def test_nmf(self):
test_cases = [np.mat([[1,2,3],[2,4,6],[4,17,13]], dtype = np.double),
np.mat([[1,0,0]], dtype = np.double)]
for in_mat in test_cases:
red = Nmf(2)
d_mat = DenseMatrix(in_mat)
#wd_init, hd_init = red.random_init(d_mat)
wd_init, hd_init = red.v_col_init(d_mat)
s_mat = SparseMatrix(in_mat)
ws_init = SparseMatrix(wd_init)
hs_init = SparseMatrix(hd_init)
wd_mat, hd_mat = Linalg.nmf(d_mat, wd_init, hd_init)
ws_mat, hs_mat = Linalg.nmf(s_mat, ws_init, hs_init)
#TESTED IT AGAINST MATLAB IMPLEMENTATION - ALL GOOD
#print wd_mat.mat
#print hd_mat.mat
#print ws_mat.mat.todense()
#print hs_mat.mat.todense()
print "V:", in_mat
print "WH:", (ws_mat*hs_mat).mat.todense()
np.testing.assert_array_almost_equal(wd_mat.mat,
ws_mat.mat.todense(), 2)
np.testing.assert_array_almost_equal(hd_mat.mat,
hs_mat.mat.todense(), 2)
def test_nmf(self):
test_cases = [np.mat([[1,2,3],[2,4,6],[4,17,13]], dtype = np.double),
np.mat([[1,0,0]], dtype = np.double)]
for in_mat in test_cases:
red = Nmf(2)
d_mat = DenseMatrix(in_mat)
#wd_init, hd_init = red.random_init(d_mat)
wd_init, hd_init = red.v_col_init(d_mat)
s_mat = SparseMatrix(in_mat)
ws_init = SparseMatrix(wd_init)
hs_init = SparseMatrix(hd_init)
wd_mat, hd_mat = Linalg.nmf(d_mat, wd_init, hd_init)
ws_mat, hs_mat = Linalg.nmf(s_mat, ws_init, hs_init)
#TESTED IT AGAINST MATLAB IMPLEMENTATION - ALL GOOD
#print wd_mat.mat
#print hd_mat.mat
#print ws_mat.mat.todense()
#print hs_mat.mat.todense()
print "V:", in_mat
print "WH:", (ws_mat*hs_mat).mat.todense()
np.testing.assert_array_almost_equal(wd_mat.mat,
ws_mat.mat.todense(), 2)
np.testing.assert_array_almost_equal(hd_mat.mat,
hs_mat.mat.todense(), 2)
def apply(self, matrix_):
matrix_.assert_positive()
#w_init, h_init = self.nndsvd_init(matrix_)
w_init, h_init = self.v_col_init(matrix_)
#w_init, h_init = self.random_init(matrix_)
w, h = Linalg.nmf(matrix_, w_init, h_init)
return w, Linalg.pinv(h)
def apply(self, matrix_):
matrix_.assert_positive()
#w_init, h_init = self.nndsvd_init(matrix_)
w_init, h_init = self.v_col_init(matrix_)
#w_init, h_init = self.random_init(matrix_)
w, h = Linalg.nmf(matrix_, w_init, h_init)
return w, Linalg.pinv(h)
