# coding:utf-8
from ksvd import ApproximateKSVD
from sklearn.decomposition import DictionaryLearning
from sklearn.utils.testing import assert_array_almost_equal
import numpy as np
import scipy as sp
from scipy.linalg import norm
def test_initialize_with_small_n_features():
N = 500
n_components = 128
n_features = 64
X = np.random.randn(N, n_features)
dico = ApproximateKSVD(n_components=n_components)
dico.fit(X)
def test_fit():
np.random.seed(0)
N = 1000
L = 64
n_features = 128
B = np.array(sp.sparse.random(N, L, density=0.5).todense())
D = np.random.randn(L, n_features)
X = np.dot(B, D)
dico = ApproximateKSVD(n_components=L, transform_n_nonzero_coefs=L)
dico.fit(X)
gamma = dico.transform(X)
assert_array_almost_equal(X, gamma.dot(dico.components_))
def test_size():
np.random.seed(0)
N = 50
L = 12
n_features = 16
D = np.random.randn(L, n_features)
B = np.array(sp.sparse.random(N, L, density=0.5).todense())
X = np.dot(B, D)
dico1 = ApproximateKSVD(n_components=L, transform_n_nonzero_coefs=L)
dico1.fit(X)
gamma1 = dico1.transform(X)
e1 = norm(X - gamma1.dot(dico1.components_))
dico2 = DictionaryLearning(n_components=L, transform_n_nonzero_coefs=L)
dico2.fit(X)
gamma2 = dico2.transform(X)
e2 = norm(X - gamma2.dot(dico2.components_))
assert dico1.components_.shape == dico2.components_.shape
assert gamma1.shape == gamma2.shape
assert e1 < e2
