/**
* Convert to an array. This is used with some training algorithms that require that the "memory"
* of the neuron(the weight and bias values) be expressed as a linear array.
*
* @param network The network to encode.
* @return The memory of the neuron.
*/
public static double[] networkToArray(final BasicNetwork network) {
final int size = network.getStructure().calculateSize();
// allocate an array to hold
final double[] result = new double[size];
int index = 0;
for (final Layer layer : network.getStructure().getLayers()) {
// process layer bias
if (layer.hasBias()) {
for (int i = 0; i < layer.getNeuronCount(); i++) {
result[index++] = layer.getBiasWeight(i);
}
}
// process synapses
for (final Synapse synapse : network.getStructure().getPreviousSynapses(layer)) {
if (synapse.getMatrix() != null) {
// process each weight matrix
for (int x = 0; x < synapse.getToNeuronCount(); x++) {
for (int y = 0; y < synapse.getFromNeuronCount(); y++) {
result[index++] = synapse.getMatrix().get(y, x);
}
}
}
}
}
return result;
}
/** @return The list of layers that the outbound synapses connect to. */
public Collection<Layer> getNextLayers() {
final Collection<Layer> result = new HashSet<Layer>();
for (final Synapse synapse : this.next) {
result.add(synapse.getToLayer());
}
return result;
}
/** @return True if this layer is connected to intself. */
public boolean isSelfConnected() {
for (final Synapse synapse : this.next) {
if (synapse.isSelfConnected()) {
return true;
}
}
return false;
}
/**
* Determine if this layer is connected to another layer.
*
* @param layer A layer to check and see if this layer is connected to.
* @return True if the two layers are connected.
*/
public boolean isConnectedTo(final Layer layer) {
for (final Synapse synapse : this.next) {
if (synapse.getToLayer() == layer) {
return true;
}
}
return false;
}
/**
* Process a fully connected synapse.
*
* @param network The network to process.
* @param layer The layer to process.
* @param array The array to process.
* @param index The current index.
* @return The index after this synapse has been read.
*/
private static int processSynapseFull(
final BasicNetwork network, final Layer layer, final double[] array, final int index) {
int result = index;
// process synapses
for (final Synapse synapse : network.getStructure().getPreviousSynapses(layer)) {
if (synapse.getMatrix() != null) {
// process each weight matrix
for (int x = 0; x < synapse.getToNeuronCount(); x++) {
for (int y = 0; y < synapse.getFromNeuronCount(); y++) {
synapse.getMatrix().set(y, x, array[result++]);
}
}
}
}
return result;
}
/**
* Process a partially connected synapse.
*
* @param network The network to process.
* @param layer The layer to process.
* @param array The array to process.
* @param index The current index.
* @return The index after this synapse has been read.
*/
private static int processSynapseLimited(
final BasicNetwork network, final Layer layer, final double[] array, final int index) {
int result = index;
// process synapses
for (final Synapse synapse : network.getStructure().getPreviousSynapses(layer)) {
if (synapse.getMatrix() != null) {
// process each weight matrix
for (int x = 0; x < synapse.getToNeuronCount(); x++) {
for (int y = 0; y < synapse.getFromNeuronCount(); y++) {
final double oldValue = synapse.getMatrix().get(y, x);
double value = array[result++];
if (Math.abs(oldValue) < network.getStructure().getConnectionLimit()) {
value = 0;
}
synapse.getMatrix().set(y, x, value);
}
}
}
}
return result;
}
