ZCA whitening (http://ufldl.stanford.edu/wiki/index.php/Implementing_PCA/Whitening)

gistfile1.py

import numpy as np
from scipy import linalg
from sklearn.utils import array2d, as_float_array
from sklearn.base import TransformerMixin, BaseEstimator


class ZCA(BaseEstimator, TransformerMixin):

    def __init__(self, regularization=10**-5, copy=False):
        self.regularization = regularization
        self.copy = copy

    def fit(self, X, y=None):
        X = array2d(X)
        X = as_float_array(X, copy = self.copy)
        self.mean_ = np.mean(X, axis=0)
        X -= self.mean_
        sigma = np.dot(X.T,X) / X.shape[1]
        U, S, V = linalg.svd(sigma)
        tmp = np.dot(U, np.diag(1/np.sqrt(S+self.regularization)))
        self.components_ = np.dot(tmp, U.T)
        return self

    def transform(self, X):
        X = array2d(X)
        X_transformed = X - self.mean_
        X_transformed = np.dot(X_transformed, self.components_.T)
        return X_transformed

Hi @duschendestroyer and thank you for sharing your code!

Despite my efforts, I didn't find out how apply this method to RGB images.

According to UFLDL Tutorial and to your code, I understand that X is supposed to be a matrix (2d-array) of 0. to 1. floats with shape: (n_samples, Height * Width * 3,). Is that right?

If so, how do you transform an original dataset with shape (n_samples, Height, Witdh, 3) to (n_samples, Height * Witdh * 3) ? do you simply reshape?

"do you simply reshape?" Yes
(the answer is prob far too late but may it help anyone who stumbles on this gist via google)

How is that done in python (Im new to python)I'd be grateful if you could as well show a snippet for that.

if you are using numpy it is very easy just use https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.reshape.html

if you got the code from https://github.com/ltrottier/ZCA-Whitening-Python/blob/master/zca.py plz cite