def test_NeuronalNetwork():
# DataSet
x = np.linspace(-7,7,20)
y = np.sin(x) * 0.5
input = x.reshape(len(x), 1)
target = y.reshape(len(x), 1)
inputL = [[-7,7]]
hiddenOutputL = [7,7,7,1]
nn = NeuronalNetwork(input, target, inputL, hiddenOutputL)
prediction, error = nn.run()
print ""
print prediction
print ""
print error
__author__ = 'tmorales' import numpy as np from src.NeuronalNetwork import NeuronalNetwork from src.plotData import * # dataSet x = np.linspace(-7,7,20) y = np.sin(x) * 0.5 # plotting the dataSet plot_dataset(x, y, xLabel="X", yLabel="y", legend="sin(x)") # Network architecture input = x.reshape(len(x), 1) target = y.reshape(len(x), 1) inputL = [[-7,7]] hiddenOutputL = [7,7,7,1] # Fit and prediction nn = NeuronalNetwork(input, target, inputL, hiddenOutputL) prediction, error = nn.run() # Plotting results plot_error(x, y, prediction, error, title="Backpropagation Algoritm", xLabel="Epoch", yLabel="error (default SSE)")
