/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.networks.training.pnn;
import org.encog.ml.MLMethod;
import org.encog.ml.TrainingImplementationType;
import org.encog.ml.data.MLData;
import org.encog.ml.data.MLDataPair;
import org.encog.ml.data.MLDataSet;
import org.encog.ml.data.basic.BasicMLData;
import org.encog.ml.data.basic.BasicMLDataPair;
import org.encog.ml.data.basic.BasicMLDataSet;
import org.encog.ml.train.BasicTraining;
import org.encog.neural.networks.training.propagation.TrainingContinuation;
import org.encog.neural.pnn.BasicPNN;
import org.encog.neural.pnn.PNNKernelType;
import org.encog.neural.pnn.PNNOutputMode;
import org.encog.util.EngineArray;
/**
* Train a PNN.
*/
public class TrainBasicPNN extends BasicTraining implements CalculationCriteria {
/**
* The default max error.
*/
public static final double DEFAULT_MAX_ERROR = 0.05;
/**
* The default minimum improvement before stop.
*/
public static final double DEFAULT_MIN_IMPROVEMENT = 0.0001;
/**
* THe default sigma low value.
*/
public static final double DEFAULT_SIGMA_LOW = 0.0001;
/**
* The default sigma high value.
*/
public static final double DEFAULT_SIGMA_HIGH = 10.0;
/**
* The default number of sigmas to evaluate between the low and high.
*/
public static final int DEFAULT_NUM_SIGMAS = 10;
/**
* Temp storage for derivative computation.
*/
private double[] v;
/**
* Temp storage for derivative computation.
*/
private double[] w;
/**
* Temp storage for derivative computation.
*/
private double[] dsqr;
/**
* The network to train.
*/
private final BasicPNN network;
/**
* The training data.
*/
private final MLDataSet training;
/**
* The maximum error to allow.
*/
private double maxError;
/**
* The minimum improvement allowed.
*/
private double minImprovement;
/**
* The low value for the sigma search.
*/
private double sigmaLow;
/**
* The high value for the sigma search.
*/
private double sigmaHigh;
/**
* The number of sigmas to evaluate between the low and high.
*/
private int numSigmas;
/**
* Have the samples been loaded.
*/
private boolean samplesLoaded;
/**
* Train a BasicPNN.
*
* @param network
* The network to train.
* @param training
* The training data.
*/
public TrainBasicPNN(final BasicPNN network, final MLDataSet training) {
super(TrainingImplementationType.OnePass);
this.network = network;
this.training = training;
this.maxError = TrainBasicPNN.DEFAULT_MAX_ERROR;
this.minImprovement = TrainBasicPNN.DEFAULT_MIN_IMPROVEMENT;
this.sigmaLow = TrainBasicPNN.DEFAULT_SIGMA_LOW;
this.sigmaHigh = TrainBasicPNN.DEFAULT_SIGMA_HIGH;
this.numSigmas = TrainBasicPNN.DEFAULT_NUM_SIGMAS;
this.samplesLoaded = false;
}
/**
* Calculate the error with multiple sigmas.
*
* @param x
* The data.
* @param der1
* The first derivative.
* @param der2
* The 2nd derivatives.
* @param der
* Calculate the derivative.
* @return The error.
*/
@Override
public double calcErrorWithMultipleSigma(final double[] x,
final double[] der1, final double[] der2, final boolean der) {
int ivar;
double err;
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
this.network.getSigma()[ivar] = x[ivar];
}
if (!der) {
return calculateError(this.network.getSamples(), false);
}
err = calculateError(this.network.getSamples(), true);
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
der1[ivar] = this.network.getDeriv()[ivar];
der2[ivar] = this.network.getDeriv2()[ivar];
}
return err;
}
/**
* Calculate the error using a common sigma.
*
* @param sig
* The sigma to use.
* @return The training error.
*/
@Override
public double calcErrorWithSingleSigma(final double sig) {
int ivar;
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
this.network.getSigma()[ivar] = sig;
}
return calculateError(this.network.getSamples(), false);
}
/**
* Calculate the error for the entire training set.
*
* @param training
* Training set to use.
* @param deriv
* Should we find the derivative.
* @return The error.
*/
public double calculateError(final MLDataSet training,
final boolean deriv) {
double err, totErr;
double diff;
totErr = 0.0;
if (deriv) {
final int num = (this.network.isSeparateClass()) ? this.network
.getInputCount() * this.network.getOutputCount()
: this.network.getInputCount();
for (int i = 0; i < num; i++) {
this.network.getDeriv()[i] = 0.0;
this.network.getDeriv2()[i] = 0.0;
}
}
this.network.setExclude((int) training.getRecordCount());
final MLDataPair pair = BasicMLDataPair.createPair(
training.getInputSize(), training.getIdealSize());
final double[] out = new double[this.network.getOutputCount()];
for (int r = 0; r < training.getRecordCount(); r++) {
training.getRecord(r, pair);
this.network.setExclude(this.network.getExclude() - 1);
err = 0.0;
final MLData input = pair.getInput();
final MLData target = pair.getIdeal();
if (this.network.getOutputMode() == PNNOutputMode.Unsupervised) {
if (deriv) {
final MLData output = computeDeriv(input, target);
for (int z = 0; z < this.network.getOutputCount(); z++) {
out[z] = output.getData(z);
}
} else {
final MLData output = this.network.compute(input);
for (int z = 0; z < this.network.getOutputCount(); z++) {
out[z] = output.getData(z);
}
}
for (int i = 0; i < this.network.getOutputCount(); i++) {
diff = input.getData(i) - out[i];
err += diff * diff;
}
} else if (this.network.getOutputMode() == PNNOutputMode.Classification) {
final int tclass = (int) target.getData(0);
MLData output;
if (deriv) {
output = computeDeriv(input, pair.getIdeal());
} else {
output = this.network.compute(input);
}
EngineArray.arrayCopy(output.getData(),out);
for (int i = 0; i < out.length; i++) {
if (i == tclass) {
diff = 1.0 - out[i];
err += diff * diff;
} else {
err += out[i] * out[i];
}
}
}
else if (this.network.getOutputMode() == PNNOutputMode.Regression) {
if (deriv) {
final MLData output = this.network.compute(input);
for (int z = 0; z < this.network.getOutputCount(); z++) {
out[z] = output.getData(z);
}
} else {
final MLData output = this.network.compute(input);
for (int z = 0; z < this.network.getOutputCount(); z++) {
out[z] = output.getData(z);
}
}
for (int i = 0; i < this.network.getOutputCount(); i++) {
diff = target.getData(i) - out[i];
err += diff * diff;
}
}
totErr += err;
}
this.network.setExclude(-1);
this.network.setError(totErr / training.getRecordCount());
if (deriv) {
for (int i = 0; i < this.network.getDeriv().length; i++) {
this.network.getDeriv()[i] /= training.getRecordCount();
this.network.getDeriv2()[i] /= training.getRecordCount();
}
}
if ((this.network.getOutputMode() == PNNOutputMode.Unsupervised)
|| (this.network.getOutputMode() == PNNOutputMode.Regression)) {
this.network.setError(this.network.getError()
/ this.network.getOutputCount());
if (deriv) {
for (int i = 0; i < this.network.getInputCount(); i++) {
this.network.getDeriv()[i] /= this.network.getOutputCount();
this.network.getDeriv2()[i] /= this.network
.getOutputCount();
}
}
}
return this.network.getError();
}
/**
* {@inheritDoc}
*/
@Override
public boolean canContinue() {
return false;
}
/**
* Compute the derivative for target data.
*
* @param input
* The input.
* @param target
* The target data.
* @return The output.
*/
public MLData computeDeriv(final MLData input,
final MLData target) {
int pop, ivar;
final int ibest = 0;
int outvar;
double diff, dist, truedist;
double vtot, wtot;
double temp, der1, der2, psum;
int vptr, wptr, vsptr = 0, wsptr = 0;
final double[] out = new double[this.network.getOutputCount()];
for (pop = 0; pop < this.network.getOutputCount(); pop++) {
out[pop] = 0.0;
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
this.v[pop * this.network.getInputCount() + ivar] = 0.0;
this.w[pop * this.network.getInputCount() + ivar] = 0.0;
}
}
psum = 0.0;
if (this.network.getOutputMode() != PNNOutputMode.Classification) {
vsptr = this.network.getOutputCount()
* this.network.getInputCount();
wsptr = this.network.getOutputCount()
* this.network.getInputCount();
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
this.v[vsptr + ivar] = 0.0;
this.w[wsptr + ivar] = 0.0;
}
}
final MLDataPair pair = BasicMLDataPair.createPair(this.network
.getSamples().getInputSize(), this.network.getSamples()
.getIdealSize());
for (int r = 0; r < this.network.getSamples().getRecordCount(); r++) {
this.network.getSamples().getRecord(r, pair);
if (r == this.network.getExclude()) {
continue;
}
dist = 0.0;
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
diff = input.getData(ivar) - pair.getInput().getData(ivar);
diff /= this.network.getSigma()[ivar];
this.dsqr[ivar] = diff * diff;
dist += this.dsqr[ivar];
}
if (this.network.getKernel() == PNNKernelType.Gaussian) {
dist = Math.exp(-dist);
} else if (this.network.getKernel() == PNNKernelType.Reciprocal) {
dist = 1.0 / (1.0 + dist);
}
truedist = dist;
if (dist < 1.e-40) {
dist = 1.e-40;
}
if (this.network.getOutputMode() == PNNOutputMode.Classification) {
pop = (int) pair.getIdeal().getData(0);
out[pop] += dist;
vptr = pop * this.network.getInputCount();
wptr = pop * this.network.getInputCount();
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
temp = truedist * this.dsqr[ivar];
this.v[vptr + ivar] += temp;
this.w[wptr + ivar] += temp * (2.0 * this.dsqr[ivar] - 3.0);
}
}
else if (this.network.getOutputMode() == PNNOutputMode.Unsupervised) {
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
out[ivar] += dist * pair.getInput().getData(ivar);
temp = truedist * this.dsqr[ivar];
this.v[vsptr + ivar] += temp;
this.w[wsptr + ivar] += temp
* (2.0 * this.dsqr[ivar] - 3.0);
}
vptr = 0;
wptr = 0;
for (outvar = 0; outvar < this.network.getOutputCount(); outvar++) {
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
temp = truedist * this.dsqr[ivar]
* pair.getInput().getData(ivar);
this.v[vptr++] += temp;
this.w[wptr++] += temp * (2.0 * this.dsqr[ivar] - 3.0);
}
}
psum += dist;
} else if (this.network.getOutputMode() == PNNOutputMode.Regression) {
for (ivar = 0; ivar < this.network.getOutputCount(); ivar++) {
out[ivar] += dist * pair.getIdeal().getData(ivar);
}
vptr = 0;
wptr = 0;
for (outvar = 0; outvar < this.network.getOutputCount(); outvar++) {
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
temp = truedist * this.dsqr[ivar]
* pair.getIdeal().getData(outvar);
this.v[vptr++] += temp;
this.w[wptr++] += temp * (2.0 * this.dsqr[ivar] - 3.0);
}
}
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
temp = truedist * this.dsqr[ivar];
this.v[vsptr + ivar] += temp;
this.w[wsptr + ivar] += temp
* (2.0 * this.dsqr[ivar] - 3.0);
}
psum += dist;
}
}
if (this.network.getOutputMode() == PNNOutputMode.Classification) {
psum = 0.0;
for (pop = 0; pop < this.network.getOutputCount(); pop++) {
if (this.network.getPriors()[pop] >= 0.0) {
out[pop] *= this.network.getPriors()[pop]
/ this.network.getCountPer()[pop];
}
psum += out[pop];
}
if (psum < 1.e-40) {
psum = 1.e-40;
}
}
for (pop = 0; pop < this.network.getOutputCount(); pop++) {
out[pop] /= psum;
}
for (ivar = 0; ivar < this.network.getInputCount(); ivar++) {
if (this.network.getOutputMode() == PNNOutputMode.Classification) {
vtot = wtot = 0.0;
} else {
vtot = this.v[vsptr + ivar] * 2.0
/ (psum * this.network.getSigma()[ivar]);
wtot = this.w[wsptr + ivar]
* 2.0
/ (psum * this.network.getSigma()[ivar] * this.network
.getSigma()[ivar]);
}
for (outvar = 0; outvar < this.network.getOutputCount(); outvar++) {
if ((this.network.getOutputMode() == PNNOutputMode.Classification)
&& (this.network.getPriors()[outvar] >= 0.0)) {
this.v[outvar * this.network.getInputCount() + ivar] *= this.network
.getPriors()[outvar]
/ this.network.getCountPer()[outvar];
this.w[outvar * this.network.getInputCount() + ivar] *= this.network
.getPriors()[outvar]
/ this.network.getCountPer()[outvar];
}
this.v[outvar * this.network.getInputCount() + ivar] *= 2.0 / (psum * this.network
.getSigma()[ivar]);
this.w[outvar * this.network.getInputCount() + ivar] *= 2.0 / (psum
* this.network.getSigma()[ivar] * this.network
.getSigma()[ivar]);
if (this.network.getOutputMode() == PNNOutputMode.Classification) {
vtot += this.v[outvar * this.network.getInputCount() + ivar];
wtot += this.w[outvar * this.network.getInputCount() + ivar];
}
}
for (outvar = 0; outvar < this.network.getOutputCount(); outvar++) {
der1 = this.v[outvar * this.network.getInputCount() + ivar]
- out[outvar] * vtot;
der2 = this.w[outvar * this.network.getInputCount() + ivar]
+ 2.0 * out[outvar] * vtot * vtot - 2.0
* this.v[outvar * this.network.getInputCount() + ivar]
* vtot - out[outvar] * wtot;
if (this.network.getOutputMode() == PNNOutputMode.Classification) {
if (outvar == (int) target.getData(0)) {
temp = 2.0 * (out[outvar] - 1.0);
} else {
temp = 2.0 * out[outvar];
}
} else {
temp = 2.0 * (out[outvar] - target.getData(outvar));
}
this.network.getDeriv()[ivar] += temp * der1;
this.network.getDeriv2()[ivar] += temp * der2 + 2.0 * der1
* der1;
}
}
return new BasicMLData(out);
}
/**
* @return the maxError
*/
public double getMaxError() {
return this.maxError;
}
/**
* {@inheritDoc}
*/
@Override
public MLMethod getMethod() {
return this.network;
}
/**
* @return the minImprovement
*/
public double getMinImprovement() {
return this.minImprovement;
}
/**
* @return the numSigmas
*/
public int getNumSigmas() {
return this.numSigmas;
}
/**
* @return the sigmaHigh
*/
public double getSigmaHigh() {
return this.sigmaHigh;
}
/**
* @return the sigmaLow
*/
public double getSigmaLow() {
return this.sigmaLow;
}
/**
* {@inheritDoc}
*/
@Override
public void iteration() {
preIteration();
if (!this.samplesLoaded) {
this.network.setSamples(new BasicMLDataSet(this.training));
this.samplesLoaded = true;
}
final GlobalMinimumSearch globalMinimum = new GlobalMinimumSearch();
final DeriveMinimum dermin = new DeriveMinimum();
int k;
if (this.network.getOutputMode() == PNNOutputMode.Classification) {
k = this.network.getOutputCount();
} else {
k = this.network.getOutputCount() + 1;
}
this.dsqr = new double[this.network.getInputCount()];
this.v = new double[this.network.getInputCount() * k];
this.w = new double[this.network.getInputCount() * k];
final double[] x = new double[this.network.getInputCount()];
final double[] base = new double[this.network.getInputCount()];
final double[] direc = new double[this.network.getInputCount()];
final double[] g = new double[this.network.getInputCount()];
final double[] h = new double[this.network.getInputCount()];
final double[] dwk2 = new double[this.network.getInputCount()];
if (this.network.isTrained()) {
k = 0;
for (int i = 0; i < this.network.getInputCount(); i++) {
x[i] = this.network.getSigma()[i];
}
globalMinimum.setY2(1.e30);
} else {
globalMinimum.findBestRange(this.sigmaLow, this.sigmaHigh,
this.numSigmas, true, this.maxError, this);
for (int i = 0; i < this.network.getInputCount(); i++) {
x[i] = globalMinimum.getX2();
}
}
final double d = dermin.calculate(32767, this.maxError, 1.e-8,
this.minImprovement, this, this.network.getInputCount(), x,
globalMinimum.getY2(), base, direc, g, h, dwk2);
globalMinimum.setY2(d);
for (int i = 0; i < this.network.getInputCount(); i++) {
this.network.getSigma()[i] = x[i];
}
this.network.setError(Math.abs(globalMinimum.getY2()));
this.network.setTrained(true); // Tell other routines net is trained
this.setError(network.getError());
postIteration();
}
/**
* {@inheritDoc}
*/
@Override
public TrainingContinuation pause() {
return null;
}
/**
* {@inheritDoc}
*/
@Override
public void resume(final TrainingContinuation state) {
}
/**
* @param maxError
* the maxError to set
*/
public void setMaxError(final double maxError) {
this.maxError = maxError;
}
/**
* @param minImprovement
* the minImprovement to set
*/
public void setMinImprovement(final double minImprovement) {
this.minImprovement = minImprovement;
}
/**
* @param numSigmas
* the numSigmas to set
*/
public void setNumSigmas(final int numSigmas) {
this.numSigmas = numSigmas;
}
/**
* @param sigmaHigh
* the sigmaHigh to set
*/
public void setSigmaHigh(final double sigmaHigh) {
this.sigmaHigh = sigmaHigh;
}
/**
* @param sigmaLow
* the sigmaLow to set
*/
public void setSigmaLow(final double sigmaLow) {
this.sigmaLow = sigmaLow;
}
}
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