Fix imports.

extracted_ackley_GA.py

@@ -1,13 +1,9 @@
 import matplotlib.pyplot as plt
 import numpy as np
-from mpl_toolkits.mplot3d import Axes3D
+from util_plot import AddPlot
 
-fig = plt.figure(figsize=(6, 8))
-ax1 = fig.add_subplot(211, projection='3d')
-ax2 = fig.add_subplot(212)
-ax2.set_title("Learning curve")
-ax2.set_xlim(0, 30)
-ax2.set_ylim(0, 1)
+p = AddPlot(is_3d=True, with_lc=True)
+ax1, _ = p.returns
 
 x_min = -4
 x_max = 4
@@ -82,8 +78,6 @@ def plot_ackley():
     max_idx = np.argmax(fitness_arr)
     max_chromosome = population[max_idx]
 
-    old_fitness = best_fitness
-
     if fitness_arr[max_idx] > best_fitness:
         best_fitness = fitness_arr[max_idx]
         best_goal = np.copy(max_chromosome)
@@ -93,8 +87,7 @@ def plot_ackley():
     ax1.scatter(*population.T, s=50, alpha=0.5)
     ax1.scatter(*test_goal, s=200, marker="*", alpha=1.0)
     ax1.scatter(*best_goal, s=200, marker="+", alpha=1.0)
-    if iteration > 0:
-        ax2.plot((iteration - 1, iteration), (old_fitness, best_fitness), color='C0')
+    p.plot_curve(iteration, best_fitness)
     # We assume converged when arrive early_stop_fitness.
     if best_fitness > early_stop_fitness:
         ax1.set_title("Stop at iteration %s, best_fitness: %.4f" % (iteration, best_fitness))

genetic_algorithm.py

@@ -1,9 +1,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from util_3d import add_plot
+from util_plot import AddPlot
 
 is_3d = True
-ax, gene_num = add_plot(is_3d)
+ax, gene_num = AddPlot(is_3d).returns
 
 chromosome_num = 10
 population_shape = (chromosome_num, gene_num)

iris_knn.py

@@ -1,10 +1,10 @@
 import matplotlib.pyplot as plt
 import numpy as np
 from sklearn import datasets
-from util_3d import add_plot
+from util_plot import AddPlot
 
 is_3d = True
-ax, point_dim = add_plot(is_3d)
+ax, point_dim = AddPlot(is_3d).returns
 
 # import some data to play with
 iris = datasets.load_iris()

k_means.py

@@ -2,10 +2,10 @@
 
 import numpy as np
 import matplotlib.pyplot as plt
-from util_3d import add_plot
+from util_plot import AddPlot
 
 is_3d = True
-ax, point_dim = add_plot(is_3d)
+ax, point_dim = AddPlot(is_3d).returns
 
 n_cluster_points = 100
 cluster_shape = (n_cluster_points, point_dim)

k_nearest_neighbor.py

@@ -2,10 +2,10 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from util_3d import add_plot
+from util_plot import AddPlot
 
 is_3d = True
-ax, point_dim = add_plot(is_3d)
+ax, point_dim = AddPlot(is_3d).returns
 
 # Number of points of cluster
 n_cluster_points = 20

mean_shift.py

@@ -1,9 +1,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from util_3d import add_plot
+from util_plot import AddPlot
 
 is_3d = True
-ax, point_dim = add_plot(is_3d)
+ax, point_dim = AddPlot(is_3d).returns
 
 n_cluster_points = 100
 cluster_shape = (n_cluster_points, point_dim)

midterm_2.py

@@ -58,7 +58,6 @@ def plot_rastrigin():
 iteration = 0
 max_iteration = 30
 while True:
-    ax1.clear()
 
     plot_rastrigin()
 
@@ -67,7 +66,7 @@ def plot_rastrigin():
     fitness_arr = population[:, 2]
 
     max_idx = np.argmax(fitness_arr)
-    old_fitness = best_fitness
+    prev_fitness = best_fitness
 
     if fitness_arr[max_idx] > best_fitness:
         best_fitness = fitness_arr[max_idx]
@@ -77,7 +76,7 @@ def plot_rastrigin():
     ax1.scatter(*best_goal, s=200, c='blue', marker='*')
 
     if iteration > 0:
-        ax2.plot((iteration - 1, iteration), (old_fitness, best_fitness), c='C0')
+        ax2.plot((iteration - 1, iteration), (prev_fitness, best_fitness), c='C0')
 
     if best_fitness < early_stop_fitness:
         ax1.set_title("iteration %d, fitness %.4f" % (iteration, best_fitness))
@@ -90,13 +89,14 @@ def plot_rastrigin():
 
     plt.draw()
     plt.pause(0.5)
+    ax1.clear()
 
     # Genetic Algorithm
 
     # Selection
     sorted_idx = np.argsort(fitness_arr)
     seled_chromo = population[sorted_idx][-sel_num:]
-    copy_idx = np.random.choice(seled_chromo.shape[0], copy_num)
+    copy_idx = np.random.choice(sel_num, copy_num)
     copy_chromo = seled_chromo[copy_idx]
     population = np.concatenate((seled_chromo, copy_chromo))
 
@@ -122,5 +122,4 @@ def plot_rastrigin():
     iteration += 1
 
 # Plot forever after finished
-plt.draw()
 plt.show()

path_planning.py

@@ -1,10 +1,10 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-from util_3d import add_plot
+from util_plot import AddPlot
 
 is_3d = True
-ax, data_dim = add_plot(is_3d)
+ax, data_dim = AddPlot(is_3d).returns
 
 # Make goal as gene
 gene_num = 5
@@ -28,7 +28,6 @@
 
 iteration = 0
 iter_num = 100
-plt.pause(3)
 while True:
     # Update all paths
     paths[:, 1:] = points[1:][population]

random_search.py

@@ -1,9 +1,9 @@
 import numpy as np
 import matplotlib.pyplot as plt
-from util_3d import add_plot
+from util_plot import AddPlot
 
 is_3d = True
-ax, particle_dim = add_plot(is_3d)
+ax, particle_dim = AddPlot(is_3d).returns
 
 n_particles = 10
 particles_shape = (n_particles, particle_dim)

util_plot.py

@@ -0,0 +1,37 @@
+from matplotlib import pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+
+class AddPlot:
+    def __init__(self, is_3d, pos=111, with_lc=False, is_record=False):
+        if with_lc:
+            pos = 211
+            self.fig = plt.figure(figsize=(6, 8))
+            self._add_learning_curve()
+        else:
+            self.fig = plt.figure()
+        if is_record:
+            mngr = plt.get_current_fig_manager()
+            # to put it into the upper left corner for example:
+            mngr.window.wm_geometry("+350+100")
+        if is_3d:
+            point_dim = 3
+            ax = self.fig.add_subplot(pos, projection='3d')
+        else:
+            point_dim = 2
+            ax = self.fig.add_subplot(pos)
+        self.returns = ax, point_dim
+
+    def _add_learning_curve(self):
+        self.ax2 = self.fig.add_subplot(212)
+        self.ax2.set_title("Learning Curve")
+        self.ax2.set_xlabel("Iteration")
+        self.ax2.set_ylabel("Fitness Value")
+        self.ax2.grid()
+        self.prev_fitness = None
+
+    def plot_curve(self, iteration, best_fitness):
+        if self.prev_fitness is not None:
+            self.ax2.plot((iteration - 1, iteration), (self.prev_fitness, best_fitness), c='C0')
+        self.prev_fitness = best_fitness
+