def test_lu_arbitrary(self):
a_sym = tests.generate_pos_dif(1000, -1000, 1000)
assert_array_almost_equal(lu_block_slow.lu_block(a_sym, 42)[0], sp.lu(a_sym)[1], decimal=12)
assert_array_almost_equal(lu_block_slow.lu_block(a_sym, 42)[1], sp.lu(a_sym)[2], decimal=8)
U = np.zeros((m, m))
for k in range(0, m, r):
PLU = lu_partial(A[k:, k:k+r])
small_P = PLU[0]
small_P_t = invert_array(small_P)
P[k:] = permute_array(small_P, P[k:])
L[k:, k:k+r] = PLU[1]
U[k:k+r, k:k+r] = PLU[2][:r, :r]
L[k:, :k] = permute_rows(small_P_t, L[k:, :k])
A[k:, k:] = permute_rows(small_P_t, A[k:, k:])
U[k:k+r, k+r:] = row_substitution(L[k:k+r, k:k+r], A[k:k+r, k+r:])
A[k+r:, k+r:] -= np.dot(L[k+r:, k:k+r], U[k:k+r, k+r:])
return P_to_Pmatrix(P), L, U
rand_int_matrix = np.random.randint(-1000, 1000, size=(6, 6))
a_sym = tests.generate_pos_dif(4, -1000, 1000)
#print sp.lu(rand_int_matrix[:, 2:])[0]
#print sp.lu(rand_int_matrix[2:, :])[0]
matrix = np.array([[-874, -965, 18, -71],
[230, -457, -817, -508],
[570, -781, -109, -751],
[-4, -497, -630, 230]])
matrix2 = np.array([[11, 12, 13, 14],
[21, 22, 23, 24],
[31, 32, 33, 34],
[41, 42, 43, 44]])
plal = np.array([[0, 1, 0, 0],
[0, 0, 0, 1],
