qubo_lab/util/random_instance_pool.py

#!/usr/bin/env python3

from . import randomSAT

import math
import random
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt

class Random_instance_pool:
    
    def __init__(self, parameter_range):
        self.__parameter_range = parameter_range
    
    def __iter__(self):
        return self
            
    def __next__(self):
        return self.Random(self.__parameter_range.next())
    
    class Random:
        def __init__(self, parameters):
            self.__params = parameters
        
        def random(self):
            return randomSAT.generateRandomKSAT(self.__params.number_of_clauses,
                                                self.__params.number_of_variables,
                                                self.__params.variables_per_clause)
        
    
    
class Instance_parameters:
    
    def __init__(self,
                 number_of_clauses,
                 number_of_variables,
                 variables_per_clause = 3):
        
        self.number_of_clauses = number_of_clauses
        self.number_of_variables = number_of_variables
        self.variables_per_clause = variables_per_clause
        
    def __str__(self):
        return ("number of clauses: {}\n"
                "number of variables: {}\n"
                "variables per clause: {}").format(self.number_of_clauses,
                                                    self.number_of_variables,
                                                    self.variables_per_clause)
        
class Instance_parameter_variable_range:
    
    def __init__(self, start_parameter, variable_range): 
        self.start_parameter = start_parameter
        self.__variable_range = variable_range
        
    def __iter__(self):
        return self
    
    def __next__(self):
        self.start_parameter.number_of_variables = self.__variable_range.next()
        
        return self.start_parameter
    
    def next(self):
        return self.__next__()
        
    
class Manual_range:
    
    def __init__(self, start, stop, step = 1):
        self.start = start
        self.stop = stop
        self.step = step
        
        self.__current = start
        
    def __iter__(self):
        return self
    
    def __next__(self):
        if self.__current >= self.stop:
            raise StopIteration
        
        self.__current += self.step
        
        return self.__current
    
    def next(self):
        return self.__next__()
    
class Random_range:
    
    def __init__(self, random_generator, steps):
        self.__random_generator = random_generator
        self.__steps = steps
        self.__current_step = 0
        
    def __iter__(self):
        return self
    
    def __next__(self):
        if self.__current_step < self.__steps:
            self.__current_step += 1
            return self.__random_generator.random()
        else:
            raise StopIteration
    
    def next(self):
        return self.__next__()
    
class Random_logistic_variable_distribution:
    
    def __init__(self,
                 number_of_clauses,
                 min_variables,
                 max_variables,
                 alpha_point_of_interest,
                 steepnesss):
        self.__number_of_clauses = number_of_clauses
        self.__min_variables = min_variables
        self.__max_variables = max_variables
        self.__alpha_point_of_interest = alpha_point_of_interest
        self.__steepnesss = steepnesss
        
    def random(self):
        number_of_variables = 0
        
        while (number_of_variables < self.__min_variables or 
               number_of_variables > self.__max_variables):
            
            alpha = inv_logistic(random.random(),
                                 1,
                                 self.__steepnesss,
                                 self.__alpha_point_of_interest)
            
            number_of_variables = int(self.__number_of_clauses / alpha)
        
        return number_of_variables

def inv_logistic(x, L, k, x0):
    return math.log(math.pow(x / (L-x), 1/k)) + x0
    
def test():
    sns.set()
    
    data = []
    
    logistic_distr = Random_logistic_variable_distribution(42, 5, 84, 4.5, 1)
    rnd_range = Random_range(logistic_distr, 500)
    
    for i in range(500):
        #data.append(50/math.exp(random.uniform(5, 50)))
        #v = 0
        
        #while v < 5 or v > 84:
            #v = int(42 / inv_logistic(random.random(), 1, 1, 4.5))
            
        data.append(rnd_range.next())
    
    data = np.array(data)
    
    sns.distplot(data,
                 #bins=30,
                 norm_hist=True)
    #sns.lineplot(list(range(2, 100)), data)
    plt.show()
    
    sns.distplot(42/data,
                 #bins=30,
                 norm_hist=True)
    #sns.lineplot(list(range(2, 100)), data)
    plt.show()
    
    return data