Merge pull request #40 from jbytecode/main

introduce GeneType enum to replace Real, Permutation, and Binary stri…

src/individual.py

@@ -1,10 +1,18 @@
+from enum import Enum 
 from numpy import random
 import numpy as np
 import random as rd
 from problems.abstract_problem import AbstractProblem
 
 
 
+class GeneType(Enum):
+    BINARY = 1
+    PERMUTATION = 2
+    REAL = 3
+
+
+
 class Individual:
     """
     A class to represent an individual in an evolutionary algorithm.
@@ -21,14 +29,14 @@ class Individual:
         The positions of the individual's neighbors.
     neighbors : list or None
         The list of neighbors for the individual.
-    gen_type : str
-        The type of genome representation ("Binary", "Permutation", "Real").
+    gen_type : GeneType
+        The enum type of genome representation (GeneType.BINARY, GeneType.PERMUTATION, GeneType.REAL).
     ch_size : int
         The size of the chromosome.
     """
 
     def __init__(self, 
-                 gen_type: str = "", 
+                 gen_type: GeneType = GeneType.BINARY, 
                  ch_size: int = 0,
                  problem: AbstractProblem = None,
                  mins : list[float] = [],
@@ -39,7 +47,7 @@ def __init__(self,
         Parameters
         ----------
         gen_type : str, optional
-            The type of genome representation. Must be one of "Binary", "Permutation", or "Real". (default is "Binary")
+            The type of genome representation. Must be one of GeneType.BINARY, "Permutation", or "Real". (default is GeneType.BINARY)
         ch_size : int
             The size of the chromosome.
         mins: list[float]
@@ -73,18 +81,17 @@ def randomize(self):
         NotImplementedError
             If the genome type is not implemented.
         """
-        problem_name = self.problem.__class__.__name__
 
-        if self.gen_type == "Binary":
+        if self.gen_type == GeneType.BINARY:
             self.chromosome = [random.randint(2) for i in range(self.ch_size)]
 
-        elif self.gen_type == "Permutation":
+        elif self.gen_type == GeneType.PERMUTATION:
             # Generate a random permutation of the numbers 1 to ch_size.
             # by default random.permutation emits numbers from 0 to ch_size-1
             # so we add 1 to each number to get the desired range.
             self.chromosome = list(np.random.permutation(self.ch_size) + 1)
 
-        elif self.gen_type == "Real":
+        elif self.gen_type == GeneType.REAL:
             if len(self.mins) > 0:
                 assert len(self.mins) == len(self.maxs) == self.ch_size
                 self.chromosome = [rd.uniform(self.mins[i], self.maxs[i]) for i in range(self.ch_size)]
@@ -95,7 +102,8 @@ def randomize(self):
             raise NotImplementedError(self.gen_type + " not implemented yet.")
         return self.chromosome
 
-    def generate_candidate(self, vector: list) -> list:
+
+    def generate_candidate(self, probvector: list) -> list:
         """
         Generate a candidate chromosome based on the given probability vector.
 
@@ -109,7 +117,7 @@ def generate_candidate(self, vector: list) -> list:
         list
             The generated candidate chromosome as a list of 0s and 1s.
         """
-        ind = [1 if random.rand() < p else 0 for p in vector]
+        ind = [1 if random.rand() < p else 0 for p in probvector]
         return ind
 
     def getneighbors_positions(self) -> list:

src/main.py

@@ -153,7 +153,7 @@
 #     n_rows = 5,
 #     n_gen = 100,
 #     ch_size = 10,
-#     gen_type = "Binary",
+#     gen_type = GeneType.BINARY,
 #     problem = optimizer.OneMax(),
 #     selection = optimizer.TournamentSelection)
 

src/optimizer.py

@@ -360,7 +360,7 @@ def alpha_cga(
     ch_size : int
         Size of the chromosome.
     gen_type : str
-        Type of genome representation ("Binary", "Permutation", "Real").
+        Type of genome representation (GeneType.BINARY, "Permutation", "Real").
     p_crossover : float
         Probability of crossover, should be between 0 and 1.
     p_mutation : float
@@ -522,7 +522,7 @@ def ccga(
     ch_size : int
         Size of the chromosome.
     gen_type : str
-        Type of genome representation ("Binary", "Permutation", "Real").
+        Type of genome representation (GeneType.BINARY, "Permutation", "Real").
     problem : AbstractProblem
         The problem instance used to evaluate fitness.
     selection : Callable
@@ -840,26 +840,5 @@ def generate_probability_vector(mins: List[float], maxs: List[float], ntries: in
     return probvector
 
 
-def sample(probvector: List[float]) -> List[int]:
-    """
-    Sample a vector based on the provided probability vector.
-
-    Parameters
-    ----------
-    probvector : List[float]
-        Probability vector for sampling.
-
-    Returns
-    -------
-    List[int]
-        Sampled binary vector.
-    """
-    n = len(probvector)
-    newvector = [0] * n
-
-    for i in range(n):
-        if random.random() < probvector[i]:
-            newvector[i] = 1
 
-    return newvector
 

src/population.py

@@ -26,7 +26,7 @@ class Population:
     n_cols : int
         The number of columns in the grid.
     gen_type : str
-        The type of genome representation ("Binary", "Permutation", "Real").
+        The type of genome representation (GeneType.BINARY, Genetype.PERMUTATION, GeneType.REAL).
     problem : AbstractProblem
         The problem instance used to evaluate fitness.
     vector : list

src/tests/test_arithmetic_crossover.py

@@ -1,6 +1,6 @@
 import pytest
 import random
-from individual import Individual
+from individual import Individual, GeneType
 from problems.abstract_problem import AbstractProblem
 from recombination.arithmetic_crossover import ArithmeticCrossover  # Replace with the actual path if different
 
@@ -34,8 +34,8 @@ def setup_parents():
     list
         A list containing two parent individuals with predefined chromosomes and size.
     """
-    ind1 = Individual("Real", 5)
-    ind2 = Individual("Real", 5)
+    ind1 = Individual(GeneType.REAL, 5)
+    ind2 = Individual(GeneType.REAL, 5)
     ind1.chromosome = [1.0, 2.0, 3.0, 4.0, 5.0]
     ind2.chromosome = [5.0, 4.0, 3.0, 2.0, 1.0]
     ind1.ch_size = 5

src/tests/test_bit_flip_mutation.py

@@ -2,7 +2,7 @@
 import pytest
 from mutation.bit_flip_mutation import BitFlipMutation
 from problems.single_objective.discrete.binary.one_max import OneMax
-from individual import Individual
+from individual import Individual, GeneType
 
 def test_bit_flip_mutation():
     """
@@ -37,7 +37,7 @@ def test_bit_flip_mutation():
     CHSIZE = 20
 
     # Create an individual with all zeros
-    ind = Individual("Binary", CHSIZE)
+    ind = Individual(GeneType.BINARY, CHSIZE)
     ind.chromosome = [0] * CHSIZE
     ind.ch_size = CHSIZE
 

src/tests/test_blxalpha_crossover.py

@@ -1,6 +1,6 @@
 import pytest
 import random
-from individual import Individual
+from individual import Individual, GeneType
 from problems.abstract_problem import AbstractProblem
 from recombination.blxalpha_crossover import BlxalphaCrossover  # Replace with the actual path if different
 
@@ -34,8 +34,8 @@ def setup_parents():
     list
         A list containing two parent individuals with predefined chromosomes and size.
     """
-    ind1 = Individual("Real", 5)
-    ind2 = Individual("Real", 5)
+    ind1 = Individual(GeneType.REAL, 5)
+    ind2 = Individual(GeneType.REAL, 5)
     ind1.chromosome = [1.0, 2.0, 3.0, 4.0, 5.0]
     ind2.chromosome = [5.0, 4.0, 3.0, 2.0, 1.0]
     ind1.ch_size = 5

src/tests/test_byte_mutation.py

@@ -1,9 +1,9 @@
 import pytest
 import numpy as np
-from individual import Individual
+from individual import Individual, GeneType
 from problems.abstract_problem import AbstractProblem
 from mutation.byte_mutation import ByteMutation  # Replace with the actual path if different
-import struct
+
 
 class MockProblem(AbstractProblem):
     """
@@ -35,7 +35,7 @@ def setup_individual():
     Individual
         An individual instance with a predefined chromosome and size.
     """
-    ind = Individual("Real", 5)
+    ind = Individual(GeneType.REAL, 5)
     ind.chromosome = [1.0, 2.0, 3.0, 4.0, 5.0]
     ind.ch_size = 5
     return ind

src/tests/test_byte_mutation_random.py

@@ -1,9 +1,8 @@
 import pytest
 import numpy as np
-from individual import Individual
+from individual import Individual, GeneType
 from problems.abstract_problem import AbstractProblem
 from mutation.byte_mutation_random import ByteMutationRandom  # Replace with the actual path if different
-import struct
 
 class MockProblem(AbstractProblem):
     """
@@ -35,7 +34,7 @@ def setup_individual():
     Individual
         An individual instance with a predefined chromosome and size.
     """
-    ind = Individual("Real", 5)
+    ind = Individual(GeneType.REAL, 5)
     ind.chromosome = [1.0, 2.0, 3.0, 4.0, 5.0]
     ind.ch_size = 5
     return ind

src/tests/test_byte_one_point_crossover.py

@@ -1,9 +1,9 @@
 import pytest
 import numpy as np
-from individual import Individual
+from individual import Individual, GeneType
 from problems.abstract_problem import AbstractProblem
 from recombination.byte_one_point_crossover import ByteOnePointCrossover  # Replace with the actual path if different
-import struct
+
 
 class MockProblem(AbstractProblem):
     """
@@ -35,8 +35,8 @@ def setup_parents():
     list
         A list containing two parent individuals with predefined chromosomes and size.
     """
-    ind1 = Individual("Real", 5)
-    ind2 = Individual("Real", 5)
+    ind1 = Individual(GeneType.REAL, 5)
+    ind2 = Individual(GeneType.REAL, 5)
     ind1.chromosome = [1.15, 2.45, 3.03, 4.76, 5.34]
     ind2.chromosome = [5.36, 4.98, 3.23, 2.45, 1.67]
     ind1.ch_size = 5

src/tests/test_byte_uniform_crossover.py

@@ -1,6 +1,6 @@
 import pytest
 import numpy.random as randomgenerator
-from individual import Individual
+from individual import Individual, GeneType
 from problems.abstract_problem import AbstractProblem
 from recombination.byte_uniform_crossover import ByteUniformCrossover  # Replace with the actual path if different
 import struct
@@ -35,8 +35,8 @@ def setup_parents():
     list
         A list containing two parent individuals with predefined chromosomes and size.
     """
-    ind1 = Individual("Real", 5)
-    ind2 = Individual("Real", 5)
+    ind1 = Individual(GeneType.REAL, 5)
+    ind2 = Individual(GeneType.REAL, 5)
     ind1.chromosome = [1.0, 2.0, 3.0, 4.0, 5.0]
     ind2.chromosome = [5.0, 4.0, 3.0, 2.0, 1.0]
     ind1.ch_size = 5

src/tests/test_flat_crossover.py

@@ -1,6 +1,6 @@
 import pytest
 import random
-from individual import Individual
+from individual import Individual, GeneType
 from problems.abstract_problem import AbstractProblem
 from recombination.flat_crossover import FlatCrossover  # Replace with the actual path if different
 
@@ -34,8 +34,8 @@ def setup_parents():
     list
         A list containing two parent individuals with predefined chromosomes and size.
     """
-    ind1 = Individual("Real", 5)
-    ind2 = Individual("Real", 5)
+    ind1 = Individual(GeneType.REAL, 5)
+    ind2 = Individual(GeneType.REAL, 5)
     ind1.chromosome = [1.0, 2.0, 3.0, 4.0, 5.0]
     ind2.chromosome = [5.0, 4.0, 3.0, 2.0, 1.0]
     ind1.ch_size = 5

src/tests/test_float_uniform_mutation.py

@@ -1,6 +1,6 @@
 import pytest
 import random
-from individual import Individual
+from individual import Individual, GeneType
 from problems.abstract_problem import AbstractProblem
 from mutation.float_uniform_mutation import FloatUniformMutation  # Replace with the actual path if different
 
@@ -34,7 +34,7 @@ def setup_individual():
     Individual
         An individual instance with a predefined chromosome and size.
     """
-    ind = Individual("Real", 5)
+    ind = Individual(GeneType.REAL, 5)
     ind.chromosome = [1.0, 2.0, 3.0, 4.0, 5.0]
     ind.ch_size = 5
     return ind

src/tests/test_individual.py

@@ -1,7 +1,7 @@
 import pytest
 from numpy import random
 import random as rd
-from individual import Individual 
+from individual import Individual, GeneType 
 from problems.single_objective.discrete.binary.one_max import OneMax
 from problems.single_objective.continuous.ackley import Ackley
 from problems.single_objective.discrete.permutation.tsp import Tsp
@@ -12,14 +12,14 @@ def setup_individual():
     """
     Fixture to provide an instance of the Individual class with different configurations.
     """
-    return Individual(gen_type="Binary", ch_size=10, problem=OneMax())
+    return Individual(gen_type=GeneType.BINARY, ch_size=10, problem=OneMax())
 
 def test_individual_init():
     """
     Test the initialization of the Individual class.
     """
-    ind = Individual(gen_type="Binary", ch_size=10, problem=OneMax())
-    assert ind.gen_type == "Binary"
+    ind = Individual(gen_type=GeneType.BINARY, ch_size=10, problem=OneMax())
+    assert ind.gen_type == GeneType.BINARY
     assert ind.ch_size == 10
     assert ind.chromosome == []
     assert ind.fitness_value == 0
@@ -31,7 +31,7 @@ def test_randomize_binary():
     """
     Test the randomization of the chromosome for a binary genome type.
     """
-    ind = Individual(gen_type="Binary", ch_size=10, problem=OneMax())
+    ind = Individual(gen_type=GeneType.BINARY, ch_size=10, problem=OneMax())
     ind.randomize()
     assert len(ind.chromosome) == 10
     assert all(gene in [0, 1] for gene in ind.chromosome)
@@ -41,7 +41,7 @@ def test_randomize_permutation():
     Test the randomization of the chromosome for a permutation genome type.
     """
     chsize = 14
-    ind = Individual(gen_type="Permutation", ch_size=chsize, problem=Tsp())
+    ind = Individual(gen_type=GeneType.PERMUTATION, ch_size=chsize, problem=Tsp())
     ind.randomize()
     assert len(ind.chromosome) == chsize
     for i in range(1, chsize+1):
@@ -54,7 +54,7 @@ def test_randomize_real_valued():
     Test the randomization of the chromosome for a real-valued genome type.
     """
     chsize = 10
-    ind = Individual(gen_type="Real", ch_size=chsize, problem=Ackley())
+    ind = Individual(gen_type=GeneType.REAL, ch_size=chsize, problem=Ackley())
     ind.randomize()
     assert len(ind.chromosome) == chsize
     assert all(isinstance(gene, float) for gene in ind.chromosome)

src/tests/test_insertion_mutation.py

@@ -1,6 +1,6 @@
 from mutation.insertion_mutation import InsertionMutation
 from problems.single_objective.discrete.permutation.tsp import Tsp
-from individual import Individual
+from individual import Individual, GeneType
 import random
 
 def test_insertion_mutation():
@@ -32,7 +32,7 @@ def test_insertion_mutation():
     CHSIZE = 14
 
     # Create an individual with a random permutation of integers from 1 to CHSIZE
-    ind = Individual("Permutation", CHSIZE)
+    ind = Individual(GeneType.PERMUTATION, CHSIZE)
     ind.chromosome = list(random.sample(range(1, CHSIZE + 1), CHSIZE))
 
     # Initialize the TSP problem