abrazhe

abrazhe

# A quick demo of how to produce a loglog histogram plot of very large
# amounts of data, by using log-histogram bins

import numpy as np
import matplotlib.pyplot as plt
import itertools as it

# We shall draw millions of samples from a Zipf distribution.  Using linear 
# bins this is too much data for a fast and attactive plot.

abrazhe

import numpy as np, numpy.linalg as linalg
def fast_svd(M, k):
    p = k+5
    Y = np.dot(M, np.random.normal(size=(M.shape[1],p)))
    Q,r = linalg.qr(Y)
    B = np.dot(Q.T,M)
    Uhat, s, v = linalg.svd(B, full_matrices=False)
    U = np.dot(Q, Uhat)
    return U.T[:k].T, s[:k], v[:k]

abrazhe

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