joelouismarino

keras_top_inds = keras_act[0].argsort()[::-1][:5]
zip(keras_act[0][keras_top_inds], labels[keras_top_inds])

joelouismarino

import numpy as np

n_samples = 500
mean = 15
std_dev = 5

X = np.random.normal(mean, std_dev, n_samples)
Z = (X - np.mean(X)) / np.std(X)

joelouismarino

import numpy as np

n_samples = 500
mean_1 = 15
std_dev_1 = 5
mean_2 = -20
std_dev_2 = 3

X = np.concatenate([np.random.normal(mean_1, std_dev_1, n_samples / 2),
                                 np.random.normal(mean_2, std_dev_2, n_samples / 2)], axis=0)

joelouismarino

import numpy as np


def whiten(X, method='zca'):
    """
    Whitens the input matrix X using specified whitening method.
    Inputs:
        X:      Input data matrix with data examples along the first dimension

        method: Whitening method. Must be one of 'zca', 'zca_cor', 'pca',

joelouismarino

import math
import torch
from torch.distributions import Normal

# standard univariate Gaussian (Normal)
mean = torch.zeros(1)
std = torch.ones(1)

# evaluate at the origin
value = torch.zeros(1)

joelouismarino

import math
import torch
from torch.distributions import Normal

# standard univariate Gaussian (Normal)
mean = torch.zeros(1)
std = torch.ones(1)

# evaluate from -0.5 to 0.5
x_min = -0.5 * torch.ones(1)