/*
* 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.perceptron;
import java.io.BufferedReader;
import java.io.File;
import java.io.InputStreamReader;
import java.text.DecimalFormat;
import java.util.Map;
import opennlp.model.AbstractModel;
import opennlp.model.Context;
import opennlp.model.EvalParameters;
public class PerceptronModel extends AbstractModel {
public PerceptronModel(Context[] params, String[] predLabels, Map<String,Integer> pmap, String[] outcomeNames) {
super(params,predLabels,outcomeNames);
modelType = ModelType.Perceptron;
}
public PerceptronModel(Context[] params, String[] predLabels, String[] outcomeNames) {
super(params,predLabels,outcomeNames);
modelType = ModelType.Perceptron;
}
public double[] eval(String[] context) {
return eval(context,new double[evalParams.getNumOutcomes()]);
}
public double[] eval(String[] context, float[] values) {
return eval(context,values,new double[evalParams.getNumOutcomes()]);
}
public double[] eval(String[] context, double[] probs) {
return eval(context,null,probs);
}
public double[] eval(String[] context, float[] values,double[] outsums) {
int[] scontexts = new int[context.length];
java.util.Arrays.fill(outsums, 0);
for (int i=0; i<context.length; i++) {
Integer ci = pmap.get(context[i]);
scontexts[i] = ci == null ? -1 : ci;
}
return eval(scontexts,values,outsums,evalParams,true);
}
public static double[] eval(int[] context, double[] prior, EvalParameters model) {
return eval(context,null,prior,model,true);
}
public static double[] eval(int[] context, float[] values, double[] prior, EvalParameters model, boolean normalize) {
Context[] params = model.getParams();
double[] activeParameters;
int[] activeOutcomes;
double value = 1;
for (int ci = 0; ci < context.length; ci++) {
if (context[ci] >= 0) {
Context predParams = params[context[ci]];
activeOutcomes = predParams.getOutcomes();
activeParameters = predParams.getParameters();
if (values != null) {
value = values[ci];
}
for (int ai = 0; ai < activeOutcomes.length; ai++) {
int oid = activeOutcomes[ai];
prior[oid] += activeParameters[ai] * value;
}
}
}
if (normalize) {
int numOutcomes = model.getNumOutcomes();
for (int oid = 0; oid < numOutcomes; oid++)
prior[oid] = Math.exp(prior[oid]);
double normal = 0.0;
for (int oid = 0; oid < numOutcomes; oid++)
normal += prior[oid];
for (int oid = 0; oid < numOutcomes; oid++)
prior[oid] /= normal;
}
return prior;
}
public static void main(String[] args) throws java.io.IOException {
if (args.length == 0) {
System.err.println("Usage: PerceptronModel modelname < contexts");
System.exit(1);
}
AbstractModel m = new PerceptronModelReader(new File(args[0])).getModel();
BufferedReader in = new BufferedReader(new InputStreamReader(System.in));
DecimalFormat df = new java.text.DecimalFormat(".###");
for (String line = in.readLine(); line != null; line = in.readLine()) {
String[] context = line.split(" ");
double[] dist = m.eval(context);
for (int oi=0;oi<dist.length;oi++) {
System.out.print("["+m.getOutcome(oi)+" "+df.format(dist[oi])+"] ");
}
System.out.println();
}
}
}