def test_ccipca_equivalence():
array = np.random.random(size=(10, 10))
col_mean = np.mean(array, axis=1)
row_mean = np.mean(array, axis=0)
overall_mean = np.mean(array)
zero_mean_array = array - col_mean[:, np.newaxis] - row_mean[
np.newaxis, :] + overall_mean
print zero_mean_array
print np.mean(zero_mean_array, axis=0)
print np.mean(zero_mean_array, axis=1)
norm_array = normalize(zero_mean_array)
U, Sigma, Vt = np.linalg.svd(norm_array)
ccipca = CCIPCA(6, vector_size=10, amnesia=3.0)
for iteration in xrange(500):
for column in xrange(10):
ccipca.fixed_iteration(norm_array[:, column], learn=True)
a = ccipca._v[1:6] * 10
b = (U * Sigma * Sigma).T[:5]
print(np.abs(a / b))
print Sigma[:5]
print rmse(np.abs(a / b) - 1)
def test_ccipca_equivalence():
array = np.random.random(size=(10,10))
col_mean = np.mean(array, axis=1)
row_mean = np.mean(array, axis=0)
overall_mean = np.mean(array)
zero_mean_array = array - col_mean[:, np.newaxis] - row_mean[np.newaxis,:] + overall_mean
print zero_mean_array
print np.mean(zero_mean_array, axis=0)
print np.mean(zero_mean_array, axis=1)
norm_array = normalize(zero_mean_array)
U, Sigma, Vt = np.linalg.svd(norm_array)
ccipca = CCIPCA(6, vector_size=10, amnesia=3.0)
for iteration in xrange(500):
for column in xrange(10):
ccipca.fixed_iteration(norm_array[:,column], learn=True)
a = ccipca._v[1:6] * 10
b = (U * Sigma * Sigma).T[:5]
print (np.abs(a/b))
print Sigma[:5]
print rmse(np.abs(a/b)-1)
