ass 3 done!!

03_euler_gen_alg/main.py

@@ -70,8 +70,8 @@ def eval_fitness(bin_pop_values):
     return fitness_arr
 
 def select(fitness_arr):
-    gray_pop_copy = gray_pop.copy()  # Create a copy of the population
-    fitness_arr_copy = fitness_arr.copy()
+    gray_pop_copy = gray_pop.copy() # copy of population
+    fitness_arr_copy = fitness_arr.copy() # copy of fitness array
     selected_pop = []
     
     while len(selected_pop) < SELECTION_SIZE:
@@ -140,7 +140,7 @@ bin_pop_values = generate_random_population(POPULATION_SIZE)
 
 print("Working...")
 iteration = 0 # debugging
-while not np.any((np.array(fitness_arr)) > 300):  # Continue while any fitness value is > 1
+while not np.any((np.array(fitness_arr)) > 200):  # Continue while any fitness value is > 1
     print("\nIteration: " + str(iteration)) # debugging
     
     # Evaluate fitness

03_euler_gen_alg/utils.py

@@ -14,15 +14,6 @@ def gray_to_bin(gray):
         num ^= mask
     return format(num, '032b')  # Always return 32-bit string
 
-def bin_to_gray(binary):
-    """
-    Convert binary to Gray code using XOR with right shift
-    :returns: 32-bit String
-    """
-    num = int(binary, 2)  # Convert string to integer
-    gray = num ^ (num >> 1)  # Gray code formula: G = B ^ (B >> 1)
-    return format(gray, '032b')  # Always return 32-bit string
-
 def bin_to_param(binary, q_min = 0.0, q_max = 5.0):
     """
     Convert one binary string to float parameter in range [q_min, q_max]
@@ -34,7 +25,7 @@ def bin_to_param(binary, q_min = 0.0, q_max = 5.0):
     return q
 
 def plot_graph(a, b, c, d): 
-    x = np.arange(-5., 5., 0.1)
+    x = np.arange(-1., 1., 0.1)
     
     fig, ax = plt.subplots()
     y_approx = a*x**3 + b*x**2 + c*x + d
@@ -43,7 +34,7 @@ def plot_graph(a, b, c, d):
     ax.plot(x, y_approx, label='approx. func.')
     ax.plot(x, y_taylor, label='taylor')
     ax.plot(x, y_exact, label='e^x')
-    ax.set_xlim(-5, 5)
+    ax.set_xlim(-1, 1)
     ax.set_ylim(-1, 5)
     plt.legend()
     plt.show()