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import numpy as np
def get_cindex(Y, P):
summ = 0
pair = 0
for i in range(1, len(Y)):
for j in range(0, i):
if i is not j:
if(Y[i] > Y[j]):
pair +=1
summ += 1* (P[i] > P[j]) + 0.5 * (P[i] == P[j])
if pair is not 0:
return summ/pair
else:
return 0
def r_squared_error(y_obs,y_pred):
y_obs = np.array(y_obs)
y_pred = np.array(y_pred)
y_obs_mean = [np.mean(y_obs) for y in y_obs]
y_pred_mean = [np.mean(y_pred) for y in y_pred]
mult = sum((y_pred - y_pred_mean) * (y_obs - y_obs_mean))
mult = mult * mult
y_obs_sq = sum((y_obs - y_obs_mean)*(y_obs - y_obs_mean))
y_pred_sq = sum((y_pred - y_pred_mean) * (y_pred - y_pred_mean) )
return mult / float(y_obs_sq * y_pred_sq)
def get_k(y_obs,y_pred):
y_obs = np.array(y_obs)
y_pred = np.array(y_pred)
return sum(y_obs*y_pred) / float(sum(y_pred*y_pred))
def squared_error_zero(y_obs,y_pred):
k = get_k(y_obs,y_pred)
y_obs = np.array(y_obs)
y_pred = np.array(y_pred)
y_obs_mean = [np.mean(y_obs) for y in y_obs]
upp = sum((y_obs - (k*y_pred)) * (y_obs - (k* y_pred)))
down= sum((y_obs - y_obs_mean)*(y_obs - y_obs_mean))
return 1 - (upp / float(down))
def get_rm2(ys_orig,ys_line):
r2 = r_squared_error(ys_orig, ys_line)
r02 = squared_error_zero(ys_orig, ys_line)
return r2 * (1 - np.sqrt(np.absolute((r2*r2)-(r02*r02)))) |