dealt with the indent style in util.py

util/util.py

@@ -348,45 +348,45 @@ def HoeffdingRuleMarkov(beta, G, H, W, FlowNum):
 
 def ChainGen(N, beta):
     # Get the initial distribution mu_0
-        mu_0 = mu_ini(N**2)
+    mu_0 = mu_ini(N**2)
 
     # Get the original transition matrix Q
-        Q = probability_matrix_Q(N)
+    Q = probability_matrix_Q(N)
 
     # Get the new transition matrix P
-        P = P_est(Q)
+    P = P_est(Q)
 
     # Get the actual stationary distribution mu; 1 x (N**2)
-        PP = LA.matrix_power(P, 1000)
-        mu = PP[0, :]
+    PP = LA.matrix_power(P, 1000)
+    mu = PP[0, :]
 
     # Get a sample path of the Markov chain with length n_1; this path is used to estimate the stationary distribution
-        n_1 = 1000 * N * N  # the length of a sample path
-        x_1 = chain(mu_0, P, n_1)
+    n_1 = 1000 * N * N  # the length of a sample path
+    x_1 = chain(mu_0, P, n_1)
 
     # Get the estimated stationary distribution mu_1
-        mu_1 = mu_est(x_1, N)
+    mu_1 = mu_est(x_1, N)
 
     # Get the estimate of Q
-        Q_1 = Q_est(mu_1)
+    Q_1 = Q_est(mu_1)
 
     # Get the estimate of P
-        P_1 = P_est(Q_1)
+    P_1 = P_est(Q_1)
 
     # Get the estimate of the gradient
-        G_1 = G_est(Q_1)
+    G_1 = G_est(Q_1)
 
     # Get the estimate of the Hessian
-        H_1 = H_est(mu_1)
+    H_1 = H_est(mu_1)
 
     # Get the estimate of the covariance matrix
-        Sigma_1 = Sigma_est(P_1, mu_1)
+    Sigma_1 = Sigma_est(P_1, mu_1)
 
     # Get an estimated sample path of W
-        SampNum = 1000
-        W_1 = W_est(Sigma_1, SampNum)
+    SampNum = 1000
+    W_1 = W_est(Sigma_1, SampNum)
 
-        return mu_0, mu, mu_1, P, G_1, H_1, W_1
+    return mu_0, mu, mu_1, P, G_1, H_1, W_1
 
 from ..Simulator.ThresCalc import ThresSanov, ThresActual, ThresWeakConv
 def visualization(N, beta):
@@ -415,4 +415,4 @@ def visualization(N, beta):
     pylab.xlim(2 * N * N - 2, 6 * N * N + 5)
     pylab.ylim(0, 1)
     savefig('./Results/test.eps')
-    plt.show()
+    plt.show()