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| import numpy as np | |
| num = 1 | |
| def KL(a, b): | |
| """Calculate the Kullback Leibler divergence between a and b """ | |
| D_KL = np.nansum(np.multiply(a, np.log(np.divide(a, b+np.spacing(1)))), axis=1) | |
| return D_KL | |
| def calc_information(probTgivenXs, PYgivenTs, PXs, PYs): | |
| """Calculate the MI - I(X;T) and I(Y;T)""" | |
| PTs = np.nansum(probTgivenXs*PXs, axis=1) | |
| Ht = np.nansum(-np.dot(PTs, np.log2(PTs))) | |
| Htx = - np.nansum((np.dot(np.multiply(probTgivenXs, np.log2(probTgivenXs)), PXs))) | |
| Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs)) | |
| Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1))) | |
| IYT = Hy - Hyt | |
| ITX = Ht - Htx | |
| return ITX, IYT | |
| def calc_information_1(probTgivenXs, PYgivenTs, PXs, PYs, PTs): | |
| """Calculate the MI - I(X;T) and I(Y;T)""" | |
| #PTs = np.nansum(probTgivenXs*PXs, axis=1) | |
| Ht = np.nansum(-np.dot(PTs, np.log2(PTs+np.spacing(1)))) | |
| Htx = - np.nansum((np.dot(np.multiply(probTgivenXs, np.log2(probTgivenXs+np.spacing(1))), PXs))) | |
| Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs)) | |
| Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1))) | |
| IYT = Hy - Hyt | |
| ITX = Ht - Htx | |
| return ITX, IYT | |
| def calc_information(probTgivenXs, PYgivenTs, PXs, PYs, PTs): | |
| """Calculate the MI - I(X;T) and I(Y;T)""" | |
| #PTs = np.nansum(probTgivenXs*PXs, axis=1) | |
| t_indeces = np.nonzero(PTs) | |
| Ht = np.nansum(-np.dot(PTs, np.log2(PTs+np.spacing(1)))) | |
| Htx = - np.nansum((np.dot(np.multiply(probTgivenXs, np.log2(probTgivenXs)), PXs))) | |
| Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs)) | |
| Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1))) | |
| IYT = Hy - Hyt | |
| ITX = Ht - Htx | |
| return ITX, IYT | |
| def t_calc_information(p_x_given_t, PYgivenTs, PXs, PYs): | |
| """Calculate the MI - I(X;T) and I(Y;T)""" | |
| Hx = np.nansum(-np.dot(PXs, np.log2(PXs))) | |
| Hxt = - np.nansum((np.dot(np.multiply(p_x_given_t, np.log2(p_x_given_t)), PXs))) | |
| Hyt = - np.nansum(np.dot(PYgivenTs*np.log2(PYgivenTs+np.spacing(1)), PTs)) | |
| Hy = np.nansum(-PYs * np.log2(PYs+np.spacing(1))) | |
| IYT = Hy - Hyt | |
| ITX = Hx - Hxt | |
| return ITX, IYT |