From b2b1775e16b5bbb3f8d4e873a2d3a8a2e7cb3d5c Mon Sep 17 00:00:00 2001
From: SimpleArt <71458112+SimpleArt@users.noreply.github.com>
Date: Fri, 1 Jan 2021 16:09:27 -0500
Subject: [PATCH] Added decorators file

---
 src/crossover/Crossover.py     |  50 +------
 src/decorators.py              | 261 +++++++++++++++++++++++++++++++++
 src/mutation/Mutation.py       |  68 +--------
 src/parent/Parent.py           |  54 +------
 src/survivor/Survivor.py       |  37 +++--
 src/termination/Termination.py |  80 +---------
 6 files changed, 292 insertions(+), 258 deletions(-)
 create mode 100644 src/decorators.py

diff --git a/src/crossover/Crossover.py b/src/crossover/Crossover.py
index a3e16e3..298e5cb 100644
--- a/src/crossover/Crossover.py
+++ b/src/crossover/Crossover.py
@@ -1,47 +1,13 @@
-from EasyGA import function_info
 import random
 
+# Import all crossover decorators
+from decorators import _check_weight, _gene_by_gene
+
 # Round to an integer near x with higher probability
 # the closer it is to that integer.
 randround = lambda x: int(x + random.random())
 
 
-@function_info
-def _check_weight(individual_method):
-    """Checks if the weight is between 0 and 1 before running.
-    Exception may occur when using ga.adapt, which will catch
-    the error and try again with valid weight.
-    """
-
-    def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', None)):
-
-        if weight is None:
-            individual_method(ga, parent_1, parent_2)
-        elif 0 < weight < 1:
-            individual_method(ga, parent_1, parent_2, weight = weight)
-        else:
-            raise ValueError(f"Weight must be between 0 and 1 when using {individual_method.__name__}.")
-
-    return new_method
-
-
-@function_info
-def _gene_by_gene(individual_method):
-    """Perform crossover by making a single new chromosome by combining each gene by gene."""
-
-    def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', 'None')):
-
-        ga.population.add_child(
-            individual_method(ga, value_1, value_2)
-            if weight == 'None' else
-            individual_method(ga, value_1, value_2, weight = weight)
-            for value_1, value_2
-            in zip(parent_1.gene_value_iter, parent_2.gene_value_iter)
-        )
-
-    return new_method
-
-
 class Population:
     """Methods for selecting chromosomes to crossover."""
 
@@ -61,7 +27,7 @@ class Population:
             )
 
 
-    def random_mate(ga):
+    def random(ga):
         """Select random pairs from the mating pool.
         Every parent is paired with a random parent.
         """
@@ -100,9 +66,9 @@ class Individual:
 
     @_check_weight
     @_gene_by_gene
-    def uniform(ga, *gene_values, *, weight = 0.5):
+    def uniform(ga, value_1, value_2, *, weight = 0.5):
         """Cross two parents by swapping all genes randomly."""
-        return random.choices(gene_values, cum_weights = [weight, 1])[0]
+        return random.choices(gene_pair, cum_weights = [weight, 1])[0]
 
 
     class Arithmetic:
@@ -136,7 +102,7 @@ class Individual:
 
         @_check_weight
         @_gene_by_gene
-        def random_value(ga, value_1, value_2, *, weight = 0.5):
+        def random(ga, value_1, value_2, *, weight = 0.5):
             """Cross two parents by taking a random integer or float value between each of the genes."""
 
             value = value_1 + ga.weighted_random(weight) * (value_2-value_1)
@@ -158,7 +124,7 @@ class Individual:
 
             # Too small to cross
             if len(parent_1) < 2:
-                raise ValueError("Parent lengths must be at least 2.")
+                return parent_1.gene_list
 
             # Unequal parent lengths
             if len(parent_1) != len(parent_2):
diff --git a/src/decorators.py b/src/decorators.py
new file mode 100644
index 0000000..b0e119f
--- /dev/null
+++ b/src/decorators.py
@@ -0,0 +1,261 @@
+import random
+from math import ceil
+
+def function_info(decorator):
+    """Recovers the name and doc-string for decorators throughout EasyGA for documentation purposes."""
+
+    def new_decorator(method):
+
+        # Apply old decorator
+        new_method = decorator(method)
+
+        # Recover name and doc-string
+        new_method.__name__ = method.__name__
+        new_method.__doc__  = method.__doc__
+
+        # Return new method with proper name and doc-string
+        return new_method
+
+    return new_decorator
+
+
+#=======================#
+# Crossover decorators: #
+#=======================#
+
+@function_info
+def _check_weight(individual_method):
+    """Checks if the weight is between 0 and 1 before running.
+    Exception may occur when using ga.adapt, which will catch
+    the error and try again with valid weight.
+    """
+
+    def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', None)):
+
+        if weight is None:
+            individual_method(ga, parent_1, parent_2)
+        elif 0 < weight < 1:
+            individual_method(ga, parent_1, parent_2, weight = weight)
+        else:
+            raise ValueError(f"Weight must be between 0 and 1 when using {individual_method.__name__}.")
+
+    return new_method
+
+@function_info
+def _gene_by_gene(individual_method):
+    """Perform crossover by making a single new chromosome by combining each gene by gene."""
+
+    def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', 'None')):
+
+        ga.population.add_child(
+            individual_method(ga, value_1, value_2)
+            if weight == 'None' else
+            individual_method(ga, value_1, value_2, weight = weight)
+            for value_1, value_2
+            in zip(parent_1.gene_value_iter, parent_2.gene_value_iter)
+        )
+
+    return new_method
+
+
+#====================#
+# Parent decorators: #
+#====================#
+
+@function_info
+def _check_selection_probability(selection_method):
+    """Raises a ValueError if the selection_probability is not between 0 and 1 inclusively.
+    Otherwise runs the selection method."""
+
+    def new_method(ga):
+        if 0 <= ga.selection_probability <= 1:
+            selection_method(ga)
+        else:
+            raise ValueError("Selection probability must be between 0 and 1 to select parents.")
+
+    return new_method
+
+
+@function_info
+def _check_positive_fitness(selection_method):
+    """Raises a ValueError if the population contains a chromosome with negative fitness.
+    Otherwise runs the selection method."""
+
+    def new_method(ga):
+        if ga.get_chromosome_fitness(0) > 0 and ga.get_chromosome_fitness(-1) >= 0:
+            selection_method(ga)
+        else:
+            raise ValueError("Converted fitness values can't have negative values or be all 0."
+                             + " Consider using rank selection or stochastic selection instead.")
+
+    return new_method
+
+@function_info
+def _ensure_sorted(selection_method):
+    """Sorts the population by fitness and then runs the selection method."""
+
+    def new_method(ga):
+        ga.sort_by_best_fitness()
+        selection_method(ga)
+
+    return new_method
+
+@function_info
+def _compute_parent_amount(selection_method):
+    """Computes the amount of parents needed to be selected,
+    and passes it as another argument for the method."""
+
+    def new_method(ga):
+        parent_amount = max(2, round(len(ga.population)*ga.parent_ratio))
+        selection_method(ga, parent_amount)
+
+    return new_method
+
+
+#======================#
+# Mutation decorators: #
+#======================#
+
+
+@function_info
+def _check_chromosome_mutation_rate(population_method):
+    """Checks if the chromosome mutation rate is a float between 0 and 1 before running."""
+
+    def new_method(ga):
+
+        if not isinstance(ga.chromosome_mutation_rate, float):
+            raise TypeError("Chromosome mutation rate must be a float.")
+
+        elif 0 < ga.chromosome_mutation_rate < 1:
+            population_method(ga)
+
+        else:
+            raise ValueError("Chromosome mutation rate must be between 0 and 1.")
+
+    return new_method
+
+
+@function_info
+def _check_gene_mutation_rate(individual_method):
+    """Checks if the gene mutation rate is a float between 0 and 1 before running."""
+
+    def new_method(ga, index):
+
+        if not isinstance(ga.gene_mutation_rate, float):
+            raise TypeError("Gene mutation rate must be a float.")
+
+        elif 0 < ga.gene_mutation_rate <= 1:
+            individual_method(ga, index)
+
+        else:
+            raise ValueError("Gene mutation rate must be between 0 and 1.")
+
+    return new_method
+
+
+@function_info
+def _reset_fitness(individual_method):
+    """Resets the fitness value of the chromosome."""
+
+    def new_method(ga, chromosome):
+        chromosome.fitness = None
+        individual_method(ga, chromosome)
+
+    return new_method
+
+
+@function_info
+def _loop_random_mutations(individual_method):
+    """Runs the individual method until enough
+    genes are mutated on the indexed chromosome."""
+
+    # Change input to include the gene index being mutated.
+    def new_method(ga, chromosome):
+
+        sample_space = range(len(chromosome))
+        sample_size  = ceil(len(chromosome)*ga.gene_mutation_rate)
+
+        # Loop the individual method until enough genes are mutated.
+        for index in random.sample(sample_space, sample_size):
+            individual_method(ga, chromosome, index)
+
+    return new_method
+
+
+#======================#
+# Survivor decorators: #
+#======================#
+
+
+#=========================#
+# Termination decorators: #
+#=========================#
+
+@function_info
+def _add_by_fitness_goal(termination_impl):
+    """Adds termination by fitness goal to the method."""
+
+    def new_method(ga):
+
+        # Try to check the fitness goal
+        try:
+
+            # If minimum fitness goal reached, stop ga.
+            if ga.target_fitness_type == 'min' and ga.population[0].fitness <= ga.fitness_goal:
+                return False
+
+            # If maximum fitness goal reached, stop ga.
+            elif ga.target_fitness_type == 'max' and ga.population[0].fitness >= ga.fitness_goal:
+                return False
+
+        # Fitness or fitness goals are None, or Population not initialized
+        except (TypeError, AttributeError):
+            pass
+
+        # Check other termination methods
+        return termination_impl(ga)
+
+    return new_method
+
+
+@function_info
+def _add_by_generation_goal(termination_impl):
+    """Adds termination by generation goal to the method."""
+
+    def new_method(ga):
+
+        # If generation goal is set, check it.
+        if ga.generation_goal is not None and ga.current_generation >= ga.generation_goal:
+            return False
+
+        # Check other termination methods
+        return termination_impl(ga)
+
+    return new_method
+
+
+@function_info
+def _add_by_tolerance_goal(termination_impl):
+    """Adds termination by tolerance goal to the method."""
+
+    def new_method(ga):
+
+        # If tolerance is set, check it, if possible.
+        try:
+            best_fitness = ga.population[0].fitness
+            threshhold_fitness = ga.population[round(ga.percent_converged*len(ga.population))].fitness
+            tol = ga.tolerance_goal * (1 + abs(best_fitness))
+
+            # Terminate if the specified amount of the population has converged to the specified tolerance
+            if abs(best_fitness - threshhold_fitness) < tol:
+                return False
+
+        # Fitness or tolerance goals are None, or population is not initialized
+        except (TypeError, AttributeError):
+            pass
+
+        # Check other termination methods
+        return termination_impl(ga)
+
+    return new_method
+
diff --git a/src/mutation/Mutation.py b/src/mutation/Mutation.py
index fcb8ef8..33f18dc 100644
--- a/src/mutation/Mutation.py
+++ b/src/mutation/Mutation.py
@@ -1,72 +1,8 @@
-from EasyGA import function_info
 import random
 from math import ceil
 
-
-@function_info
-def _check_chromosome_mutation_rate(population_method):
-    """Checks if the chromosome mutation rate is a float between 0 and 1 before running."""
-
-    def new_method(ga):
-
-        if not isinstance(ga.chromosome_mutation_rate, float):
-            raise TypeError("Chromosome mutation rate must be a float.")
-
-        elif 0 < ga.chromosome_mutation_rate < 1:
-            population_method(ga)
-
-        else:
-            raise ValueError("Chromosome mutation rate must be between 0 and 1.")
-
-    return new_method
-
-
-@function_info
-def _check_gene_mutation_rate(individual_method):
-    """Checks if the gene mutation rate is a float between 0 and 1 before running."""
-
-    def new_method(ga, index):
-
-        if not isinstance(ga.gene_mutation_rate, float):
-            raise TypeError("Gene mutation rate must be a float.")
-
-        elif 0 < ga.gene_mutation_rate <= 1:
-            individual_method(ga, index)
-
-        else:
-            raise ValueError("Gene mutation rate must be between 0 and 1.")
-
-    return new_method
-
-
-@function_info
-def _reset_fitness(individual_method):
-    """Resets the fitness value of the chromosome."""
-
-    def new_method(ga, chromosome):
-        chromosome.fitness = None
-        individual_method(ga, chromosome)
-
-    return new_method
-
-
-@function_info
-def _loop_random_mutations(individual_method):
-    """Runs the individual method until enough
-    genes are mutated on the indexed chromosome.
-    """
-
-    # Change input to include the gene index being mutated.
-    def new_method(ga, chromosome):
-
-        sample_space = range(len(chromosome))
-        sample_size  = ceil(len(chromosome)*ga.gene_mutation_rate)
-
-        # Loop the individual method until enough genes are mutated.
-        for index in random.sample(sample_space, sample_size):
-            individual_method(ga, chromosome, index)
-
-    return new_method
+# Import all mutation decorators
+from decorators import _check_chromosome_mutation_rate, _check_gene_mutation_rate, _reset_fitness, _loop_random_mutations
 
 
 class Population:
diff --git a/src/parent/Parent.py b/src/parent/Parent.py
index 8b2e95a..0c01335 100644
--- a/src/parent/Parent.py
+++ b/src/parent/Parent.py
@@ -1,57 +1,7 @@
-from EasyGA import function_info
 import random
 
-
-@function_info
-def _check_selection_probability(selection_method):
-    """Raises a ValueError if the selection_probability is not between 0 and 1 inclusively.
-    Otherwise runs the selection method."""
-
-    def new_method(ga):
-        if 0 <= ga.selection_probability <= 1:
-            selection_method(ga)
-        else:
-            raise ValueError("Selection probability must be between 0 and 1 to select parents.")
-
-    return new_method
-
-
-@function_info
-def _check_positive_fitness(selection_method):
-    """Raises a ValueError if the population contains a chromosome with negative fitness.
-    Otherwise runs the selection method."""
-
-    def new_method(ga):
-        if ga.get_chromosome_fitness(0) > 0 and ga.get_chromosome_fitness(-1) >= 0:
-            selection_method(ga)
-        else:
-            raise ValueError("Converted fitness values can't have negative values or be all 0."
-                             + " Consider using rank selection or stochastic selection instead.")
-
-    return new_method
-
-
-@function_info
-def _ensure_sorted(selection_method):
-    """Sorts the population by fitness and then runs the selection method."""
-
-    def new_method(ga):
-        ga.sort_by_best_fitness()
-        selection_method(ga)
-
-    return new_method
-
-
-@function_info
-def _compute_parent_amount(selection_method):
-    """Computes the amount of parents needed to be selected,
-    and passes it as another argument for the method."""
-
-    def new_method(ga):
-        parent_amount = max(2, round(len(ga.population)*ga.parent_ratio))
-        selection_method(ga, parent_amount)
-
-    return new_method
+# Import all parent decorators
+from decorators import _check_selection_probability, _check_positive_fitness, _ensure_sorted, _compute_parent_amount
 
 
 class Rank:
diff --git a/src/survivor/Survivor.py b/src/survivor/Survivor.py
index ce41653..ed6307c 100644
--- a/src/survivor/Survivor.py
+++ b/src/survivor/Survivor.py
@@ -1,34 +1,23 @@
-from EasyGA import function_info
 import random
 
-
-@function_info
-def _append_to_next_population(survivor_method):
-    """Appends the selected chromosomes to the next population."""
-
-    def new_method(ga):
-        ga.population.append_children(survivor_method(ga))
-
-    return new_method
+# Import all survivor decorators
+from decorators import *
 
 
-@_append_to_next_population
 def fill_in_best(ga):
     """Fills in the next population with the best chromosomes from the last population"""
 
     needed_amount = len(ga.population) - len(ga.population.next_population)
-    return ga.population[:needed_amount]
+    ga.population.append_children(ga.population[:needed_amount])
 
 
-@_append_to_next_population
 def fill_in_random(ga):
     """Fills in the next population with random chromosomes from the last population"""
 
     needed_amount = len(ga.population) - len(ga.population.next_population)
-    return random.sample(ga.population, needed_amount)
+    ga.population.append_children(random.sample(ga.population, needed_amount))
 
 
-@_append_to_next_population
 def fill_in_parents_then_random(ga):
     """Fills in the next population with all parents followed by random chromosomes from the last population"""
 
@@ -41,11 +30,19 @@ def fill_in_parents_then_random(ga):
 
     # Only parents are used.
     if random_amount == 0:
-        return ga.population.mating_pool[:parent_amount]
+        ga.population.append_children(
+            chromosome
+            for i, chromosome
+            in enumerate(mating_pool)
+            if i < parent_amount
+        )
 
     # Parents need to be removed from the random sample to avoid dupes.
     else:
-        return mating_pool + random.sample(
-                                 set(ga.population) - mating_pool,
-                                 random_amount
-                             )
+        ga.population.append_children(mating_pool)
+        ga.population.append_children(
+               random.sample(
+                  set(ga.population) - mating_pool,
+                  random_amount
+              )
+        )
diff --git a/src/termination/Termination.py b/src/termination/Termination.py
index 959c5ee..b36453d 100644
--- a/src/termination/Termination.py
+++ b/src/termination/Termination.py
@@ -1,81 +1,5 @@
-from EasyGA import function_info
-
-@function_info
-def _add_by_fitness_goal(termination_impl):
-    """Adds termination by fitness goal to the method."""
-
-    def new_method(ga):
-
-        # Try to check the fitness goal
-        try:
-
-            # If minimum fitness goal reached, stop ga.
-            if ga.target_fitness_type == 'min' and ga.population[0].fitness <= ga.fitness_goal:
-                return False
-
-            # If maximum fitness goal reached, stop ga.
-            elif ga.target_fitness_type == 'max' and ga.population[0].fitness >= ga.fitness_goal:
-                return False
-
-        # Fitness or fitness goals are None
-        except TypeError:
-            pass
-
-        # Population not initialized
-        except AttributeError:
-            pass
-
-        # Check other termination methods
-        return termination_impl(ga)
-
-    return new_method
-
-
-@function_info
-def _add_by_generation_goal(termination_impl):
-    """Adds termination by generation goal to the method."""
-
-    def new_method(ga):
-
-        # If generation goal is set, check it.
-        if ga.generation_goal is not None and ga.current_generation >= ga.generation_goal:
-            return False
-
-        # Check other termination methods
-        return termination_impl(ga)
-
-    return new_method
-
-
-@function_info
-def _add_by_tolerance_goal(termination_impl):
-    """Adds termination by tolerance goal to the method."""
-
-    def new_method(ga):
-
-        # If tolerance is set, check it, if possible.
-        try:
-            best_fitness = ga.population[0].fitness
-            threshhold_fitness = ga.population[round(ga.percent_converged*len(ga.population))].fitness
-            tol = ga.tolerance_goal * (1 + abs(best_fitness))
-
-            # Terminate if the specified amount of the population has converged to the specified tolerance
-            if abs(best_fitness - threshhold_fitness) < tol:
-                return False
-
-        # Fitness or tolerance goals are None
-        except TypeError:
-            pass
-
-        # Population not initialized
-        except AttributeError:
-            pass
-
-        # Check other termination methods
-        return termination_impl(ga)
-
-    return new_method
-
+# Import all termination decorators
+from decorators import _add_by_fitness_goal, _add_by_generation_goal, _add_by_tolerance_goal
 
 @_add_by_fitness_goal
 @_add_by_generation_goal