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| import warnings | |
| # 2018-05-29, PendingDeprecationWarning added to matrix.__new__ | |
| # 2020-01-23, numpy 1.19.0 PendingDeprecatonWarning | |
| warnings.warn("Importing from numpy.matlib is deprecated since 1.19.0. " | |
| "The matrix subclass is not the recommended way to represent " | |
| "matrices or deal with linear algebra (see " | |
| "https://docs.scipy.org/doc/numpy/user/numpy-for-matlab-users.html). " | |
| "Please adjust your code to use regular ndarray. ", | |
| PendingDeprecationWarning, stacklevel=2) | |
| import numpy as np | |
| from numpy.matrixlib.defmatrix import matrix, asmatrix | |
| # Matlib.py contains all functions in the numpy namespace with a few | |
| # replacements. See doc/source/reference/routines.matlib.rst for details. | |
| # Need * as we're copying the numpy namespace. | |
| from numpy import * # noqa: F403 | |
| __version__ = np.__version__ | |
| __all__ = np.__all__[:] # copy numpy namespace | |
| __all__ += ['rand', 'randn', 'repmat'] | |
| def empty(shape, dtype=None, order='C'): | |
| """Return a new matrix of given shape and type, without initializing entries. | |
| Parameters | |
| ---------- | |
| shape : int or tuple of int | |
| Shape of the empty matrix. | |
| dtype : data-type, optional | |
| Desired output data-type. | |
| order : {'C', 'F'}, optional | |
| Whether to store multi-dimensional data in row-major | |
| (C-style) or column-major (Fortran-style) order in | |
| memory. | |
| See Also | |
| -------- | |
| empty_like, zeros | |
| Notes | |
| ----- | |
| `empty`, unlike `zeros`, does not set the matrix values to zero, | |
| and may therefore be marginally faster. On the other hand, it requires | |
| the user to manually set all the values in the array, and should be | |
| used with caution. | |
| Examples | |
| -------- | |
| >>> import numpy.matlib | |
| >>> np.matlib.empty((2, 2)) # filled with random data | |
| matrix([[ 6.76425276e-320, 9.79033856e-307], # random | |
| [ 7.39337286e-309, 3.22135945e-309]]) | |
| >>> np.matlib.empty((2, 2), dtype=int) | |
| matrix([[ 6600475, 0], # random | |
| [ 6586976, 22740995]]) | |
| """ | |
| return ndarray.__new__(matrix, shape, dtype, order=order) | |
| def ones(shape, dtype=None, order='C'): | |
| """ | |
| Matrix of ones. | |
| Return a matrix of given shape and type, filled with ones. | |
| Parameters | |
| ---------- | |
| shape : {sequence of ints, int} | |
| Shape of the matrix | |
| dtype : data-type, optional | |
| The desired data-type for the matrix, default is np.float64. | |
| order : {'C', 'F'}, optional | |
| Whether to store matrix in C- or Fortran-contiguous order, | |
| default is 'C'. | |
| Returns | |
| ------- | |
| out : matrix | |
| Matrix of ones of given shape, dtype, and order. | |
| See Also | |
| -------- | |
| ones : Array of ones. | |
| matlib.zeros : Zero matrix. | |
| Notes | |
| ----- | |
| If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``, | |
| `out` becomes a single row matrix of shape ``(1,N)``. | |
| Examples | |
| -------- | |
| >>> np.matlib.ones((2,3)) | |
| matrix([[1., 1., 1.], | |
| [1., 1., 1.]]) | |
| >>> np.matlib.ones(2) | |
| matrix([[1., 1.]]) | |
| """ | |
| a = ndarray.__new__(matrix, shape, dtype, order=order) | |
| a.fill(1) | |
| return a | |
| def zeros(shape, dtype=None, order='C'): | |
| """ | |
| Return a matrix of given shape and type, filled with zeros. | |
| Parameters | |
| ---------- | |
| shape : int or sequence of ints | |
| Shape of the matrix | |
| dtype : data-type, optional | |
| The desired data-type for the matrix, default is float. | |
| order : {'C', 'F'}, optional | |
| Whether to store the result in C- or Fortran-contiguous order, | |
| default is 'C'. | |
| Returns | |
| ------- | |
| out : matrix | |
| Zero matrix of given shape, dtype, and order. | |
| See Also | |
| -------- | |
| numpy.zeros : Equivalent array function. | |
| matlib.ones : Return a matrix of ones. | |
| Notes | |
| ----- | |
| If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``, | |
| `out` becomes a single row matrix of shape ``(1,N)``. | |
| Examples | |
| -------- | |
| >>> import numpy.matlib | |
| >>> np.matlib.zeros((2, 3)) | |
| matrix([[0., 0., 0.], | |
| [0., 0., 0.]]) | |
| >>> np.matlib.zeros(2) | |
| matrix([[0., 0.]]) | |
| """ | |
| a = ndarray.__new__(matrix, shape, dtype, order=order) | |
| a.fill(0) | |
| return a | |
| def identity(n,dtype=None): | |
| """ | |
| Returns the square identity matrix of given size. | |
| Parameters | |
| ---------- | |
| n : int | |
| Size of the returned identity matrix. | |
| dtype : data-type, optional | |
| Data-type of the output. Defaults to ``float``. | |
| Returns | |
| ------- | |
| out : matrix | |
| `n` x `n` matrix with its main diagonal set to one, | |
| and all other elements zero. | |
| See Also | |
| -------- | |
| numpy.identity : Equivalent array function. | |
| matlib.eye : More general matrix identity function. | |
| Examples | |
| -------- | |
| >>> import numpy.matlib | |
| >>> np.matlib.identity(3, dtype=int) | |
| matrix([[1, 0, 0], | |
| [0, 1, 0], | |
| [0, 0, 1]]) | |
| """ | |
| a = array([1]+n*[0], dtype=dtype) | |
| b = empty((n, n), dtype=dtype) | |
| b.flat = a | |
| return b | |
| def eye(n,M=None, k=0, dtype=float, order='C'): | |
| """ | |
| Return a matrix with ones on the diagonal and zeros elsewhere. | |
| Parameters | |
| ---------- | |
| n : int | |
| Number of rows in the output. | |
| M : int, optional | |
| Number of columns in the output, defaults to `n`. | |
| k : int, optional | |
| Index of the diagonal: 0 refers to the main diagonal, | |
| a positive value refers to an upper diagonal, | |
| and a negative value to a lower diagonal. | |
| dtype : dtype, optional | |
| Data-type of the returned matrix. | |
| order : {'C', 'F'}, optional | |
| Whether the output should be stored in row-major (C-style) or | |
| column-major (Fortran-style) order in memory. | |
| .. versionadded:: 1.14.0 | |
| Returns | |
| ------- | |
| I : matrix | |
| A `n` x `M` matrix where all elements are equal to zero, | |
| except for the `k`-th diagonal, whose values are equal to one. | |
| See Also | |
| -------- | |
| numpy.eye : Equivalent array function. | |
| identity : Square identity matrix. | |
| Examples | |
| -------- | |
| >>> import numpy.matlib | |
| >>> np.matlib.eye(3, k=1, dtype=float) | |
| matrix([[0., 1., 0.], | |
| [0., 0., 1.], | |
| [0., 0., 0.]]) | |
| """ | |
| return asmatrix(np.eye(n, M=M, k=k, dtype=dtype, order=order)) | |
| def rand(*args): | |
| """ | |
| Return a matrix of random values with given shape. | |
| Create a matrix of the given shape and propagate it with | |
| random samples from a uniform distribution over ``[0, 1)``. | |
| Parameters | |
| ---------- | |
| \\*args : Arguments | |
| Shape of the output. | |
| If given as N integers, each integer specifies the size of one | |
| dimension. | |
| If given as a tuple, this tuple gives the complete shape. | |
| Returns | |
| ------- | |
| out : ndarray | |
| The matrix of random values with shape given by `\\*args`. | |
| See Also | |
| -------- | |
| randn, numpy.random.RandomState.rand | |
| Examples | |
| -------- | |
| >>> np.random.seed(123) | |
| >>> import numpy.matlib | |
| >>> np.matlib.rand(2, 3) | |
| matrix([[0.69646919, 0.28613933, 0.22685145], | |
| [0.55131477, 0.71946897, 0.42310646]]) | |
| >>> np.matlib.rand((2, 3)) | |
| matrix([[0.9807642 , 0.68482974, 0.4809319 ], | |
| [0.39211752, 0.34317802, 0.72904971]]) | |
| If the first argument is a tuple, other arguments are ignored: | |
| >>> np.matlib.rand((2, 3), 4) | |
| matrix([[0.43857224, 0.0596779 , 0.39804426], | |
| [0.73799541, 0.18249173, 0.17545176]]) | |
| """ | |
| if isinstance(args[0], tuple): | |
| args = args[0] | |
| return asmatrix(np.random.rand(*args)) | |
| def randn(*args): | |
| """ | |
| Return a random matrix with data from the "standard normal" distribution. | |
| `randn` generates a matrix filled with random floats sampled from a | |
| univariate "normal" (Gaussian) distribution of mean 0 and variance 1. | |
| Parameters | |
| ---------- | |
| \\*args : Arguments | |
| Shape of the output. | |
| If given as N integers, each integer specifies the size of one | |
| dimension. If given as a tuple, this tuple gives the complete shape. | |
| Returns | |
| ------- | |
| Z : matrix of floats | |
| A matrix of floating-point samples drawn from the standard normal | |
| distribution. | |
| See Also | |
| -------- | |
| rand, numpy.random.RandomState.randn | |
| Notes | |
| ----- | |
| For random samples from :math:`N(\\mu, \\sigma^2)`, use: | |
| ``sigma * np.matlib.randn(...) + mu`` | |
| Examples | |
| -------- | |
| >>> np.random.seed(123) | |
| >>> import numpy.matlib | |
| >>> np.matlib.randn(1) | |
| matrix([[-1.0856306]]) | |
| >>> np.matlib.randn(1, 2, 3) | |
| matrix([[ 0.99734545, 0.2829785 , -1.50629471], | |
| [-0.57860025, 1.65143654, -2.42667924]]) | |
| Two-by-four matrix of samples from :math:`N(3, 6.25)`: | |
| >>> 2.5 * np.matlib.randn((2, 4)) + 3 | |
| matrix([[1.92771843, 6.16484065, 0.83314899, 1.30278462], | |
| [2.76322758, 6.72847407, 1.40274501, 1.8900451 ]]) | |
| """ | |
| if isinstance(args[0], tuple): | |
| args = args[0] | |
| return asmatrix(np.random.randn(*args)) | |
| def repmat(a, m, n): | |
| """ | |
| Repeat a 0-D to 2-D array or matrix MxN times. | |
| Parameters | |
| ---------- | |
| a : array_like | |
| The array or matrix to be repeated. | |
| m, n : int | |
| The number of times `a` is repeated along the first and second axes. | |
| Returns | |
| ------- | |
| out : ndarray | |
| The result of repeating `a`. | |
| Examples | |
| -------- | |
| >>> import numpy.matlib | |
| >>> a0 = np.array(1) | |
| >>> np.matlib.repmat(a0, 2, 3) | |
| array([[1, 1, 1], | |
| [1, 1, 1]]) | |
| >>> a1 = np.arange(4) | |
| >>> np.matlib.repmat(a1, 2, 2) | |
| array([[0, 1, 2, 3, 0, 1, 2, 3], | |
| [0, 1, 2, 3, 0, 1, 2, 3]]) | |
| >>> a2 = np.asmatrix(np.arange(6).reshape(2, 3)) | |
| >>> np.matlib.repmat(a2, 2, 3) | |
| matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2], | |
| [3, 4, 5, 3, 4, 5, 3, 4, 5], | |
| [0, 1, 2, 0, 1, 2, 0, 1, 2], | |
| [3, 4, 5, 3, 4, 5, 3, 4, 5]]) | |
| """ | |
| a = asanyarray(a) | |
| ndim = a.ndim | |
| if ndim == 0: | |
| origrows, origcols = (1, 1) | |
| elif ndim == 1: | |
| origrows, origcols = (1, a.shape[0]) | |
| else: | |
| origrows, origcols = a.shape | |
| rows = origrows * m | |
| cols = origcols * n | |
| c = a.reshape(1, a.size).repeat(m, 0).reshape(rows, origcols).repeat(n, 0) | |
| return c.reshape(rows, cols) | |