/*
* Artificial Intelligence for Humans
* Volume 1: Fundamental Algorithms
* Java Version
* http://www.aifh.org
* http://www.jeffheaton.com
*
* Code repository:
* https://github.com/jeffheaton/aifh
* Copyright 2013 by Jeff Heaton
*
* Licensed 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.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package com.heatonresearch.aifh.examples.regression;
import com.heatonresearch.aifh.examples.learning.SimpleLearn;
import com.heatonresearch.aifh.general.data.BasicData;
import com.heatonresearch.aifh.normalize.DataSet;
import com.heatonresearch.aifh.regression.MultipleLinearRegression;
import com.heatonresearch.aifh.regression.TrainLeastSquares;
import java.io.InputStream;
import java.util.Arrays;
import java.util.List;
/**
* Linear regression example.
*/
public class LinearRegressionExample extends SimpleLearn {
public void process() {
try {
final InputStream istream = this.getClass().getResourceAsStream("/abalone.csv");
if( istream==null ) {
System.out.println("Cannot access data set, make sure the resources are available.");
System.exit(1);
}
final DataSet ds = DataSet.load(istream);
// The following ranges are setup for the Abalone data set. If you wish to normalize other files you will
// need to modify the below function calls other files.
ds.encodeOneOfN(0, 0, 1);
istream.close();
final List<BasicData> trainingData = ds.extractSupervised(0, 10, 10, 1);
final MultipleLinearRegression reg = new MultipleLinearRegression(10);
final TrainLeastSquares train = new TrainLeastSquares(reg, trainingData);
train.iteration();
System.out.println(Arrays.toString(reg.getLongTermMemory()));
query(reg, trainingData);
System.out.println("Error: " + train.getError());
} catch (Throwable t) {
t.printStackTrace();
}
}
public static void main(final String[] args) {
final LinearRegressionExample prg = new LinearRegressionExample();
prg.process();
}
}