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Regression examples
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@ahmedfgad
ahmedfgad committed Jul 6, 2023
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32 changes: 16 additions & 16 deletions docs/source/gann.rst
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Expand Up @@ -535,7 +535,7 @@ value (i.e. accuracy) of 100 is reached after around 180 generations.
ga_instance.plot_fitness()
.. figure:: https://user-images.githubusercontent.com/16560492/82078638-c11e0700-96e1-11ea-8aa9-c36761c5e9c7.png
.. image:: https://user-images.githubusercontent.com/16560492/82078638-c11e0700-96e1-11ea-8aa9-c36761c5e9c7.png
:alt:

By running the code again, a different initial population is created and
Expand Down Expand Up @@ -930,7 +930,7 @@ The number of wrong classifications is only 1 and the accuracy is
The next figure shows how fitness value evolves by generation.

.. figure:: https://user-images.githubusercontent.com/16560492/82152993-21898180-9865-11ea-8387-b995f88b83f7.png
.. image:: https://user-images.githubusercontent.com/16560492/82152993-21898180-9865-11ea-8387-b995f88b83f7.png
:alt:

Regression Example 1
Expand Down Expand Up @@ -998,10 +998,10 @@ for regression.
GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices)
print("Generation = {generation}".format(generation=ga_instance.generations_completed))
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1]))
print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]))
print("Change = {change}".format(change=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - last_fitness))
last_fitness = ga_instance.best_solution()[1].copy()
last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1].copy()
# Holds the fitness value of the previous generation.
last_fitness = 0
Expand All @@ -1011,8 +1011,8 @@ for regression.
[8, 15, 20, 13]])
# Preparing the NumPy array of the outputs.
data_outputs = numpy.array([0.1,
1.5])
data_outputs = numpy.array([[0.1, 0.2],
[1.8, 1.5]])
# The length of the input vector for each sample (i.e. number of neurons in the input layer).
num_inputs = data_inputs.shape[1]
Expand All @@ -1022,7 +1022,7 @@ for regression.
GANN_instance = pygad.gann.GANN(num_solutions=num_solutions,
num_neurons_input=num_inputs,
num_neurons_hidden_layers=[2],
num_neurons_output=1,
num_neurons_output=2,
hidden_activations=["relu"],
output_activation="None")
Expand Down Expand Up @@ -1071,7 +1071,7 @@ for regression.
ga_instance.plot_fitness()
# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
solution, solution_fitness, solution_idx = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)
print("Parameters of the best solution : {solution}".format(solution=solution))
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
Expand All @@ -1092,7 +1092,7 @@ for regression.
The next figure shows how the fitness value changes for the generations
used.

.. figure:: https://user-images.githubusercontent.com/16560492/92948154-3cf24b00-f459-11ea-94ea-952b66ab2145.png
.. image:: https://user-images.githubusercontent.com/16560492/92948154-3cf24b00-f459-11ea-94ea-952b66ab2145.png
:alt:

Regression Example 2 - Fish Weight Prediction
Expand Down Expand Up @@ -1164,15 +1164,15 @@ Here is the complete code.
GANN_instance.update_population_trained_weights(population_trained_weights=population_matrices)
print("Generation = {generation}".format(generation=ga_instance.generations_completed))
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution()[1]))
print("Change = {change}".format(change=ga_instance.best_solution()[1] - last_fitness))
print("Fitness = {fitness}".format(fitness=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1]))
print("Change = {change}".format(change=ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1] - last_fitness))
last_fitness = ga_instance.best_solution()[1].copy()
last_fitness = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)[1].copy()
# Holds the fitness value of the previous generation.
last_fitness = 0
data = numpy.array(pandas.read_csv("Fish.csv"))
data = numpy.array(pandas.read_csv("../data/Fish.csv"))
# Preparing the NumPy array of the inputs.
data_inputs = numpy.asarray(data[:, 2:], dtype=numpy.float32)
Expand Down Expand Up @@ -1237,7 +1237,7 @@ Here is the complete code.
ga_instance.plot_fitness()
# Returning the details of the best solution.
solution, solution_fitness, solution_idx = ga_instance.best_solution()
solution, solution_fitness, solution_idx = ga_instance.best_solution(pop_fitness=ga_instance.last_generation_fitness)
print("Parameters of the best solution : {solution}".format(solution=solution))
print("Fitness value of the best solution = {solution_fitness}".format(solution_fitness=solution_fitness))
print("Index of the best solution : {solution_idx}".format(solution_idx=solution_idx))
Expand All @@ -1258,5 +1258,5 @@ Here is the complete code.
The next figure shows how the fitness value changes for the 500
generations used.

.. figure:: https://user-images.githubusercontent.com/16560492/92948486-bbe78380-f459-11ea-9e31-0d4c7269d606.png
.. image:: https://user-images.githubusercontent.com/16560492/92948486-bbe78380-f459-11ea-9e31-0d4c7269d606.png
:alt:

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