/*
* 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.error;
import com.heatonresearch.aifh.error.ErrorCalculation;
import com.heatonresearch.aifh.error.ErrorCalculationMSE;
import com.heatonresearch.aifh.error.ErrorCalculationRMS;
import com.heatonresearch.aifh.error.ErrorCalculationSSE;
import com.heatonresearch.aifh.randomize.GenerateRandom;
import com.heatonresearch.aifh.randomize.MersenneTwisterGenerateRandom;
import java.text.NumberFormat;
/**
* Example that demonstrates how to calculate errors. This allows you to see how different types of distortion affect
* the final error for various error calculation methods.
* <p/>
* Type ESS MSE RMS
* Small 1252 0.01 0.1
* Medium 31317 0.251 0.501
* Large 125269 1.002 1.001
* Huge 12526940 100.216 10.011
*/
public class EvaluateErrors {
/**
* The random seed to use.
*/
public static final int SEED = 1420;
/**
* The number of rows.
*/
public static final int ROWS = 10000;
/**
* The number of columns.
*/
public static final int COLS = 25;
/**
* The low value.
*/
public static final double LOW = -1;
/**
* The high value.
*/
public static final double HIGH = 1;
/**
* Generate random data.
*
* @param seed The seed to use.
* @param rows The number of rows to generate.
* @param cols The number of columns to generate.
* @param low The low value.
* @param high The high value.
* @param distort The distortion factor.
* @return The data set.
*/
public DataHolder generate(final int seed, final int rows, final int cols, final double low, final double high, final double distort) {
final GenerateRandom rnd = new MersenneTwisterGenerateRandom(seed);
final double[][] ideal = new double[rows][cols];
final double[][] actual = new double[rows][cols];
for (int row = 0; row < rows; row++) {
for (int col = 0; col < cols; col++) {
ideal[row][col] = rnd.nextDouble(low, high);
actual[row][col] = ideal[row][col] + (rnd.nextGaussian() * distort);
}
}
final DataHolder result = new DataHolder();
result.setActual(actual);
result.setIdeal(ideal);
return result;
}
/**
* Run the example.
*/
public void process() {
final NumberFormat nf = NumberFormat.getInstance();
final ErrorCalculation calcESS = new ErrorCalculationSSE();
final ErrorCalculation calcMSE = new ErrorCalculationMSE();
final ErrorCalculation calcRMS = new ErrorCalculationRMS();
final DataHolder smallErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 0.1);
final DataHolder mediumErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 0.5);
final DataHolder largeErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 1.0);
final DataHolder hugeErrors = generate(SEED, ROWS, COLS, LOW, HIGH, 10.0);
final double smallESS = smallErrors.calculateError(calcESS);
final double smallMSE = smallErrors.calculateError(calcMSE);
final double smallRMS = smallErrors.calculateError(calcRMS);
final double mediumESS = mediumErrors.calculateError(calcESS);
final double mediumMSE = mediumErrors.calculateError(calcMSE);
final double mediumRMS = mediumErrors.calculateError(calcRMS);
final double largeESS = largeErrors.calculateError(calcESS);
final double largeMSE = largeErrors.calculateError(calcMSE);
final double largeRMS = largeErrors.calculateError(calcRMS);
final double hugeESS = hugeErrors.calculateError(calcESS);
final double hugeMSE = hugeErrors.calculateError(calcMSE);
final double hugeRMS = hugeErrors.calculateError(calcRMS);
System.out.println("Type\tSSE\t\t\tMSE\t\tRMS");
System.out.println("Small\t" + (int) smallESS + "\t\t" + nf.format(smallMSE) + "\t" + nf.format(smallRMS));
System.out.println("Medium\t" + (int) mediumESS + "\t\t" + nf.format(mediumMSE) + "\t" + nf.format(mediumRMS));
System.out.println("Large\t" + (int) largeESS + "\t\t" + nf.format(largeMSE) + "\t" + nf.format(largeRMS));
System.out.println("Huge\t" + (int) hugeESS + "\t" + nf.format(hugeMSE) + "\t" + nf.format(hugeRMS));
}
/**
* The main method.
*
* @param args Not used.
*/
public static void main(final String[] args) {
final EvaluateErrors prg = new EvaluateErrors();
prg.process();
}
}