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package util;

import java.util.ArrayList;

import java.util.List;

import java.util.Random;

import model.FeedForwardLayer;

import model.GruLayer;

import model.LinearLayer;

import model.LstmLayer;

import model.Model;

import model.NeuralNetwork;

import model.Nonlinearity;

import model.RnnLayer;

public class NeuralNetworkHelper {

public static NeuralNetwork makeLstm(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {

List<Model> layers = new ArrayList<>();

for (int h = 0; h < hiddenLayers; h++) {

if (h == 0) {

layers.add(new LstmLayer(inputDimension, hiddenDimension, initParamsStdDev, rng));

}

else {

layers.add(new LstmLayer(hiddenDimension, hiddenDimension, initParamsStdDev, rng));

}

}

layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));

return new NeuralNetwork(layers);

}

public static NeuralNetwork makeLstmWithInputBottleneck(int inputDimension, int bottleneckDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {

List<Model> layers = new ArrayList<>();

layers.add(new LinearLayer(inputDimension, bottleneckDimension, initParamsStdDev, rng));

for (int h = 0; h < hiddenLayers; h++) {

if (h == 0) {

layers.add(new LstmLayer(bottleneckDimension, hiddenDimension, initParamsStdDev, rng));

}

else {

layers.add(new LstmLayer(hiddenDimension, hiddenDimension, initParamsStdDev, rng));

}

}

layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));

return new NeuralNetwork(layers);

}

public static NeuralNetwork makeFeedForward(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity hiddenUnit, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {

List<Model> layers = new ArrayList<>();

if (hiddenLayers == 0) {

layers.add(new FeedForwardLayer(inputDimension, outputDimension, decoderUnit, initParamsStdDev, rng));

return new NeuralNetwork(layers);

}

else {

for (int h = 0; h < hiddenLayers; h++) {

if (h == 0) {

layers.add(new FeedForwardLayer(inputDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));

}

else {

layers.add(new FeedForwardLayer(hiddenDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));

}

}

layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));

return new NeuralNetwork(layers);

}

}

public static NeuralNetwork makeGru(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {

List<Model> layers = new ArrayList<>();

for (int h = 0; h < hiddenLayers; h++) {

if (h == 0) {

layers.add(new GruLayer(inputDimension, hiddenDimension, initParamsStdDev, rng));

}

else {

layers.add(new GruLayer(hiddenDimension, hiddenDimension, initParamsStdDev, rng));

}

}

layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));

return new NeuralNetwork(layers);

}

public static NeuralNetwork makeRnn(int inputDimension, int hiddenDimension, int hiddenLayers, int outputDimension, Nonlinearity hiddenUnit, Nonlinearity decoderUnit, double initParamsStdDev, Random rng) {

List<Model> layers = new ArrayList<>();

for (int h = 0; h < hiddenLayers; h++) {

if (h == 0) {

layers.add(new RnnLayer(inputDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));

}

else {

layers.add(new RnnLayer(hiddenDimension, hiddenDimension, hiddenUnit, initParamsStdDev, rng));

}

}

layers.add(new FeedForwardLayer(hiddenDimension, outputDimension, decoderUnit, initParamsStdDev, rng));

return new NeuralNetwork(layers);

}

}