Examples
Multilayer Network
Step 1: Data Processing
Firstly, we will load and prepare the IRIS dataset:
| sepal.length |
sepal.width |
petal.length |
petal.width |
variety |
| 5.1 |
3.5 |
1.4 |
0.2 |
Setosa |
var featureColumnIndices=new int[]{0,1,2,3}; // Specify your feature columns
var targetColumnIndex=4; // Specify your target column
var dataProcessor=new DataProcessor(new CSVIDataReader());
var dataset=dataProcessor.loadDataSetFromCSV(
new File("path/to/your/data.csv"), // Replace with your CSV file path
',', // CSV delimiter
true, // Whether to skip the header
featureColumnIndices,
targetColumnIndex
);
dataset.shuffle();
dataset.normalize();
dataProcessor.split(0.8); // Replace with your train-test split ratio
Step 2: Configure the MultilayerPerceptron
Extract the features and labels for both training and test sets:
var trainFeatures=dataProcessor.getTrainFeatures(featureColumnIndices);
var trainLabels=dataProcessor.getTrainLabels(targetColumnIndex);
var testFeatures=dataProcessor.getTestFeatures(featureColumnIndices);
var testLabels=dataProcessor.getTestLabels(targetColumnIndex);
Step 3: Network Configuration
var networkConfiguration=new NetworkConfiguration(
trainFeatures[0].length, // Number of input neurons
List.of(128,256,512), // Number of neurons in each hidden layer
3, // Number of output neurons
0.01, // Learning rate
300, // Number of epochs
ActivationFunction.LEAKY_RELU, // Activation function for hidden layers
ActivationFunction.SOFTMAX, // Activation function for output layer
LossFunction.CATEGORICAL_CROSS_ENTROPY, // Loss function
Initialization.XAVIER, // Weight initialization for hidden layers
Initialization.XAVIER // Weight initialization for output layer
);
Step 4: Training and Evaluation
MultilayerPerceptron multilayerPerceptron=new MultilayerPerceptron(
networkConfiguration,
testFeatures,
testLabels
);
multilayerPerceptron.train(trainFeatures,trainLabels);
multilayerPerceptron.evaluate(testFeatures,testLabels);
Results
...
MultilayerPerceptron - Best accuracy after restoring best MLP model: 93.33%
Decision Tree
var datasetProvider = new IrisProvider(NORMALIZE, SHUFFLE, 0.6);
datasetProvider.printStatistics();
double[][] features = datasetProvider.getTrainFeatures();
double[][] labels = datasetProvider.getTrainLabels();
double[][] testFeatures = datasetProvider.getTestFeatures();
double[][] testLabels = datasetProvider.getTestLabels();
Classifier decisionTree = new DecisionTree(8, 2, 1, 4);
decisionTree.train(features, labels);
decisionTree.evaluate(testFeatures, testLabels);
Support Vector Machine - Example on IRIS Dataset
Step 1 : Initialize the variables
private static final double TRAIN_TEST_SPLIT_RATIO = 0.70;
private static final File CSV_FILE =
new File("path/to/your/iris.csv");
private static final boolean SKIP_HEAD = true;
Step 2 : Create,train and evaluate classifier
/*
+-------------+------------+-------------+------------+---------+
| sepal.length| sepal.width| petal.length| petal.width| variety |
+-------------+------------+-------------+------------+---------+
| 5.1 | 3.5 | 1.4 | .2 | Setosa |
+-------------+------------+-------------+------------+---------+
*/
// First 4 columns are features
var featureColumnIndices = new int[]{0, 1, 2, 3};
// Last column is the target
var targetColumnIndex = 4;
var irisDataProcessor =
new DataProcessor(new CSVIDataReader())
.loadDataSetFromCSV(CSV_FILE, ',', SKIP_HEAD,featureColumnIndices,targetColumnIndex)
.normalize().shuffle()
.split(TRAIN_TEST_SPLIT_RATIO);
Classifier classifier = new DecisionTree(2, 2, 3, 12);
var trainFeatures = irisDataProcessor.getTrainFeatures(featureColumnIndices);
var trainTestFeatures = irisDataProcessor.getTestFeatures(featureColumnIndices);
var trainLabels = irisDataProcessor.getTrainLabels(targetColumnIndex);
var trainTestLabels = irisDataProcessor.getTestLabels(targetColumnIndex);
classifier.train(trainFeatures, trainLabels);
classifier.evaluate(trainTestFeatures, trainTestLabels);