def result_is_positive_matrix(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix) ok_(numpy.amin(w) >= 0, 'W of a matrix is not positive') ok_(numpy.amin(h) >= 0, 'H of a matrix is not positive') matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix) ok_(numpy.amin(w) >= 0, 'W of zero matrix is not positive') ok_(numpy.amin(h) >= 0, 'W of zero matrix is not positive')
def result_is_positive_matrix_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, distance="kl") ok_(numpy.amin(w) >= 0, 'W of a matrix is not positive') ok_(numpy.amin(h) >= 0, 'H of a matrix is not positive') matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix, distance="kl") ok_(numpy.amin(w) >= 0, 'W of zero matrix is not positive') ok_(numpy.amin(h) >= 0, 'W of zero matrix is not positive')
def result_is_positive_matrix(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix) ok_(numpy.amin(w) >= 0, 'W of a matrix is not positive') ok_(numpy.amin(h) >= 0, 'H of a matrix is not positive') matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix) ok_(numpy.amin(w) >= 0, 'W of zero matrix is not positive') ok_(numpy.amin(h) >= 0, 'W of zero matrix is not positive')
def result_is_positive_matrix_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, distance="kl") ok_(numpy.amin(w) >= 0, 'W of a matrix is not positive') ok_(numpy.amin(h) >= 0, 'H of a matrix is not positive') matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix, distance="kl") ok_(numpy.amin(w) >= 0, 'W of zero matrix is not positive') ok_(numpy.amin(h) >= 0, 'W of zero matrix is not positive')
def result_is_positive_matrix_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") w_min = numpy.amin(w) h_min = numpy.amin(h) ok_(w_min >= 0, 'W of zero matrix is not positive (%s < 0)' % w_min) ok_(h_min >= 0, 'H of zero matrix is not positive (%s < 0)' % h_min) matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") w_min = numpy.amin(w) h_min = numpy.amin(h) ok_(w_min >= 0, 'W of zero matrix is not positive (%s < 0)' % w_min) ok_(h_min >= 0, 'H of zero matrix is not positive (%s < 0)' % h_min)
def can_approx_original_matrix_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, distance="kl") diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff / 100.0 < 1, 'NMF cannot apporximate a matrix (%s > 1.)' % diff)
def can_treat_matrix_without_errors_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") ok_(True, 'Failed to decomposit a matrix')
def can_approx_zero_matrix_with_beta_divergence_with_random_init(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, init=nmf.random_init, max_iter=1000, distance="beta") diff += numpy.amax(w.dot(h)) ok_(diff/100.0 < 0.2, 'NMF cannot apporoximate zero matrix (%s > 0.2)' % diff)
def can_approx_zero_matrix(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, max_iter=1000) diff += numpy.amax(w.dot(h)) ok_(diff/100.0 < 0.12, 'NMF cannot apporximate zero matrix (%s > 0.12)' % diff)
def can_treat_matrix_without_errors_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, distance="kl") ok_(True, 'Failed to decomposit a matrix')
def can_treat_matrix_without_errors(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix) ok_(True, 'Failed to decomposit a matrix')
def can_approx_zero_matrix(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, max_iter=1000) diff += numpy.amax(w.dot(h)) ok_(diff / 100.0 < 0.12, 'NMF cannot apporximate zero matrix (%s > 0.12)' % diff)
def can_approx_original_matrix(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix) diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff / 100.0 < 0.12, 'NMF cannot apporximate a matrix (%s > 0.12)' % diff)
def can_reduce_dimension_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, 2, distance="kl") eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct')
def can_approx_zero_matrix_with_kl_divergent(): matrix = numpy.zeros((4, 3)) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, max_iter=1000, distance="kl") diff += numpy.amax(w.dot(h)) ok_(diff / 100.0 < 0.2, 'NMF cannot apporoximate zero matrix (%s > 0.2)' % diff)
def can_reduce_dimension(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, 2) eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct')
def can_reduce_dimension_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, dim=2, init=nmf.random_init, distance="beta") eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct')
def result_is_positive_matrix_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") w_min = numpy.amin(w) h_min = numpy.amin(h) ok_(w_min >= 0, 'W of zero matrix is not positive (%s < 0)' % w_min) ok_(h_min >= 0, 'H of zero matrix is not positive (%s < 0)' % h_min) matrix = numpy.zeros((4, 3)) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") w_min = numpy.amin(w) h_min = numpy.amin(h) ok_(w_min >= 0, 'W of zero matrix is not positive (%s < 0)' % w_min) ok_(h_min >= 0, 'H of zero matrix is not positive (%s < 0)' % h_min)
def can_approx_original_matrix(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix) diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff/100.0 < 0.12, 'NMF cannot apporximate a matrix (%s > 0.12)' % diff)
def can_approx_original_matrix_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) diff = 0.0 for _ in range(100): w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") diff += numpy.amax(abs(matrix - w.dot(h))) ok_(diff/100.0 < 1, 'NMF cannot apporximate a matrix (%s > 1.0)' % diff)
def can_treat_matrix_without_errors_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, init=nmf.random_init, distance="beta") ok_(True, 'Failed to decomposit a matrix')
def can_reduce_dimension_with_beta_divergence_with_random_init(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, dim=2, init=nmf.random_init, distance="beta") eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct')
def can_reduce_dimension(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, 2) eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct')
def can_treat_matrix_without_errors_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, distance="kl") ok_(True, 'Failed to decomposit a matrix')
def can_reduce_dimension_with_kl_divergent(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix, 2, distance="kl") eq_(w.shape, (4, 2), 'dim(W) is not correct') eq_(h.shape, (2, 3), 'dim(W) is not correct')
def can_treat_matrix_without_errors(): matrix = numpy.array([[1, 2, 3], [0, 1, 7], [7, 8, 1], [9, 0, 1]]) w, h = nmf.nmf(matrix) ok_(True, 'Failed to decomposit a matrix')