/**
* Neural networks with only one type of activation function offer certain optimization options.
* This method determines if only a single activation function is used.
*
* @return The number of the single activation function, or -1 if there are no activation
* functions or more than one type of activation function.
*/
public Class<?> hasSameActivationFunction() {
final List<Class<?>> map = new ArrayList<Class<?>>();
for (final ActivationFunction activation : this.activationFunctions) {
if (!map.contains(activation.getClass())) {
map.add(activation.getClass());
}
}
if (map.size() != 1) {
return null;
} else {
return map.get(0);
}
}
/** {@inheritDoc} */
@Override
public void save(final OutputStream os, final Object obj) {
final EncogWriteHelper out = new EncogWriteHelper(os);
final RBFNetwork net = (RBFNetwork) obj;
final FlatNetworkRBF flat = (FlatNetworkRBF) net.getFlat();
out.addSection("RBF-NETWORK");
out.addSubSection("PARAMS");
out.addProperties(net.getProperties());
out.addSubSection("NETWORK");
out.writeProperty(BasicNetwork.TAG_BEGIN_TRAINING, flat.getBeginTraining());
out.writeProperty(BasicNetwork.TAG_CONNECTION_LIMIT, flat.getConnectionLimit());
out.writeProperty(BasicNetwork.TAG_CONTEXT_TARGET_OFFSET, flat.getContextTargetOffset());
out.writeProperty(BasicNetwork.TAG_CONTEXT_TARGET_SIZE, flat.getContextTargetSize());
out.writeProperty(BasicNetwork.TAG_END_TRAINING, flat.getEndTraining());
out.writeProperty(BasicNetwork.TAG_HAS_CONTEXT, flat.getHasContext());
out.writeProperty(PersistConst.INPUT_COUNT, flat.getInputCount());
out.writeProperty(BasicNetwork.TAG_LAYER_COUNTS, flat.getLayerCounts());
out.writeProperty(BasicNetwork.TAG_LAYER_FEED_COUNTS, flat.getLayerFeedCounts());
out.writeProperty(BasicNetwork.TAG_LAYER_CONTEXT_COUNT, flat.getLayerContextCount());
out.writeProperty(BasicNetwork.TAG_LAYER_INDEX, flat.getLayerIndex());
out.writeProperty(PersistConst.OUTPUT, flat.getLayerOutput());
out.writeProperty(PersistConst.OUTPUT_COUNT, flat.getOutputCount());
out.writeProperty(BasicNetwork.TAG_WEIGHT_INDEX, flat.getWeightIndex());
out.writeProperty(PersistConst.WEIGHTS, flat.getWeights());
out.writeProperty(BasicNetwork.TAG_BIAS_ACTIVATION, flat.getBiasActivation());
out.addSubSection("ACTIVATION");
for (final ActivationFunction af : flat.getActivationFunctions()) {
out.addColumn(af.getClass().getSimpleName());
for (int i = 0; i < af.getParams().length; i++) {
out.addColumn(af.getParams()[i]);
}
out.writeLine();
}
out.addSubSection("RBF");
for (final RadialBasisFunction rbf : flat.getRBF()) {
out.addColumn(rbf.getClass().getSimpleName());
out.addColumn(rbf.getWidth());
out.addColumn(rbf.getPeak());
for (int i = 0; i < rbf.getCenters().length; i++) {
out.addColumn(rbf.getCenters()[i]);
}
out.writeLine();
}
out.flush();
}
/** {@inheritDoc} */
@Override
public void save(final OutputStream os, final Object obj) {
final EncogWriteHelper out = new EncogWriteHelper(os);
final BasicNetwork net = (BasicNetwork) obj;
final FlatNetwork flat = net.getStructure().getFlat();
out.addSection("BASIC");
out.addSubSection("PARAMS");
out.addProperties(net.getProperties());
out.addSubSection("NETWORK");
out.writeProperty(BasicNetwork.TAG_BEGIN_TRAINING, flat.getBeginTraining());
out.writeProperty(BasicNetwork.TAG_CONNECTION_LIMIT, flat.getConnectionLimit());
out.writeProperty(BasicNetwork.TAG_CONTEXT_TARGET_OFFSET, flat.getContextTargetOffset());
out.writeProperty(BasicNetwork.TAG_CONTEXT_TARGET_SIZE, flat.getContextTargetSize());
out.writeProperty(BasicNetwork.TAG_END_TRAINING, flat.getEndTraining());
out.writeProperty(BasicNetwork.TAG_HAS_CONTEXT, flat.getHasContext());
out.writeProperty(PersistConst.INPUT_COUNT, flat.getInputCount());
out.writeProperty(BasicNetwork.TAG_LAYER_COUNTS, flat.getLayerCounts());
out.writeProperty(BasicNetwork.TAG_LAYER_FEED_COUNTS, flat.getLayerFeedCounts());
out.writeProperty(BasicNetwork.TAG_LAYER_CONTEXT_COUNT, flat.getLayerContextCount());
out.writeProperty(BasicNetwork.TAG_LAYER_INDEX, flat.getLayerIndex());
out.writeProperty(PersistConst.OUTPUT, flat.getLayerOutput());
out.writeProperty(PersistConst.OUTPUT_COUNT, flat.getOutputCount());
out.writeProperty(BasicNetwork.TAG_WEIGHT_INDEX, flat.getWeightIndex());
out.writeProperty(PersistConst.WEIGHTS, flat.getWeights());
out.writeProperty(BasicNetwork.TAG_BIAS_ACTIVATION, flat.getBiasActivation());
out.addSubSection("ACTIVATION");
for (final ActivationFunction af : flat.getActivationFunctions()) {
String sn = af.getClass().getSimpleName();
// if this is an Encog class then only add the simple name, so it works with C#
if (sn.startsWith("org.encog.")) {
out.addColumn(sn);
} else {
out.addColumn(af.getClass().getName());
}
for (int i = 0; i < af.getParams().length; i++) {
out.addColumn(af.getParams()[i]);
}
out.writeLine();
}
out.flush();
}
/** {@inheritDoc} */
@Override
public ActivationFunction createActivationFunction(String fn) {
String name;
double[] params;
int index = fn.indexOf('[');
if (index != -1) {
name = fn.substring(0, index).toLowerCase();
int index2 = fn.indexOf(']');
if (index2 == -1) {
throw new EncogError("Unbounded [ while parsing activation function.");
}
String a = fn.substring(index + 1, index2);
params = NumberList.fromList(CSVFormat.EG_FORMAT, a);
} else {
name = fn.toLowerCase();
params = new double[0];
}
ActivationFunction af = allocateAF(name);
if (af == null) {
return null;
}
if (af.getParamNames().length != params.length) {
throw new EncogError(
name
+ " expected "
+ af.getParamNames().length
+ ", but "
+ params.length
+ " were provided.");
}
for (int i = 0; i < af.getParamNames().length; i++) {
af.setParam(i, params[i]);
}
return af;
}
/** {@inheritDoc} */
@Override
public void calculateError(
ActivationFunction af,
double[] b,
double[] a,
double[] ideal,
double[] actual,
double[] error,
double derivShift,
double significance) {
for (int i = 0; i < actual.length; i++) {
double deriv = af.derivativeFunction(b[i], a[i]) + derivShift;
error[i] = ((ideal[i] - actual[i]) * significance) * deriv;
}
}
/** {@inheritDoc} */
@Override
public Object read(final InputStream is) {
final BasicNetwork result = new BasicNetwork();
final FlatNetwork flat = new FlatNetwork();
final EncogReadHelper in = new EncogReadHelper(is);
EncogFileSection section;
while ((section = in.readNextSection()) != null) {
if (section.getSectionName().equals("BASIC")
&& section.getSubSectionName().equals("PARAMS")) {
final Map<String, String> params = section.parseParams();
result.getProperties().putAll(params);
}
if (section.getSectionName().equals("BASIC")
&& section.getSubSectionName().equals("NETWORK")) {
final Map<String, String> params = section.parseParams();
flat.setBeginTraining(EncogFileSection.parseInt(params, BasicNetwork.TAG_BEGIN_TRAINING));
flat.setConnectionLimit(
EncogFileSection.parseDouble(params, BasicNetwork.TAG_CONNECTION_LIMIT));
flat.setContextTargetOffset(
EncogFileSection.parseIntArray(params, BasicNetwork.TAG_CONTEXT_TARGET_OFFSET));
flat.setContextTargetSize(
EncogFileSection.parseIntArray(params, BasicNetwork.TAG_CONTEXT_TARGET_SIZE));
flat.setEndTraining(EncogFileSection.parseInt(params, BasicNetwork.TAG_END_TRAINING));
flat.setHasContext(EncogFileSection.parseBoolean(params, BasicNetwork.TAG_HAS_CONTEXT));
flat.setInputCount(EncogFileSection.parseInt(params, PersistConst.INPUT_COUNT));
flat.setLayerCounts(EncogFileSection.parseIntArray(params, BasicNetwork.TAG_LAYER_COUNTS));
flat.setLayerFeedCounts(
EncogFileSection.parseIntArray(params, BasicNetwork.TAG_LAYER_FEED_COUNTS));
flat.setLayerContextCount(
EncogFileSection.parseIntArray(params, BasicNetwork.TAG_LAYER_CONTEXT_COUNT));
flat.setLayerIndex(EncogFileSection.parseIntArray(params, BasicNetwork.TAG_LAYER_INDEX));
flat.setLayerOutput(section.parseDoubleArray(params, PersistConst.OUTPUT));
flat.setLayerSums(new double[flat.getLayerOutput().length]);
flat.setOutputCount(EncogFileSection.parseInt(params, PersistConst.OUTPUT_COUNT));
flat.setWeightIndex(EncogFileSection.parseIntArray(params, BasicNetwork.TAG_WEIGHT_INDEX));
flat.setWeights(section.parseDoubleArray(params, PersistConst.WEIGHTS));
flat.setBiasActivation(section.parseDoubleArray(params, BasicNetwork.TAG_BIAS_ACTIVATION));
} else if (section.getSectionName().equals("BASIC")
&& section.getSubSectionName().equals("ACTIVATION")) {
int index = 0;
flat.setActivationFunctions(new ActivationFunction[flat.getLayerCounts().length]);
for (final String line : section.getLines()) {
ActivationFunction af = null;
final List<String> cols = EncogFileSection.splitColumns(line);
// if this is a class name with a path, then do not default to inside of the Encog
// package.
String name;
if (cols.get(0).indexOf('.') != -1) {
name = cols.get(0);
} else {
name = "org.encog.engine.network.activation." + cols.get(0);
}
try {
final Class<?> clazz = Class.forName(name);
af = (ActivationFunction) clazz.newInstance();
} catch (final ClassNotFoundException e) {
throw new PersistError(e);
} catch (final InstantiationException e) {
throw new PersistError(e);
} catch (final IllegalAccessException e) {
throw new PersistError(e);
}
for (int i = 0; i < af.getParamNames().length; i++) {
af.setParam(i, CSVFormat.EG_FORMAT.parse(cols.get(i + 1)));
}
flat.getActivationFunctions()[index++] = af;
}
}
}
result.getStructure().setFlat(flat);
result.updateProperties();
return result;
}
private double calculateRange(ActivationFunction af, double r) {
double[] d = {r};
af.activationFunction(d, 0, 1);
return d[0];
}
/** {@inheritDoc} */
@Override
public Object read(final InputStream is) {
final RBFNetwork result = new RBFNetwork();
final FlatNetworkRBF flat = (FlatNetworkRBF) result.getFlat();
final EncogReadHelper in = new EncogReadHelper(is);
EncogFileSection section;
while ((section = in.readNextSection()) != null) {
if (section.getSectionName().equals("RBF-NETWORK")
&& section.getSubSectionName().equals("PARAMS")) {
final Map<String, String> params = section.parseParams();
result.getProperties().putAll(params);
}
if (section.getSectionName().equals("RBF-NETWORK")
&& section.getSubSectionName().equals("NETWORK")) {
final Map<String, String> params = section.parseParams();
flat.setBeginTraining(EncogFileSection.parseInt(params, BasicNetwork.TAG_BEGIN_TRAINING));
flat.setConnectionLimit(
EncogFileSection.parseDouble(params, BasicNetwork.TAG_CONNECTION_LIMIT));
flat.setContextTargetOffset(
EncogFileSection.parseIntArray(params, BasicNetwork.TAG_CONTEXT_TARGET_OFFSET));
flat.setContextTargetSize(
EncogFileSection.parseIntArray(params, BasicNetwork.TAG_CONTEXT_TARGET_SIZE));
flat.setEndTraining(EncogFileSection.parseInt(params, BasicNetwork.TAG_END_TRAINING));
flat.setHasContext(EncogFileSection.parseBoolean(params, BasicNetwork.TAG_HAS_CONTEXT));
flat.setInputCount(EncogFileSection.parseInt(params, PersistConst.INPUT_COUNT));
flat.setLayerCounts(EncogFileSection.parseIntArray(params, BasicNetwork.TAG_LAYER_COUNTS));
flat.setLayerFeedCounts(
EncogFileSection.parseIntArray(params, BasicNetwork.TAG_LAYER_FEED_COUNTS));
flat.setLayerContextCount(
EncogFileSection.parseIntArray(params, BasicNetwork.TAG_LAYER_CONTEXT_COUNT));
flat.setLayerIndex(EncogFileSection.parseIntArray(params, BasicNetwork.TAG_LAYER_INDEX));
flat.setLayerOutput(section.parseDoubleArray(params, PersistConst.OUTPUT));
flat.setLayerSums(new double[flat.getLayerOutput().length]);
flat.setOutputCount(EncogFileSection.parseInt(params, PersistConst.OUTPUT_COUNT));
flat.setWeightIndex(EncogFileSection.parseIntArray(params, BasicNetwork.TAG_WEIGHT_INDEX));
flat.setWeights(section.parseDoubleArray(params, PersistConst.WEIGHTS));
flat.setBiasActivation(section.parseDoubleArray(params, BasicNetwork.TAG_BIAS_ACTIVATION));
} else if (section.getSectionName().equals("RBF-NETWORK")
&& section.getSubSectionName().equals("ACTIVATION")) {
int index = 0;
flat.setActivationFunctions(new ActivationFunction[flat.getLayerCounts().length]);
for (final String line : section.getLines()) {
ActivationFunction af = null;
final List<String> cols = EncogFileSection.splitColumns(line);
final String name = "org.encog.engine.network.activation." + cols.get(0);
try {
final Class<?> clazz = Class.forName(name);
af = (ActivationFunction) clazz.newInstance();
} catch (final ClassNotFoundException e) {
throw new PersistError(e);
} catch (final InstantiationException e) {
throw new PersistError(e);
} catch (final IllegalAccessException e) {
throw new PersistError(e);
}
for (int i = 0; i < af.getParamNames().length; i++) {
af.setParam(i, CSVFormat.EG_FORMAT.parse(cols.get(i + 1)));
}
flat.getActivationFunctions()[index++] = af;
}
} else if (section.getSectionName().equals("RBF-NETWORK")
&& section.getSubSectionName().equals("RBF")) {
int index = 0;
final int hiddenCount = flat.getLayerCounts()[1];
final int inputCount = flat.getLayerCounts()[2];
flat.setRBF(new RadialBasisFunction[hiddenCount]);
for (final String line : section.getLines()) {
RadialBasisFunction rbf = null;
final List<String> cols = EncogFileSection.splitColumns(line);
final String name = "org.encog.mathutil.rbf." + cols.get(0);
try {
final Class<?> clazz = Class.forName(name);
rbf = (RadialBasisFunction) clazz.newInstance();
} catch (final ClassNotFoundException e) {
throw new PersistError(e);
} catch (final InstantiationException e) {
throw new PersistError(e);
} catch (final IllegalAccessException e) {
throw new PersistError(e);
}
rbf.setWidth(CSVFormat.EG_FORMAT.parse(cols.get(1)));
rbf.setPeak(CSVFormat.EG_FORMAT.parse(cols.get(2)));
rbf.setCenters(new double[inputCount]);
for (int i = 0; i < inputCount; i++) {
rbf.getCenters()[i] = CSVFormat.EG_FORMAT.parse(cols.get(i + 3));
}
flat.getRBF()[index++] = rbf;
}
}
}
return result;
}