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# 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. |
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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] |
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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 |