137 lines
2.8 KiB
Python
137 lines
2.8 KiB
Python
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import numpy as np
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import itertools
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import json
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import math
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import collections
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from scipy.optimize import minimize
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from gekko import GEKKO
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PATH_IN = "files/timings.json"
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PATH_OUT = "files/optimized.json"
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def load_data():
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l_list, timings_list = [], []
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with open(PATH_IN, "r") as f:
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data = json.load(f)
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for key, value in data.items():
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l_list.append(key)
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timings_list.append(value)
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return l_list, timings_list
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L, T = load_data()
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"""
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Function to minimize: worst time case for generating the key
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This one is for python
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"""
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def to_optimize_(x):
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global L, T
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res = 0
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for i in range(len(L)): res += x[i] * T[i]
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return res
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"""
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Gekko function.
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Function to minimize: worst time case for generating the key
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"""
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def to_optimize(x, m):
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global L, T
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res = 0
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for i in range(len(L)): res += x[i] * T[i]
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return res
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"""
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Key-space size from GEKKO
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"""
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def keySpace_size(x, m):
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res = 0
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for i in range(len(x)): res += m.log(2*x[i]+1) / m.log(2)
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return res
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"""
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Key-space size from python
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"""
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def keySpace_size_(x):
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res = 0
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for i in range(len(x)): res += math.log(2*x[i]+1) / math.log(2)
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return res
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"""
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GEKKO optimizer
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"""
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def optimize():
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global L, T
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m = GEKKO(remote=False)
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x = m.Array(m.Var,len(L), lb=1, ub=1000, integer=False)
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for i in range(len(L)):
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f = T[i]
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if 5 <= f <= 50:
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x[i].value = 1
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x[i].lower = 0
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x[i].upper = 10
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if 0.5 <= f <= 5:
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x[i].value = 10
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x[i].lower = 0
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x[i].upper = 100
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elif 0.05 <= f and f < 0.5:
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x[i].value = 500
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x[i].lower = 0
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x[i].upper = 1000
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elif 0.005 <= f and f < 0.05:
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x[i].value = 5000
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x[i].lower = 0
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x[i].upper = 10000
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elif 0.001 <= f and f < 0.005:
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x[i].value = 10000
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x[i].lower = 0
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x[i].upper = 100000
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elif 0.0001 <= f and f < 0.001:
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x[i].value = 10000
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x[i].lower = 0
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x[i].upper = 100000
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print("Initial parameters:")
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y = [y.value.value for y in x]
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print(y)
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print("Initial time:")
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print(to_optimize_(y))
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print("Initial keySpace:")
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print(keySpace_size_(y))
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m.Equation(keySpace_size(x, m) >= 200)
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m.options.SOLVER=1
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# m.options.IMODE = 3
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m.options.COLDSTART=1
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#m.solver_options = ['minlp_maximum_iterations 10000']
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m.Minimize(to_optimize(x, m))
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m.solve(disp=True)
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return x
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def save_result(res):
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print(res)
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d = dict()
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for i in range(len(L)):
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d[L[i]] = round(int(res[i].value[0]))
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with open(PATH_OUT, "w") as f:
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json.dump(d, f)
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def main():
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L, T = load_data()
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res = optimize()
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save_result(res);
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main()
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