Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file 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.tools.ml.naivebayes;
import java.util.Map;
import opennlp.tools.ml.model.AbstractModel;
import opennlp.tools.ml.model.Context;
import opennlp.tools.ml.model.EvalParameters;
import opennlp.tools.ml.model.MaxentModel;
/**
* A {@link MaxentModel} implementation of the multinomial Naive Bayes classifier model.
*
* @see AbstractModel
* @see MaxentModel
*/
public class NaiveBayesModel extends AbstractModel {
protected final double[] outcomeTotals;
protected long vocabulary;
/**
* Initializes a {@link NaiveBayesModel}.
*
* @param params The {@link Context parameters} to set.
* @param predLabels The predicted labels.
* @param pmap A {@link Map} that provides a mapping between predicates and contexts.
* @param outcomeNames The names of the outcomes.
*/
NaiveBayesModel(Context[] params, String[] predLabels, Map pmap,
String[] outcomeNames) {
super(params, predLabels, pmap, outcomeNames);
outcomeTotals = initOutcomeTotals(outcomeNames, params);
this.evalParams = new NaiveBayesEvalParameters(params, outcomeNames.length,
outcomeTotals, predLabels.length);
modelType = ModelType.NaiveBayes;
}
/**
* Initializes a {@link NaiveBayesModel}.
*
* @param params The {@link Context parameters} to set.
* @param predLabels The predicted labels.
* @param outcomeNames The names of the outcomes.
*/
public NaiveBayesModel(Context[] params, String[] predLabels, String[] outcomeNames) {
super(params, predLabels, outcomeNames);
outcomeTotals = initOutcomeTotals(outcomeNames, params);
this.evalParams = new NaiveBayesEvalParameters(params, outcomeNames.length,
outcomeTotals, predLabels.length);
modelType = ModelType.NaiveBayes;
}
protected double[] initOutcomeTotals(String[] outcomeNames, Context[] params) {
double[] outcomeTotals = new double[outcomeNames.length];
for (Context context : params) {
for (int j = 0; j < context.getOutcomes().length; ++j) {
int outcome = context.getOutcomes()[j];
double count = context.getParameters()[j];
outcomeTotals[outcome] += count;
}
}
return outcomeTotals;
}
/**
* {@inheritDoc}
*/
@Override
public double[] eval(String[] context) {
return eval(context, new double[evalParams.getNumOutcomes()]);
}
/**
* {@inheritDoc}
*/
@Override
public double[] eval(String[] context, float[] values) {
return eval(context, values, new double[evalParams.getNumOutcomes()]);
}
/**
* {@inheritDoc}
*/
@Override
public double[] eval(String[] context, double[] probs) {
return eval(context, null, probs);
}
public double[] eval(String[] context, float[] values, double[] outsums) {
Context[] scontexts = new Context[context.length];
java.util.Arrays.fill(outsums, 0);
for (int i = 0; i < context.length; i++) {
scontexts[i] = pmap.get(context[i]);
}
return eval(scontexts, values, outsums, evalParams, true);
}
/**
* Evaluates a {@link NaiveBayesModel}.
*
* @param context The context parameters as {@code int[]}.
* @param prior The data prior to the evaluation as {@code double[]}.
* @param model The {@link EvalParameters} used for evaluation.
*
* @return The resulting evaluation data as {@code double[]}.
*/
public static double[] eval(int[] context, double[] prior, EvalParameters model) {
return eval(context, null, prior, model, true);
}
/**
* Evaluates a {@link NaiveBayesModel}.
*
* @param context The {@link Context[] parameters} to set..
* @param values The {@code float[]} values to be used.
* @param prior The data prior to the evaluation as {@code double[]}.
* @param model The {@link EvalParameters} used for evaluation.
* @param normalize Whether to normalize, or not.
*
* @return The resulting evaluation data as {@code double[]}.
*/
static double[] eval(Context[] context, float[] values, double[] prior,
EvalParameters model, boolean normalize) {
Probabilities probabilities = new LogProbabilities<>();
double[] outcomeTotals = model instanceof NaiveBayesEvalParameters
? ((NaiveBayesEvalParameters) model).getOutcomeTotals() : new double[prior.length];
long vocabulary = model instanceof NaiveBayesEvalParameters
? ((NaiveBayesEvalParameters) model).getVocabulary() : 0;
double[] activeParameters;
int[] activeOutcomes;
double value = 1;
for (int ci = 0; ci < context.length; ci++) {
if (context[ci] != null) {
Context predParams = context[ci];
activeOutcomes = predParams.getOutcomes();
activeParameters = predParams.getParameters();
if (values != null) {
value = values[ci];
}
int ai = 0;
for (int i = 0; i < outcomeTotals.length && ai < activeOutcomes.length; ++i) {
int oid = activeOutcomes[ai];
double numerator = oid == i ? activeParameters[ai++] * value : 0;
double denominator = outcomeTotals[i];
probabilities.addIn(i, getProbability(numerator, denominator, vocabulary, true), 1);
}
}
}
double total = 0;
for (double outcomeTotal : outcomeTotals) {
total += outcomeTotal;
}
for (int i = 0; i < outcomeTotals.length; ++i) {
double numerator = outcomeTotals[i];
probabilities.addIn(i, numerator / total, 1);
}
for (int i = 0; i < outcomeTotals.length; ++i) {
prior[i] = probabilities.get(i);
}
return prior;
}
/**
* Evaluates a {@link NaiveBayesModel}.
*
* @param context The context parameters as {@code int[]}.
* @param values The {@code float[]} values to be used.
* @param prior The data prior to the evaluation as {@code double[]}.
* @param model The {@link EvalParameters} used for evaluation.
* @param normalize Whether to normalize, or not.
*
* @return The resulting evaluation data as {@code double[]}.
*/
static double[] eval(int[] context, float[] values, double[] prior,
EvalParameters model, boolean normalize) {
Context[] scontexts = new Context[context.length];
for (int i = 0; i < context.length; i++) {
scontexts[i] = model.getParams()[context[i]];
}
return eval(scontexts, values, prior, model, normalize);
}
private static double getProbability(double numerator, double denominator,
double vocabulary, boolean isSmoothed) {
if (isSmoothed)
return getSmoothedProbability(numerator, denominator, vocabulary);
else if (denominator == 0 || denominator <= Double.MIN_VALUE)
return 0;
else
return numerator / denominator;
}
private static double getSmoothedProbability(double numerator, double denominator, double vocabulary) {
final double delta = 0.05; // Lidstone smoothing
return (numerator + delta) / (denominator + delta * vocabulary);
}
}
