PerceptronModel.java
/*
* Copyright (c) 2017, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
*
* WSO2 Inc. 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 org.wso2.extension.siddhi.execution.streamingml.classification.perceptron.util;
import org.wso2.extension.siddhi.execution.streamingml.util.MathUtil;
import java.io.Serializable;
import java.util.Arrays;
/**
* Represents a linear Perceptron Model
*/
public class PerceptronModel implements Serializable {
private static final long serialVersionUID = -5179648194841293764L;
private double[] weights;
private double bias = 0.0;
private double threshold = 0.5;
private double learningRate = 0.1;
public PerceptronModel() {
}
public PerceptronModel(PerceptronModel model) {
this.weights = model.weights;
this.bias = model.bias;
this.threshold = model.threshold;
this.learningRate = model.learningRate;
}
public double[] update(Boolean label, double[] features) {
boolean predictedLabel = this.classify(this.getPredictionProbability(features));
if (!label.equals(predictedLabel)) {
double error = Boolean.TRUE.equals(label) ? 1.0 : -1.0;
// Get correction
double correction;
for (int i = 0; i < features.length; i++) {
correction = features[i] * error * this.learningRate;
this.weights[i] = this.weights[i] + correction;
}
}
return Arrays.copyOf(weights, weights.length);
}
private double getPredictionProbability(double[] features) {
if (this.weights == null) {
this.initWeights(features.length);
}
return MathUtil.dot(features, weights) + this.bias;
}
private boolean classify(double evaluation) {
return evaluation > this.threshold ? true : false;
}
public Object[] classify(double[] features) {
double evaluation = getPredictionProbability(features);
boolean prediction = classify(evaluation);
return new Object[]{prediction, evaluation};
}
public void initWeights(int size) {
this.weights = new double[size];
}
public int getFeatureSize() {
if (weights == null) {
return -1;
}
return weights.length;
}
public void setBias(double bias) {
this.bias = bias;
}
public void setThreshold(double threshold) {
this.threshold = threshold;
}
public void setLearningRate(double learningRate) {
this.learningRate = learningRate;
}
}