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package datasets;

import java.util.ArrayList;

import java.util.List;

import java.util.Random;

import loss.LossMultiDimensionalBinary;

import loss.LossSumOfSquares;

import matrix.Matrix;

import model.Model;

import model.Nonlinearity;

import model.SigmoidUnit;

import datastructs.DataSequence;

import datastructs.DataSet;

import datastructs.DataStep;

public class SequentialParity extends DataSet {

public SequentialParity(Random r, int total_sequences, int max_sequence_length_train, int max_sequence_length_test) {

inputDimension = 1;

outputDimension = 1;

lossTraining = new LossSumOfSquares();

lossReporting = new LossMultiDimensionalBinary();

training = generateSequences(r, total_sequences, max_sequence_length_train);

//training.addAll(generateSequences(r, total_sequences, max_sequence_length_test));

validation = generateSequences(r, total_sequences, max_sequence_length_train);

testing = generateSequences(r, total_sequences, max_sequence_length_test);

}

private static List<DataSequence> generateSequences(Random r, int total_sequences, int max_sequence_length) {

List<DataSequence> result = new ArrayList<>();;

for (int s = 0; s < total_sequences; s++) {

DataSequence sequence = new DataSequence();

int tot = 0;

int tempSequenceLength = r.nextInt(max_sequence_length) + 1;

for (int t = 0; t < tempSequenceLength; t++) {

DataStep step = new DataStep();

double[] input = {0.0};

if (r.nextDouble() < 0.5) {

input[0] = 1.0;

tot++;

}

step.input = new Matrix(input);

double[] targetOutput = null;

if (t == tempSequenceLength - 1) {

targetOutput = new double[1];

targetOutput[0] = tot%2;

step.targetOutput = new Matrix(targetOutput);

}

sequence.steps.add(step);

}

result.add(sequence);

}

return result;

}

@Override

public void DisplayReport(Model model, Random rng) throws Exception {

// TODO Auto-generated method stub

}

@Override

public Nonlinearity getModelOutputUnitToUse() {

return new SigmoidUnit();

}

}