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')