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* Licensed to the Apache Software Foundation (ASF) under one
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* distributed with this work for additional information
* regarding copyright ownership. The ASF licenses this file
* to you 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.
*/
package opennlp.maxent.quasinewton;
import opennlp.model.AbstractModel;
import opennlp.model.Context;
import opennlp.model.EvalParameters;
import opennlp.model.UniformPrior;
public class QNModel extends AbstractModel {
private static final double SMOOTHING_VALUE = 0.1;
private double[] parameters;
// FROM trainer
public QNModel(LogLikelihoodFunction monitor, double[] parameters) {
super(null, monitor.getPredLabels(), monitor.getOutcomeLabels());
int[][] outcomePatterns = monitor.getOutcomePatterns();
Context[] params = new Context[monitor.getPredLabels().length];
for (int ci = 0; ci < params.length; ci++) {
int[] outcomePattern = outcomePatterns[ci];
double[] alpha = new double[outcomePattern.length];
for (int oi = 0; oi < outcomePattern.length; oi++) {
alpha[oi] = parameters[ci + (outcomePattern[oi] * monitor.getPredLabels().length)];
}
params[ci] = new Context(outcomePattern, alpha);
}
this.evalParams = new EvalParameters(params, monitor.getOutcomeLabels().length);
this.prior = new UniformPrior();
this.modelType = ModelType.MaxentQn;
this.parameters = parameters;
}
// FROM model reader
public QNModel(String[] predNames, String[] outcomeNames, Context[] params, double[] parameters) {
super(params, predNames, outcomeNames);
this.prior = new UniformPrior();
this.modelType = ModelType.MaxentQn;
this.parameters = parameters;
}
public double[] eval(String[] context) {
return eval(context, new double[evalParams.getNumOutcomes()]);
}
private int getPredIndex(String predicate) {
return pmap.get(predicate);
}
public double[] eval(String[] context, double[] probs) {
return eval(context, null, probs);
}
public double[] eval(String[] context, float[] values) {
return eval(context, values, new double[evalParams.getNumOutcomes()]);
}
// TODO need implments for handlling with "probs".
private double[] eval(String[] context, float[] values, double[] probs) {
double[] result = new double[outcomeNames.length];
double[] table = new double[outcomeNames.length + 1];
for (int pi = 0; pi < context.length; pi++) {
int predIdx = getPredIndex(context[pi]);
for (int oi = 0; oi < outcomeNames.length; oi++) {
int paraIdx = oi * pmap.size() + predIdx;
double predValue = 1.0;
if (values != null) predValue = values[pi];
if (paraIdx < 0) {
table[oi] += predValue * SMOOTHING_VALUE;
} else {
table[oi] += predValue * parameters[paraIdx];
}
}
}
for (int oi = 0; oi < outcomeNames.length; oi++) {
table[oi] = Math.exp(table[oi]);
table[outcomeNames.length] += table[oi];
}
for (int oi = 0; oi < outcomeNames.length; oi++) {
result[oi] = table[oi] / table[outcomeNames.length];
}
return result;
// double[] table = new double[outcomeNames.length];
// Arrays.fill(table, 1.0 / outcomeNames.length);
// return table;
}
public int getNumOutcomes() {
return this.outcomeNames.length;
}
public double[] getParameters() {
return this.parameters;
}
public boolean equals(Object obj) {
if (!(obj instanceof QNModel))
return false;
QNModel objModel = (QNModel) obj;
if (this.outcomeNames.length != objModel.outcomeNames.length)
return false;
for (int i = 0; i < this.outcomeNames.length; i++) {
if (!this.outcomeNames[i].equals(objModel.outcomeNames[i]))
return false;
}
if (this.pmap.size() != objModel.pmap.size())
return false;
String[] pmapArray = new String[pmap.size()];
pmap.toArray(pmapArray);
for (int i = 0; i < this.pmap.size(); i++) {
if (i != objModel.pmap.get(pmapArray[i]))
return false;
}
// compare evalParameters
Context[] contextComparing = objModel.evalParams.getParams();
if (this.evalParams.getParams().length != contextComparing.length)
return false;
for (int i = 0; i < this.evalParams.getParams().length; i++) {
if (this.evalParams.getParams()[i].getOutcomes().length != contextComparing[i].getOutcomes().length)
return false;
for (int j = 0; i < this.evalParams.getParams()[i].getOutcomes().length; i++) {
if (this.evalParams.getParams()[i].getOutcomes()[j] != contextComparing[i].getOutcomes()[j])
return false;
}
if (this.evalParams.getParams()[i].getParameters().length != contextComparing[i].getParameters().length)
return false;
for (int j = 0; i < this.evalParams.getParams()[i].getParameters().length; i++) {
if (this.evalParams.getParams()[i].getParameters()[j] != contextComparing[i].getParameters()[j])
return false;
}
}
return true;
}
}