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Commit 759ed4e

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Merge pull request #230 from ahmedfgad/github-actions
PyGAD 3.2.0
2 parents 5bb342e + d50f118 commit 759ed4e

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‎NSGA-II/non_dominant_sorting.py‎

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‎README.md‎

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# PyGAD: Genetic Algorithm in Python
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[PyGAD](https://pypi.org/project/pygad) is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch.
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[PyGAD](https://pypi.org/project/pygad) is an open-source easy-to-use Python 3 library for building the genetic algorithm and optimizing machine learning algorithms. It supports Keras and PyTorch. PyGAD supports optimizing both single-objective and multi-objective problems.
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Check documentation of the [PyGAD](https://pygad.readthedocs.io/en/latest).
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# Example
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Check the [PyGAD's documentation](https://pygad.readthedocs.io/en/latest/pygad.html) for information about the implementation of this example.
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Check the [PyGAD's documentation](https://pygad.readthedocs.io/en/latest/pygad.html) for information about the implementation of this example. It solves a single-objective problem.
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```python
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import pygad
@@ -183,9 +183,9 @@ num_genes = len(function_inputs)
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last_fitness = 0
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def callback_generation(ga_instance):
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global last_fitness
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print("Generation = {generation}".format(generation=ga_instance.generations_completed))
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print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1]))
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print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))
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print(f"Generation = {ga_instance.generations_completed}")
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print(f"Fitness = {ga_instance.best_solution()[1]}")
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print(f"Change = {ga_instance.best_solution()[1] - last_fitness}")
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last_fitness = ga_instance.best_solution()[1]
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# Creating an instance of the GA class inside the ga module. Some parameters are initialized within the constructor.
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# Returning the details of the best solution.
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solution, solution_fitness, solution_idx = ga_instance.best_solution()
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print("Parameters of the best solution : {solution}".format(solution=solution))
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print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
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print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
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print(f"Parameters of the best solution : {solution}")
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print(f"Fitness value of the best solution = {solution_fitness}")
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print(f"Index of the best solution : {solution_idx}")
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prediction = numpy.sum(numpy.array(function_inputs)*solution)
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print("Predicted output based on the best solution : {prediction}".format(prediction=prediction))
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print(f"Predicted output based on the best solution : {prediction}")
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if ga_instance.best_solution_generation != -1:
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print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))
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print(g"Best fitness value reached after {ga_instance.best_solution_generation} generations.")
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# Saving the GA instance.
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filename = 'genetic' # The filename to which the instance is saved. The name is without extension.

‎docs/source/cnn.rst‎

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@@ -644,9 +644,9 @@ addition to the classification accuracy.
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num_wrong = numpy.where(predictions != train_outputs)[0]
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num_correct = train_outputs.size - num_wrong.size
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accuracy = 100 * (num_correct/train_outputs.size)
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print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct))
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print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size))
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print("Classification accuracy : {accuracy}.".format(accuracy=accuracy))
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print(f"Number of correct classifications : {num_correct}.")
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print(f"Number of wrong classifications : {num_wrong.size}.")
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print(f"Classification accuracy : {accuracy}.")
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It is very important to note that it is not expected that the
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classification accuracy is high because no training algorithm is used.
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num_wrong = numpy.where(predictions != train_outputs)[0]
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num_correct = train_outputs.size - num_wrong.size
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accuracy = 100 * (num_correct/train_outputs.size)
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print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct))
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print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size))
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print("Classification accuracy : {accuracy}.".format(accuracy=accuracy))
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print(f"Number of correct classifications : {num_correct}.")
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print(f"Number of wrong classifications : {num_wrong.size}.")
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print(f"Classification accuracy : {accuracy}.")

‎docs/source/conf.py‎

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author = 'Ahmed Fawzy Gad'
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# The full version, including alpha/beta/rc tags
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release = '3.1.0'
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release = '3.2.0'
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master_doc = 'index'
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‎docs/source/gacnn.rst‎

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@@ -405,7 +405,7 @@ solutions within the population.
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population_matrices = gacnn.population_as_matrices(population_networks=GACNN_instance.population_networks, population_vectors=ga_instance.population)
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GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices)
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print("Generation = {generation}".format(generation=ga_instance.generations_completed))
408+
print(f"Generation = {ga_instance.generations_completed}")
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After preparing the fitness and callback function, next is to create an
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instance of the ``pygad.GA`` class.
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ga_instance.plot_fitness()
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.. figure:: https://user-images.githubusercontent.com/16560492/83429675-ab744580-a434-11ea-8f21-9d3804b50d15.png
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.. image:: https://user-images.githubusercontent.com/16560492/83429675-ab744580-a434-11ea-8f21-9d3804b50d15.png
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:alt:
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Information about the Best Solution
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.. code:: python
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solution, solution_fitness, solution_idx = ga_instance.best_solution()
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print("Parameters of the best solution : {solution}".format(solution=solution))
487-
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
488-
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
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print(f"Parameters of the best solution : {solution}")
487+
print(f"Fitness value of the best solution = {solution_fitness}")
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print(f"Index of the best solution : {solution_idx}")
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.. code::
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.. code:: python
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predictions = pygad.cnn.predict(last_layer=GANN_instance.population_networks[solution_idx], data_inputs=data_inputs)
507-
print("Predictions of the trained network : {predictions}".format(predictions=predictions))
507+
print(f"Predictions of the trained network : {predictions}")
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Calculating Some Statistics
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---------------------------
@@ -518,9 +518,9 @@ addition to the classification accuracy.
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num_wrong = numpy.where(predictions != data_outputs)[0]
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num_correct = data_outputs.size - num_wrong.size
520520
accuracy = 100 * (num_correct/data_outputs.size)
521-
print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct))
522-
print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size))
523-
print("Classification accuracy : {accuracy}.".format(accuracy=accuracy))
521+
print(f"Number of correct classifications : {num_correct}.")
522+
print(f"Number of wrong classifications : {num_wrong.size}.")
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print(f"Classification accuracy : {accuracy}.")
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.. code::
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GACNN_instance.update_population_trained_weights(population_trained_weights=population_matrices)
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578-
print("Generation = {generation}".format(generation=ga_instance.generations_completed))
579-
print("Fitness = {fitness}".format(fitness=ga_instance.best_solutions_fitness))
578+
print(f"Generation = {ga_instance.generations_completed}")
579+
print(f"Fitness = {ga_instance.best_solutions_fitness}")
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data_inputs = numpy.load("dataset_inputs.npy")
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data_outputs = numpy.load("dataset_outputs.npy")
@@ -642,21 +642,21 @@ complete code is listed below.
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# Returning the details of the best solution.
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solution, solution_fitness, solution_idx = ga_instance.best_solution()
645-
print("Parameters of the best solution : {solution}".format(solution=solution))
646-
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
647-
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
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print(f"Parameters of the best solution : {solution}")
646+
print(f"Fitness value of the best solution = {solution_fitness}")
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print(f"Index of the best solution : {solution_idx}")
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if ga_instance.best_solution_generation != -1:
650-
print("Best fitness value reached after {best_solution_generation} generations.".format(best_solution_generation=ga_instance.best_solution_generation))
650+
print(f"Best fitness value reached after {ga_instance.best_solution_generation} generations.")
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# Predicting the outputs of the data using the best solution.
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predictions = GACNN_instance.population_networks[solution_idx].predict(data_inputs=data_inputs)
654-
print("Predictions of the trained network : {predictions}".format(predictions=predictions))
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print(f"Predictions of the trained network : {predictions}")
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# Calculating some statistics
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num_wrong = numpy.where(predictions != data_outputs)[0]
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num_correct = data_outputs.size - num_wrong.size
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accuracy = 100 * (num_correct/data_outputs.size)
660-
print("Number of correct classifications : {num_correct}.".format(num_correct=num_correct))
661-
print("Number of wrong classifications : {num_wrong}.".format(num_wrong=num_wrong.size))
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print("Classification accuracy : {accuracy}.".format(accuracy=accuracy))
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print(f"Number of correct classifications : {num_correct}.")
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print(f"Number of wrong classifications : {num_wrong.size}.")
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print(f"Classification accuracy : {accuracy}.")

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