Add midterm_2, plan_path.

midterm_2.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from mpl_toolkits.mplot3d import Axes3D
+
+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_xlabel("Iteration")
+ax2.set_ylabel("Fitness Value")
+ax2.grid()
+
+x_min = -5
+x_max = 5
+y_min = -5
+y_max = 5
+z_min = 0
+z_max = 80
+
+chromo_num = 20
+gene_num = 2
+
+sel_rate = 0.3
+crossover_rate = 0.3
+mutation_rate = 0.3
+
+sel_num = int(chromo_num * sel_rate)
+copy_num = chromo_num - sel_num
+
+
+def reversed_rastrigin_function(x, y):
+    A = 10
+    z = 80 - (2 * A + x ** 2 + y ** 2 - A * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))
+    return z
+
+
+x = np.arange(x_min, x_max, 0.1)
+y = np.arange(y_min, y_max, 0.1)
+xx, yy = np.meshgrid(x, y, sparse=True)
+
+zz = reversed_rastrigin_function(xx, yy)
+
+
+def plot_rastrigin():
+    ax1.set_xlim(x_min, x_max)
+    ax1.set_ylim(y_min, y_max)
+    ax1.set_zlim(z_min, z_max)
+    ax1.plot_surface(xx, yy, zz, rstride=1, cstride=1, cmap='terrain', alpha=0.2)
+
+
+rand_population = np.random.rand(chromo_num, gene_num) * [x_max - x_min, y_max - y_min] + [x_min, y_min]
+# Add addition zero column
+population = np.zeros((chromo_num, gene_num + 1))
+population[:, :-1] = rand_population
+
+best_fitness = 0.0
+early_stop_fitness = 79
+iteration = 0
+max_iteration = 30
+while True:
+    ax1.clear()
+
+    plot_rastrigin()
+
+    # Get fitness as z
+    population[:, 2] = reversed_rastrigin_function(*population[:, :2].T)
+    fitness_arr = population[:, 2]
+
+    max_idx = np.argmax(fitness_arr)
+    old_fitness = best_fitness
+
+    if fitness_arr[max_idx] > best_fitness:
+        best_fitness = fitness_arr[max_idx]
+        best_goal = np.copy(population[max_idx])
+
+    ax1.scatter(*population.T, s=50, c='red')
+    ax1.scatter(*best_goal, s=200, c='blue', marker='*')
+
+    if iteration > 0:
+        ax2.plot((iteration - 1, iteration), (old_fitness, best_fitness), c='C0')
+
+    if best_fitness < early_stop_fitness:
+        ax1.set_title("iteration %d, fitness %.4f" % (iteration, best_fitness))
+    elif iteration >= max_iteration-1:
+        ax1.set_title("Early Stop at iteration %d, fitness %.4f" % (iteration, best_fitness))
+        break
+    else:
+        ax1.set_title("Stop at iteration %d, fitness %.4f" % (iteration, best_fitness))
+        break
+
+    plt.draw()
+    plt.pause(0.5)
+
+    # 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_chromo = seled_chromo[copy_idx]
+    population = np.concatenate((seled_chromo, copy_chromo))
+
+    # Crossover
+    for i in range(chromo_num):
+        if np.random.rand(1) <= crossover_rate:
+            gene_idx = np.random.randint(gene_num)
+            # Prevent crossover with self
+            parent_idx = np.random.randint(chromo_num - 1)
+            if i == parent_idx:
+                parent_idx = chromo_num - 1
+
+            buff_gene = population[parent_idx, gene_idx]
+            population[parent_idx, gene_idx] = population[i, gene_idx]
+            population[i, gene_idx] = buff_gene
+
+    # Mutation
+    for i in range(chromo_num):
+        if np.random.rand(1) <= mutation_rate:
+            gene_idx = np.random.randint(gene_num)
+            population[i, gene_idx] = np.random.normal(best_goal[gene_idx]) / (iteration+1)
+
+    iteration += 1
+
+# Plot forever after finished
+plt.draw()
+plt.show()

path_planning.py

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+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn import datasets
+
+data_dim = 2
+goal_num = 5
+
+goals = np.random.rand(goal_num, data_dim)
+start = np.random.rand(data_dim)
+
+temp = np.arange(goal_num)
+np.random.shuffle(temp)
+
+
+def plot_path(temp, color):
+    points = np.concatenate((start[np.newaxis], goals[temp]))
+    plt.plot(*points.T, c="blue")
+
+
+plot_path(temp, color='blue')
+
+plt.scatter(*goals.T, c="red", s=250, alpha=0.7, marker="*")
+plt.scatter(*start, c="green", s=250, alpha=0.7, marker="*")
+
+plt.xlim(-0.1, 1.1)
+plt.ylim(-0.1, 1.1)
+plt.grid()
+plt.show()