reformatted with black

exercise.ipynb

@@ -129,9 +129,16 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "\n",
     "# Reversed Ackley function\n",
     "def ackley(x, y):\n",
-    "    return -1*(-20.0 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2))) - np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y))) + np.e + 20)\n",
+    "    return -1 * (\n",
+    "        -20.0 * np.exp(-0.2 * np.sqrt(0.5 * (x**2 + y**2)))\n",
+    "        - np.exp(0.5 * (np.cos(2 * np.pi * x) + np.cos(2 * np.pi * y)))\n",
+    "        + np.e\n",
+    "        + 20\n",
+    "    )\n",
+    "\n",
     "\n",
     "# define range for input\n",
     "r_min, r_max = -5.0, 5.0\n",
@@ -144,8 +151,8 @@
     "results = ackley(x, y)\n",
     "# create a surface plot with the jet color scheme\n",
     "fig = plt.figure()\n",
-    "ax = fig.add_subplot(projection='3d')\n",
-    "surf = ax.plot_surface(x, y, results, cmap='jet')\n",
+    "ax = fig.add_subplot(projection=\"3d\")\n",
+    "surf = ax.plot_surface(x, y, results, cmap=\"jet\")\n",
     "ax.set_xlabel(\"param x\")\n",
     "ax.set_ylabel(\"param y\")\n",
     "ax.set_zlabel(\"Ackley\")\n",
@@ -216,11 +223,12 @@
     "env.reset()\n",
     "\n",
     "# Create a widget for displaying the environment\n",
-    "image_widget = widgets.Image(format='jpeg')\n",
+    "image_widget = widgets.Image(format=\"jpeg\")\n",
     "\n",
     "# Display the widget\n",
     "display(image_widget)\n",
     "\n",
+    "\n",
     "def update_image(env):\n",
     "    img = env.render()\n",
     "    img = Image.fromarray(img)\n",
@@ -229,14 +237,15 @@
     "        image_data = output.getvalue()\n",
     "        image_widget.value = image_data\n",
     "\n",
+    "\n",
     "# Run and update environment\n",
     "for _ in range(1000):\n",
     "    action = env.action_space.sample()  # Replace with your action policy\n",
     "    observation, reward, terminated, truncated, info = env.step(action)\n",
-    "    \n",
+    "\n",
     "    if terminated or truncated:\n",
     "        observation, info = env.reset()\n",
-    "    \n",
+    "\n",
     "    update_image(env)\n",
     "    clear_output(wait=True)\n",
     "\n",
@@ -299,11 +308,12 @@
     "env.reset()\n",
     "\n",
     "# Create a widget for displaying the environment\n",
-    "image_widget = widgets.Image(format='jpeg')\n",
+    "image_widget = widgets.Image(format=\"jpeg\")\n",
     "\n",
     "# Display the widget\n",
     "display(image_widget)\n",
     "\n",
+    "\n",
     "def update_image(env):\n",
     "    img = env.render()\n",
     "    img = Image.fromarray(img)\n",
@@ -312,14 +322,15 @@
     "        image_data = output.getvalue()\n",
     "        image_widget.value = image_data\n",
     "\n",
+    "\n",
     "# Run and update environment\n",
     "for _ in range(400):\n",
     "    action = env.action_space.sample()  # Replace with your action policy\n",
     "    observation, reward, terminated, truncated, info = env.step(action)\n",
-    "    \n",
+    "\n",
     "    if terminated or truncated:\n",
     "        observation, info = env.reset()\n",
-    "    \n",
+    "\n",
     "    update_image(env)\n",
     "    clear_output(wait=True)\n",
     "\n",

exercise_1_ga/assets/utility.py

@@ -1,31 +1,33 @@
 import numpy as np
 
+
 def highlight_mutations(original, mutated):
     if original.shape != mutated.shape:
         raise ValueError("Original and mutated populations must have the same shape")
 
     # ANSI escape code for red text and reset
-    RED = '\033[91m'
-    RESET = '\033[0m'
+    RED = "\033[91m"
+    RESET = "\033[0m"
 
     for i in range(original.shape[0]):
-        highlighted_row = ''
+        highlighted_row = ""
         for j in range(original.shape[1]):
             if original[i, j] != mutated[i, j]:
-                highlighted_row += f'{RED}{mutated[i, j]}{RESET} '
+                highlighted_row += f"{RED}{mutated[i, j]}{RESET} "
             else:
-                highlighted_row += f'{mutated[i, j]} '
-                
+                highlighted_row += f"{mutated[i, j]} "
+
         if i == 0:
             print(f"[[{highlighted_row[:-1]}]")
             continue
-            
-        if i == original.shape[0]-1:
+
+        if i == original.shape[0] - 1:
             print(f" [{highlighted_row[:-1]}]]")
             continue
-            
+
         print(f" [{highlighted_row[:-1]}]")
-
+
+
 def search_index_in_columns(population, winner):
     index = None
     for i in range(population.shape[0]):
@@ -36,5 +38,5 @@ def search_index_in_columns(population, winner):
     # Print the result
     if index is None:
         print("Winner not found in population!")
-        
-    return index
+
+    return index

exercise_1_ga/genetic_algorithm.ipynb

@@ -48,7 +48,7 @@
     "import numpy as np\n",
     "\n",
     "%run assets/utility.py\n",
-    "np.set_printoptions(precision=4, suppress=True) # for better printing"
+    "np.set_printoptions(precision=4, suppress=True)  # for better printing"
    ]
   },
   {
@@ -129,12 +129,12 @@
     "# Solution 1:\n",
     "def generate_initial_population(population_size, chromosome_length):\n",
     "    population = np.zeros((population_size, chromosome_length))\n",
-    "    \n",
+    "\n",
     "    chromosome = np.random.randint(0, 2, size=(chromosome_length,))\n",
     "    for i in range(population_size):\n",
-    "        np.random.shuffle(chromosome) # shuffles in-place\n",
-    "        population[i,:] = chromosome\n",
-    "        \n",
+    "        np.random.shuffle(chromosome)  # shuffles in-place\n",
+    "        population[i, :] = chromosome\n",
+    "\n",
     "    return population"
    ]
   },
@@ -158,7 +158,9 @@
     "def generate_initial_population(population_size, chromosome_length):\n",
     "    binary_values = [0, 1]\n",
     "    probabilities = [0.5, 0.5]\n",
-    "    population = np.random.choice(binary_values, size=(population_size, chromosome_length), p=probabilities)\n",
+    "    population = np.random.choice(\n",
+    "        binary_values, size=(population_size, chromosome_length), p=probabilities\n",
+    "    )\n",
     "\n",
     "    return population"
    ]
@@ -321,14 +323,14 @@
     "def tournament_selection(population, fitnesses, tournament_size):\n",
     "    # Choose tournament participants\n",
     "    indices = np.random.choice(population.shape[0], tournament_size, replace=False)\n",
-    "    \n",
+    "\n",
     "    # Evaluate winner\n",
     "    contenders_fitness = fitnesses[indices]\n",
     "    max_contenders_fitness_index = np.argmax(contenders_fitness)\n",
     "    winner_index = indices[max_contenders_fitness_index]\n",
-    "    \n",
-    "    winner = population[winner_index,:]\n",
-    "    \n",
+    "\n",
+    "    winner = population[winner_index, :]\n",
+    "\n",
     "    return winner.reshape(1, -1)"
    ]
   },
@@ -374,7 +376,7 @@
     "\n",
     "population = np.random.randint(0, 2, size=(population_size, chromosome_length))\n",
     "fitness = np.random.uniform(size=(population_size,))\n",
-    "tournament_size = 8 # we can only test for the full population, otherwise you need to add prints in your function.\n",
+    "tournament_size = 8  # we can only test for the full population, otherwise you need to add prints in your function.\n",
     "\n",
     "winner = tournament_selection(population, fitness, tournament_size)\n",
     "\n",
@@ -465,15 +467,15 @@
     "def stochastic_one_point_crossover(parent_1, parent_2, crossover_probability):\n",
     "    if np.random.rand() < crossover_probability:\n",
     "        idx = np.random.randint(1, parent_1.shape[1])\n",
-    "        \n",
+    "\n",
     "        child_1 = np.concatenate((parent_1[0, :idx], parent_2[0, idx:]))\n",
     "        child_2 = np.concatenate((parent_2[0, :idx], parent_1[0, idx:]))\n",
-    "        \n",
+    "\n",
     "        return child_1, child_2\n",
     "    else:\n",
     "        child_1 = parent_1\n",
     "        child_2 = parent_2\n",
-    "        \n",
+    "\n",
     "        return child_1, child_2"
    ]
   },
@@ -497,15 +499,15 @@
     "def stochastic_one_point_crossover(parent_1, parent_2, crossover_probability):\n",
     "    if np.random.rand() >= crossover_probability:\n",
     "        return parent_1, parent_2\n",
-    "    \n",
+    "\n",
     "    idx = np.random.randint(low=1, high=parent_1.shape[1])\n",
-    "    \n",
+    "\n",
     "    child_1 = np.concatenate((parent_1[0, :idx], parent_2[0, idx:]))\n",
     "    child_2 = np.concatenate((parent_2[0, :idx], parent_1[0, idx:]))\n",
-    "    \n",
+    "\n",
     "    child_1 = child_1.reshape(1, -1)\n",
     "    child_2 = child_2.reshape(1, -1)\n",
-    "    \n",
+    "\n",
     "    return child_1, child_2"
    ]
   },
@@ -551,7 +553,9 @@
     "parent_1 = np.random.randint(0, 2, size=(1, chromosome_length))\n",
     "parent_2 = np.random.randint(0, 2, size=(1, chromosome_length))\n",
     "\n",
-    "child_1, child_2 = stochastic_one_point_crossover(parent_1, parent_2, crossover_probability)\n",
+    "child_1, child_2 = stochastic_one_point_crossover(\n",
+    "    parent_1, parent_2, crossover_probability\n",
+    ")\n",
     "\n",
     "print(f\"Parent 1: {parent_1}\\nParent 2: {parent_2}\\n\")\n",
     "print(f\"Child 1: {child_1}\\nChild 2: {child_2}\\n\")\n",
@@ -648,7 +652,7 @@
     "def mutate_genotypes(population, mutation_probability):\n",
     "    # Create a boolean mask where True indicates potential mutation sites\n",
     "    mutation_mask = np.random.rand(*population.shape) < mutation_probability\n",
-    "    \n",
+    "\n",
     "    # Flip the bits where the mask is True\n",
     "    mutated_population = population ^ mutation_mask\n",
     "\n",