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189 lines
5.2 KiB
189 lines
5.2 KiB
#!/usr/bin/env python3
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from . import randomSAT
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import math
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import random
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import numpy as np
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import seaborn as sns
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import matplotlib.pyplot as plt
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class Random_instance_pool:
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def __init__(self, parameter_range):
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self.__parameter_range = parameter_range
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def __iter__(self):
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return self
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def __next__(self):
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return self.Random(self.__parameter_range.next())
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class Random:
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def __init__(self, parameters):
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self.__params = parameters
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def random(self):
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return randomSAT.generateRandomKSAT(self.__params.number_of_clauses,
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self.__params.number_of_variables,
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self.__params.variables_per_clause)
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class Instance_parameters:
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def __init__(self,
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number_of_clauses,
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number_of_variables,
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variables_per_clause = 3):
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self.number_of_clauses = number_of_clauses
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self.number_of_variables = number_of_variables
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self.variables_per_clause = variables_per_clause
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def __str__(self):
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return ("number of clauses: {}\n"
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"number of variables: {}\n"
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"variables per clause: {}").format(self.number_of_clauses,
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self.number_of_variables,
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self.variables_per_clause)
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class Instance_parameter_variable_range:
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def __init__(self, start_parameter, variable_range):
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self.start_parameter = start_parameter
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self.__variable_range = variable_range
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def __iter__(self):
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return self
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def __next__(self):
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self.start_parameter.number_of_variables = self.__variable_range.next()
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return self.start_parameter
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def next(self):
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return self.__next__()
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class Manual_range:
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def __init__(self, start, stop, step = 1):
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self.start = start
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self.stop = stop
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self.step = step
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self.__current = start
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def __iter__(self):
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return self
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def __next__(self):
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if self.__current >= self.stop:
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raise StopIteration
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self.__current += self.step
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return self.__current
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def next(self):
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return self.__next__()
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class Random_range:
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def __init__(self, random_generator, steps):
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self.__random_generator = random_generator
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self.__steps = steps
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self.__current_step = 0
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def __iter__(self):
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return self
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def __next__(self):
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if self.__current_step < self.__steps:
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self.__current_step += 1
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return self.__random_generator.random()
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else:
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raise StopIteration
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def next(self):
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return self.__next__()
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class Random_logistic_variable_distribution:
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def __init__(self,
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number_of_clauses,
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min_variables,
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max_variables,
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alpha_point_of_interest,
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steepness):
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self.__number_of_clauses = number_of_clauses
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self.__min_variables = min_variables
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self.__max_variables = max_variables
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self.__alpha_point_of_interest = alpha_point_of_interest
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self.__steepness = steepness
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def random(self):
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number_of_variables = 0
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while (number_of_variables < self.__min_variables or
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number_of_variables > self.__max_variables):
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alpha = inv_logistic(random.random(),
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1,
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self.__steepness,
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self.__alpha_point_of_interest)
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number_of_variables = int(self.__number_of_clauses / alpha)
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return number_of_variables
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def inv_logistic(x, L, k, x0):
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return math.log(math.pow(x / (L-x), 1/k)) + x0
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def create_random_logistic_pool(logistic_variable_distr_params, number_of_instances,
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instance_params):
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logist_distr = Random_logistic_variable_distribution(**logistic_variable_distr_params)
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rnd_range = Random_range(logist_distr, number_of_instances)
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param_range = Instance_parameter_variable_range(instance_params, rnd_range)
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return Random_instance_pool(param_range)
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def test():
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sns.set()
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data = []
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logistic_distr = Random_logistic_variable_distribution(42, 5, 84, 4.5, 1)
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rnd_range = Random_range(logistic_distr, 500)
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for i in range(500):
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#data.append(50/math.exp(random.uniform(5, 50)))
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#v = 0
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#while v < 5 or v > 84:
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#v = int(42 / inv_logistic(random.random(), 1, 1, 4.5))
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data.append(rnd_range.next())
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data = np.array(data)
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sns.distplot(data,
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#bins=30,
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norm_hist=True)
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#sns.lineplot(list(range(2, 100)), data)
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plt.show()
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sns.distplot(42/data,
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#bins=30,
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norm_hist=True)
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#sns.lineplot(list(range(2, 100)), data)
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plt.show()
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return data
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