grey to bin and bin to grey progress

03_euler_gen_alg.py

@@ -1,23 +1,61 @@
 import numpy as np
 import random
-import scipy.integrate
+import struct
-
 # import matplotlib.pyplot as plt
 
 def generate_random_individuals():
-    a = random.randrange(10)
+    g = format(random.getrandbits(32), '32b')
-    b = random.randrange(10)
+    # val = int(b, 2) / 25.5 * 10 # conversion to 0.0 - 10.0 float
-    c = random.randrange(10)
-    d = random.randrange(10)
 
-    return [a, b, c, d]
 
-def integrate_individual(function):
+    return val
-    result = scipy.integrate.quad(function, -3, 3)
+
-    return result
+# Function to flip a bit
+# represented as character.
+def flip(c): 
+    return '1' if c == '0' else '0'
+
+# accepts string binary array
+def grey_to_bin(gray):
+    binary = ""
+
+    # MSB of binary code is same as gray code
+    binary += gray[0]
+
+    # Compute remaining bits
+    for i in range(1, len(gray)):
+        
+        # If current bit is 0, concatenate
+        # previous bit
+        if gray[i] == '0':
+            binary += binary[i - 1]
+
+        # Else, concatenate invert of
+        # previous bit
+        else:
+            binary += flip(binary[i - 1])
+
+    return binary
+
+# accepts string binary array
+def bin_to_grey(binary):
+    gray = ""
+
+    # MSB of gray code is same as binary code
+    gray += binary[0]
+
+    # Compute remaining bits, next bit is computed by
+    # doing XOR of previous and current in Binary
+    for i in range(1, len(binary)):
+        
+        # Concatenate XOR of previous bit
+        # with current bit
+        gray += xorChar(binary[i - 1], binary[i])
+
+    return gray
 
 def quadratic_error(original_fn, approx_fn, n):
-    error = 0
+    error = 0.0
     
     for i in range(n):
         error += (original_fn(i) - approx_fn(i))**2
@@ -27,14 +65,20 @@ def quadratic_error(original_fn, approx_fn, n):
 def e_fn_approx(a, b, c, d, x = 1):
     return a*x**3 + b*x**2 + c*x + d
 
+def fuck_that_shit_up():
     e_func = lambda x: np.e**x
-fixed_approx = 1 # TODO
+    fixed_approx = lambda x: e_fn_approx(1.0, 0.1, 0.2, 1.0, x)
     
-while quadratic_error(e_func, fixed_approx, n) > 0.01:
+    while quadratic_error(e_func, fixed_approx, 6) > 0.01:
+        pass
     # berechne fitness
     # selection
     # crossover
     # mutation
     
     # neue population
-    print("Hello World")
+    return 0
+
+b = format(random.getrandbits(32), '32b')
+print(b)
+# print(quadratic_error(e_func, fixed_approx, 6)) # hopefully works