def test_is_rate_matrix(self):
K_copy = self.K.copy()
self.assertTrue(is_rate_matrix(self.K, self.tol),
"K should be evaluated as rate matrix.")
self.assertTrue(
np.allclose(self.K.data, K_copy.data)
and np.allclose(self.K.offsets, K_copy.offsets),
"object modified!")
def jump_matrix(rate_matrix):
"""Extract the jump probabilities from a rate matrix.
Parameters
----------
rate_matrix : (M, M) array_like
A transition rate matrix, with row sums equal to zero.
Returns
-------
(M, M) ndarray
The jump matrix (embedded transition matrix) derived from
`rate_matrix`.
"""
rate_matrix = np.asarray(rate_matrix)
if not msmana.is_rate_matrix(rate_matrix):
raise ValueError('matrix must be row infinitesimal stochastic')
jump_rates = -rate_matrix.diagonal()
jump_matrix = rate_matrix / jump_rates[:, np.newaxis]
np.fill_diagonal(jump_matrix, 0)
return jump_matrix
def rate_matrix(self, value):
value = np.asarray(value)
if not msmana.is_rate_matrix(value):
raise ValueError('matrix must be row infinitesimal stochastic')
self._rate_matrix = value
def test_IsRateMatrix(self):
self.assertTrue(is_rate_matrix(self.A), 'A should be a rate matrix')
# manipulate matrix so it isn't a rate matrix any more
self.A[0][0] = 3
self.assertFalse(is_rate_matrix(self.A), 'matrix is not a rate matrix')