package datasets;

import java.util.*;

import datastructs.DataSequence;

import datastructs.DataSet;

import datastructs.DataStep;

import loss.LossMultiDimensionalBinary;

import loss.LossSumOfSquares;

import model.Model;

import model.Nonlinearity;

import model.SigmoidUnit;

public class EmbeddedReberGrammar extends DataSet {

static public class State {

public State(Transition[] transitions) {

this.transitions = transitions;

}

public Transition[] transitions;

}

static public class Transition {

public Transition(int next_state_id, int token) {

this.next_state_id = next_state_id;

this.token = token;

}

public int next_state_id;

public int token;

}

public EmbeddedReberGrammar(Random r) throws Exception {

int total_sequences = 1000;

inputDimension = 7;

outputDimension = 7;

lossTraining = new LossSumOfSquares();

lossReporting = new LossMultiDimensionalBinary();

training = generateSequences(r, total_sequences);

validation = generateSequences(r, total_sequences);

testing = generateSequences(r, total_sequences);

}

public static List<DataSequence> generateSequences(Random r, int sequences) {

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

final int B = 0;

final int T = 1;

final int P = 2;

final int S = 3;

final int X = 4;

final int V = 5;

final int E = 6;

State[] states = new State[19];

states[0] = new State(new Transition[] {new Transition(1,B)});

states[1] = new State(new Transition[] {new Transition(2,T), new Transition(11,P)});

states[2] = new State(new Transition[] {new Transition(3,B)});

states[3] = new State(new Transition[] {new Transition(4,T), new Transition(9,P)});

states[4] = new State(new Transition[] {new Transition(4,S), new Transition(5,X)});

states[5] = new State(new Transition[] {new Transition(6,S), new Transition(9,X)});

states[6] = new State(new Transition[] {new Transition(7,E)});

states[7] = new State(new Transition[] {new Transition(8,T)});

states[8] = new State(new Transition[] {new Transition(0,E)});

states[9] = new State(new Transition[] {new Transition(9,T), new Transition(10,V)});

states[10] = new State(new Transition[] {new Transition(5,P), new Transition(6,V)});

states[11] = new State(new Transition[] {new Transition(12,B)});

states[12] = new State(new Transition[] {new Transition(13,T), new Transition(17,P)});

states[13] = new State(new Transition[] {new Transition(13,S), new Transition(14,X)});

states[14] = new State(new Transition[] {new Transition(15,S), new Transition(17,X)});

states[15] = new State(new Transition[] {new Transition(16,E)});

states[16] = new State(new Transition[] {new Transition(8,P)});

states[17] = new State(new Transition[] {new Transition(17,T), new Transition(18,V)});

states[18] = new State(new Transition[] {new Transition(14,P), new Transition(15,V)});

for (int sequence = 0; sequence < sequences; sequence++) {

List<DataStep> steps = new ArrayList<>();;

int state_id = 0;

while (true) {

int transition = -1;

if (states[state_id].transitions.length == 1) {

transition = 0;

}

else if (states[state_id].transitions.length == 2) {

transition = r.nextInt(2);

}

double[] observation = null;

observation = new double[7];

observation[states[state_id].transitions[transition].token] = 1.0;

state_id = states[state_id].transitions[transition].next_state_id;

if (state_id == 0) { //exit at end of sequence

break;

}

double[] target_output = new double[7];

for (int i = 0; i < states[state_id].transitions.length; i++) {

target_output[states[state_id].transitions[i].token] = 1.0;

}

steps.add(new DataStep(observation, target_output));

}

result.add(new DataSequence(steps));

}

return result;

}

@Override

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

// TODO Auto-generated method stub

}

@Override

public Nonlinearity getModelOutputUnitToUse() {

return new SigmoidUnit();

}

}