LGTM code quality suggestions

quantecon/game_theory/__init__.py

@@ -13,5 +13,7 @@
 from .lemke_howson import lemke_howson
 from .mclennan_tourky import mclennan_tourky
 from .vertex_enumeration import vertex_enumeration, vertex_enumeration_gen
-from .game_generators import *
+from .game_generators import (
+    blotto_game, ranking_game, sgc_game, tournament_game, unit_vector_game
+)
 from .repeated_game import RepeatedGame

quantecon/lqcontrol.py

@@ -6,7 +6,6 @@
 """
 from textwrap import dedent
 import numpy as np
-from numpy import dot
 from scipy.linalg import solve
 from .matrix_eqn import solve_discrete_riccati, solve_discrete_riccati_system
 from .util import check_random_state
@@ -186,15 +185,15 @@ def update_values(self):
         Q, R, A, B, N, C = self.Q, self.R, self.A, self.B, self.N, self.C
         P, d = self.P, self.d
         # == Some useful matrices == #
-        S1 = Q + self.beta * dot(B.T, dot(P, B))
-        S2 = self.beta * dot(B.T, dot(P, A)) + N
-        S3 = self.beta * dot(A.T, dot(P, A))
+        S1 = Q + self.beta * np.dot(B.T, np.dot(P, B))
+        S2 = self.beta * np.dot(B.T, np.dot(P, A)) + N
+        S3 = self.beta * np.dot(A.T, np.dot(P, A))
         # == Compute F as (Q + B'PB)^{-1} (beta B'PA + N) == #
         self.F = solve(S1, S2)
         # === Shift P back in time one step == #
-        new_P = R - dot(S2.T, self.F) + S3
+        new_P = R - np.dot(S2.T, self.F) + S3
         # == Recalling that trace(AB) = trace(BA) == #
-        new_d = self.beta * (d + np.trace(dot(P, dot(C, C.T))))
+        new_d = self.beta * (d + np.trace(np.dot(P, np.dot(C, C.T))))
         # == Set new state == #
         self.P, self.d = new_P, new_d
 
@@ -240,15 +239,15 @@ def stationary_values(self, method='doubling'):
         P = solve_discrete_riccati(A0, B0, R, Q, N, method=method)
 
         # == Compute F == #
-        S1 = Q + self.beta * dot(B.T, dot(P, B))
-        S2 = self.beta * dot(B.T, dot(P, A)) + N
+        S1 = Q + self.beta * np.dot(B.T, np.dot(P, B))
+        S2 = self.beta * np.dot(B.T, np.dot(P, A)) + N
         F = solve(S1, S2)
 
         # == Compute d == #
         if self.beta == 1:
             d = 0
         else:
-            d = self.beta * np.trace(dot(P, dot(C, C.T))) / (1 - self.beta)
+            d = self.beta * np.trace(np.dot(P, np.dot(C, C.T))) / (1 - self.beta)
 
         # == Bind states and return values == #
         self.P, self.F, self.d = P, F, d
@@ -315,7 +314,7 @@ def compute_sequence(self, x0, ts_length=None, method='doubling',
         x_path = np.empty((self.n, T+1))
         u_path = np.empty((self.k, T))
         w_path = random_state.randn(self.j, T+1)
-        Cw_path = dot(C, w_path)
+        Cw_path = np.dot(C, w_path)
 
         # == Compute and record the sequence of policies == #
         policies = []
@@ -327,13 +326,13 @@ def compute_sequence(self, x0, ts_length=None, method='doubling',
         # == Use policy sequence to generate states and controls == #
         F = policies.pop()
         x_path[:, 0] = x0.flatten()
-        u_path[:, 0] = - dot(F, x0).flatten()
+        u_path[:, 0] = - np.dot(F, x0).flatten()
         for t in range(1, T):
             F = policies.pop()
-            Ax, Bu = dot(A, x_path[:, t-1]), dot(B, u_path[:, t-1])
+            Ax, Bu = np.dot(A, x_path[:, t-1]), np.dot(B, u_path[:, t-1])
             x_path[:, t] = Ax + Bu + Cw_path[:, t]
-            u_path[:, t] = - dot(F, x_path[:, t])
-        Ax, Bu = dot(A, x_path[:, T-1]), dot(B, u_path[:, T-1])
+            u_path[:, t] = - np.dot(F, x_path[:, t])
+        Ax, Bu = np.dot(A, x_path[:, T-1]), np.dot(B, u_path[:, T-1])
         x_path[:, T] = Ax + Bu + Cw_path[:, T]
 
         return x_path, u_path, w_path

quantecon/lqnash.py

@@ -1,5 +1,4 @@
 import numpy as np
-from numpy import dot, eye
 from scipy.linalg import solve
 from .util import check_random_state
 
@@ -107,8 +106,8 @@ def nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2,
         k_2 = B2.shape[1]
 
     random_state = check_random_state(random_state)
-    v1 = eye(k_1)
-    v2 = eye(k_2)
+    v1 = np.eye(k_1)
+    v2 = np.eye(k_2)
     P1 = np.zeros((n, n))
     P2 = np.zeros((n, n))
     F1 = random_state.randn(k_1, n)
@@ -119,28 +118,28 @@ def nnash(A, B1, B2, R1, R2, Q1, Q2, S1, S2, W1, W2, M1, M2,
         F10 = F1
         F20 = F2
 
-        G2 = solve(dot(B2.T, P2.dot(B2))+Q2, v2)
-        G1 = solve(dot(B1.T, P1.dot(B1))+Q1, v1)
-        H2 = dot(G2, B2.T.dot(P2))
-        H1 = dot(G1, B1.T.dot(P1))
+        G2 = solve(np.dot(B2.T, P2.dot(B2))+Q2, v2)
+        G1 = solve(np.dot(B1.T, P1.dot(B1))+Q1, v1)
+        H2 = np.dot(G2, B2.T.dot(P2))
+        H1 = np.dot(G1, B1.T.dot(P1))
 
         # break up the computation of F1, F2
-        F1_left = v1 - dot(H1.dot(B2)+G1.dot(M1.T),
+        F1_left = v1 - np.dot(H1.dot(B2)+G1.dot(M1.T),
                            H2.dot(B1)+G2.dot(M2.T))
-        F1_right = H1.dot(A)+G1.dot(W1.T) - dot(H1.dot(B2)+G1.dot(M1.T),
+        F1_right = H1.dot(A)+G1.dot(W1.T) - np.dot(H1.dot(B2)+G1.dot(M1.T),
                                                 H2.dot(A)+G2.dot(W2.T))
         F1 = solve(F1_left, F1_right)
-        F2 = H2.dot(A)+G2.dot(W2.T) - dot(H2.dot(B1)+G2.dot(M2.T), F1)
+        F2 = H2.dot(A)+G2.dot(W2.T) - np.dot(H2.dot(B1)+G2.dot(M2.T), F1)
 
         Lambda1 = A - B2.dot(F2)
         Lambda2 = A - B1.dot(F1)
-        Pi1 = R1 + dot(F2.T, S1.dot(F2))
-        Pi2 = R2 + dot(F1.T, S2.dot(F1))
+        Pi1 = R1 + np.dot(F2.T, S1.dot(F2))
+        Pi2 = R2 + np.dot(F1.T, S2.dot(F1))
 
-        P1 = dot(Lambda1.T, P1.dot(Lambda1)) + Pi1 - \
-             dot(dot(Lambda1.T, P1.dot(B1)) + W1 - F2.T.dot(M1), F1)
-        P2 = dot(Lambda2.T, P2.dot(Lambda2)) + Pi2 - \
-             dot(dot(Lambda2.T, P2.dot(B2)) + W2 - F1.T.dot(M2), F2)
+        P1 = np.dot(Lambda1.T, P1.dot(Lambda1)) + Pi1 - \
+             np.dot(np.dot(Lambda1.T, P1.dot(B1)) + W1 - F2.T.dot(M1), F1)
+        P2 = np.dot(Lambda2.T, P2.dot(Lambda2)) + Pi2 - \
+             np.dot(np.dot(Lambda2.T, P2.dot(B2)) + W2 - F1.T.dot(M2), F2)
 
         dd = np.max(np.abs(F10 - F1)) + np.max(np.abs(F20 - F2))
 

quantecon/matrix_eqn.py

@@ -10,7 +10,6 @@
       2. Fix warnings from checking conditioning of matrices
 """
 import numpy as np
-from numpy import dot
 from numpy.linalg import solve
 from scipy.linalg import solve_discrete_lyapunov as sp_solve_discrete_lyapunov
 from scipy.linalg import solve_discrete_are as sp_solve_discrete_are
@@ -172,19 +171,19 @@ def solve_discrete_riccati(A, B, Q, R, N=None, tolerance=1e-10, max_iter=500,
     # == Choose optimal value of gamma in R_hat = R + gamma B'B == #
     current_min = np.inf
     candidates = (0.01, 0.1, 0.25, 0.5, 1.0, 2.0, 10.0, 100.0, 10e5)
-    BB = dot(B.T, B)
-    BTA = dot(B.T, A)
+    BB = np.dot(B.T, B)
+    BTA = np.dot(B.T, A)
     for gamma in candidates:
         Z = R + gamma * BB
         cn = np.linalg.cond(Z)
         if cn * EPS < 1:
-            Q_tilde = - Q + dot(N.T, solve(Z, N + gamma * BTA)) + gamma * I
-            G0 = dot(B, solve(Z, B.T))
-            A0 = dot(I - gamma * G0, A) - dot(B, solve(Z, N))
-            H0 = gamma * dot(A.T, A0) - Q_tilde
+            Q_tilde = - Q + np.dot(N.T, solve(Z, N + gamma * BTA)) + gamma * I
+            G0 = np.dot(B, solve(Z, B.T))
+            A0 = np.dot(I - gamma * G0, A) - np.dot(B, solve(Z, N))
+            H0 = gamma * np.dot(A.T, A0) - Q_tilde
             f1 = np.linalg.cond(Z, np.inf)
             f2 = gamma * f1
-            f3 = np.linalg.cond(I + dot(G0, H0))
+            f3 = np.linalg.cond(I + np.dot(G0, H0))
             f_gamma = max(f1, f2, f3)
             if f_gamma < current_min:
                 best_gamma = gamma
@@ -199,10 +198,10 @@ def solve_discrete_riccati(A, B, Q, R, N=None, tolerance=1e-10, max_iter=500,
     R_hat = R + gamma * BB
 
     # == Initial conditions == #
-    Q_tilde = - Q + dot(N.T, solve(R_hat, N + gamma * BTA)) + gamma * I
-    G0 = dot(B, solve(R_hat, B.T))
-    A0 = dot(I - gamma * G0, A) - dot(B, solve(R_hat, N))
-    H0 = gamma * dot(A.T, A0) - Q_tilde
+    Q_tilde = - Q + np.dot(N.T, solve(R_hat, N + gamma * BTA)) + gamma * I
+    G0 = np.dot(B, solve(R_hat, B.T))
+    A0 = np.dot(I - gamma * G0, A) - np.dot(B, solve(R_hat, N))
+    H0 = gamma * np.dot(A.T, A0) - Q_tilde
     i = 1
 
     # == Main loop == #
@@ -212,9 +211,9 @@ def solve_discrete_riccati(A, B, Q, R, N=None, tolerance=1e-10, max_iter=500,
             raise ValueError(fail_msg.format(i))
 
         else:
-            A1 = dot(A0, solve(I + dot(G0, H0), A0))
-            G1 = G0 + dot(dot(A0, G0), solve(I + dot(H0, G0), A0.T))
-            H1 = H0 + dot(A0.T, solve(I + dot(H0, G0), dot(H0, A0)))
+            A1 = np.dot(A0, solve(I + np.dot(G0, H0), A0))
+            G1 = G0 + np.dot(np.dot(A0, G0), solve(I + np.dot(H0, G0), A0.T))
+            H1 = H0 + np.dot(A0.T, solve(I + np.dot(H0, G0), np.dot(H0, A0)))
 
             error = np.max(np.abs(H1 - H0))
             A0 = A1

quantecon/robustlq.py

@@ -6,7 +6,6 @@
 import numpy as np
 from .lqcontrol import LQ
 from .quadsums import var_quadratic_sum
-from numpy import dot, log, sqrt, identity, hstack, vstack, trace
 from scipy.linalg import solve, inv, det
 from .matrix_eqn import solve_discrete_lyapunov
 
@@ -108,10 +107,10 @@ def d_operator(self, P):
         """
         C, theta = self.C, self.theta
         I = np.identity(self.j)
-        S1 = dot(P, C)
-        S2 = dot(C.T, S1)
+        S1 = np.dot(P, C)
+        S2 = np.dot(C.T, S1)
 
-        dP = P + dot(S1, solve(theta * I - S2, S1.T))
+        dP = P + np.dot(S1, solve(theta * I - S2, S1.T))
 
         return dP
 
@@ -143,12 +142,12 @@ def b_operator(self, P):
 
         """
         A, B, Q, R, beta = self.A, self.B, self.Q, self.R, self.beta
-        S1 = Q + beta * dot(B.T, dot(P, B))
-        S2 = beta * dot(B.T, dot(P, A))
-        S3 = beta * dot(A.T, dot(P, A))
+        S1 = Q + beta * np.dot(B.T, np.dot(P, B))
+        S2 = beta * np.dot(B.T, np.dot(P, A))
+        S3 = beta * np.dot(A.T, np.dot(P, A))
         F = solve(S1, S2) if not self.pure_forecasting else np.zeros(
             (self.k, self.n))
-        new_P = R - dot(S2.T, F) + S3
+        new_P = R - np.dot(S2.T, F) + S3
 
         return F, new_P
 
@@ -188,7 +187,7 @@ def robust_rule(self, method='doubling'):
         beta, theta = self.beta, self.theta
         k, j = self.k, self.j
         # == Set up LQ version == #
-        I = identity(j)
+        I = np.identity(j)
         Z = np.zeros((k, j))
 
         if self.pure_forecasting:
@@ -200,8 +199,8 @@ def robust_rule(self, method='doubling'):
             K = -f[:k, :]
 
         else:
-            Ba = hstack([B, C])
-            Qa = vstack([hstack([Q, Z]), hstack([Z.T, -beta*I*theta])])
+            Ba = np.hstack([B, C])
+            Qa = np.vstack([np.hstack([Q, Z]), np.hstack([Z.T, -beta*I*theta])])
             lq = LQ(Qa, R, A, Ba, beta=beta)
 
             # == Solve and convert back to robust problem == #
@@ -281,8 +280,8 @@ def F_to_K(self, F, method='doubling'):
 
         """
         Q2 = self.beta * self.theta
-        R2 = - self.R - dot(F.T, dot(self.Q, F))
-        A2 = self.A - dot(self.B, F)
+        R2 = - self.R - np.dot(F.T, np.dot(self.Q, F))
+        A2 = self.A - np.dot(self.B, F)
         B2 = self.C
         lq = LQ(Q2, R2, A2, B2, beta=self.beta)
         neg_P, neg_K, d = lq.stationary_values(method=method)
@@ -309,10 +308,10 @@ def K_to_F(self, K, method='doubling'):
             The value function for a given K
 
         """
-        A1 = self.A + dot(self.C, K)
+        A1 = self.A + np.dot(self.C, K)
         B1 = self.B
         Q1 = self.Q
-        R1 = self.R - self.beta * self.theta * dot(K.T, K)
+        R1 = self.R - self.beta * self.theta * np.dot(K.T, K)
         lq = LQ(Q1, R1, A1, B1, beta=self.beta)
         P, F, d = lq.stationary_values(method=method)
 
@@ -349,9 +348,9 @@ def compute_deterministic_entropy(self, F, K, x0):
             The deterministic entropy
 
         """
-        H0 = dot(K.T, K)
+        H0 = np.dot(K.T, K)
         C0 = np.zeros((self.n, 1))
-        A0 = self.A - dot(self.B, F) + dot(self.C, K)
+        A0 = self.A - np.dot(self.B, F) + np.dot(self.C, K)
         e = var_quadratic_sum(A0, C0, H0, self.beta, x0)
 
         return e
@@ -389,13 +388,13 @@ def evaluate_F(self, F):
         K_F, P_F = self.F_to_K(F)
         I = np.identity(self.j)
         H = inv(I - C.T.dot(P_F.dot(C)) / theta)
-        d_F = log(det(H))
+        d_F = np.log(det(H))
 
         # == Compute O_F and o_F == #
-        AO = sqrt(beta) * (A - dot(B, F) + dot(C, K_F))
-        O_F = solve_discrete_lyapunov(AO.T, beta * dot(K_F.T, K_F))
-        ho = (trace(H - 1) - d_F) / 2.0
-        tr = trace(dot(O_F, C.dot(H.dot(C.T))))
+        AO = np.sqrt(beta) * (A - np.dot(B, F) + np.dot(C, K_F))
+        O_F = solve_discrete_lyapunov(AO.T, beta * np.dot(K_F.T, K_F))
+        ho = (np.trace(H - 1) - d_F) / 2.0
+        tr = np.trace(np.dot(O_F, C.dot(H.dot(C.T))))
         o_F = (ho + beta * tr) / (1 - beta)
 
         return K_F, P_F, d_F, O_F, o_F