Merge pull request #13 from danielwilczak101/ryley_beta

Selection/Crossover/Mutation Framework Update

src/EasyGA.py

@@ -4,71 +4,75 @@ from initialization import Population as create_population
 from initialization import Chromosome as create_chromosome
 from initialization import Gene as create_gene
 # Structure Methods
-from fitness_function import Fitness_methods
-from initialization import Initialization_methods
-from termination_point import Termination_methods
+from fitness_function import Fitness_Examples
+from initialization import Initialization_Methods
+from termination_point import Termination_Methods
 # Population Methods
-from survivor_selection import Survivor_methods
+from selection import Selection_Methods
 # Manipulation Methods
-from parent_selection import Parent_methods
-from mutation import Mutation_methods
-from crossover import Crossover_methods
+from mutation import Mutation_Methods
+from crossover import Crossover_Methods
 
 class GA:
     def __init__(self):
         """Initialize the GA."""
         # Initilization variables
-        self.chromosome_length = 3
-        self.population_size = 5
+        self.chromosome_length = 10
+        self.population_size = 150
         self.chromosome_impl = None
         self.gene_impl = None
         self.population = None
-        # Termination varibles
+        self.target_fitness_type = 'maximum'
+        self.update_fitness = True
+
+        # Selection variables
+        self.parent_ratio = 0.1
+        self.selection_probablity = 0.95
+
+        # Termination variables
         self.current_generation = 0
-        self.generation_goal = 3
+        self.generation_goal = 50
 
         self.current_fitness = 0
-        self.fitness_goal = 3
+        self.generation_goal = 250
+        self.fitness_goal = 9
+
         # Mutation variables
-        self.mutation_rate = 0.03
+        self.mutation_rate = 0.10        
 
-        # Rerun already computed fitness
-        self.update_fitness = False
-
-        # Defualt EastGA implimentation structure
-        self.initialization_impl = Initialization_methods.random_initialization
-        self.fitness_funciton_impl = Fitness_methods.is_it_5
+        # Default EasyGA implimentation structure
+        self.initialization_impl = Initialization_Methods().random_initialization
+        self.fitness_function_impl = Fitness_Examples().index_dependent_values
         # Selects which chromosomes should be automaticly moved to the next population
-        #self.survivor_selection_impl = Survivor_methods.
+        self.survivor_selection_impl = Selection_Methods().Survivor_Selection().remove_two_worst
         # Methods for accomplishing parent-selection -> Crossover -> Mutation
-        # self.parent_selection_impl = Parent_methods.roulette_selection
-        #self.crossover_impl = Crossover_methods.
-        #self.mutation_impl = Mutation_methods.
+        self.parent_selection_impl = Selection_Methods().Parent_Selection().Roulette().roulette_selection
+        self.crossover_impl = Crossover_Methods().single_point_crossover
+        self.mutation_impl = Mutation_Methods().per_gene_mutation
         # The type of termination to impliment
-        self.termination_impl = Termination_methods.generation_based
+        self.termination_impl = Termination_Methods().generation_based
+        
 
-    def evolve_generation(self, number_of_generations = 1):
+    def evolve_generation(self, number_of_generations = 1, consider_termination = True):
         """Evolves the ga the specified number of generations."""
-        while(number_of_generations > 0):
+        while(number_of_generations > 0 and (consider_termination == False or self.termination_impl(self))):
             # If its the first generation then initialize the population
-            if(self.current_generation == 0):
-                # Initialize the population
+            if self.current_generation == 0:
                 self.initialize_population()
-            # First get the fitness of the population
-            self.get_population_fitness(self.population.chromosome_list)
-            # Selection - Triggers flags in the chromosome if its been selected
-            # self.selection_impl(self)
-            # Crossover - Takes the flagged chromosome_list and crosses there genetic
-            # makup to make new offsprings.
-            # self.crossover_impl(self)
-            # Repopulate - Manipulates the population to some desired way
-            # self.repopulate_impl(self)
-            # Mutation - Manipulates the population very slightly
-            # self.mutation_impl(self)
-            # self.parent_selection_impl(self)
-            # Counter for the local number of generations in evolve_generation
+                self.set_all_fitness(self.population.chromosome_list)
+                self.population.set_all_chromosomes(self.sort_by_best_fitness())
+            else:
+                self.parent_selection_impl(self)
+                next_population = self.crossover_impl(self)
+                next_population = self.survivor_selection_impl(self, next_population)
+                next_population.set_all_chromosomes(self.mutation_impl(self, next_population.get_all_chromosomes()))
+
+                self.population = next_population
+                self.set_all_fitness(self.population.chromosome_list)
+                self.population.set_all_chromosomes(self.sort_by_best_fitness())
+
             number_of_generations -= 1
-            # Add one to the current overall generation
+
             self.current_generation += 1
 
     def evolve(self):
@@ -87,18 +91,38 @@ class GA:
          implimentation that is currently set"""
         self.population = self.initialization_impl(self)
 
-    def get_population_fitness(self,population):
+    def set_all_fitness(self,chromosome_set):
         """Will get and set the fitness of each chromosome in the population.
         If update_fitness is set then all fitness values are updated.
         Otherwise only fitness values set to None (i.e. uninitialized
         fitness values) are updated."""
         # Get each chromosome in the population
-        for chromosome in population:
-            # If the fitness is not set then get its fitness or if allways getting
-            # fitness is turn on then always get the fitness of the chromosome.
+
+        for chromosome in chromosome_set:
             if(chromosome.fitness == None or self.update_fitness == True):
                 # Set the chromosomes fitness using the fitness function
-                chromosome.fitness = self.fitness_funciton_impl(chromosome)
+                chromosome.set_fitness(self.fitness_function_impl(chromosome))
+
+    def sort_by_best_fitness(self, chromosome_set = None):
+
+        if chromosome_set == None:
+            chromosome_set = self.population.get_all_chromosomes()
+
+        chromosome_set_temp = chromosome_set
+        not_sorted_check = 0
+        while (not_sorted_check != len(chromosome_set_temp)):
+            not_sorted_check = 0
+            for i in range(len(chromosome_set_temp)):
+                if ((i + 1 < len(chromosome_set_temp)) and (chromosome_set_temp[i + 1].fitness > chromosome_set_temp[i].fitness)):
+                    temp = chromosome_set[i]
+                    chromosome_set_temp[i] = chromosome_set[i + 1]
+                    chromosome_set_temp[i + 1] = temp
+                else:
+                    not_sorted_check += 1
+
+        chromosome_set = chromosome_set_temp 
+
+        return chromosome_set
 
     def make_gene(self,value):
         """Let's the user create a gene."""

src/crossover/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT function_name
-from .methods import Crossover_methods
+from .crossover_methods import Crossover_Methods

src/crossover/crossover_methods.py

@@ -0,0 +1,37 @@
+import random
+from initialization.chromosome_structure.chromosome import Chromosome
+from initialization.population_structure.population import Population
+
+class Crossover_Methods:
+    """ Crossover explination goes here.
+
+    Points - Defined as sections between the chromosomes genetic makeup
+    """
+    def __init__(self):
+        pass
+    
+    def single_point_crossover(self, ga):
+        """Single  point crossover is when a "point" is selected and the genetic
+        make up of the two parent chromosomes are "Crossed" or better known as swapped"""
+
+        crossover_pool = ga.population.mating_pool
+        
+        new_population = Population()
+        for i in range(len(crossover_pool)):
+            if i + 1 < len(crossover_pool):
+                new_gene_set = []
+                parent_one = crossover_pool[i].get_genes()
+                parent_two = crossover_pool[i+1].get_genes()
+                #halfway_point = int(ga.chromosome_length/2)
+                split_point = random.randint(0,ga.chromosome_length)    
+                new_gene_set.extend(parent_one[0:split_point])
+                new_gene_set.extend(parent_two[split_point:])
+                new_chromosome = Chromosome(new_gene_set)
+                new_population.add_chromosome(new_chromosome)
+
+        return new_population
+    
+    def multi_point_crossover(self, ga,number_of_points = 2):
+        """Multi point crossover is when a specific number (More then one) of
+        "points" are created to merge the genetic makup of the chromosomes."""
+        pass

src/fitness_function/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT class name
-from .methods import Fitness_methods
+from .fitness_examples import Fitness_Examples

src/fitness_function/fitness_examples.py

@@ -0,0 +1,28 @@
+class Fitness_Examples:
+    """Fitness function examples used"""
+    def is_it_5(self, chromosome):
+        """A very simple case test function - If the chromosomes gene value is a 5 add one
+         to the chromosomes overall fitness value."""
+        # Overall fitness value
+        fitness = 0
+        # For each gene in the chromosome
+        for gene in chromosome.gene_list:
+            # Check if its value = 5
+            if(gene.value == 5):
+                # If its value is 5 then add one to
+                # the overal fitness of the chromosome.
+                fitness += 1
+                
+        return fitness
+
+    def index_dependent_values(self, chromosome):
+        """A very simple case test function - If the chromosomes gene value is a 5 add one
+         to the chromosomes overall fitness value."""
+        # Overall fitness value
+        fitness = 0
+        # For each gene in the chromosome
+        for i in range(len(chromosome.gene_list)):
+            if (chromosome.gene_list[i].value == i+1):
+                fitness += 1
+                
+        return fitness

src/fitness_function/test_examples.py

@@ -0,0 +1,12 @@
+class test_fitness_funciton:
+    def get_fitness(self, chromosome):
+        # For every gene in chromosome
+        for i in range(len(chromosome.genes)):
+            # If the gene has a five then add one to the fitness
+            # Example -> Chromosome = [5],[2],[2],[5],[5] then fitness = 3
+            if (chromosome.genes[i].get_value == 5):
+                # Add to the genes fitness
+                chromosome.genes[i].fitness += 1
+                # Add to the chromosomes fitness
+                chromosome.fitness += 1
+        return chromosome.fitness

src/initialization/__init__.py

@@ -1,5 +1,3 @@
 # FROM (. means local) file_name IMPORT function_name
-from .methods import Initialization_methods
-from .population_structure.population import Population
-from .chromosome_structure.chromosome import Chromosome
-from .gene_structure.gene import Gene
+from .initialization_methods import Initialization_Methods
+

src/initialization/chromosome_structure/chromosome.py

@@ -1,12 +1,10 @@
 class Chromosome:
-
     def __init__(self, gene_list = None):
-        """Initialize the chromosome based on input gene list, defaulted to an empty list"""
         if gene_list is None:
             self.gene_list = []
         else:
-            self.gene_list = gene_list
-        # The fitness of the overal chromosome
+            self.gene_list = genes
+
         self.fitness = None
         # If the chromosome has been selected then the flag would switch to true
         self.selected = False
@@ -18,11 +16,9 @@ class Chromosome:
         self.gene_list.insert(index, gene)
 
     def remove_gene(self, index):
-        """Remove a gene from the chromosome at the specified index"""
         del self.gene_list[index]
 
     def get_genes(self):
-        """Return all genes in the chromosome"""
         return self.gene_list
 
     def get_fitness(self):
@@ -30,12 +26,10 @@ class Chromosome:
         return self.fitness
 
     def set_gene(self, gene, index):
-        """Set a gene at a specific index"""
         self.gene_list[index] = gene
 
-    def set_genes(self, gene_list):
-        """Set the entire gene set of the chromosome"""
-        self.gene_list = gene_list
+    def set_genes(self, genes):
+        self.gene_list = genes
 
     def set_fitness(self, fitness):
         """Set the fitness value of the chromosome"""

src/initialization/gene_creation/gene_random.py

@@ -0,0 +1,13 @@
+# Imported library
+import random
+
+def check_values(low,high):
+    #Check to make sure its not less then zero
+    assert low > 0 , "The random gene low can not be less then zero"
+    # Check to make sure the high value is not
+    # lower than or equal to low and not 0.
+    assert high > low , "High value can not be smaller then low value"
+    assert high != 0, "High value can not be zero"
+
+def random_gene():
+    return random.randint(1,100)

src/initialization/gene_structure/gene.py

@@ -22,7 +22,7 @@ class Gene:
         """Set fitness of the gene"""
         self.fitness = fitness
 
-    def set_value(self):
+    def set_value(self, value):
         """Set value of the gene"""
         self.value = value
 

src/initialization/initialization_methods.py

@@ -0,0 +1,32 @@
+# Import the data structure
+from .population_structure.population import Population as create_population
+from .chromosome_structure.chromosome import Chromosome as create_chromosome
+from .gene_structure.gene import Gene as create_gene
+
+class Initialization_Methods:
+    """Initialization examples that are used as defaults and examples"""
+    
+    def random_initialization(self, ga):
+        """Takes the initialization inputs and choregraphs them to output the type of population with the given parameters."""
+
+        # Create the population object
+        population = create_population()
+
+        # Fill the population with chromosomes
+        for i in range(ga.population_size):
+            chromosome = create_chromosome()
+            #Fill the Chromosome with genes
+            for j in range(ga.chromosome_length):
+                # Using the chromosome_impl to set every index inside of the chromosome
+                if ga.chromosome_impl != None:
+                    # Each chromosome location is specified with its own function
+                    chromosome.add_gene(create_gene(ga.chromosome_impl(j)))
+                    # Will break if chromosome_length != len(lists) in domain
+                elif ga.gene_impl != None:
+                    function = ga.gene_impl[0]
+                    chromosome.add_gene(create_gene(function(*ga.gene_impl[1:])))
+                else:
+                    #Exit because either were not specified
+                    print("You did not specify any initialization constraints.")
+            population.add_chromosome(chromosome)
+        return population

src/initialization/population_structure/population.py

@@ -1,5 +1,4 @@
 class Population:
-
     def __init__(self, chromosome_list = None):
         """Intiialize the population with fitness of value None, and a set of chromosomes dependant on user-passed parameter"""
         if chromosome_list is None:
@@ -7,6 +6,7 @@ class Population:
         else:
           self.chromosome_list = chromosome_list
         self.fitness = None
+        self.mating_pool = []
 
     def get_closet_fitness(self,value):
         """Get the chomosome that has the closets fitness to the value defined"""
@@ -24,18 +24,18 @@ class Population:
 
     def get_all_chromosomes(self):
         """returns all chromosomes in the population"""
-        return chromosome_list
+        return self.chromosome_list
 
     def get_fitness(self):
         """returns the population's fitness"""
         return self.fitness
 
-    def set_all_chromosomes(self, chromosome_list):
-        """sets the chromosome set of the population"""
-        self.chromosome_list = chromosome_list
+    def set_all_chromosomes(self, chromosomes):
+        self.chromosome_list = chromosomes
 
-    def set_chromosome(self, chromosome, index):
-        """sets a specific chromosome at a specific index"""
+    def set_chromosome(self, chromosome, index = -1):
+        if index == -1:
+            index = len(self.chromosomes)-1
         self.chromosome_list[index] = chromosome
 
     def set_fitness(self, fitness):
@@ -43,8 +43,8 @@ class Population:
         self.fitness = fitness
 
     def __repr__(self):
-        """Sets the repr() output format"""
-        return ''.join([chromosome.__repr__() for chromosome in self.chromosome_list])
+        for index in range(len(self.chromosomes)):
+            return f'{self.chromosome_list[index]}'
 
     def print_all(self):
         """Prints information about the population in the following format:"""

src/mutation/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT function_name
-from .methods import Mutation_methods
+from .mutation_methods import Mutation_Methods

src/mutation/mutation_methods.py

@@ -0,0 +1,42 @@
+import random
+
+class Mutation_Methods:
+    
+    def __init__(self):
+        pass
+
+    def random_mutation(self, ga, chromosome_set = None):
+        
+        if chromosome_set == None:
+            chromosome_set = ga.population.get_all_chromosomes()
+
+        chromosome_mutate_num = int(len(chromosome_set)*ga.mutation_rate)
+        temp_population = ga.initialization_impl(ga)
+
+        while chromosome_mutate_num > 0:
+            chromosome_set[random.randint(0,ga.population_size-1)] = temp_population.get_all_chromosomes()[chromosome_mutate_num]
+            chromosome_mutate_num -= 1
+        
+        return chromosome_set
+    
+    def per_gene_mutation(self, ga, chromosome_set = None, gene_mutate_count = 1):
+        
+        gene_mutate_count_static = int(gene_mutate_count)
+
+        if chromosome_set == None:
+            chromosome_set = ga.population.get_all_chromosomes()
+
+        for i in range(len(chromosome_set)):
+            random_num = random.uniform(0,1)
+
+            if (random_num <= ga.mutation_rate):
+                while gene_mutate_count > 0:
+                    dummy_population = ga.initialization_impl(ga) #Really inefficient, but works for now
+                    random_index = random.randint(0, ga.chromosome_length-1)
+                    chromosome_set[i].get_genes()[random_index] = dummy_population.get_all_chromosomes()[random.randint(0,ga.population_size-1)].get_genes()[random_index]
+                    gene_mutate_count -= 1
+            gene_mutate_count = int(gene_mutate_count_static)
+
+        return chromosome_set
+
+        

src/new_initialization_method_testing.py

@@ -0,0 +1,36 @@
+import EasyGA
+import random
+
+#1. GA should take in range for gene input
+#2. GA should take in index-dependent range for gene input
+#3. GA should take in domain input
+#4. GA should take in index-dependent domain for gene input
+#5. GA should accept mix of range and domain for gene input
+
+
+# Create the Genetic algorithm
+ga = EasyGA.GA()
+
+test_gene_input = [["left", "right"],[1,100],[5.0,10],[22,"up"]]
+ga.gene_input_type[1] = "float-range"
+ga.gene_input_type[2] = "domain"
+
+ga.initialize(test_gene_input)
+ga.population.print_all() 
+
+
+#Example tests
+#Note, the following examples assume a chromosome length of 4.
+#if the test_gene_input is longer than the chromosomes, it will get truncated at the length of the chromosome
+#for example, for chromosomes with length 2, [["left", "right"],[1,100],[5.0,10],[22,"up"]] becomes [["left", "right"],[1,100]]
+#if the test_gene_input is shorter than the chromosomes, the remaining elements will be populated with None
+
+#test_gene_input = [1,100]
+#test_gene_input = [["left", "right"],[1,100],[5.0,10],[22,"up"]]
+#test_gene_input = ["left", "right", "up", "down"]
+#test_gene_input = [[1,100],[0,1],[33,35],[5,6]]
+#test_gene_input = [["left", "right"], ["up", "down"], ["left", "down"], ["down", "right"]]
+
+#ga.gene_input_type = "float-range"
+#ga.gene_input_type[1] = "domain"
+#ga.gene_input_type[1] = "float-range"

src/run_testing.py

@@ -1,16 +1,15 @@
 import EasyGA
 import random
+
+
 # Create the Genetic algorithm
 ga = EasyGA.GA()
 
-ga.chromosome_length = 3
-ga.max_generations = 5
-# If the user wants to use a domain
-ga.gene_impl = [random.randrange,1,10]
 
-ga.generation = 36
 
-# Run Everyhting
+ga.gene_impl = [random.randrange,1,100]
+
+# Run Everything
 ga.evolve()
 
 # Print the current population

src/selection/__init__.py

@@ -0,0 +1,2 @@
+# FROM (. means local) file_name IMPORT function_name
+from .selection_methods import Selection_Methods

src/selection/selection_methods.py

@@ -0,0 +1,95 @@
+import random
+from initialization.chromosome_structure.chromosome import Chromosome as create_chromosome
+from initialization.gene_structure.gene import Gene as create_gene
+from initialization.population_structure.population import Population
+from initialization.chromosome_structure.chromosome import Chromosome
+
+class Selection_Methods:
+    """Selection is the process by which chromosomes are selected for crossover and eventually, influence the next generation of chromosomes."""
+    def __init__(self):
+        pass
+
+    class Parent_Selection:
+        class Tournament:
+            def with_replacement(self, ga):
+                tournament_size = int(len(ga.population.get_all_chromosomes())*ga.parent_ratio/10)
+                if tournament_size < 3:
+                    tournament_size = int(len(ga.population.get_all_chromosomes())*ga.parent_ratio/3)
+                
+                # Probability used for determining if a chromosome should enter the mating pool.
+                selection_probability = ga.selection_probability
+                
+                # Repeat tournaments until the mating pool is large enough.
+                while (len(ga.population.mating_pool) < len(ga.population.get_all_chromosomes())*ga.parent_ratio):
+                    
+                    # Generate a random tournament group and sort by fitness.
+                    tournament_group = ga.sort_by_best_fitness([random.choice(ga.population.get_all_chromosomes()) for n in range(tournament_size)])
+                    
+                    # For each chromosome, add it to the mating pool based on its rank in the tournament.
+                    for index in range(tournament_size):
+                        # Probability required is selection_probability * (1-selection_probability) ^ (tournament_size-index+1)
+                        # e.g. top ranked fitness has probability: selection_probability
+                        #   second ranked fitness has probability: selection_probability * (1-selection_probability)
+                        #   third  ranked fitness has probability: selection_probability * (1-selection_probability)^2
+                        # etc.
+                        if random.uniform(0, 1) < selection_probability * pow(1-selection_probability, index+1):
+                            ga.population.mating_pool.append(tournament_group[index])
+
+        class Roulette:
+            def roulette_selection(self, ga):
+                """Roulette selection works based off of how strong the fitness is of the
+                chromosomes in the population. The stronger the fitness the higher the probability
+                that it will be selected. Using the example of a casino roulette wheel.
+                Where the chromosomes are the numbers to be selected and the board size for
+                those numbers are directly proportional to the chromosome's current fitness. Where
+                the ball falls is a randomly generated number between 0 and 1"""
+                total_fitness = sum(ga.population.chromosome_list[i].get_fitness() for i in range(len(ga.population.chromosome_list)))
+                rel_fitnesses = []
+        
+                for chromosome in ga.population.chromosome_list:
+                    if (total_fitness != 0):
+                        rel_fitnesses.append(float(chromosome.fitness)/total_fitness)
+                
+                probability = [sum(rel_fitnesses[:i+1]) for i in range(len(rel_fitnesses))]
+        
+                while (len(ga.population.mating_pool) < len(ga.population.get_all_chromosomes())*ga.parent_ratio):
+                    rand_number = random.random()
+        
+                    # Loop through the list of probabilities
+                    for i in range(len(probability)):
+                        # If the probability is greater than the random_number, then select that chromosome
+                        if (probability[i] >= rand_number):
+                            ga.population.mating_pool.append(ga.population.chromosome_list[i])
+                            # print (f'Selected chromosome : {i}')
+                            break
+
+    class Survivor_Selection:
+        def repeated_crossover(self, ga, next_population): #Might be cheating? I don't know honestly - RG
+            while len(next_population.get_all_chromosomes()) < ga.population_size:
+                crossover_pool = ga.population.mating_pool
+
+                split_point = random.randint(0,ga.chromosome_length)    
+                chromosome_list = []
+                for i in range(len(crossover_pool)):
+                    if i + 1 < len(crossover_pool):
+                        new_gene_set = []
+                        parent_one = crossover_pool[i].get_genes()
+                        parent_two = crossover_pool[i+1].get_genes()
+                        new_gene_set.extend(parent_one[0:split_point])
+                        new_gene_set.extend(parent_two[split_point:])
+                        new_chromosome = create_chromosome(new_gene_set)
+                        chromosome_list.append(new_chromosome)
+                        
+                
+                for i in range(len(chromosome_list)):
+                    next_population.add_chromosome(chromosome_list[i])
+                    if len(next_population.get_all_chromosomes()) >= ga.population_size:
+                        break
+            return next_population
+    
+        def remove_two_worst(self, ga, next_population):
+            iterator = 0
+            while len(next_population.get_all_chromosomes()) < ga.population_size:
+                next_population.add_chromosome(ga.population.get_all_chromosomes()[iterator])
+                iterator += 1
+            return next_population

src/termination_point/__init__.py

@@ -1,2 +1,2 @@
 # FROM (. means local) file_name IMPORT class name
-from .methods import Termination_methods
+from .termination_methods import Termination_Methods

src/termination_point/termination_methods.py

@@ -0,0 +1,21 @@
+class Termination_Methods:
+    """Example functions that can be used to terminate the the algorithms loop"""
+
+    def fitness_based(self, ga):
+        """Fitness based approach to terminate when the goal fitness has been reached"""
+            
+        status = True
+        if ga.population == None:
+            return status
+        for i in range(len(ga.population.get_all_chromosomes())):
+            if(ga.population.get_all_chromosomes()[i].fitness >= ga.fitness_goal):
+                status = False
+                break
+        return status
+
+    def generation_based(self, ga):
+        """Generation based approach to terminate when the goal generation has been reached"""
+        status = True
+        if(ga.current_generation > ga.generation_goal):
+            status = False
+        return status