T1TSK and T1Mamdani ML Classes ...

examples/ex_19.py

@@ -1,4 +1,4 @@
-from pyit2fls import (T1TSK_ML, )
+from pyit2fls import (T1TSK_ML, T1FS_plot, )
 from numpy import (linspace, array, abs, pi, sin, cos, meshgrid, zeros_like, )
 from scipy.optimize import (Bounds, )
 import matplotlib.pyplot as plt
@@ -15,8 +15,10 @@
         y.append(sin(x1) + cos(x2))
 X = array(X)
 
-myTSK = T1TSK_ML(2, 16, (-4., 4.), algorithm="PSO", 
-                 algorithm_params=[20, 200, 0.3, 0.3, 2.4])
+M = 2
+N = 4
+myTSK = T1TSK_ML(M, N, (-4., 4.), algorithm="PSO", 
+                 algorithm_params=[200, 200, 0.3, 0.3, 2.4])
 print(myTSK.fit(X, y))
 
 x1, x2 = meshgrid(X1, X2)
@@ -26,6 +28,7 @@
     for j in range(10):
         y2[i, j] = myTSK.score(array([X1[j], X2[i], ]))
 
+
 fig = plt.figure()
 ax = fig.add_subplot(111, projection="3d")
 original = ax.plot_surface(x1, x2, y1, cmap="viridis", vmin=y1.min(), vmax=y1.max())
@@ -44,15 +47,22 @@
 
 fig = plt.figure()
 ax = fig.add_subplot(111, projection="3d")
-error_surface = ax.plot_surface(x1, x2, abs(y2 - y1), cmap="Greens", alpha=0.3)
+error_surface = ax.plot_surface(x1, x2, abs(y2 - y1), cmap="Greens", alpha=0.8)
 fig.colorbar(error_surface, ax=ax, shrink=0.5, aspect=10, 
              label="Error Magnitude")
 ax.plot_surface(x1, x2, y1, cmap="Blues", alpha=0.7)
 ax.set_title("3D Surface Plot")
 plt.show()
 
 
-
+achievedSystem = myTSK.get_T1TSK()
+for r, rule in zip(range(N), achievedSystem.rules):
+    sets = []
+    labels = []
+    for i in range(M):
+        sets.append(rule[0][i][1])
+        labels.append(f"X{i}")
+    T1FS_plot(*sets, title=f"Rule {r+1}", legends=labels)
 
 
 

examples/ex_20.py

@@ -18,7 +18,7 @@
 M = 2
 N = 4
 myMamdani = T1Mamdani_ML(M, N, (-4., 4.), algorithm="GA", 
-                         algorithm_params=[50, 200, 100, 100, ])
+                         algorithm_params=[200, 200, 100, 100, 0.05])
 print(myMamdani.fit(X, y))
 
 x1, x2 = meshgrid(X1, X2)
@@ -62,7 +62,7 @@
         labels.append(f"X{i}")
     sets.append(rule[1][0][1])
     labels.append("Y")
-    T1FS_plot(*sets, title="Rule {r}", legends=labels)
+    T1FS_plot(*sets, title=f"Rule {r+1}", legends=labels)
 
 
 

pyit2fls/learning.py

@@ -99,8 +99,8 @@ def crossover(self, parent1, parent2):
         b = rand(self.M)
         return (a * parent1.solution + b * parent2.solution) / (a + b)
 
-    def iterate(self, mutation_num, crossover_num):
-        parent_list = self.tournament_selection(2 * crossover_num, 1)
+    def iterate(self, mutation_num, crossover_num, tp):
+        parent_list = self.tournament_selection(2 * crossover_num, 1.0)
 
         for i, j in zip(parent_list[::2], parent_list[1::2]):
             child_solution = self.crossover(self.population[i], self.population[j])
@@ -111,7 +111,7 @@ def iterate(self, mutation_num, crossover_num):
                 self.population[j].solution = child_solution.copy()
                 self.population[j].fitness = self.func(child_solution, *self.args)
 
-        parent_list = self.tournament_selection(mutation_num, 0.1)
+        parent_list = self.tournament_selection(mutation_num, tp)
         for i in parent_list:
             mutated_solution = self.mutate(self.population[i])
             if self.func(mutated_solution, *self.args) < self.population[i].fitness:
@@ -246,7 +246,8 @@ def fit(self, X, y):
                       self.Bounds[0], args=(X, y, ))
             for i in range(self.algorithm_params[1]):
                 myGA.iterate(self.algorithm_params[2], 
-                              self.algorithm_params[3], )
+                             self.algorithm_params[3], 
+                             self.algorithm_params[4], )
                 print("Iteration ", i+1, ".", myGA.population[0].fitness)
             self.params = myGA.population[0].solution
         else:
@@ -325,7 +326,7 @@ def get_T1TSK(self, std=1., ):
                                    T1FS(domain, gaussian_mf, 
                                         params=[self.model.p[i][j][0], self.model.p[i][j][1], 1., ])))
 
-            consequent = [("Y", lambda **X: self.model.q[i]), 
+            consequent = [("Y", lambda *X: self.model.q[i]), 
                          ]
             generated_T1TSK.add_rule(antecedent, consequent)