def test_R_inf(self): self.G.add_edge(3,0) self.G.add_edge(3,0) self.G.add_edge(3,1) AF_exp = np.array([ [7/15,0], [7/30,0], [0,0]]) np.testing.assert_array_almost_equal(cp.mat_AF(self.G,self.alpha),AF_exp,7,'error in AF calculation') RF = np.array([[1],[-1]]) I = np.identity(3) # mat_A ad vec_h are used in previosus tests so if they do not fail they can be used in this test A = cp.mat_A(self.G,self.alpha) h = cp.vec_h(self.G,self.alpha) R_inf_exp = (linalg.inv(I-A)).dot(h+(AF_exp.dot(RF))) np.testing.assert_array_almost_equal(cp.R_inf(self.G,self.alpha),R_inf_exp,7,'Error in R_inf function')
def test_R_inf(self):
self.G.add_edge(3, 0)
self.G.add_edge(3, 0)
self.G.add_edge(3, 1)
AF_exp = np.array([[7 / 15, 0], [7 / 30, 0], [0, 0]])
np.testing.assert_array_almost_equal(cp.mat_AF(self.G,
self.alpha), AF_exp, 7,
'error in AF calculation')
RF = np.array([[1], [-1]])
I = np.identity(3)
# mat_A ad vec_h are used in previosus tests so if they do not fail they can be used in this test
A = cp.mat_A(self.G, self.alpha)
h = cp.vec_h(self.G, self.alpha)
R_inf_exp = (linalg.inv(I - A)).dot(h + (AF_exp.dot(RF)))
np.testing.assert_array_almost_equal(cp.R_inf(self.G,
self.alpha), R_inf_exp,
7, 'Error in R_inf function')
def test_mat_A(self):
A = np.array([[0, 0.7, 0], [0.35, 0, 0.35], [0, 0.7, 0]])
np.testing.assert_array_equal(cp.mat_A(self.G, self.alpha), A,
'error in matrix A calculation')
def test_mat_A(self): A = np.array([ [0,0.7,0], [0.35,0,0.35], [0,0.7,0]]) np.testing.assert_array_equal(cp.mat_A(self.G,self.alpha),A,'error in matrix A calculation')
