
if depth > rs._depth:
rs._warn("Deep recursion while scanning topic trees!")
return []
# Collect an array of all topics.
topics = [topic]
# Does this topic include others?
if topic in rs._includes:
# Try each of these.
for includes in sorted(rs._includes[topic]):
topics.extend(get_topic_tree(rs, includes, depth + 1))
# Does this topic inherit others?
if topic in rs._lineage:
# Try each of these.
for inherits in sorted(rs._lineage[topic]):
topics.extend(get_topic_tree(rs, inherits, depth + 1))
return topics | Given one topic, get the list of all included/inherited topics.
:param str topic: The topic to start the search at.
:param int depth: The recursion depth counter.
:return []str: Array of topics. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/inheritance.py#L115-L144 |
aichaos/rivescript-python | rivescript/utils.py | word_count | def word_count(trigger, all=False):
"""Count the words that aren't wildcards or options in a trigger.
:param str trigger: The trigger to count words for.
:param bool all: Count purely based on whitespace separators, or
consider wildcards not to be their own words.
:return int: The word count."""
words = []
if all:
words = re.split(RE.ws, trigger)
else:
words = re.split(RE.wilds_and_optionals, trigger)
wc = 0 # Word count
for word in words:
if len(word) > 0:
wc += 1
return wc | python | def word_count(trigger, all=False):
"""Count the words that aren't wildcards or options in a trigger.
:param str trigger: The trigger to count words for.
:param bool all: Count purely based on whitespace separators, or
consider wildcards not to be their own words.
:return int: The word count."""
words = []
if all:
words = re.split(RE.ws, trigger)
else:
words = re.split(RE.wilds_and_optionals, trigger)
wc = 0 # Word count
for word in words:
if len(word) > 0:
wc += 1
return wc | Count the words that aren't wildcards or options in a trigger.
:param str trigger: The trigger to count words for.
:param bool all: Count purely based on whitespace separators, or
consider wildcards not to be their own words.
:return int: The word count. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/utils.py#L14-L33 |
aichaos/rivescript-python | rivescript/utils.py | string_format | def string_format(msg, method):
"""Format a string (upper, lower, formal, sentence).
:param str msg: The user's message.
:param str method: One of ``uppercase``, ``lowercase``,
``sentence`` or ``formal``.
:return str: The reformatted string.
"""
if method == "uppercase":
return msg.upper()
elif method == "lowercase":
return msg.lower()
elif method == "sentence":
return msg.capitalize()
elif method == "formal":
return string.capwords(msg) | python | def string_format(msg, method):
"""Format a string (upper, lower, formal, sentence).
:param str msg: The user's message.
:param str method: One of ``uppercase``, ``lowercase``,
``sentence`` or ``formal``.
:return str: The reformatted string.
"""
if method == "uppercase":
return msg.upper()
elif method == "lowercase":
return msg.lower()
elif method == "sentence":
return msg.capitalize()
elif method == "formal":
return string.capwords(msg) | Format a string (upper, lower, formal, sentence).
:param str msg: The user's message.
:param str method: One of ``uppercase``, ``lowercase``,
``sentence`` or ``formal``.
:return str: The reformatted string. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/utils.py#L63-L79 |
aichaos/rivescript-python | eg/perl-objects/perl.py | PerlObject.load | def load(self, name, code):
"""Prepare a Perl code object given by the RS interpreter."""
source = "\n".join(code)
self._objects[name] = source | python | def load(self, name, code):
"""Prepare a Perl code object given by the RS interpreter."""
source = "\n".join(code)
self._objects[name] = source | Prepare a Perl code object given by the RS interpreter. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/eg/perl-objects/perl.py#L13-L17 |
aichaos/rivescript-python | eg/twilio/app.py | hello_rivescript | def hello_rivescript():
"""Receive an inbound SMS and send a reply from RiveScript."""
from_number = request.values.get("From", "unknown")
message = request.values.get("Body")
reply = "(Internal error)"
# Get a reply from RiveScript.
if message:
reply = bot.reply(from_number, message)
# Send the response.
resp = twilio.twiml.Response()
resp.message(reply)
return str(resp) | python | def hello_rivescript():
"""Receive an inbound SMS and send a reply from RiveScript."""
from_number = request.values.get("From", "unknown")
message = request.values.get("Body")
reply = "(Internal error)"
# Get a reply from RiveScript.
if message:
reply = bot.reply(from_number, message)
# Send the response.
resp = twilio.twiml.Response()
resp.message(reply)
return str(resp) | Receive an inbound SMS and send a reply from RiveScript. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/eg/twilio/app.py#L28-L42 |
aichaos/rivescript-python | rivescript/interactive.py | interactive_mode | def interactive_mode():
"""The built-in RiveScript Interactive Mode.
This feature of RiveScript allows you to test and debug a chatbot in your
terminal window. There are two ways to invoke this mode::
# By running the Python RiveScript module directly:
python rivescript eg/brain
# By running the shell.py in the source distribution:
python shell.py eg/brain
The only required command line parameter is a filesystem path to a directory
containing RiveScript source files (with the ``*.rive`` file extension).
Additionally, it accepts command line flags.
Parameters:
--utf8: Enable UTF-8 mode.
--json: Use JSON to communicate with the bot instead of plain text.
See the JSON Mode documentation below for advanced details.
--debug: Enable verbose debug logging.
--log (str): The path to a text file you want the debug logging to
be written to. This is to be used in conjunction with ``--debug``,
for the case where you don't want your terminal window to be flooded
with debug messages.
--depth (int): Override the recursion depth limit (default ``50``).
--nostrict: Disable strict syntax checking when parsing the RiveScript
files. By default a syntax error raises an exception and will
terminate the interactive mode.
--help: Show the documentation of command line flags.
path (str): The path to a directory containing ``.rive`` files.
**JSON Mode**
By invoking the interactive mode with the ``--json`` (or ``-j``) flag,
the interactive mode will communicate with you via JSON messages. This
can be used as a "bridge" to enable the use of RiveScript from another
programming language that doesn't have its own native RiveScript
implementation.
For example, a program could open a shell pipe to the RiveScript interactive
mode and send/receive JSON payloads to communicate with the bot.
In JSON mode, you send a message to the bot in the following format::
{
"username": "str username",
"message": "str message",
"vars": {
"topic": "random",
"name": "Alice"
}
}
The ``username`` and ``message`` keys are required, and ``vars`` is a
key/value object of all the variables about the user.
After sending the JSON payload over standard input, you can either close the
input file handle (send the EOF signal; or Ctrl-D in a terminal), or send
the string ``__END__`` on a line of text by itself. This will cause the bot
to parse your payload, get a reply for the message, and respond with a
similar JSON payload::
{
"status": "ok",
"reply": "str response",
"vars": {
"topic": "random",
"name": "Alice"
}
}
The ``vars`` structure in the response contains all of the key/value pairs
the bot knows about the username you passed in. This will also contain a
lot of internal data, such as the user's history and last matched trigger.
To keep a stateful session, you should parse the ``vars`` returned by
RiveScript and pass them in with your next request so that the bot can
remember them for the next reply.
If you closed the filehandle (Ctrl-D, EOF) after your input payload, the
interactive mode will exit after giving the response. If, on the other
hand, you sent the string ``__END__`` on a line by itself after your
payload, the RiveScript interactive mode will do the same after its response
is returned. This way, you can re-use the shell pipe to send and receive
many messages over a single session.
"""
parser = argparse.ArgumentParser(description="RiveScript interactive mode.")
parser.add_argument("--debug", "-d",
help="Enable debug logging within RiveScript.",
action="store_true",
)
parser.add_argument("--json", "-j",
help="Enable JSON mode. In this mode, you communicate with the bot by "
"sending a JSON-encoded object with keys 'username', 'message' and "
"'vars' (an object of user variables) over standard input, and "
"then close the input (^D) or send the string '__END__' on a line "
"by itself. The bot will respond with a similarly formatted JSON "
"response over standard output, and then will either exit or send "
"'__END__' depending on how you ended your input.",
action="store_true",
)
parser.add_argument("--utf8", "-u",
help="Enable UTF-8 mode (default is disabled)",
action="store_true",
)
parser.add_argument("--log",
help="The path to a log file to send debugging output to (when debug "
"mode is enabled) instead of standard output.",
type=text_type,
)
parser.add_argument("--nostrict",
help="Disable strict mode (where syntax errors are fatal)",
action="store_true",
)
parser.add_argument("--depth",
help="Override the default recursion depth limit when fetching a reply "
"(default 50)",
type=int,
default=50,
)
parser.add_argument("path",
help="A directory containing RiveScript files (*.rive) to load.",
type=text_type,
# required=True,
)
args = parser.parse_args()
# Make the bot.
bot = RiveScript(
debug=args.debug,
strict=not args.nostrict,
depth=args.depth,
utf8=args.utf8,
log=args.log
)
bot.load_directory(args.path)
bot.sort_replies()
# Interactive mode?
if args.json:
# Read from standard input.
buffer = ""
stateful = False
while True:
line = ""
try:
line = input()
except EOFError:
break
# Look for the __END__ line.
end = re.match(r'^__END__$', line)
if end:
# Process it.
stateful = True # This is a stateful session
json_in(bot, buffer, stateful)
buffer = ""
continue
else:
buffer += line + "\n"
# We got the EOF. If the session was stateful, just exit,
# otherwise process what we just read.
if stateful:
quit()
json_in(bot, buffer, stateful)
quit()
print(
" . . \n"
" .:...:: RiveScript Interpreter (Python)\n"
" .:: ::. Library Version: v{version}\n"
" ..:;;. ' .;;:.. \n"
" . ''' . Type '/quit' to quit.\n"
" :;,:,;: Type '/help' for more options.\n"
" : : \n"
"\n"
"Using the RiveScript bot found in: {path}\n"
"Type a message to the bot and press Return to send it.\n"
.format(version=bot.VERSION(), path=args.path)
)
while True:
msg = input("You> ")
if PY2:
# For Python 2 only: cast the message to Unicode.
msg = msg.decode("utf-8")
# Commands
if msg == '/help':
print("> Supported Commands:")
print("> /help - Displays this message.")
print("> /quit - Exit the program.")
elif msg == '/quit':
exit()
else:
reply = bot.reply("localuser", msg)
print("Bot>", reply) | python | def interactive_mode():
"""The built-in RiveScript Interactive Mode.
This feature of RiveScript allows you to test and debug a chatbot in your
terminal window. There are two ways to invoke this mode::
# By running the Python RiveScript module directly:
python rivescript eg/brain
# By running the shell.py in the source distribution:
python shell.py eg/brain
The only required command line parameter is a filesystem path to a directory
containing RiveScript source files (with the ``*.rive`` file extension).
Additionally, it accepts command line flags.
Parameters:
--utf8: Enable UTF-8 mode.
--json: Use JSON to communicate with the bot instead of plain text.
See the JSON Mode documentation below for advanced details.
--debug: Enable verbose debug logging.
--log (str): The path to a text file you want the debug logging to
be written to. This is to be used in conjunction with ``--debug``,
for the case where you don't want your terminal window to be flooded
with debug messages.
--depth (int): Override the recursion depth limit (default ``50``).
--nostrict: Disable strict syntax checking when parsing the RiveScript
files. By default a syntax error raises an exception and will
terminate the interactive mode.
--help: Show the documentation of command line flags.
path (str): The path to a directory containing ``.rive`` files.
**JSON Mode**
By invoking the interactive mode with the ``--json`` (or ``-j``) flag,
the interactive mode will communicate with you via JSON messages. This
can be used as a "bridge" to enable the use of RiveScript from another
programming language that doesn't have its own native RiveScript
implementation.
For example, a program could open a shell pipe to the RiveScript interactive
mode and send/receive JSON payloads to communicate with the bot.
In JSON mode, you send a message to the bot in the following format::
{
"username": "str username",
"message": "str message",
"vars": {
"topic": "random",
"name": "Alice"
}
}
The ``username`` and ``message`` keys are required, and ``vars`` is a
key/value object of all the variables about the user.
After sending the JSON payload over standard input, you can either close the
input file handle (send the EOF signal; or Ctrl-D in a terminal), or send
the string ``__END__`` on a line of text by itself. This will cause the bot
to parse your payload, get a reply for the message, and respond with a
similar JSON payload::
{
"status": "ok",
"reply": "str response",
"vars": {
"topic": "random",
"name": "Alice"
}
}
The ``vars`` structure in the response contains all of the key/value pairs
the bot knows about the username you passed in. This will also contain a
lot of internal data, such as the user's history and last matched trigger.
To keep a stateful session, you should parse the ``vars`` returned by
RiveScript and pass them in with your next request so that the bot can
remember them for the next reply.
If you closed the filehandle (Ctrl-D, EOF) after your input payload, the
interactive mode will exit after giving the response. If, on the other
hand, you sent the string ``__END__`` on a line by itself after your
payload, the RiveScript interactive mode will do the same after its response
is returned. This way, you can re-use the shell pipe to send and receive
many messages over a single session.
"""
parser = argparse.ArgumentParser(description="RiveScript interactive mode.")
parser.add_argument("--debug", "-d",
help="Enable debug logging within RiveScript.",
action="store_true",
)
parser.add_argument("--json", "-j",
help="Enable JSON mode. In this mode, you communicate with the bot by "
"sending a JSON-encoded object with keys 'username', 'message' and "
"'vars' (an object of user variables) over standard input, and "
"then close the input (^D) or send the string '__END__' on a line "
"by itself. The bot will respond with a similarly formatted JSON "
"response over standard output, and then will either exit or send "
"'__END__' depending on how you ended your input.",
action="store_true",
)
parser.add_argument("--utf8", "-u",
help="Enable UTF-8 mode (default is disabled)",
action="store_true",
)
parser.add_argument("--log",
help="The path to a log file to send debugging output to (when debug "
"mode is enabled) instead of standard output.",
type=text_type,
)
parser.add_argument("--nostrict",
help="Disable strict mode (where syntax errors are fatal)",
action="store_true",
)
parser.add_argument("--depth",
help="Override the default recursion depth limit when fetching a reply "
"(default 50)",
type=int,
default=50,
)
parser.add_argument("path",
help="A directory containing RiveScript files (*.rive) to load.",
type=text_type,
# required=True,
)
args = parser.parse_args()
# Make the bot.
bot = RiveScript(
debug=args.debug,
strict=not args.nostrict,
depth=args.depth,
utf8=args.utf8,
log=args.log
)
bot.load_directory(args.path)
bot.sort_replies()
# Interactive mode?
if args.json:
# Read from standard input.
buffer = ""
stateful = False
while True:
line = ""
try:
line = input()
except EOFError:
break
# Look for the __END__ line.
end = re.match(r'^__END__$', line)
if end:
# Process it.
stateful = True # This is a stateful session
json_in(bot, buffer, stateful)
buffer = ""
continue
else:
buffer += line + "\n"
# We got the EOF. If the session was stateful, just exit,
# otherwise process what we just read.
if stateful:
quit()
json_in(bot, buffer, stateful)
quit()
print(
" . . \n"
" .:...:: RiveScript Interpreter (Python)\n"
" .:: ::. Library Version: v{version}\n"
" ..:;;. ' .;;:.. \n"
" . ''' . Type '/quit' to quit.\n"
" :;,:,;: Type '/help' for more options.\n"
" : : \n"
"\n"
"Using the RiveScript bot found in: {path}\n"
"Type a message to the bot and press Return to send it.\n"
.format(version=bot.VERSION(), path=args.path)
)
while True:
msg = input("You> ")
if PY2:
# For Python 2 only: cast the message to Unicode.
msg = msg.decode("utf-8")
# Commands
if msg == '/help':
print("> Supported Commands:")
print("> /help - Displays this message.")
print("> /quit - Exit the program.")
elif msg == '/quit':
exit()
else:
reply = bot.reply("localuser", msg)
print("Bot>", reply) | The built-in RiveScript Interactive Mode.
This feature of RiveScript allows you to test and debug a chatbot in your
terminal window. There are two ways to invoke this mode::
# By running the Python RiveScript module directly:
python rivescript eg/brain
# By running the shell.py in the source distribution:
python shell.py eg/brain
The only required command line parameter is a filesystem path to a directory
containing RiveScript source files (with the ``*.rive`` file extension).
Additionally, it accepts command line flags.
Parameters:
--utf8: Enable UTF-8 mode.
--json: Use JSON to communicate with the bot instead of plain text.
See the JSON Mode documentation below for advanced details.
--debug: Enable verbose debug logging.
--log (str): The path to a text file you want the debug logging to
be written to. This is to be used in conjunction with ``--debug``,
for the case where you don't want your terminal window to be flooded
with debug messages.
--depth (int): Override the recursion depth limit (default ``50``).
--nostrict: Disable strict syntax checking when parsing the RiveScript
files. By default a syntax error raises an exception and will
terminate the interactive mode.
--help: Show the documentation of command line flags.
path (str): The path to a directory containing ``.rive`` files.
**JSON Mode**
By invoking the interactive mode with the ``--json`` (or ``-j``) flag,
the interactive mode will communicate with you via JSON messages. This
can be used as a "bridge" to enable the use of RiveScript from another
programming language that doesn't have its own native RiveScript
implementation.
For example, a program could open a shell pipe to the RiveScript interactive
mode and send/receive JSON payloads to communicate with the bot.
In JSON mode, you send a message to the bot in the following format::
{
"username": "str username",
"message": "str message",
"vars": {
"topic": "random",
"name": "Alice"
}
}
The ``username`` and ``message`` keys are required, and ``vars`` is a
key/value object of all the variables about the user.
After sending the JSON payload over standard input, you can either close the
input file handle (send the EOF signal; or Ctrl-D in a terminal), or send
the string ``__END__`` on a line of text by itself. This will cause the bot
to parse your payload, get a reply for the message, and respond with a
similar JSON payload::
{
"status": "ok",
"reply": "str response",
"vars": {
"topic": "random",
"name": "Alice"
}
}
The ``vars`` structure in the response contains all of the key/value pairs
the bot knows about the username you passed in. This will also contain a
lot of internal data, such as the user's history and last matched trigger.
To keep a stateful session, you should parse the ``vars`` returned by
RiveScript and pass them in with your next request so that the bot can
remember them for the next reply.
If you closed the filehandle (Ctrl-D, EOF) after your input payload, the
interactive mode will exit after giving the response. If, on the other
hand, you sent the string ``__END__`` on a line by itself after your
payload, the RiveScript interactive mode will do the same after its response
is returned. This way, you can re-use the shell pipe to send and receive
many messages over a single session. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/interactive.py#L62-L262 |
aichaos/rivescript-python | eg/json-server/server.py | reply | def reply():
"""Fetch a reply from RiveScript.
Parameters (JSON):
* username
* message
* vars
"""
params = request.json
if not params:
return jsonify({
"status": "error",
"error": "Request must be of the application/json type!",
})
username = params.get("username")
message = params.get("message")
uservars = params.get("vars", dict())
# Make sure the required params are present.
if username is None or message is None:
return jsonify({
"status": "error",
"error": "username and message are required keys",
})
# Copy and user vars from the post into RiveScript.
if type(uservars) is dict:
for key, value in uservars.items():
bot.set_uservar(username, key, value)
# Get a reply from the bot.
reply = bot.reply(username, message)
# Get all the user's vars back out of the bot to include in the response.
uservars = bot.get_uservars(username)
# Send the response.
return jsonify({
"status": "ok",
"reply": reply,
"vars": uservars,
}) | python | def reply():
"""Fetch a reply from RiveScript.
Parameters (JSON):
* username
* message
* vars
"""
params = request.json
if not params:
return jsonify({
"status": "error",
"error": "Request must be of the application/json type!",
})
username = params.get("username")
message = params.get("message")
uservars = params.get("vars", dict())
# Make sure the required params are present.
if username is None or message is None:
return jsonify({
"status": "error",
"error": "username and message are required keys",
})
# Copy and user vars from the post into RiveScript.
if type(uservars) is dict:
for key, value in uservars.items():
bot.set_uservar(username, key, value)
# Get a reply from the bot.
reply = bot.reply(username, message)
# Get all the user's vars back out of the bot to include in the response.
uservars = bot.get_uservars(username)
# Send the response.
return jsonify({
"status": "ok",
"reply": reply,
"vars": uservars,
}) | Fetch a reply from RiveScript.
Parameters (JSON):
* username
* message
* vars | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/eg/json-server/server.py#L24-L66 |
aichaos/rivescript-python | eg/json-server/server.py | index | def index(path=None):
"""On all other routes, just return an example `curl` command."""
payload = {
"username": "soandso",
"message": "Hello bot",
"vars": {
"name": "Soandso",
}
}
return Response(r"""Usage: curl -i \
-H "Content-Type: application/json" \
-X POST -d '{}' \
http://localhost:5000/reply""".format(json.dumps(payload)),
mimetype="text/plain") | python | def index(path=None):
"""On all other routes, just return an example `curl` command."""
payload = {
"username": "soandso",
"message": "Hello bot",
"vars": {
"name": "Soandso",
}
}
return Response(r"""Usage: curl -i \
-H "Content-Type: application/json" \
-X POST -d '{}' \
http://localhost:5000/reply""".format(json.dumps(payload)),
mimetype="text/plain") | On all other routes, just return an example `curl` command. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/eg/json-server/server.py#L70-L83 |
aichaos/rivescript-python | contrib/redis/rivescript_redis.py | RedisSessionManager._key | def _key(self, username, frozen=False):
"""Translate a username into a key for Redis."""
if frozen:
return self.frozen + username
return self.prefix + username | python | def _key(self, username, frozen=False):
"""Translate a username into a key for Redis."""
if frozen:
return self.frozen + username
return self.prefix + username | Translate a username into a key for Redis. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/contrib/redis/rivescript_redis.py#L42-L46 |
aichaos/rivescript-python | contrib/redis/rivescript_redis.py | RedisSessionManager._get_user | def _get_user(self, username):
"""Custom helper method to retrieve a user's data from Redis."""
data = self.client.get(self._key(username))
if data is None:
return None
return json.loads(data.decode()) | python | def _get_user(self, username):
"""Custom helper method to retrieve a user's data from Redis."""
data = self.client.get(self._key(username))
if data is None:
return None
return json.loads(data.decode()) | Custom helper method to retrieve a user's data from Redis. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/contrib/redis/rivescript_redis.py#L48-L53 |
aichaos/rivescript-python | rivescript/sorting.py | sort_trigger_set | def sort_trigger_set(triggers, exclude_previous=True, say=None):
"""Sort a group of triggers in optimal sorting order.
The optimal sorting order is, briefly:
* Atomic triggers (containing nothing but plain words and alternation
groups) are on top, with triggers containing the most words coming
first. Triggers with equal word counts are sorted by length, and then
alphabetically if they have the same length.
* Triggers containing optionals are sorted next, by word count like
atomic triggers.
* Triggers containing wildcards are next, with ``_`` (alphabetic)
wildcards on top, then ``#`` (numeric) and finally ``*``.
* At the bottom of the sorted list are triggers consisting of only a
single wildcard, in the order: ``_``, ``#``, ``*``.
Triggers that have ``{weight}`` tags are grouped together by weight
value and sorted amongst themselves. Higher weighted groups are then
ordered before lower weighted groups regardless of the normal sorting
algorithm.
Triggers that come from topics which inherit other topics are also
sorted with higher priority than triggers from the inherited topics.
Arguments:
triggers ([]str): Array of triggers to sort.
exclude_previous (bool): Create a sort buffer for 'previous' triggers.
say (function): A reference to ``RiveScript._say()`` or provide your
own function.
"""
if say is None:
say = lambda x: x
# KEEP IN MIND: the `triggers` array is composed of array elements of the form
# ["trigger text", pointer to trigger data]
# So this code will use e.g. `trig[0]` when referring to the trigger text.
# Create a list of trigger objects map.
trigger_object_list = []
for index, trig in enumerate(triggers):
if exclude_previous and trig[1]["previous"]:
continue
pattern = trig[0] # Extract only the text of the trigger, with possible tag of inherit
# See if it has a weight tag
match, weight = re.search(RE.weight, trig[0]), 0
if match: # Value of math is not None if there is a match.
weight = int(match.group(1)) # Get the weight from the tag ``{weight}``
# See if it has an inherits tag.
match = re.search(RE.inherit, pattern)
if match:
inherit = int(match.group(1)) # Get inherit value from the tag ``{inherit}``
say("\t\t\tTrigger belongs to a topic which inherits other topics: level=" + str(inherit))
triggers[index][0] = pattern = re.sub(RE.inherit, "", pattern) # Remove the inherit tag if any
else:
inherit = sys.maxsize # If not found any inherit, set it to the maximum value, to place it last in the sort
trigger_object_list.append(TriggerObj(pattern, index, weight, inherit))
# Priority order of sorting criteria:
# weight, inherit, is_empty, star, pound, under, option, wordcount, len, alphabet
sorted_list = sorted(trigger_object_list,
key=attrgetter('weight', 'inherit', 'is_empty', 'star', 'pound',
'under', 'option', 'wordcount', 'len', 'alphabet'))
return [triggers[item.index] for item in sorted_list] | python | def sort_trigger_set(triggers, exclude_previous=True, say=None):
"""Sort a group of triggers in optimal sorting order.
The optimal sorting order is, briefly:
* Atomic triggers (containing nothing but plain words and alternation
groups) are on top, with triggers containing the most words coming
first. Triggers with equal word counts are sorted by length, and then
alphabetically if they have the same length.
* Triggers containing optionals are sorted next, by word count like
atomic triggers.
* Triggers containing wildcards are next, with ``_`` (alphabetic)
wildcards on top, then ``#`` (numeric) and finally ``*``.
* At the bottom of the sorted list are triggers consisting of only a
single wildcard, in the order: ``_``, ``#``, ``*``.
Triggers that have ``{weight}`` tags are grouped together by weight
value and sorted amongst themselves. Higher weighted groups are then
ordered before lower weighted groups regardless of the normal sorting
algorithm.
Triggers that come from topics which inherit other topics are also
sorted with higher priority than triggers from the inherited topics.
Arguments:
triggers ([]str): Array of triggers to sort.
exclude_previous (bool): Create a sort buffer for 'previous' triggers.
say (function): A reference to ``RiveScript._say()`` or provide your
own function.
"""
if say is None:
say = lambda x: x
# KEEP IN MIND: the `triggers` array is composed of array elements of the form
# ["trigger text", pointer to trigger data]
# So this code will use e.g. `trig[0]` when referring to the trigger text.
# Create a list of trigger objects map.
trigger_object_list = []
for index, trig in enumerate(triggers):
if exclude_previous and trig[1]["previous"]:
continue
pattern = trig[0] # Extract only the text of the trigger, with possible tag of inherit
# See if it has a weight tag
match, weight = re.search(RE.weight, trig[0]), 0
if match: # Value of math is not None if there is a match.
weight = int(match.group(1)) # Get the weight from the tag ``{weight}``
# See if it has an inherits tag.
match = re.search(RE.inherit, pattern)
if match:
inherit = int(match.group(1)) # Get inherit value from the tag ``{inherit}``
say("\t\t\tTrigger belongs to a topic which inherits other topics: level=" + str(inherit))
triggers[index][0] = pattern = re.sub(RE.inherit, "", pattern) # Remove the inherit tag if any
else:
inherit = sys.maxsize # If not found any inherit, set it to the maximum value, to place it last in the sort
trigger_object_list.append(TriggerObj(pattern, index, weight, inherit))
# Priority order of sorting criteria:
# weight, inherit, is_empty, star, pound, under, option, wordcount, len, alphabet
sorted_list = sorted(trigger_object_list,
key=attrgetter('weight', 'inherit', 'is_empty', 'star', 'pound',
'under', 'option', 'wordcount', 'len', 'alphabet'))
return [triggers[item.index] for item in sorted_list] | Sort a group of triggers in optimal sorting order.
The optimal sorting order is, briefly:
* Atomic triggers (containing nothing but plain words and alternation
groups) are on top, with triggers containing the most words coming
first. Triggers with equal word counts are sorted by length, and then
alphabetically if they have the same length.
* Triggers containing optionals are sorted next, by word count like
atomic triggers.
* Triggers containing wildcards are next, with ``_`` (alphabetic)
wildcards on top, then ``#`` (numeric) and finally ``*``.
* At the bottom of the sorted list are triggers consisting of only a
single wildcard, in the order: ``_``, ``#``, ``*``.
Triggers that have ``{weight}`` tags are grouped together by weight
value and sorted amongst themselves. Higher weighted groups are then
ordered before lower weighted groups regardless of the normal sorting
algorithm.
Triggers that come from topics which inherit other topics are also
sorted with higher priority than triggers from the inherited topics.
Arguments:
triggers ([]str): Array of triggers to sort.
exclude_previous (bool): Create a sort buffer for 'previous' triggers.
say (function): A reference to ``RiveScript._say()`` or provide your
own function. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/sorting.py#L52-L118 |
aichaos/rivescript-python | rivescript/sorting.py | sort_list | def sort_list(items):
"""Sort a simple list by number of words and length."""
# Track by number of words.
track = {}
def by_length(word1, word2):
return len(word2) - len(word1)
# Loop through each item.
for item in items:
# Count the words.
cword = utils.word_count(item, all=True)
if cword not in track:
track[cword] = []
track[cword].append(item)
# Sort them.
output = []
for count in sorted(track.keys(), reverse=True):
sort = sorted(track[count], key=len, reverse=True)
output.extend(sort)
return output | python | def sort_list(items):
"""Sort a simple list by number of words and length."""
# Track by number of words.
track = {}
def by_length(word1, word2):
return len(word2) - len(word1)
# Loop through each item.
for item in items:
# Count the words.
cword = utils.word_count(item, all=True)
if cword not in track:
track[cword] = []
track[cword].append(item)
# Sort them.
output = []
for count in sorted(track.keys(), reverse=True):
sort = sorted(track[count], key=len, reverse=True)
output.extend(sort)
return output | Sort a simple list by number of words and length. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/sorting.py#L120-L143 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.load_directory | def load_directory(self, directory, ext=None):
"""Load RiveScript documents from a directory.
:param str directory: The directory of RiveScript documents to load
replies from.
:param []str ext: List of file extensions to consider as RiveScript
documents. The default is ``[".rive", ".rs"]``.
"""
self._say("Loading from directory: " + directory)
if ext is None:
# Use the default extensions - .rive is preferable.
ext = ['.rive', '.rs']
elif type(ext) == str:
# Backwards compatibility for ext being a string value.
ext = [ext]
if not os.path.isdir(directory):
self._warn("Error: " + directory + " is not a directory.")
return
for root, subdirs, files in os.walk(directory):
for file in files:
for extension in ext:
if file.lower().endswith(extension):
# Load this file.
self.load_file(os.path.join(root, file))
break | python | def load_directory(self, directory, ext=None):
"""Load RiveScript documents from a directory.
:param str directory: The directory of RiveScript documents to load
replies from.
:param []str ext: List of file extensions to consider as RiveScript
documents. The default is ``[".rive", ".rs"]``.
"""
self._say("Loading from directory: " + directory)
if ext is None:
# Use the default extensions - .rive is preferable.
ext = ['.rive', '.rs']
elif type(ext) == str:
# Backwards compatibility for ext being a string value.
ext = [ext]
if not os.path.isdir(directory):
self._warn("Error: " + directory + " is not a directory.")
return
for root, subdirs, files in os.walk(directory):
for file in files:
for extension in ext:
if file.lower().endswith(extension):
# Load this file.
self.load_file(os.path.join(root, file))
break | Load RiveScript documents from a directory.
:param str directory: The directory of RiveScript documents to load
replies from.
:param []str ext: List of file extensions to consider as RiveScript
documents. The default is ``[".rive", ".rs"]``. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L165-L192 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.load_file | def load_file(self, filename):
"""Load and parse a RiveScript document.
:param str filename: The path to a RiveScript file.
"""
self._say("Loading file: " + filename)
fh = codecs.open(filename, 'r', 'utf-8')
lines = fh.readlines()
fh.close()
self._say("Parsing " + str(len(lines)) + " lines of code from " + filename)
self._parse(filename, lines) | python | def load_file(self, filename):
"""Load and parse a RiveScript document.
:param str filename: The path to a RiveScript file.
"""
self._say("Loading file: " + filename)
fh = codecs.open(filename, 'r', 'utf-8')
lines = fh.readlines()
fh.close()
self._say("Parsing " + str(len(lines)) + " lines of code from " + filename)
self._parse(filename, lines) | Load and parse a RiveScript document.
:param str filename: The path to a RiveScript file. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L194-L206 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.stream | def stream(self, code):
"""Stream in RiveScript source code dynamically.
:param code: Either a string containing RiveScript code or an array of
lines of RiveScript code.
"""
self._say("Streaming code.")
if type(code) in [str, text_type]:
code = code.split("\n")
self._parse("stream()", code) | python | def stream(self, code):
"""Stream in RiveScript source code dynamically.
:param code: Either a string containing RiveScript code or an array of
lines of RiveScript code.
"""
self._say("Streaming code.")
if type(code) in [str, text_type]:
code = code.split("\n")
self._parse("stream()", code) | Stream in RiveScript source code dynamically.
:param code: Either a string containing RiveScript code or an array of
lines of RiveScript code. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L208-L217 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript._parse | def _parse(self, fname, code):
"""Parse RiveScript code into memory.
:param str fname: The arbitrary file name used for syntax reporting.
:param []str code: Lines of RiveScript source code to parse.
"""
# Get the "abstract syntax tree"
ast = self._parser.parse(fname, code)
# Get all of the "begin" type variables: global, var, sub, person, ...
for kind, data in ast["begin"].items():
internal = getattr(self, "_" + kind) # The internal name for this attribute
for name, value in data.items():
if value == "<undef>":
del internal[name]
else:
internal[name] = value
# Precompile substitutions.
if kind in ["sub", "person"]:
self._precompile_substitution(kind, name)
# Let the scripts set the debug mode and other special globals.
if self._global.get("debug"):
self._debug = str(self._global["debug"]).lower() == "true"
if self._global.get("depth"):
self._depth = int(self._global["depth"])
# Consume all the parsed triggers.
for topic, data in ast["topics"].items():
# Keep a map of the topics that are included/inherited under this topic.
if not topic in self._includes:
self._includes[topic] = {}
if not topic in self._lineage:
self._lineage[topic] = {}
self._includes[topic].update(data["includes"])
self._lineage[topic].update(data["inherits"])
# Consume the triggers.
if not topic in self._topics:
self._topics[topic] = []
for trigger in data["triggers"]:
self._topics[topic].append(trigger)
# Precompile the regexp for this trigger.
self._precompile_regexp(trigger["trigger"])
# Does this trigger have a %Previous? If so, make a pointer to
# this exact trigger in _thats.
if trigger["previous"] is not None:
if not topic in self._thats:
self._thats[topic] = {}
if not trigger["trigger"] in self._thats[topic]:
self._thats[topic][trigger["trigger"]] = {}
self._thats[topic][trigger["trigger"]][trigger["previous"]] = trigger
# Load all the parsed objects.
for obj in ast["objects"]:
# Have a handler for it?
if obj["language"] in self._handlers:
self._objlangs[obj["name"]] = obj["language"]
self._handlers[obj["language"]].load(obj["name"], obj["code"]) | python | def _parse(self, fname, code):
"""Parse RiveScript code into memory.
:param str fname: The arbitrary file name used for syntax reporting.
:param []str code: Lines of RiveScript source code to parse.
"""
# Get the "abstract syntax tree"
ast = self._parser.parse(fname, code)
# Get all of the "begin" type variables: global, var, sub, person, ...
for kind, data in ast["begin"].items():
internal = getattr(self, "_" + kind) # The internal name for this attribute
for name, value in data.items():
if value == "<undef>":
del internal[name]
else:
internal[name] = value
# Precompile substitutions.
if kind in ["sub", "person"]:
self._precompile_substitution(kind, name)
# Let the scripts set the debug mode and other special globals.
if self._global.get("debug"):
self._debug = str(self._global["debug"]).lower() == "true"
if self._global.get("depth"):
self._depth = int(self._global["depth"])
# Consume all the parsed triggers.
for topic, data in ast["topics"].items():
# Keep a map of the topics that are included/inherited under this topic.
if not topic in self._includes:
self._includes[topic] = {}
if not topic in self._lineage:
self._lineage[topic] = {}
self._includes[topic].update(data["includes"])
self._lineage[topic].update(data["inherits"])
# Consume the triggers.
if not topic in self._topics:
self._topics[topic] = []
for trigger in data["triggers"]:
self._topics[topic].append(trigger)
# Precompile the regexp for this trigger.
self._precompile_regexp(trigger["trigger"])
# Does this trigger have a %Previous? If so, make a pointer to
# this exact trigger in _thats.
if trigger["previous"] is not None:
if not topic in self._thats:
self._thats[topic] = {}
if not trigger["trigger"] in self._thats[topic]:
self._thats[topic][trigger["trigger"]] = {}
self._thats[topic][trigger["trigger"]][trigger["previous"]] = trigger
# Load all the parsed objects.
for obj in ast["objects"]:
# Have a handler for it?
if obj["language"] in self._handlers:
self._objlangs[obj["name"]] = obj["language"]
self._handlers[obj["language"]].load(obj["name"], obj["code"]) | Parse RiveScript code into memory.
:param str fname: The arbitrary file name used for syntax reporting.
:param []str code: Lines of RiveScript source code to parse. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L219-L281 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.deparse | def deparse(self):
"""Dump the in-memory RiveScript brain as a Python data structure.
This would be useful, for example, to develop a user interface for
editing RiveScript replies without having to edit the RiveScript
source code directly.
:return dict: JSON-serializable Python data structure containing the
contents of all RiveScript replies currently loaded in memory.
"""
# Data to return.
result = {
"begin": {
"global": {},
"var": {},
"sub": {},
"person": {},
"array": {},
"triggers": [],
},
"topics": {},
}
# Populate the config fields.
if self._debug:
result["begin"]["global"]["debug"] = self._debug
if self._depth != 50:
result["begin"]["global"]["depth"] = 50
# Definitions
result["begin"]["var"] = self._var.copy()
result["begin"]["sub"] = self._sub.copy()
result["begin"]["person"] = self._person.copy()
result["begin"]["array"] = self._array.copy()
result["begin"]["global"].update(self._global.copy())
# Topic Triggers.
for topic in self._topics:
dest = None # Where to place the topic info
if topic == "__begin__":
# Begin block.
dest = result["begin"]
else:
# Normal topic.
if topic not in result["topics"]:
result["topics"][topic] = {
"triggers": [],
"includes": {},
"inherits": {},
}
dest = result["topics"][topic]
# Copy the triggers.
for trig in self._topics[topic]:
dest["triggers"].append(copy.deepcopy(trig))
# Inherits/Includes.
for label, mapping in {"inherits": self._lineage, "includes": self._includes}.items():
if topic in mapping and len(mapping[topic]):
dest[label] = mapping[topic].copy()
return result | python | def deparse(self):
"""Dump the in-memory RiveScript brain as a Python data structure.
This would be useful, for example, to develop a user interface for
editing RiveScript replies without having to edit the RiveScript
source code directly.
:return dict: JSON-serializable Python data structure containing the
contents of all RiveScript replies currently loaded in memory.
"""
# Data to return.
result = {
"begin": {
"global": {},
"var": {},
"sub": {},
"person": {},
"array": {},
"triggers": [],
},
"topics": {},
}
# Populate the config fields.
if self._debug:
result["begin"]["global"]["debug"] = self._debug
if self._depth != 50:
result["begin"]["global"]["depth"] = 50
# Definitions
result["begin"]["var"] = self._var.copy()
result["begin"]["sub"] = self._sub.copy()
result["begin"]["person"] = self._person.copy()
result["begin"]["array"] = self._array.copy()
result["begin"]["global"].update(self._global.copy())
# Topic Triggers.
for topic in self._topics:
dest = None # Where to place the topic info
if topic == "__begin__":
# Begin block.
dest = result["begin"]
else:
# Normal topic.
if topic not in result["topics"]:
result["topics"][topic] = {
"triggers": [],
"includes": {},
"inherits": {},
}
dest = result["topics"][topic]
# Copy the triggers.
for trig in self._topics[topic]:
dest["triggers"].append(copy.deepcopy(trig))
# Inherits/Includes.
for label, mapping in {"inherits": self._lineage, "includes": self._includes}.items():
if topic in mapping and len(mapping[topic]):
dest[label] = mapping[topic].copy()
return result | Dump the in-memory RiveScript brain as a Python data structure.
This would be useful, for example, to develop a user interface for
editing RiveScript replies without having to edit the RiveScript
source code directly.
:return dict: JSON-serializable Python data structure containing the
contents of all RiveScript replies currently loaded in memory. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L283-L346 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.write | def write(self, fh, deparsed=None):
"""Write the currently parsed RiveScript data into a file.
Pass either a file name (string) or a file handle object.
This uses ``deparse()`` to dump a representation of the loaded data and
writes it to the destination file. If you provide your own data as the
``deparsed`` argument, it will use that data instead of calling
``deparse()`` itself. This way you can use ``deparse()``, edit the data,
and use that to write the RiveScript document (for example, to be used
by a user interface for editing RiveScript without writing the code
directly).
Parameters:
fh (str or file): a string or a file-like object.
deparsed (dict): a data structure in the same format as what
``deparse()`` returns. If not passed, this value will come from
the current in-memory data from ``deparse()``.
"""
# Passed a string instead of a file handle?
if type(fh) is str:
fh = codecs.open(fh, "w", "utf-8")
# Deparse the loaded data.
if deparsed is None:
deparsed = self.deparse()
# Start at the beginning.
fh.write("// Written by rivescript.deparse()\n")
fh.write("! version = 2.0\n\n")
# Variables of all sorts!
for kind in ["global", "var", "sub", "person", "array"]:
if len(deparsed["begin"][kind].keys()) == 0:
continue
for var in sorted(deparsed["begin"][kind].keys()):
# Array types need to be separated by either spaces or pipes.
data = deparsed["begin"][kind][var]
if type(data) not in [str, text_type]:
needs_pipes = False
for test in data:
if " " in test:
needs_pipes = True
break
# Word-wrap the result, target width is 78 chars minus the
# kind, var, and spaces and equals sign.
# TODO: not implemented yet.
# width = 78 - len(kind) - len(var) - 4
if needs_pipes:
data = self._write_wrapped("|".join(data), sep="|")
else:
data = " ".join(data)
fh.write("! {kind} {var} = {data}\n".format(
kind=kind,
var=var,
data=data,
))
fh.write("\n")
# Begin block.
if len(deparsed["begin"]["triggers"]):
fh.write("> begin\n\n")
self._write_triggers(fh, deparsed["begin"]["triggers"], indent="\t")
fh.write("< begin\n\n")
# The topics. Random first!
topics = ["random"]
topics.extend(sorted(deparsed["topics"].keys()))
done_random = False
for topic in topics:
if topic not in deparsed["topics"]: continue
if topic == "random" and done_random: continue
if topic == "random": done_random = True
tagged = False # Used > topic tag
data = deparsed["topics"][topic]
if topic != "random" or len(data["includes"]) or len(data["inherits"]):
tagged = True
fh.write("> topic " + topic)
if data["inherits"]:
fh.write(" inherits " + " ".join(sorted(data["inherits"].keys())))
if data["includes"]:
fh.write(" includes " + " ".join(sorted(data["includes"].keys())))
fh.write("\n\n")
indent = "\t" if tagged else ""
self._write_triggers(fh, data["triggers"], indent=indent)
if tagged:
fh.write("< topic\n\n")
return True | python | def write(self, fh, deparsed=None):
"""Write the currently parsed RiveScript data into a file.
Pass either a file name (string) or a file handle object.
This uses ``deparse()`` to dump a representation of the loaded data and
writes it to the destination file. If you provide your own data as the
``deparsed`` argument, it will use that data instead of calling
``deparse()`` itself. This way you can use ``deparse()``, edit the data,
and use that to write the RiveScript document (for example, to be used
by a user interface for editing RiveScript without writing the code
directly).
Parameters:
fh (str or file): a string or a file-like object.
deparsed (dict): a data structure in the same format as what
``deparse()`` returns. If not passed, this value will come from
the current in-memory data from ``deparse()``.
"""
# Passed a string instead of a file handle?
if type(fh) is str:
fh = codecs.open(fh, "w", "utf-8")
# Deparse the loaded data.
if deparsed is None:
deparsed = self.deparse()
# Start at the beginning.
fh.write("// Written by rivescript.deparse()\n")
fh.write("! version = 2.0\n\n")
# Variables of all sorts!
for kind in ["global", "var", "sub", "person", "array"]:
if len(deparsed["begin"][kind].keys()) == 0:
continue
for var in sorted(deparsed["begin"][kind].keys()):
# Array types need to be separated by either spaces or pipes.
data = deparsed["begin"][kind][var]
if type(data) not in [str, text_type]:
needs_pipes = False
for test in data:
if " " in test:
needs_pipes = True
break
# Word-wrap the result, target width is 78 chars minus the
# kind, var, and spaces and equals sign.
# TODO: not implemented yet.
# width = 78 - len(kind) - len(var) - 4
if needs_pipes:
data = self._write_wrapped("|".join(data), sep="|")
else:
data = " ".join(data)
fh.write("! {kind} {var} = {data}\n".format(
kind=kind,
var=var,
data=data,
))
fh.write("\n")
# Begin block.
if len(deparsed["begin"]["triggers"]):
fh.write("> begin\n\n")
self._write_triggers(fh, deparsed["begin"]["triggers"], indent="\t")
fh.write("< begin\n\n")
# The topics. Random first!
topics = ["random"]
topics.extend(sorted(deparsed["topics"].keys()))
done_random = False
for topic in topics:
if topic not in deparsed["topics"]: continue
if topic == "random" and done_random: continue
if topic == "random": done_random = True
tagged = False # Used > topic tag
data = deparsed["topics"][topic]
if topic != "random" or len(data["includes"]) or len(data["inherits"]):
tagged = True
fh.write("> topic " + topic)
if data["inherits"]:
fh.write(" inherits " + " ".join(sorted(data["inherits"].keys())))
if data["includes"]:
fh.write(" includes " + " ".join(sorted(data["includes"].keys())))
fh.write("\n\n")
indent = "\t" if tagged else ""
self._write_triggers(fh, data["triggers"], indent=indent)
if tagged:
fh.write("< topic\n\n")
return True | Write the currently parsed RiveScript data into a file.
Pass either a file name (string) or a file handle object.
This uses ``deparse()`` to dump a representation of the loaded data and
writes it to the destination file. If you provide your own data as the
``deparsed`` argument, it will use that data instead of calling
``deparse()`` itself. This way you can use ``deparse()``, edit the data,
and use that to write the RiveScript document (for example, to be used
by a user interface for editing RiveScript without writing the code
directly).
Parameters:
fh (str or file): a string or a file-like object.
deparsed (dict): a data structure in the same format as what
``deparse()`` returns. If not passed, this value will come from
the current in-memory data from ``deparse()``. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L348-L448 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript._write_triggers | def _write_triggers(self, fh, triggers, indent=""):
"""Write triggers to a file handle.
Parameters:
fh (file): file object.
triggers (list): list of triggers to write.
indent (str): indentation for each line.
"""
for trig in triggers:
fh.write(indent + "+ " + self._write_wrapped(trig["trigger"], indent=indent) + "\n")
d = trig
if d.get("previous"):
fh.write(indent + "% " + self._write_wrapped(d["previous"], indent=indent) + "\n")
for cond in d["condition"]:
fh.write(indent + "* " + self._write_wrapped(cond, indent=indent) + "\n")
if d.get("redirect"):
fh.write(indent + "@ " + self._write_wrapped(d["redirect"], indent=indent) + "\n")
for reply in d["reply"]:
fh.write(indent + "- " + self._write_wrapped(reply, indent=indent) + "\n")
fh.write("\n") | python | def _write_triggers(self, fh, triggers, indent=""):
"""Write triggers to a file handle.
Parameters:
fh (file): file object.
triggers (list): list of triggers to write.
indent (str): indentation for each line.
"""
for trig in triggers:
fh.write(indent + "+ " + self._write_wrapped(trig["trigger"], indent=indent) + "\n")
d = trig
if d.get("previous"):
fh.write(indent + "% " + self._write_wrapped(d["previous"], indent=indent) + "\n")
for cond in d["condition"]:
fh.write(indent + "* " + self._write_wrapped(cond, indent=indent) + "\n")
if d.get("redirect"):
fh.write(indent + "@ " + self._write_wrapped(d["redirect"], indent=indent) + "\n")
for reply in d["reply"]:
fh.write(indent + "- " + self._write_wrapped(reply, indent=indent) + "\n")
fh.write("\n") | Write triggers to a file handle.
Parameters:
fh (file): file object.
triggers (list): list of triggers to write.
indent (str): indentation for each line. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L450-L475 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript._write_wrapped | def _write_wrapped(self, line, sep=" ", indent="", width=78):
"""Word-wrap a line of RiveScript code for being written to a file.
:param str line: The original line of text to word-wrap.
:param str sep: The word separator.
:param str indent: The indentation to use (as a set of spaces).
:param int width: The character width to constrain each line to.
:return str: The reformatted line(s)."""
words = line.split(sep)
lines = []
line = ""
buf = []
while len(words):
buf.append(words.pop(0))
line = sep.join(buf)
if len(line) > width:
# Need to word wrap!
words.insert(0, buf.pop()) # Undo
lines.append(sep.join(buf))
buf = []
line = ""
# Straggler?
if line:
lines.append(line)
# Returned output
result = lines.pop(0)
if len(lines):
eol = ""
if sep == " ":
eol = "\s"
for item in lines:
result += eol + "\n" + indent + "^ " + item
return result | python | def _write_wrapped(self, line, sep=" ", indent="", width=78):
"""Word-wrap a line of RiveScript code for being written to a file.
:param str line: The original line of text to word-wrap.
:param str sep: The word separator.
:param str indent: The indentation to use (as a set of spaces).
:param int width: The character width to constrain each line to.
:return str: The reformatted line(s)."""
words = line.split(sep)
lines = []
line = ""
buf = []
while len(words):
buf.append(words.pop(0))
line = sep.join(buf)
if len(line) > width:
# Need to word wrap!
words.insert(0, buf.pop()) # Undo
lines.append(sep.join(buf))
buf = []
line = ""
# Straggler?
if line:
lines.append(line)
# Returned output
result = lines.pop(0)
if len(lines):
eol = ""
if sep == " ":
eol = "\s"
for item in lines:
result += eol + "\n" + indent + "^ " + item
return result | Word-wrap a line of RiveScript code for being written to a file.
:param str line: The original line of text to word-wrap.
:param str sep: The word separator.
:param str indent: The indentation to use (as a set of spaces).
:param int width: The character width to constrain each line to.
:return str: The reformatted line(s). | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L477-L515 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.sort_replies | def sort_replies(self, thats=False):
"""Sort the loaded triggers in memory.
After you have finished loading your RiveScript code, call this method
to populate the various internal sort buffers. This is absolutely
necessary for reply matching to work efficiently!
"""
# (Re)initialize the sort cache.
self._sorted["topics"] = {}
self._sorted["thats"] = {}
self._say("Sorting triggers...")
# Loop through all the topics.
for topic in self._topics.keys():
self._say("Analyzing topic " + topic)
# Collect a list of all the triggers we're going to worry about.
# If this topic inherits another topic, we need to recursively add
# those to the list as well.
alltrig = inherit_utils.get_topic_triggers(self, topic, False)
# Sort them.
self._sorted["topics"][topic] = sorting.sort_trigger_set(alltrig, True, self._say)
# Get all of the %Previous triggers for this topic.
that_triggers = inherit_utils.get_topic_triggers(self, topic, True)
# And sort them, too.
self._sorted["thats"][topic] = sorting.sort_trigger_set(that_triggers, False, self._say)
# And sort the substitution lists.
if not "lists" in self._sorted:
self._sorted["lists"] = {}
self._sorted["lists"]["sub"] = sorting.sort_list(self._sub.keys())
self._sorted["lists"]["person"] = sorting.sort_list(self._person.keys()) | python | def sort_replies(self, thats=False):
"""Sort the loaded triggers in memory.
After you have finished loading your RiveScript code, call this method
to populate the various internal sort buffers. This is absolutely
necessary for reply matching to work efficiently!
"""
# (Re)initialize the sort cache.
self._sorted["topics"] = {}
self._sorted["thats"] = {}
self._say("Sorting triggers...")
# Loop through all the topics.
for topic in self._topics.keys():
self._say("Analyzing topic " + topic)
# Collect a list of all the triggers we're going to worry about.
# If this topic inherits another topic, we need to recursively add
# those to the list as well.
alltrig = inherit_utils.get_topic_triggers(self, topic, False)
# Sort them.
self._sorted["topics"][topic] = sorting.sort_trigger_set(alltrig, True, self._say)
# Get all of the %Previous triggers for this topic.
that_triggers = inherit_utils.get_topic_triggers(self, topic, True)
# And sort them, too.
self._sorted["thats"][topic] = sorting.sort_trigger_set(that_triggers, False, self._say)
# And sort the substitution lists.
if not "lists" in self._sorted:
self._sorted["lists"] = {}
self._sorted["lists"]["sub"] = sorting.sort_list(self._sub.keys())
self._sorted["lists"]["person"] = sorting.sort_list(self._person.keys()) | Sort the loaded triggers in memory.
After you have finished loading your RiveScript code, call this method
to populate the various internal sort buffers. This is absolutely
necessary for reply matching to work efficiently! | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L521-L555 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.set_handler | def set_handler(self, language, obj):
"""Define a custom language handler for RiveScript objects.
Pass in a ``None`` value for the object to delete an existing handler (for
example, to prevent Python code from being able to be run by default).
Look in the ``eg`` folder of the rivescript-python distribution for
an example script that sets up a JavaScript language handler.
:param str language: The lowercased name of the programming language.
Examples: python, javascript, perl
:param class obj: An instance of an implementation class object.
It should provide the following interface::
class MyObjectHandler:
def __init__(self):
pass
def load(self, name, code):
# name = the name of the object from the RiveScript code
# code = the source code of the object
def call(self, rs, name, fields):
# rs = the current RiveScript interpreter object
# name = the name of the object being called
# fields = array of arguments passed to the object
return reply
"""
# Allow them to delete a handler too.
if obj is None:
if language in self._handlers:
del self._handlers[language]
else:
self._handlers[language] = obj | python | def set_handler(self, language, obj):
"""Define a custom language handler for RiveScript objects.
Pass in a ``None`` value for the object to delete an existing handler (for
example, to prevent Python code from being able to be run by default).
Look in the ``eg`` folder of the rivescript-python distribution for
an example script that sets up a JavaScript language handler.
:param str language: The lowercased name of the programming language.
Examples: python, javascript, perl
:param class obj: An instance of an implementation class object.
It should provide the following interface::
class MyObjectHandler:
def __init__(self):
pass
def load(self, name, code):
# name = the name of the object from the RiveScript code
# code = the source code of the object
def call(self, rs, name, fields):
# rs = the current RiveScript interpreter object
# name = the name of the object being called
# fields = array of arguments passed to the object
return reply
"""
# Allow them to delete a handler too.
if obj is None:
if language in self._handlers:
del self._handlers[language]
else:
self._handlers[language] = obj | Define a custom language handler for RiveScript objects.
Pass in a ``None`` value for the object to delete an existing handler (for
example, to prevent Python code from being able to be run by default).
Look in the ``eg`` folder of the rivescript-python distribution for
an example script that sets up a JavaScript language handler.
:param str language: The lowercased name of the programming language.
Examples: python, javascript, perl
:param class obj: An instance of an implementation class object.
It should provide the following interface::
class MyObjectHandler:
def __init__(self):
pass
def load(self, name, code):
# name = the name of the object from the RiveScript code
# code = the source code of the object
def call(self, rs, name, fields):
# rs = the current RiveScript interpreter object
# name = the name of the object being called
# fields = array of arguments passed to the object
return reply | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L561-L593 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.set_subroutine | def set_subroutine(self, name, code):
"""Define a Python object from your program.
This is equivalent to having an object defined in the RiveScript code,
except your Python code is defining it instead.
:param str name: The name of the object macro.
:param def code: A Python function with a method signature of
``(rs, args)``
This method is only available if there is a Python handler set up
(which there is by default, unless you've called
``set_handler("python", None)``).
"""
# Do we have a Python handler?
if 'python' in self._handlers:
self._handlers['python']._objects[name] = code
self._objlangs[name] = 'python'
else:
self._warn("Can't set_subroutine: no Python object handler!") | python | def set_subroutine(self, name, code):
"""Define a Python object from your program.
This is equivalent to having an object defined in the RiveScript code,
except your Python code is defining it instead.
:param str name: The name of the object macro.
:param def code: A Python function with a method signature of
``(rs, args)``
This method is only available if there is a Python handler set up
(which there is by default, unless you've called
``set_handler("python", None)``).
"""
# Do we have a Python handler?
if 'python' in self._handlers:
self._handlers['python']._objects[name] = code
self._objlangs[name] = 'python'
else:
self._warn("Can't set_subroutine: no Python object handler!") | Define a Python object from your program.
This is equivalent to having an object defined in the RiveScript code,
except your Python code is defining it instead.
:param str name: The name of the object macro.
:param def code: A Python function with a method signature of
``(rs, args)``
This method is only available if there is a Python handler set up
(which there is by default, unless you've called
``set_handler("python", None)``). | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L595-L615 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.set_global | def set_global(self, name, value):
"""Set a global variable.
Equivalent to ``! global`` in RiveScript code.
:param str name: The name of the variable to set.
:param str value: The value of the variable.
Set this to ``None`` to delete the variable.
"""
if value is None:
# Unset the variable.
if name in self._global:
del self._global[name]
self._global[name] = value | python | def set_global(self, name, value):
"""Set a global variable.
Equivalent to ``! global`` in RiveScript code.
:param str name: The name of the variable to set.
:param str value: The value of the variable.
Set this to ``None`` to delete the variable.
"""
if value is None:
# Unset the variable.
if name in self._global:
del self._global[name]
self._global[name] = value | Set a global variable.
Equivalent to ``! global`` in RiveScript code.
:param str name: The name of the variable to set.
:param str value: The value of the variable.
Set this to ``None`` to delete the variable. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L617-L630 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.set_variable | def set_variable(self, name, value):
"""Set a bot variable.
Equivalent to ``! var`` in RiveScript code.
:param str name: The name of the variable to set.
:param str value: The value of the variable.
Set this to ``None`` to delete the variable.
"""
if value is None:
# Unset the variable.
if name in self._var:
del self._var[name]
self._var[name] = value | python | def set_variable(self, name, value):
"""Set a bot variable.
Equivalent to ``! var`` in RiveScript code.
:param str name: The name of the variable to set.
:param str value: The value of the variable.
Set this to ``None`` to delete the variable.
"""
if value is None:
# Unset the variable.
if name in self._var:
del self._var[name]
self._var[name] = value | Set a bot variable.
Equivalent to ``! var`` in RiveScript code.
:param str name: The name of the variable to set.
:param str value: The value of the variable.
Set this to ``None`` to delete the variable. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L640-L653 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.set_substitution | def set_substitution(self, what, rep):
"""Set a substitution.
Equivalent to ``! sub`` in RiveScript code.
:param str what: The original text to replace.
:param str rep: The text to replace it with.
Set this to ``None`` to delete the substitution.
"""
if rep is None:
# Unset the variable.
if what in self._subs:
del self._subs[what]
self._subs[what] = rep | python | def set_substitution(self, what, rep):
"""Set a substitution.
Equivalent to ``! sub`` in RiveScript code.
:param str what: The original text to replace.
:param str rep: The text to replace it with.
Set this to ``None`` to delete the substitution.
"""
if rep is None:
# Unset the variable.
if what in self._subs:
del self._subs[what]
self._subs[what] = rep | Set a substitution.
Equivalent to ``! sub`` in RiveScript code.
:param str what: The original text to replace.
:param str rep: The text to replace it with.
Set this to ``None`` to delete the substitution. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L663-L676 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.set_person | def set_person(self, what, rep):
"""Set a person substitution.
Equivalent to ``! person`` in RiveScript code.
:param str what: The original text to replace.
:param str rep: The text to replace it with.
Set this to ``None`` to delete the substitution.
"""
if rep is None:
# Unset the variable.
if what in self._person:
del self._person[what]
self._person[what] = rep | python | def set_person(self, what, rep):
"""Set a person substitution.
Equivalent to ``! person`` in RiveScript code.
:param str what: The original text to replace.
:param str rep: The text to replace it with.
Set this to ``None`` to delete the substitution.
"""
if rep is None:
# Unset the variable.
if what in self._person:
del self._person[what]
self._person[what] = rep | Set a person substitution.
Equivalent to ``! person`` in RiveScript code.
:param str what: The original text to replace.
:param str rep: The text to replace it with.
Set this to ``None`` to delete the substitution. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L678-L691 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.set_uservar | def set_uservar(self, user, name, value):
"""Set a variable for a user.
This is like the ``<set>`` tag in RiveScript code.
:param str user: The user ID to set a variable for.
:param str name: The name of the variable to set.
:param str value: The value to set there.
"""
self._session.set(user, {name: value}) | python | def set_uservar(self, user, name, value):
"""Set a variable for a user.
This is like the ``<set>`` tag in RiveScript code.
:param str user: The user ID to set a variable for.
:param str name: The name of the variable to set.
:param str value: The value to set there.
"""
self._session.set(user, {name: value}) | Set a variable for a user.
This is like the ``<set>`` tag in RiveScript code.
:param str user: The user ID to set a variable for.
:param str name: The name of the variable to set.
:param str value: The value to set there. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L693-L702 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.set_uservars | def set_uservars(self, user, data=None):
"""Set many variables for a user, or set many variables for many users.
This function can be called in two ways::
# Set a dict of variables for a single user.
rs.set_uservars(username, vars)
# Set a nested dict of variables for many users.
rs.set_uservars(many_vars)
In the first syntax, ``vars`` is a simple dict of key/value string
pairs. In the second syntax, ``many_vars`` is a structure like this::
{
"username1": {
"key": "value",
},
"username2": {
"key": "value",
},
}
This way you can export *all* user variables via ``get_uservars()``
and then re-import them all at once, instead of setting them once per
user.
:param optional str user: The user ID to set many variables for.
Skip this parameter to set many variables for many users instead.
:param dict data: The dictionary of key/value pairs for user variables,
or else a dict of dicts mapping usernames to key/value pairs.
This may raise a ``TypeError`` exception if you pass it invalid data
types. Note that only the standard ``dict`` type is accepted, but not
variants like ``OrderedDict``, so if you have a dict-like type you
should cast it to ``dict`` first.
"""
# Check the parameters to see how we're being used.
if type(user) is dict and data is None:
# Setting many variables for many users.
for uid, uservars in user.items():
if type(uservars) is not dict:
raise TypeError(
"In set_uservars(many_vars) syntax, the many_vars dict "
"must be in the format of `many_vars['username'] = "
"dict(key=value)`, but the contents of many_vars['{}']"
" is not a dict.".format(uid)
)
self._session.set(uid, uservars)
elif type(user) in [text_type, str] and type(data) is dict:
# Setting variables for a single user.
self._session.set(user, data)
else:
raise TypeError(
"set_uservars() may only be called with types ({str}, dict) or "
"(dict<{str}, dict>) but you called it with types ({a}, {b})"
.format(
str="unicode" if sys.version_info[0] < 3 else "str",
a=type(user),
b=type(data),
),
) | python | def set_uservars(self, user, data=None):
"""Set many variables for a user, or set many variables for many users.
This function can be called in two ways::
# Set a dict of variables for a single user.
rs.set_uservars(username, vars)
# Set a nested dict of variables for many users.
rs.set_uservars(many_vars)
In the first syntax, ``vars`` is a simple dict of key/value string
pairs. In the second syntax, ``many_vars`` is a structure like this::
{
"username1": {
"key": "value",
},
"username2": {
"key": "value",
},
}
This way you can export *all* user variables via ``get_uservars()``
and then re-import them all at once, instead of setting them once per
user.
:param optional str user: The user ID to set many variables for.
Skip this parameter to set many variables for many users instead.
:param dict data: The dictionary of key/value pairs for user variables,
or else a dict of dicts mapping usernames to key/value pairs.
This may raise a ``TypeError`` exception if you pass it invalid data
types. Note that only the standard ``dict`` type is accepted, but not
variants like ``OrderedDict``, so if you have a dict-like type you
should cast it to ``dict`` first.
"""
# Check the parameters to see how we're being used.
if type(user) is dict and data is None:
# Setting many variables for many users.
for uid, uservars in user.items():
if type(uservars) is not dict:
raise TypeError(
"In set_uservars(many_vars) syntax, the many_vars dict "
"must be in the format of `many_vars['username'] = "
"dict(key=value)`, but the contents of many_vars['{}']"
" is not a dict.".format(uid)
)
self._session.set(uid, uservars)
elif type(user) in [text_type, str] and type(data) is dict:
# Setting variables for a single user.
self._session.set(user, data)
else:
raise TypeError(
"set_uservars() may only be called with types ({str}, dict) or "
"(dict<{str}, dict>) but you called it with types ({a}, {b})"
.format(
str="unicode" if sys.version_info[0] < 3 else "str",
a=type(user),
b=type(data),
),
) | Set many variables for a user, or set many variables for many users.
This function can be called in two ways::
# Set a dict of variables for a single user.
rs.set_uservars(username, vars)
# Set a nested dict of variables for many users.
rs.set_uservars(many_vars)
In the first syntax, ``vars`` is a simple dict of key/value string
pairs. In the second syntax, ``many_vars`` is a structure like this::
{
"username1": {
"key": "value",
},
"username2": {
"key": "value",
},
}
This way you can export *all* user variables via ``get_uservars()``
and then re-import them all at once, instead of setting them once per
user.
:param optional str user: The user ID to set many variables for.
Skip this parameter to set many variables for many users instead.
:param dict data: The dictionary of key/value pairs for user variables,
or else a dict of dicts mapping usernames to key/value pairs.
This may raise a ``TypeError`` exception if you pass it invalid data
types. Note that only the standard ``dict`` type is accepted, but not
variants like ``OrderedDict``, so if you have a dict-like type you
should cast it to ``dict`` first. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L704-L769 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.get_uservar | def get_uservar(self, user, name):
"""Get a variable about a user.
:param str user: The user ID to look up a variable for.
:param str name: The name of the variable to get.
:return: The user variable, or ``None`` or ``"undefined"``:
* If the user has no data at all, this returns ``None``.
* If the user doesn't have this variable set, this returns the
string ``"undefined"``.
* Otherwise this returns the string value of the variable.
"""
if name == '__lastmatch__': # Treat var `__lastmatch__` since it can't receive "undefined" value
return self.last_match(user)
else:
return self._session.get(user, name) | python | def get_uservar(self, user, name):
"""Get a variable about a user.
:param str user: The user ID to look up a variable for.
:param str name: The name of the variable to get.
:return: The user variable, or ``None`` or ``"undefined"``:
* If the user has no data at all, this returns ``None``.
* If the user doesn't have this variable set, this returns the
string ``"undefined"``.
* Otherwise this returns the string value of the variable.
"""
if name == '__lastmatch__': # Treat var `__lastmatch__` since it can't receive "undefined" value
return self.last_match(user)
else:
return self._session.get(user, name) | Get a variable about a user.
:param str user: The user ID to look up a variable for.
:param str name: The name of the variable to get.
:return: The user variable, or ``None`` or ``"undefined"``:
* If the user has no data at all, this returns ``None``.
* If the user doesn't have this variable set, this returns the
string ``"undefined"``.
* Otherwise this returns the string value of the variable. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L771-L787 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.get_uservars | def get_uservars(self, user=None):
"""Get all variables about a user (or all users).
:param optional str user: The user ID to retrieve all variables for.
If not passed, this function will return all data for all users.
:return dict: All the user variables.
* If a ``user`` was passed, this is a ``dict`` of key/value pairs
of that user's variables. If the user doesn't exist in memory,
this returns ``None``.
* Otherwise, this returns a ``dict`` of key/value pairs that map
user IDs to their variables (a ``dict`` of ``dict``).
"""
if user is None:
# All the users!
return self._session.get_all()
else:
# Just this one!
return self._session.get_any(user) | python | def get_uservars(self, user=None):
"""Get all variables about a user (or all users).
:param optional str user: The user ID to retrieve all variables for.
If not passed, this function will return all data for all users.
:return dict: All the user variables.
* If a ``user`` was passed, this is a ``dict`` of key/value pairs
of that user's variables. If the user doesn't exist in memory,
this returns ``None``.
* Otherwise, this returns a ``dict`` of key/value pairs that map
user IDs to their variables (a ``dict`` of ``dict``).
"""
if user is None:
# All the users!
return self._session.get_all()
else:
# Just this one!
return self._session.get_any(user) | Get all variables about a user (or all users).
:param optional str user: The user ID to retrieve all variables for.
If not passed, this function will return all data for all users.
:return dict: All the user variables.
* If a ``user`` was passed, this is a ``dict`` of key/value pairs
of that user's variables. If the user doesn't exist in memory,
this returns ``None``.
* Otherwise, this returns a ``dict`` of key/value pairs that map
user IDs to their variables (a ``dict`` of ``dict``). | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L789-L809 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.clear_uservars | def clear_uservars(self, user=None):
"""Delete all variables about a user (or all users).
:param str user: The user ID to clear variables for, or else clear all
variables for all users if not provided.
"""
if user is None:
# All the users!
self._session.reset_all()
else:
# Just this one.
self._session.reset(user) | python | def clear_uservars(self, user=None):
"""Delete all variables about a user (or all users).
:param str user: The user ID to clear variables for, or else clear all
variables for all users if not provided.
"""
if user is None:
# All the users!
self._session.reset_all()
else:
# Just this one.
self._session.reset(user) | Delete all variables about a user (or all users).
:param str user: The user ID to clear variables for, or else clear all
variables for all users if not provided. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L811-L823 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.trigger_info | def trigger_info(self, trigger=None, dump=False):
"""Get information about a trigger.
Pass in a raw trigger to find out what file name and line number it
appeared at. This is useful for e.g. tracking down the location of the
trigger last matched by the user via ``last_match()``. Returns a list
of matching triggers, containing their topics, filenames and line
numbers. Returns ``None`` if there weren't any matches found.
The keys in the trigger info is as follows:
* ``category``: Either 'topic' (for normal) or 'thats'
(for %Previous triggers)
* ``topic``: The topic name
* ``trigger``: The raw trigger text
* ``filename``: The filename the trigger was found in.
* ``lineno``: The line number the trigger was found on.
Pass in a true value for ``dump``, and the entire syntax tracking
tree is returned.
:param str trigger: The raw trigger text to look up.
:param bool dump: Whether to dump the entire syntax tracking tree.
:return: A list of matching triggers or ``None`` if no matches.
"""
if dump:
return self._syntax
response = None
# Search the syntax tree for the trigger.
for category in self._syntax:
for topic in self._syntax[category]:
if trigger in self._syntax[category][topic]:
# We got a match!
if response is None:
response = list()
fname, lineno = self._syntax[category][topic][trigger]['trigger']
response.append(dict(
category=category,
topic=topic,
trigger=trigger,
filename=fname,
line=lineno,
))
return response | python | def trigger_info(self, trigger=None, dump=False):
"""Get information about a trigger.
Pass in a raw trigger to find out what file name and line number it
appeared at. This is useful for e.g. tracking down the location of the
trigger last matched by the user via ``last_match()``. Returns a list
of matching triggers, containing their topics, filenames and line
numbers. Returns ``None`` if there weren't any matches found.
The keys in the trigger info is as follows:
* ``category``: Either 'topic' (for normal) or 'thats'
(for %Previous triggers)
* ``topic``: The topic name
* ``trigger``: The raw trigger text
* ``filename``: The filename the trigger was found in.
* ``lineno``: The line number the trigger was found on.
Pass in a true value for ``dump``, and the entire syntax tracking
tree is returned.
:param str trigger: The raw trigger text to look up.
:param bool dump: Whether to dump the entire syntax tracking tree.
:return: A list of matching triggers or ``None`` if no matches.
"""
if dump:
return self._syntax
response = None
# Search the syntax tree for the trigger.
for category in self._syntax:
for topic in self._syntax[category]:
if trigger in self._syntax[category][topic]:
# We got a match!
if response is None:
response = list()
fname, lineno = self._syntax[category][topic][trigger]['trigger']
response.append(dict(
category=category,
topic=topic,
trigger=trigger,
filename=fname,
line=lineno,
))
return response | Get information about a trigger.
Pass in a raw trigger to find out what file name and line number it
appeared at. This is useful for e.g. tracking down the location of the
trigger last matched by the user via ``last_match()``. Returns a list
of matching triggers, containing their topics, filenames and line
numbers. Returns ``None`` if there weren't any matches found.
The keys in the trigger info is as follows:
* ``category``: Either 'topic' (for normal) or 'thats'
(for %Previous triggers)
* ``topic``: The topic name
* ``trigger``: The raw trigger text
* ``filename``: The filename the trigger was found in.
* ``lineno``: The line number the trigger was found on.
Pass in a true value for ``dump``, and the entire syntax tracking
tree is returned.
:param str trigger: The raw trigger text to look up.
:param bool dump: Whether to dump the entire syntax tracking tree.
:return: A list of matching triggers or ``None`` if no matches. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L858-L905 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.current_user | def current_user(self):
"""Retrieve the user ID of the current user talking to your bot.
This is mostly useful inside of a Python object macro to get the user
ID of the person who caused the object macro to be invoked (i.e. to
set a variable for that user from within the object).
This will return ``None`` if used outside of the context of getting a
reply (the value is unset at the end of the ``reply()`` method).
"""
if self._brain._current_user is None:
# They're doing it wrong.
self._warn("current_user() is meant to be used from within a Python object macro!")
return self._brain._current_user | python | def current_user(self):
"""Retrieve the user ID of the current user talking to your bot.
This is mostly useful inside of a Python object macro to get the user
ID of the person who caused the object macro to be invoked (i.e. to
set a variable for that user from within the object).
This will return ``None`` if used outside of the context of getting a
reply (the value is unset at the end of the ``reply()`` method).
"""
if self._brain._current_user is None:
# They're doing it wrong.
self._warn("current_user() is meant to be used from within a Python object macro!")
return self._brain._current_user | Retrieve the user ID of the current user talking to your bot.
This is mostly useful inside of a Python object macro to get the user
ID of the person who caused the object macro to be invoked (i.e. to
set a variable for that user from within the object).
This will return ``None`` if used outside of the context of getting a
reply (the value is unset at the end of the ``reply()`` method). | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L907-L920 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript.reply | def reply(self, user, msg, errors_as_replies=True):
"""Fetch a reply from the RiveScript brain.
Arguments:
user (str): A unique user ID for the person requesting a reply.
This could be e.g. a screen name or nickname. It's used internally
to store user variables (including topic and history), so if your
bot has multiple users each one should have a unique ID.
msg (str): The user's message. This is allowed to contain
punctuation and such, but any extraneous data such as HTML tags
should be removed in advance.
errors_as_replies (bool): When errors are encountered (such as a
deep recursion error, no reply matched, etc.) this will make the
reply be a text representation of the error message. If you set
this to ``False``, errors will instead raise an exception, such as
a ``DeepRecursionError`` or ``NoReplyError``. By default, no
exceptions are raised and errors are set in the reply instead.
Returns:
str: The reply output.
"""
return self._brain.reply(user, msg, errors_as_replies) | python | def reply(self, user, msg, errors_as_replies=True):
"""Fetch a reply from the RiveScript brain.
Arguments:
user (str): A unique user ID for the person requesting a reply.
This could be e.g. a screen name or nickname. It's used internally
to store user variables (including topic and history), so if your
bot has multiple users each one should have a unique ID.
msg (str): The user's message. This is allowed to contain
punctuation and such, but any extraneous data such as HTML tags
should be removed in advance.
errors_as_replies (bool): When errors are encountered (such as a
deep recursion error, no reply matched, etc.) this will make the
reply be a text representation of the error message. If you set
this to ``False``, errors will instead raise an exception, such as
a ``DeepRecursionError`` or ``NoReplyError``. By default, no
exceptions are raised and errors are set in the reply instead.
Returns:
str: The reply output.
"""
return self._brain.reply(user, msg, errors_as_replies) | Fetch a reply from the RiveScript brain.
Arguments:
user (str): A unique user ID for the person requesting a reply.
This could be e.g. a screen name or nickname. It's used internally
to store user variables (including topic and history), so if your
bot has multiple users each one should have a unique ID.
msg (str): The user's message. This is allowed to contain
punctuation and such, but any extraneous data such as HTML tags
should be removed in advance.
errors_as_replies (bool): When errors are encountered (such as a
deep recursion error, no reply matched, etc.) this will make the
reply be a text representation of the error message. If you set
this to ``False``, errors will instead raise an exception, such as
a ``DeepRecursionError`` or ``NoReplyError``. By default, no
exceptions are raised and errors are set in the reply instead.
Returns:
str: The reply output. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L926-L947 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript._precompile_substitution | def _precompile_substitution(self, kind, pattern):
"""Pre-compile the regexp for a substitution pattern.
This will speed up the substitutions that happen at the beginning of
the reply fetching process. With the default brain, this took the
time for _substitute down from 0.08s to 0.02s
:param str kind: One of ``sub``, ``person``.
:param str pattern: The substitution pattern.
"""
if pattern not in self._regexc[kind]:
qm = re.escape(pattern)
self._regexc[kind][pattern] = {
"qm": qm,
"sub1": re.compile(r'^' + qm + r'$'),
"sub2": re.compile(r'^' + qm + r'(\W+)'),
"sub3": re.compile(r'(\W+)' + qm + r'(\W+)'),
"sub4": re.compile(r'(\W+)' + qm + r'$'),
} | python | def _precompile_substitution(self, kind, pattern):
"""Pre-compile the regexp for a substitution pattern.
This will speed up the substitutions that happen at the beginning of
the reply fetching process. With the default brain, this took the
time for _substitute down from 0.08s to 0.02s
:param str kind: One of ``sub``, ``person``.
:param str pattern: The substitution pattern.
"""
if pattern not in self._regexc[kind]:
qm = re.escape(pattern)
self._regexc[kind][pattern] = {
"qm": qm,
"sub1": re.compile(r'^' + qm + r'$'),
"sub2": re.compile(r'^' + qm + r'(\W+)'),
"sub3": re.compile(r'(\W+)' + qm + r'(\W+)'),
"sub4": re.compile(r'(\W+)' + qm + r'$'),
} | Pre-compile the regexp for a substitution pattern.
This will speed up the substitutions that happen at the beginning of
the reply fetching process. With the default brain, this took the
time for _substitute down from 0.08s to 0.02s
:param str kind: One of ``sub``, ``person``.
:param str pattern: The substitution pattern. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L949-L967 |
aichaos/rivescript-python | rivescript/rivescript.py | RiveScript._precompile_regexp | def _precompile_regexp(self, trigger):
"""Precompile the regex for most triggers.
If the trigger is non-atomic, and doesn't include dynamic tags like
``<bot>``, ``<get>``, ``<input>/<reply>`` or arrays, it can be
precompiled and save time when matching.
:param str trigger: The trigger text to attempt to precompile.
"""
if utils.is_atomic(trigger):
return # Don't need a regexp for atomic triggers.
# Check for dynamic tags.
for tag in ["@", "<bot", "<get", "<input", "<reply"]:
if tag in trigger:
return # Can't precompile this trigger.
self._regexc["trigger"][trigger] = self._brain.reply_regexp(None, trigger) | python | def _precompile_regexp(self, trigger):
"""Precompile the regex for most triggers.
If the trigger is non-atomic, and doesn't include dynamic tags like
``<bot>``, ``<get>``, ``<input>/<reply>`` or arrays, it can be
precompiled and save time when matching.
:param str trigger: The trigger text to attempt to precompile.
"""
if utils.is_atomic(trigger):
return # Don't need a regexp for atomic triggers.
# Check for dynamic tags.
for tag in ["@", "<bot", "<get", "<input", "<reply"]:
if tag in trigger:
return # Can't precompile this trigger.
self._regexc["trigger"][trigger] = self._brain.reply_regexp(None, trigger) | Precompile the regex for most triggers.
If the trigger is non-atomic, and doesn't include dynamic tags like
``<bot>``, ``<get>``, ``<input>/<reply>`` or arrays, it can be
precompiled and save time when matching.
:param str trigger: The trigger text to attempt to precompile. | https://github.com/aichaos/rivescript-python/blob/b55c820cf02a194605fd66af1f070e239f84ed31/rivescript/rivescript.py#L969-L986 |
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