neuroevo 0.0.5

Training neural networks with evolutionary and particle swarm algorithms

About

NeuroEvo is a platform for automated design and training of neural networks using evolutionary and particle swarm algorithms. The neuroevo Python package implements the algorithms used in the web application (neuroevo.io).

Developed by Philip Schroeder ([e-mail], [e-mail])

Example

from neuroevo import models
import uci_dataset as dataset

# Load example dataset
df=dataset.load_cardiotocography()

# Define dataset parameters
first_feature_column_number, last_feature_column_number, class_col_number, has_header = 1, 22, 23, False

# Define training method
train_method='PSO'

# Execute training
data=df.values.tolist()
res=models.train(data, first_feature_column_number, last_feature_column_number, class_col_number, has_header, train_method, n_hidden_nodes=[3,3], n_iterations=10, NP=10)

# View fitted weights of best candidate
nn1=res['best_candidate']
print(nn1.nn_weights)

# View previous class labels (based on given dataset) and new class labels (the labels used by the classifier)
print(res['old_class_labels'])
print(res['new_class_labels'])

# Example of using returned classifier to make a prediction 
example_input=[125.0, 0.004838709677419355, 0.0, 0.0016129032258064516, 0.0032258064516129032, 0.0, 0.0, 25.0, 1.7, 6.0, 11.6, 93.0, 72.0, 165.0, 3.0, 0.0, 133.0, 128.0, 132.0, 10.0, 0.0, 6.0]
print("Predicted class: " + str(nn1.predict_class(example_input)))
print("Activation function output for each class: " + str(nn1.get_last_class_act_funct_output()))
print("Input: " + str(nn1.get_last_input()))