public NeuralNetwork(int input, int hidden, int output) {
this.layers = new int[] {input, hidden, output};
df = new DecimalFormat("#0.000#");
ef = new DecimalFormat("#");
/** Create all neurons and connections Connections are created in the neuron class */
for (int i = 0; i < layers.length; i++) {
if (i == 0) { // input layer
for (int j = 0; j < layers[i]; j++) {
Neuron neuron = new Neuron();
inputLayer.add(neuron);
}
} else if (i == 1) { // hidden layer
for (int j = 0; j < layers[i]; j++) {
Neuron neuron = new Neuron();
neuron.addInConnectionsS(inputLayer);
neuron.addBiasConnection(bias);
hiddenLayer.add(neuron);
}
} else if (i == 2) { // output layer
for (int j = 0; j < layers[i]; j++) {
Neuron neuron = new Neuron();
neuron.addInConnectionsS(hiddenLayer);
neuron.addBiasConnection(bias);
outputLayer.add(neuron);
}
} else {
System.out.println("!Error NeuralNetwork init");
}
}
// initialize random weights
for (Neuron neuron : hiddenLayer) {
ArrayList<Connection> connections = neuron.getAllInConnections();
for (Connection conn : connections) {
double newWeight = getRandom();
conn.setWeight(newWeight);
}
}
for (Neuron neuron : outputLayer) {
ArrayList<Connection> connections = neuron.getAllInConnections();
for (Connection conn : connections) {
double newWeight = getRandom();
conn.setWeight(newWeight);
}
}
// reset id counters
Neuron.counter = 0;
Connection.counter = 0;
if (isTrained) {
trainedWeights();
updateAllWeights();
}
}
