creating params
parent
e67b8fd702
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839f023ee8
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@ -4,11 +4,13 @@ import struct
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# import matplotlib.pyplot as plt
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# import matplotlib.pyplot as plt
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def generate_random_individuals():
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def generate_random_individuals():
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g = format(random.getrandbits(32), '32b')
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pop_grey = format(random.getrandbits(32), '32b')
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pop_bin = grey_to_bin(pop_grey)
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a, b, c, d = pop_bin[0:7], pop_bin[8:15], pop_bin[16:23], pop_bin[24:31]
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# val = int(b, 2) / 25.5 * 10 # conversion to 0.0 - 10.0 float
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# val = int(b, 2) / 25.5 * 10 # conversion to 0.0 - 10.0 float
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return val
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return [a, b, c, d]
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def grey_to_bin(gray):
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def grey_to_bin(gray):
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"""Convert Gray code to binary, operating on the integer value directly"""
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"""Convert Gray code to binary, operating on the integer value directly"""
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@ -25,6 +27,15 @@ def bin_to_grey(binary):
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gray = num ^ (num >> 1) # Gray code formula: G = B ^ (B >> 1)
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gray = num ^ (num >> 1) # Gray code formula: G = B ^ (B >> 1)
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return format(gray, f'0{len(binary)}b') # Convert back to binary string with same length
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return format(gray, f'0{len(binary)}b') # Convert back to binary string with same length
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def bin_to_param(binary, q_min = 0.0, q_max = 10.0):
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"""Convert binary string to float parameter in range [q_min, q_max]"""
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# Convert binary string to integer
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val = int(binary, 2)
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# Scale to range [q_min, q_max]
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q = q_min + ((q_max - q_min) / (2**len(binary))) * val
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return q
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def quadratic_error(original_fn, approx_fn, n):
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def quadratic_error(original_fn, approx_fn, n):
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error = 0.0
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error = 0.0
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@ -37,15 +48,21 @@ def e_fn_approx(a, b, c, d, x = 1):
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return a*x**3 + b*x**2 + c*x + d
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return a*x**3 + b*x**2 + c*x + d
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def fuck_that_shit_up():
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def fuck_that_shit_up():
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bin_values = generate_random_individuals()
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# Convert all binary strings to parameters in range 0.0-10.0
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float_values = [bin_to_param(bin) for bin in bin_values]
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a, b, c, d = float_values
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e_func = lambda x: np.e**x
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e_func = lambda x: np.e**x
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fixed_approx = lambda x: e_fn_approx(1.0, 0.1, 0.2, 1.0, x)
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fixed_approx = lambda x: e_fn_approx(a, b, c, d, x)
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while quadratic_error(e_func, fixed_approx, 6) > 0.01:
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while quadratic_error(e_func, fixed_approx, 6) > 0.01:
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pass
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pass
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# berechne fitness
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# berechne fitness
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# selection
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# selection
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# crossover
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# crossover
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# mutation
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# mutation
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# neue population
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# neue population
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return 0
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return 0
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