/**
* Copyright 2013 Neuroph Project http://neuroph.sourceforge.net
*
* 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.
*/
package org.neuroph.samples.uci;
import java.util.Arrays;
import org.neuroph.core.NeuralNetwork;
import org.neuroph.core.data.DataSet;
import org.neuroph.core.data.DataSetRow;
import org.neuroph.core.events.LearningEvent;
import org.neuroph.core.events.LearningEventListener;
import org.neuroph.nnet.MultiLayerPerceptron;
import org.neuroph.nnet.learning.BackPropagation;
import org.neuroph.nnet.learning.MomentumBackpropagation;
/**
*
* @author Ivana Bajovic
*/
/*
INTRODUCTION TO THE PROBLEM AND DATA SET INFORMATION:
* The purpose of this experiment is to study the feasibility of classification animal species using neural networks.
* An animal class is made up of animal that are all alike in important ways.
* So we need to train a neural network to make it able to predict which species belong to a particular group.
* Once we have decided on a problem to solve using neural networks, we will need to gather data for training purposes.
* The training data set includes a number of cases, each containing values for a range of input and output variables.
* The data set that we use in this experiment can be found at http://archive.ics.uci.edu/ml/datasets.html under the category classification.
* This database includes 101 cases. Each case is the name of animal. It was found that each of these animals belonged to one of seven classes.
Class-Set of animals:
1 - (41) aardvark, antelope, bear, boar, buffalo, calf, cavy, cheetah, deer, dolphin, elephant, fruitbat, giraffe, girl, goat, gorilla, hamster, hare, leopard, lion, lynx, mink, mole, mongoose, opossum, oryx, platypus, polecat, pony, porpoise, puma, pussycat, raccoon, reindeer, seal, sealion, squirrel, vampire, vole, wallaby, wolf
2 - (20) chicken, crow, dove, duck, flamingo, gull, hawk, kiwi, lark, ostrich, parakeet, penguin, pheasant, rhea, skimmer, skua, sparrow, swan, vulture, wren
3 - (5) pitviper, seasnake, slowworm, tortoise, tuatara
4 - (13) bass, carp, catfish, chub, dogfish, haddock, herring, pike, piranha, seahorse, sole, stingray, tuna
5 - frog, frog, newt, toad
6 - flea, gnat, honeybee, housefly, ladybird, moth, termite, wasp
7 - clam, crab, crayfish, lobster, octopus, scorpion, seawasp, slug, starfish, worm
ATTRIBUTE INFORMATION:
1. hair
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
2. feathers
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
3. eggs
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
4. milk
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
5. airborne
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
6. aquatic
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
7. predator
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
8. toothed
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
9. backbone
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
10. bretahes
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
11. venomous
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
12. fins
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
13. legs
Numeric variable (set of values: {0, 2, 4, 6, 8}).
14. tail
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
15. domestic
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
16. catsize
Boolean = {true, false}. Value true will be replaced with value 1, and value false will be replaced with value 0.
17. Class-Set of animals
Numeric variable (set of values: {1, 2, 3, 4, 5, 6, 7}).
1 - (41) aardvark, antelope, bear, boar, buffalo, calf, cavy, cheetah, deer, dolphin, elephant, fruitbat, giraffe, girl, goat, gorilla, hamster, hare, leopard, lion, lynx, mink, mole, mongoose, opossum, oryx, platypus, polecat, pony, porpoise, puma, pussycat, raccoon, reindeer, seal, sealion, squirrel, vampire, vole, wallaby, wolf
2 - (20) chicken, crow, dove, duck, flamingo, gull, hawk, kiwi, lark, ostrich, parakeet, penguin, pheasant, rhea, skimmer, skua, sparrow, swan, vulture, wren
3 - (5) pitviper, seasnake, slowworm, tortoise, tuatara
4 - (13) bass, carp, catfish, chub, dogfish, haddock, herring, pike, piranha, seahorse, sole, stingray, tuna
5 - frog, frog, newt, toad
6 - flea, gnat, honeybee, housefly, ladybird, moth, termite, wasp
7 - clam, crab, crayfish, lobster, octopus, scorpion, seawasp, slug, starfish, worm
1.,2.,...15.,16. - inputs
17. - output
The original data set that will be used in this experiment can be found at link http://archive.ics.uci.edu/ml/datasets/Zoo
*/
public class AnimalsClassificationSample implements LearningEventListener {
/**
* @param args the command line arguments
*/
public static void main(String[] args) {
(new AnimalsClassificationSample()).run();
}
public void run() {
System.out.println("Creating training set...");
String trainingSetFileName = "data_sets/animals_data.txt";
int inputsCount = 20;
int outputsCount = 7;
// create training set from file
DataSet dataSet = DataSet.createFromFile(trainingSetFileName, inputsCount, outputsCount, "\t", true);
System.out.println("Creating neural network...");
// create MultiLayerPerceptron neural network
MultiLayerPerceptron neuralNet = new MultiLayerPerceptron(inputsCount, 22, outputsCount);
// attach listener to learning rule
MomentumBackpropagation learningRule = (MomentumBackpropagation) neuralNet.getLearningRule();
learningRule.addListener(this);
// set learning rate and max error
learningRule.setLearningRate(0.2);
learningRule.setMaxError(0.01);
System.out.println("Training network...");
// train the network with training set
neuralNet.learn(dataSet);
System.out.println("Training completed.");
System.out.println("Testing network...");
testNeuralNetwork(neuralNet, dataSet);
System.out.println("Saving network");
// save neural network to file
neuralNet.save("MyNeuralNetAnimals.nnet");
System.out.println("Done.");
}
public void testNeuralNetwork(NeuralNetwork neuralNet, DataSet testSet) {
for (DataSetRow testSetRow : testSet.getRows()) {
neuralNet.setInput(testSetRow.getInput());
neuralNet.calculate();
double[] networkOutput = neuralNet.getOutput();
System.out.print("Input: " + Arrays.toString(testSetRow.getInput()));
System.out.println(" Output: " + Arrays.toString(networkOutput));
}
}
@Override
public void handleLearningEvent(LearningEvent event) {
BackPropagation bp = (BackPropagation) event.getSource();
System.out.println(bp.getCurrentIteration() + ". iteration | Total network error: " + bp.getTotalNetworkError());
}
}
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