org.apache.ignite.ml.regressions.linear.LinearRegressionModel Maven / Gradle / Ivy
/*
* 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 org.apache.ignite.ml.regressions.linear;
import java.io.Serializable;
import java.util.Objects;
import org.apache.ignite.ml.Exportable;
import org.apache.ignite.ml.Exporter;
import org.apache.ignite.ml.Model;
import org.apache.ignite.ml.math.Vector;
/**
* Simple linear regression model which predicts result value Y as a linear combination of input variables:
* Y = weights * X + intercept.
*/
public class LinearRegressionModel implements Model, Exportable, Serializable {
/** */
private static final long serialVersionUID = -105984600091550226L;
/** Multiplier of the objects's vector required to make prediction. */
private final Vector weights;
/** Intercept of the linear regression model */
private final double intercept;
/** */
public LinearRegressionModel(Vector weights, double intercept) {
this.weights = weights;
this.intercept = intercept;
}
/** */
public Vector getWeights() {
return weights;
}
/** */
public double getIntercept() {
return intercept;
}
/** {@inheritDoc} */
@Override public Double apply(Vector input) {
return input.dot(weights) + intercept;
}
/** {@inheritDoc} */
@Override public void saveModel(Exporter exporter, P path) {
exporter.save(this, path);
}
/** {@inheritDoc} */
@Override public boolean equals(Object o) {
if (this == o)
return true;
if (o == null || getClass() != o.getClass())
return false;
LinearRegressionModel mdl = (LinearRegressionModel)o;
return Double.compare(mdl.intercept, intercept) == 0 &&
Objects.equals(weights, mdl.weights);
}
/** {@inheritDoc} */
@Override public int hashCode() {
return Objects.hash(weights, intercept);
}
/** {@inheritDoc} */
@Override public String toString() {
if (weights.size() < 10) {
StringBuilder builder = new StringBuilder();
for (int i = 0; i < weights.size(); i++) {
double nextItem = i == weights.size() - 1 ? intercept : weights.get(i + 1);
builder.append(String.format("%.4f", Math.abs(weights.get(i))))
.append("*x")
.append(i)
.append(nextItem > 0 ? " + " : " - ");
}
builder.append(String.format("%.4f", Math.abs(intercept)));
return builder.toString();
}
return "LinearRegressionModel{" +
"weights=" + weights +
", intercept=" + intercept +
'}';
}
}