ANN.py

import numpy

def sigmoid(inpt):
    return 1.0/(1.0+numpy.exp(-1*inpt))

def relu(inpt):
    result = inpt
    result[inpt<0] = 0
    return result

def predict_outputs(weights_mat, data_inputs, data_outputs, activation="relu"):
    predictions = numpy.zeros(shape=(data_inputs.shape[0]))
    for sample_idx in range(data_inputs.shape[0]):
        r1 = data_inputs[sample_idx, :]
        for curr_weights in weights_mat:
            r1 = numpy.matmul(r1, curr_weights)
            if activation == "relu":
                r1 = relu(r1)
            elif activation == "sigmoid":
                r1 = sigmoid(r1)
        predicted_label = numpy.where(r1 == numpy.max(r1))[0][0]
        predictions[sample_idx] = predicted_label
    correct_predictions = numpy.where(predictions == data_outputs)[0].size
    accuracy = (correct_predictions/data_outputs.size)*100
    return accuracy, predictions
    
def fitness(weights_mat, data_inputs, data_outputs, activation="relu"):
    accuracy = numpy.empty(shape=(weights_mat.shape[0]))
    for sol_idx in range(weights_mat.shape[0]):
        curr_sol_mat = weights_mat[sol_idx, :]
        accuracy[sol_idx], _ = predict_outputs(curr_sol_mat, data_inputs, data_outputs, activation=activation)
    return accuracy