def setUp(self): self.k = 4 p = np.zeros(10) q = np.zeros(10) p[0:-1] = 0.5 q[1:] = 0.5 p[4] = 0.01 q[6] = 0.1 self.bdc = birth_death_chain(q, p, sparse=True) self.mu = self.bdc.stationary_distribution self.T = self.bdc.transition_matrix """Test matrix-vector product against spectral decomposition""" R, D, L = rdl_decomposition(self.T, k=self.k) self.L = L self.R = R self.ts = timescales(self.T, k=self.k) self.times = np.array([1, 5, 10, 20, 100]) ev = np.diagonal(D) self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis] """Observable""" obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 self.obs = obs1 """Initial distribution""" w0 = np.zeros(10) w0[0:4] = 0.25 self.p0 = w0
def setUp(self): self.k = 4 p = np.zeros(10) q = np.zeros(10) p[0:-1] = 0.5 q[1:] = 0.5 p[4] = 0.01 q[6] = 0.1 self.bdc = birth_death_chain(q, p, sparse=True) self.mu = self.bdc.stationary_distribution self.T = self.bdc.transition_matrix R, D, L = rdl_decomposition(self.T, k=self.k) self.L = L self.R = R self.ts = timescales(self.T, k=self.k) self.times = np.array([1, 5, 10, 20, 100]) ev = np.diagonal(D) self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis] obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 obs2 = np.zeros(10) obs2[8] = 1 obs2[9] = 1 self.obs1 = obs1 self.obs2 = obs2 self.one_vec = np.ones(10)
def setUp(self): self.k = 4 p = np.zeros(10) q = np.zeros(10) p[0:-1] = 0.5 q[1:] = 0.5 p[4] = 0.01 q[6] = 0.1 self.bdc = birth_death_chain(q, p, sparse=True) self.mu = self.bdc.stationary_distribution self.T = self.bdc.transition_matrix R, D, L = rdl_decomposition(self.T, k=self.k) self.L = L self.R = R self.ts = timescales(self.T, k=self.k) self.times = np.array([1, 5, 10, 20]) ev = np.diagonal(D) self.ev_t = ev[np.newaxis, :]**self.times[:, np.newaxis] self.tau = 7.5 """Observables""" obs1 = np.zeros(10) obs1[0] = 1 obs1[1] = 1 obs2 = np.zeros(10) obs2[8] = 1 obs2[9] = 1 self.obs1 = obs1 self.obs2 = obs2 """Initial vector for relaxation""" w0 = np.zeros(10) w0[0:4] = 0.25 self.p0 = w0