/**
* Transition function. Currently, we only get the list of probabilities of each item.
*
* @param current - The current state
* @param action - The action
* @param possible - The possible state
* @return The list of probabilities where each index refers to the probability of that item
*/
private List<Double> transition(State current, Action action, State possible) {
List<Double> probs = new ArrayList<Double>(); // Probabilities
Map<Integer, Integer> currentStock, purchase, possibleStock; // Maps
double currentProb; // The current probability
Matrix currentMatrix; // The current probability matrix
int row, column; // The row and column
for (int i = 0; i < current.getState().size(); ++i) {
currentProb = 0.0;
currentStock = current.getState();
purchase = action.getPurchases();
possibleStock = possible.getState();
row = currentStock.get(i) + purchase.get(i);
column = row - possible.getState().get(i);
currentMatrix = this.probabilities.get(i);
if (column < 0
|| column >= currentMatrix.getNumCols()
|| row >= currentMatrix.getNumRows()) { // Invalid state
probs.add(0.0);
continue;
}
if (possibleStock.get(i) > 0
|| (possibleStock.get(i) == 0 && column == 0)) { // Sufficiently provided
currentProb = currentMatrix.get(row, column);
} else if (possibleStock.get(i) == 0 && column > 0) {
// Range of probabilities because user could have eaten plenty
for (int j = column; j < currentMatrix.getNumCols(); ++j) {
currentProb += currentMatrix.get(row, j);
}
}
probs.add(currentProb);
}
return probs;
}
/*
* We incorporate the ability to create an arbitrary network structure.
* We use array of arrays of doubles for each inter-layer matrix
* Thus, between each layer, we need a matrix of weights.
* Num rows * num columns in matrix = nodes in layer below * nodes in layer above
*
* We use the Math library's pow function to raise to exponent: double pow(double base, double exponent)
*
* Hidden Nodes in current Layer (j)
* previous layers nodes[ ]
* Features [ Wij ]
* (i) [ ]
*
* I set up a matrix with dimensions: [ nodes in previous layer ] [ nodes in next layer ]
*
* Since we are traveling through one layer at a time, we need to have another data structure
* that will be outputs for this layer
*
* I use for loops to initialize array of arrays ( allocated necessary memory)
* Please note that: number of layers + 1 = number of weight arrays needed
*/
public void train(Matrix features, Matrix labels) throws Exception {
double[] recentAccuracies = new double[5];
int currentAccuracyIndex = 0;
double currentAccuracy = 0;
Random rand = new Random();
// SHUFFLE labels, features together
features.shuffle(rand, labels);
// need to map 0,1, or 2 to the three dimensional vectors, DO N-OF-K-ENCODING FOR THE
// BACKPROPAGATION
Matrix newNOfKLabelsMatrix = new Matrix();
newNOfKLabelsMatrix.setSize(
labels.rows(), labels.valueCount(0)); // I HARD CODE IN THAT THERE SHOULD BE 3 OUTPUT NODES
for (int row = 0; row < newNOfKLabelsMatrix.rows(); row++) { // for each instance
for (int k = 0; k < labels.valueCount(0); k++) {
if (labels.get(row, 0) == k) {
for (int m = 0; m < labels.valueCount(0); m++) {
newNOfKLabelsMatrix.set(row, m, 0);
}
newNOfKLabelsMatrix.set(row, k, 1);
}
}
}
labels = newNOfKLabelsMatrix;
// IMMEDIATELY SAVE SOME OF THIS, NEVER WILL TRAIN ON THESE
// STICK THESE INTO A VALIDATION SET
// ONCE MSE STARTS TO INCREASE AGAIN ON THE VALIDATION SET, WE'VE GONE TOO FAR
int numRowsToGetIntoTrainingSet = (int) (features.rows() * validationSetPercentageOfData);
Matrix featuresForTrainingTrimmed = new Matrix();
featuresForTrainingTrimmed.setSize(numRowsToGetIntoTrainingSet, features.cols());
Matrix featuresValidationSet = new Matrix();
featuresValidationSet.setSize(features.rows() - numRowsToGetIntoTrainingSet, features.cols());
Matrix labelsForTrainingTrimmed = new Matrix();
labelsForTrainingTrimmed.setSize(numRowsToGetIntoTrainingSet, labels.cols());
Matrix labelsValidationSet = new Matrix();
labelsValidationSet.setSize(features.rows() - numRowsToGetIntoTrainingSet, labels.cols());
// LOOP THROUGH AND PUT MOST OF FEATURES INTO featuresForTrainingTrimmed
for (int row = 0; row < features.rows(); row++) {
for (int col = 0; col < features.cols(); col++) {
if (row < numRowsToGetIntoTrainingSet) {
featuresForTrainingTrimmed.set(row, col, features.get(row, col));
} else {
featuresValidationSet.set(row - numRowsToGetIntoTrainingSet, col, features.get(row, col));
}
}
}
// LOOP THROUGH AND PUT MOST OF FEATURES INTO featuresForTrainingTrimmed
for (int row = 0; row < labels.rows(); row++) {
for (int col = 0; col < labels.cols(); col++) {
if (row < numRowsToGetIntoTrainingSet) {
labelsForTrainingTrimmed.set(row, col, labels.get(row, col));
} else {
labelsValidationSet.set(row - numRowsToGetIntoTrainingSet, col, labels.get(row, col));
}
}
}
features = featuresForTrainingTrimmed;
labels = labelsForTrainingTrimmed;
// LOOP THROUGH AND PUT LEFTOVER PORTION OF FEATURES INTO validationSet
arrayListOfEachLayersWeightMatrices = new ArrayList<double[][]>();
for (int i = 0; i < numHiddenLayers + 1; i++) { // each layer
double[][] specificLayersWeightMatrix;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersWeightMatrix =
new double[features.cols()][numNodesPerHiddenLayer[i]]; // INPUTS are the rows
} else if (i == numHiddenLayers) {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][labels.cols()]; // OUTPUTS ARE THE COLUMNS
} else {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][numNodesPerHiddenLayer[i]];
}
arrayListOfEachLayersWeightMatrices.add(specificLayersWeightMatrix);
}
changeInWeightMatricesForEveryLayer = new ArrayList<double[][]>();
for (int i = 0; i < numHiddenLayers + 1; i++) { // each layer
double[][] specificLayersWeightMatrix;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersWeightMatrix =
new double[features.cols()][numNodesPerHiddenLayer[i]]; // INPUTS are the rows
} else if (i == numHiddenLayers) {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][labels.cols()]; // OUTPUTS ARE THE COLUMNS
} else {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][numNodesPerHiddenLayer[i]];
}
changeInWeightMatricesForEveryLayer.add(specificLayersWeightMatrix);
}
// allocate space/ initialize the previous change in weights that we'll use for momentum
temporaryStashChangeInWeightMatricesForEveryLayer = new ArrayList<double[][]>();
for (int i = 0; i < numHiddenLayers + 1; i++) { // each layer
double[][] specificLayersWeightMatrix;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersWeightMatrix =
new double[features.cols()][numNodesPerHiddenLayer[i]]; // INPUTS are the rows
} else if (i == numHiddenLayers) {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][labels.cols()]; // OUTPUTS ARE THE COLUMNS
} else {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][numNodesPerHiddenLayer[i]];
}
temporaryStashChangeInWeightMatricesForEveryLayer.add(specificLayersWeightMatrix);
}
// ALLOCATE SPACE FOR DELTA ( INTERMEDIATE VALUES THAT WE USE TO UPDATE THE WEIGHTS)
arrayListOfEachLayersDeltaArray = new ArrayList<double[]>();
// EACH LAYER HAS AN ARRAY OF DELTA VALUES
for (int i = 0;
i < numHiddenLayers + 2;
i++) { // each layer // OF COURSE WE COULD HAVE DONE numHiddenLayers + 1, but I want
// consistency with fnet ArrayList
double[] specificLayersDeltaArray;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersDeltaArray = new double[features.cols()]; // INPUTS are the rows
} else if (i == (numHiddenLayers + 1)) {
// specificLayersDeltaArray = new double[ numNodesPerHiddenLayer[ i-1 ] ] ; //[
// numNodesPerHiddenLayer[ labels.cols() ] ] ; // OUTPUTS ARE THE COLUMNS
specificLayersDeltaArray = new double[labels.cols()]; // FIND OUT # NODES AT EACH LEVEL
} else {
specificLayersDeltaArray = new double[numNodesPerHiddenLayer[i - 1]];
}
arrayListOfEachLayersDeltaArray.add(specificLayersDeltaArray);
}
previousChangeInWeightMatricesForEachLayer = new ArrayList<double[][]>();
for (int i = 0; i < numHiddenLayers + 1; i++) { // each layer
double[][] specificLayersWeightMatrix;
if (i == 0) { // first hidden layer (Each layer owns its own weights)
specificLayersWeightMatrix =
new double[features.cols()][numNodesPerHiddenLayer[i]]; // INPUTS are the rows
} else if (i == numHiddenLayers) {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][labels.cols()]; // OUTPUTS ARE THE COLUMNS
} else {
specificLayersWeightMatrix =
new double[numNodesPerHiddenLayer[i - 1]][numNodesPerHiddenLayer[i]];
}
previousChangeInWeightMatricesForEachLayer.add(specificLayersWeightMatrix);
}
// INITIALIZE ALL OF PREVIOUS DELTA VALUES TO 0 [ THIS IS DONE AUTOMATICALLY, CAN DELETE ALL OF
// THIS CODE ]
// initialize all weights randomly ( small random weights with 0 mean)
double[][] currentLayersWeightMatrix;
for (int i = 0; i < numNodesPerHiddenLayer.length + 1; i++) { // scroll across each layer
currentLayersWeightMatrix = arrayListOfEachLayersWeightMatrices.get(i);
for (int j = 0; j < currentLayersWeightMatrix.length; j++) {
for (int k = 0; k < currentLayersWeightMatrix[j].length; k++) {
currentLayersWeightMatrix[j][k] = (2 * rand.nextDouble()) - 1;
}
}
}
// GO THROUGH AND ADD THE SPECIFIC WEIGHTS
// Initial Weights:
// PUT ALL BIAS WEIGHTS INTO ARRAYLIST (ONE ARRAY FOR EACH LAYER'S BIAS WEIGHTS)
biasWeightsAcrossAllLayers = new ArrayList<double[]>();
for (int i = 0; i < numHiddenLayers + 1; i++) {
if (i < numHiddenLayers) {
double[] biasArrayToBeAdded = new double[numNodesPerHiddenLayer[i]];
biasWeightsAcrossAllLayers.add(biasArrayToBeAdded);
} else {
double[] biasArrayForOutputNodesToBeAdded = new double[labels.cols()];
biasWeightsAcrossAllLayers.add(biasArrayForOutputNodesToBeAdded);
}
}
double[] currentBiasLayersWeightArray;
for (int i = 0; i < numNodesPerHiddenLayer.length + 1; i++) { // scroll across each layer
currentBiasLayersWeightArray = biasWeightsAcrossAllLayers.get(i);
for (int j = 0; j < currentBiasLayersWeightArray.length; j++) {
currentBiasLayersWeightArray[j] = (2 * rand.nextDouble()) - 1;
}
}
// We'll need to store the previous bias weights
previousBiasChangeInWeightsAcrossAllLayers = new ArrayList<double[]>();
for (int i = 0; i < numHiddenLayers + 1; i++) {
if (i < numHiddenLayers) {
double[] biasArrayToBeAdded = new double[numNodesPerHiddenLayer[i]];
previousBiasChangeInWeightsAcrossAllLayers.add(biasArrayToBeAdded);
} else {
double[] biasArrayForOutputNodesToBeAdded = new double[labels.cols()];
previousBiasChangeInWeightsAcrossAllLayers.add(biasArrayForOutputNodesToBeAdded);
}
}
// temporarily stashed bias weights across all layers
temporarilyStashedChangeInBiasWeightsAcrossAllLayers = new ArrayList<double[]>();
for (int i = 0; i < numHiddenLayers + 1; i++) {
if (i < numHiddenLayers) {
double[] biasArrayToBeAdded = new double[numNodesPerHiddenLayer[i]];
temporarilyStashedChangeInBiasWeightsAcrossAllLayers.add(biasArrayToBeAdded);
} else {
double[] biasArrayForOutputNodesToBeAdded = new double[labels.cols()];
temporarilyStashedChangeInBiasWeightsAcrossAllLayers.add(biasArrayForOutputNodesToBeAdded);
}
}
changeInBiasArrayForEveryLayer = new ArrayList<double[]>();
for (int i = 0; i < numHiddenLayers + 1; i++) {
if (i < numHiddenLayers) {
double[] biasArrayToBeAdded = new double[numNodesPerHiddenLayer[i]];
changeInBiasArrayForEveryLayer.add(biasArrayToBeAdded);
} else {
double[] biasArrayForOutputNodesToBeAdded = new double[labels.cols()];
changeInBiasArrayForEveryLayer.add(biasArrayForOutputNodesToBeAdded);
}
}
// INITIALIZE BIAS FOR HIDDEN AND OUTPUT NEURONS
// Stochastic weight update
// SOMEHOW GOT TO INITIALIZE ALL OF THIS, ADD BLANKS, SO THAT LATER WE CAN
// storedFNetForEachLayer.set( i, blah );
storedFNetForEachLayer =
new ArrayList<double[]>(); // f_net is the output that is fed into the next layer
for (int i = 0;
i < numHiddenLayers + 2;
i++) { // WE HAVE ONE MORE layer of fnet( consider inputs as fnet)
double[] thisLayersFNetValues;
// COULD DO IF/ELSE STATEMENTS IF WE ARE LOOKING AT INPUTS, OR THEN HIDDEN NODES,
if (i == 0) {
thisLayersFNetValues = new double[features.cols()]; // FIND OUT # NODES AT EACH LEVEL
} else if (i == numHiddenLayers + 1) { // OR IS IT +1
thisLayersFNetValues = new double[labels.cols()]; // FIND OUT # NODES AT EACH LEVEL
} else {
thisLayersFNetValues =
new double[numNodesPerHiddenLayer[i - 1]]; // FIND OUT # NODES AT EACH LEVEL
}
storedFNetForEachLayer.add(thisLayersFNetValues);
}
// -----BEGIN THE TRAINING-----
double netValAtNode = 0;
double fOfNetValAtNode = 0;
for (int epoch = 0;
epoch < 10000;
epoch++) { // For each epoch, cap it at 10000, we want to avoid infinite loop
System.out.println("---Epoch " + epoch + "---");
for (int instance = 0;
instance < features.rows();
instance++) { // later we will swap this Matrix for featuresForTrainingTrimmed
// GO FORWARD
// ---------------------------------------------------------------------------------------------------------------------
// System.out.println("Forward propagating...");
for (int layer = 0;
layer < numHiddenLayers + 2;
layer++) { // HERE LAYER DENOTES HIDDEN LAYER
if (layer == 0) {
storedFNetForEachLayer.set(
layer, Arrays.copyOf(features.row(instance), features.row(0).length));
continue;
}
double[] thisLayersFNetValues =
storedFNetForEachLayer.get(
layer); // make a new array of doubles CAN I PLEASE DELETE THIS LINE OF CODE
for (int node = 0; node < storedFNetForEachLayer.get(layer).length; node++) {
netValAtNode = 0;
// FIND THE CROSS PRODUCT;
// use a for loop to multiply each col of weights vector by each col of
// outputsFromPreviousLayer
for (int colInInputVector = 0;
colInInputVector < storedFNetForEachLayer.get(layer - 1).length;
colInInputVector++) {
netValAtNode +=
(storedFNetForEachLayer.get(layer - 1)[colInInputVector]
* arrayListOfEachLayersWeightMatrices.get(layer - 1)[colInInputVector][node]);
}
netValAtNode += (biasWeightsAcrossAllLayers.get(layer - 1)[node]);
if (netValAtNode < 0) { // make special function
fOfNetValAtNode = (1 / (1 + Math.pow(Math.E, (-1 * netValAtNode))));
} else { // normal
fOfNetValAtNode =
(1
/ (1
+ (1
/ (Math.pow(
Math.E,
(netValAtNode)))))); // if it was positive, then we raise to neg
// exponent
}
thisLayersFNetValues[node] = fOfNetValAtNode; // stick it into the object
}
storedFNetForEachLayer.set(
layer,
thisLayersFNetValues); // or if we are editing object, this is not even necessary
// DOUBLE CHECK
}
// ---NOW FOR THIS INSTANCE, GO
// BACKWARDS-----------------------------------------------------------------------------------------------------------------------
// System.out.println("Back propagating...");
// UPDATE THE WEIGHTS
for (int layer = numHiddenLayers + 1; layer > 0; layer--) { // ACROSS EACH LAYER BACKWARD
if (layer == numHiddenLayers + 1) { // THIS IS AN OUTPUT LAYER
for (int node = 0; node < labels.cols(); node++) {
double deltaArrayForThisLayer[] = arrayListOfEachLayersDeltaArray.get(layer);
deltaArrayForThisLayer[node] =
((labels.get(instance, node) - storedFNetForEachLayer.get(layer)[node])
* (storedFNetForEachLayer.get(layer)[node])
* (1 - (storedFNetForEachLayer.get(layer)[node])));
// should automatically be set since we get the objects address from heap memory, and
// change it
for (int inputToThisNode = 0;
inputToThisNode < numNodesPerHiddenLayer[layer - 2] + 1;
inputToThisNode++) {
double changeInWeightBetweenIJ = 0;
if (inputToThisNode == numNodesPerHiddenLayer[layer - 2]) { // this is a bias node
changeInWeightBetweenIJ =
(learningRate
* 1
* arrayListOfEachLayersDeltaArray
.get(layer)[node]); // NEED TO ADD STUFF FOR MOMENTUM
double[] thisLayersBiasWeights =
changeInBiasArrayForEveryLayer.get(
layer - 1); // NEED TO ADD STUFF FOR MOMENTUM
thisLayersBiasWeights[node] =
(changeInWeightBetweenIJ); // NEED TO ADD STUFF FOR MOMENTUM
} else {
changeInWeightBetweenIJ =
(learningRate
* storedFNetForEachLayer.get(layer - 1)[inputToThisNode]
* arrayListOfEachLayersDeltaArray.get(layer)[node]);
// double[][] thisLayersWeightMatrix =
// arrayListOfEachLayersWeightMatrices.get(layer-1);
// thisLayersWeightMatrix[inputToThisNode][node] += ( changeInWeightBetweenIJ );
double[][] changeInWeightsMatrixForThisLayer =
changeInWeightMatricesForEveryLayer.get(layer - 1);
changeInWeightsMatrixForThisLayer[inputToThisNode][node] =
changeInWeightBetweenIJ;
}
}
}
} else {
for (int node = 0;
node < numNodesPerHiddenLayer[layer - 1] + 1;
node++) { // ACROSS EACH HIDDEN LAYER (ie these are not output nodes)
double deltaArrayForThisLayer[] = arrayListOfEachLayersDeltaArray.get(layer);
if (node == numNodesPerHiddenLayer[layer - 1]) { // this is a bias node
// change in weight = learningRate *
} else { // this is not a bias node
double summedOutgoingWeightsCrossOutputDelta = 0;
for (int outgoingEdgeToOutgoingNode = 0;
outgoingEdgeToOutgoingNode
< arrayListOfEachLayersDeltaArray.get(layer + 1).length;
outgoingEdgeToOutgoingNode++) {
summedOutgoingWeightsCrossOutputDelta +=
(arrayListOfEachLayersDeltaArray.get(layer + 1)[outgoingEdgeToOutgoingNode]
* arrayListOfEachLayersWeightMatrices
.get(layer)[node][outgoingEdgeToOutgoingNode]);
}
deltaArrayForThisLayer[node] =
((summedOutgoingWeightsCrossOutputDelta)
* (storedFNetForEachLayer.get(layer)[node])
* (1 - (storedFNetForEachLayer.get(layer)[node])));
// should automatically be set since we get the objects address from heap memory,
// and change it
if (layer == 1) {
// need a for loop across the neural net's input nodes
for (int inputToTheNeuralNet = 0;
inputToTheNeuralNet < features.cols() + 1;
inputToTheNeuralNet++) {
double changeInWeightBetweenIJ = 0;
if (inputToTheNeuralNet
== features.cols()) { // then we know that this is our bias node
changeInWeightBetweenIJ =
(learningRate
* 1
* arrayListOfEachLayersDeltaArray
.get(layer)[node]); // NEED TO ADD STUFF FOR MOMENTUM
double[] thisLayersBiasWeights =
changeInBiasArrayForEveryLayer.get(
layer - 1); // NEED TO ADD STUFF FOR MOMENTUM
thisLayersBiasWeights[node] =
(changeInWeightBetweenIJ); // NEED TO ADD STUFF FOR MOMENTUM
} else {
changeInWeightBetweenIJ =
(learningRate
* storedFNetForEachLayer.get(layer - 1)[inputToTheNeuralNet]
* arrayListOfEachLayersDeltaArray.get(layer)[node]);
double[][] changeInWeightsMatrixForThisLayer =
changeInWeightMatricesForEveryLayer.get(layer - 1);
changeInWeightsMatrixForThisLayer[inputToTheNeuralNet][node] =
changeInWeightBetweenIJ;
}
}
} else {
for (int inputToThisNode = 0;
inputToThisNode < numNodesPerHiddenLayer[layer - 2] + 1;
inputToThisNode++) {
double changeInWeightBetweenIJ = 0;
if (inputToThisNode
== numNodesPerHiddenLayer[layer - 2]) { // this is a bias node
changeInWeightBetweenIJ =
(learningRate
* 1
* arrayListOfEachLayersDeltaArray
.get(layer)[node]); // NEED TO ADD STUFF FOR MOMENTUM
double[] thisLayersBiasWeights =
changeInBiasArrayForEveryLayer.get(
layer - 1); // NEED TO ADD STUFF FOR MOMENTUM
thisLayersBiasWeights[node] =
(changeInWeightBetweenIJ); // NEED TO ADD STUFF FOR MOMENTUM
} else {
changeInWeightBetweenIJ =
(learningRate
* storedFNetForEachLayer.get(layer - 1)[inputToThisNode]
* arrayListOfEachLayersDeltaArray.get(layer)[node]);
// double[][] thisLayersWeightMatrix =
// arrayListOfEachLayersWeightMatrices.get(layer-1);
// thisLayersWeightMatrix[inputToThisNode][node] += ( changeInWeightBetweenIJ
// );
double[][] changeInWeightsMatrixForThisLayer =
changeInWeightMatricesForEveryLayer.get(layer - 1);
changeInWeightsMatrixForThisLayer[inputToThisNode][node] =
changeInWeightBetweenIJ;
}
}
}
}
}
}
}
// System.out.printf( "e_0=%.17f, e_1=%.17f, e_2=%.17f, e_3=%.17f\n" ,
// arrayListOfEachLayersDeltaArray.get(2)[0], arrayListOfEachLayersDeltaArray.get(1)[0] ,
// arrayListOfEachLayersDeltaArray.get(1)[1] ,
// arrayListOfEachLayersDeltaArray.get(1)[2]);
// System.out.println("Descending Gradient...");
// // PUT TEMPORARILY STASHED INTO PREVIOUS
// // ONLY HERE SHOULD WE PUT IN THE STASHED WEIGHTS INTO THE PREVIOUS-STASH-SPOT
// // PUT STASHED INTO PREVIOUS
//
// // update the bias weights
// GET NEW CHANGE IN WEIGHT THANKS TO MOMENTUM, PLACE IN PREVIOUS SPOT
// should be changeInBiasArrayForEveryLayer not
for (int w = 0; w < previousBiasChangeInWeightsAcrossAllLayers.size(); w++) {
for (int y = 0; y < previousBiasChangeInWeightsAcrossAllLayers.get(w).length; y++) {
double currentChangeInWeightVal = changeInBiasArrayForEveryLayer.get(w)[y];
double[] fullBiasWeightList = biasWeightsAcrossAllLayers.get(w);
double previousXYCoordInBiasWeightMatrix =
previousBiasChangeInWeightsAcrossAllLayers.get(w)[y];
double thisIsTheWeightChangeIncludingMomentum =
(currentChangeInWeightVal + (momentum * previousXYCoordInBiasWeightMatrix));
fullBiasWeightList[y] += thisIsTheWeightChangeIncludingMomentum;
double[] arrayOfPreviousBiases = previousBiasChangeInWeightsAcrossAllLayers.get(w);
arrayOfPreviousBiases[y] = thisIsTheWeightChangeIncludingMomentum;
}
}
// GET NEW CHANGE IN WEIGHT THANKS TO MOMENTUM, PLACE IN PREVIOUS SPOT
// We update the weights ( by adding the changes in weights to the weight matrices) after
// every layer has been processed
for (int w = 0; w < arrayListOfEachLayersWeightMatrices.size(); w++) {
for (int y = 0; y < arrayListOfEachLayersWeightMatrices.get(w).length; y++) {
for (int z = 0; z < arrayListOfEachLayersWeightMatrices.get(w)[y].length; z++) {
double currentXYCoordInMatrix = changeInWeightMatricesForEveryLayer.get(w)[y][z];
double[] fullWeightListForLayer = arrayListOfEachLayersWeightMatrices.get(w)[y];
double previousXYCoordInChangeInWeightMatrix =
previousChangeInWeightMatricesForEachLayer.get(w)[y][z];
double thisIsTheWeightChangeIncludingMomentum =
(currentXYCoordInMatrix + (previousXYCoordInChangeInWeightMatrix * momentum));
fullWeightListForLayer[z] += thisIsTheWeightChangeIncludingMomentum;
double[][] arrayOfPreviousBiases = previousChangeInWeightMatricesForEachLayer.get(w);
arrayOfPreviousBiases[y][z] = thisIsTheWeightChangeIncludingMomentum;
// newWeight(at next round t+1) = learningRate * delta_at_node_we_feed_into * Xi +
// momentum_parameter * change_in_weight_at_t
// momentum goes into the weight updates ( not in the change in weights)
}
}
}
// System.out.printf( "w_0=%.17f, w_1=%.17f, w_2=%.17f, w_3=%.17f, w_4=%.17f,
// w_5=%.17f,\n w_6=%.17f, w_7=%.17f, w_8=%.17f, w_9=%.17f," +
// "w_10=%.17f, w_11=%.17f,\n w_12=%.17f\n" ,
// biasWeightsAcrossAllLayers.get(1)[0],
// arrayListOfEachLayersWeightMatrices.get(1)[0][0] ,
// arrayListOfEachLayersWeightMatrices.get(1)[1][0] ,
// arrayListOfEachLayersWeightMatrices.get(1)[2][0] , biasWeightsAcrossAllLayers.get(0)[0],
// arrayListOfEachLayersWeightMatrices.get(0)[0][0],
// arrayListOfEachLayersWeightMatrices.get(0)[1][0], biasWeightsAcrossAllLayers.get(0)[1],
// arrayListOfEachLayersWeightMatrices.get(0)[0][1],
// arrayListOfEachLayersWeightMatrices.get(0)[1][1],
// arrayListOfEachLayersWeightMatrices.get(0)[0][2],
// biasWeightsAcrossAllLayers.get(0)[2],
// arrayListOfEachLayersWeightMatrices.get(0)[0][2],
// arrayListOfEachLayersWeightMatrices.get(0)[1][2]);
// // ONLY AFTER THIS POINT HAS EVERY LAYER BEEN PROCESSED
}
// if( STOPPING CRITERIA MET ) { // HAVE TO USE THE VALIDATION SET THIS TIME FOR THE STOPPING
// CRITERION
currentAccuracy = calculateMSEOnValidationSet(featuresValidationSet, labelsValidationSet);
// currentAccuracy = calculateMSEOnValidationSet( features , labels ); // On the training set
// now
System.out.println(" Current MSE on epoch # " + epoch + " is: " + currentAccuracy);
currentAccuracyIndex++;
recentAccuracies[currentAccuracyIndex % 5] = currentAccuracy;
double sumAccuracies = 0;
if (currentAccuracyIndex > 5) {
for (int i = 0; i < recentAccuracies.length; i++) {
sumAccuracies +=
Math.abs(recentAccuracies[currentAccuracyIndex % 5] - recentAccuracies[i]);
}
if (sumAccuracies
< 0.01) { // we only stop training when measureAccuracy after 5 epochs does not increase
// by 0.01
break;
}
}
// In theory, it would be wise here to go back to the old best weights because now we're
// already overfitting if the stopping criterion is met
features.shuffle(
rand, labels); // MUST SHUFFLE DATA ROWS AFTER EACH EPOCH,labels is the buddy matrix
}
return;
}
/*
* backward pass 1: update
* (1) \hat{mu} (mu_hat_s)
* (2) \hat{grad_mu} (grad_mu_hat_s)
* (3) \hat{V} (v_hat_s)
*/
public static void backward1(boolean update_grad) {
for (int t1 = T - 1; t1 > t0; t1--) {
int t = t1 - t0;
// System.out.println("backward 1;\tt = " + t1);
if (t != T - 1 - t0) {
double V_pre_t = v_s.get(t - 1); // V^{t-1}
double V_hat_t = v_hat_s.get(t); // \hat{V}^{t}
double[][] mu_pre_t = mu_s.get(t - 1); // \mu^{t-1}
double[][] mu_hat_t = mu_hat_s.get(t); // \hat{\mu}^{t} [t-1]
Matrix A_pre_t = new Matrix(AS.get(t - 1)); // A^{t-1}
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1)); // h'^{t-1}
Matrix ave_neighbors = A_pre_t.times(hprime_pre_t); // n * 1
/* calculate \hat{\mu} at time t-1 */
double factor_1 =
(1 - lambda) * V_pre_t / (sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t);
double factor_2 = (sigma * sigma) / (sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t);
double[][] mu_hat_pre_t = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
mu_hat_pre_t[i][k] =
factor_1 * (mu_hat_t[i][k] - lambda * ave_neighbors.get(i, k))
+ factor_2 * mu_pre_t[i][k];
}
/* calculate \hat{V} at time t-1 */
double V_hat_pre_t =
V_pre_t
+ factor_1
* factor_1
* (V_hat_t - (1 - lambda) * (1 - lambda) * V_pre_t - (sigma * sigma));
/* update \mu and V */
mu_hat_s.set(t - 1, mu_hat_pre_t);
v_hat_s.set(t - 1, V_hat_pre_t);
/* calculate and update grad_mu_hat at time t-1 */
if (update_grad)
for (int s = 0; s < T - t0; s++) {
double[][] grad_hat_t_s = grad_mu_hat_s.get(t * (T - t0) + s);
double[][] grad_pre_t_s = grad_mu_s.get((t - 1) * (T - t0) + s);
double[][] grad_hat_pre_t_s = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
grad_hat_pre_t_s[i][k] =
factor_1 * grad_hat_t_s[i][k] + factor_2 * grad_pre_t_s[i][k];
}
grad_mu_hat_s.set((t - 1) * (T - t0) + s, grad_hat_pre_t_s);
}
} else {
/*
* initial condition for backward pass:
* (1) \hat{mu}^{T} = mu^{T}
* (2) \hat{V}^{T} = V^{T}
* (3) \hat{grad_mu}^{T/s} = grad_mu^{T/s}, \forall s
*/
mu_hat_s.set(t, mu_s.get(t));
v_hat_s.set(t, v_s.get(t));
if (update_grad)
for (int s = 0; s < T - t0; s++) {
grad_mu_hat_s.set(t * (T - t0) + s, grad_mu_s.get(t * (T - t0) + s));
}
}
// Scanner sc = new Scanner(System.in);
// int gu; gu = sc.nextInt();
/* end for each t */
}
}
/**
* forward pass 1: update intrinsic features (1) mu (mu_s) (2) grad_mu (grad_mu_s) (3) variance V
* (v_s)
*/
public static void forward1(boolean update_grad, int iter) {
/*
if (iter == 4) {
int t = 15;
double[][] h_t = new double[n][K];
double[][] h_hat_t = new double[n][K];
double[][] h_prime_t = new double[n][K];
double[][] h_hat_prime_t = new double[n][K];
for (int i = 0; i < n; i++) for (int k = 0; k < K; k++) {
h_t[i][k] = h_s.get(t-1)[i][k];
h_hat_t[i][k] = h_hat_s.get(t-1)[i][k];
h_prime_t[i][k] = h_prime_s.get(t-1)[i][k];
h_hat_prime_t[i][k] = h_hat_prime_s.get(t-1)[i][k];
}
h_s.set(t, h_t); h_hat_s.set(t, h_hat_t);
h_prime_s.set(t, h_prime_t); h_hat_prime_s.set(t, h_hat_prime_t);
}
*/
for (int t = 0; t < T - t0; t++) {
// System.out.println("forward 1;\tt = " + t1);
if (t != 0) {
double delta_t = delta_s.get(t); // delta_t
double[][] h_hat_t = h_hat_s.get(t); // \hat{h}^t [t]
double[][] mu_pre_t = mu_s.get(t - 1); // mu^{t-1} (N*1)
double V_pre_t = v_s.get(t - 1); // V^{t-1}
Matrix a = new Matrix(AS.get(t - 1)); // A^{t-1}
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1)); // h'^{t-1}
Matrix ave_neighbors = a.times(hprime_pre_t);
/* calculate \mu */
double[][] mu_t = new double[n][K];
double factor_1 =
(delta_t * delta_t)
/ (delta_t * delta_t + sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t);
double factor_2 =
(sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t)
/ (delta_t * delta_t + sigma * sigma + (1 - lambda) * (1 - lambda) * V_pre_t);
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
mu_t[i][k] =
factor_1 * ((1 - lambda) * mu_pre_t[i][k] + lambda * ave_neighbors.get(i, k))
+ factor_2 * h_hat_t[i][k];
}
/* calculate V */
double V_t = factor_2 * delta_t * delta_t;
/* update \mu and V */
mu_s.set(t, mu_t);
v_s.set(t, V_t);
/* calculate and update grad_mu */
if (update_grad)
for (int s = 0; s < T - t0; s++) {
double[][] grad_pre_t_s = grad_mu_s.get((t - 1) * (T - t0) + s);
double[][] grad_t_s = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
grad_t_s[i][k] = factor_1 * (1 - lambda) * grad_pre_t_s[i][k];
if (t == s) {
grad_t_s[i][k] += factor_2;
}
}
grad_mu_s.set(t * (T - t0) + s, grad_t_s);
}
} else {
/* mu, V: random init (keep unchanged) */
/* grad_mu: set to 0 (keep unchanged) */
}
// Scanner sc = new Scanner(System.in);
// int gu; gu = sc.nextInt();
/* end for each t */
}
}
public static void compute_gradient1(int iteration) {
double[][][] tmp_grad_h_hat_s = new double[T - t0][n][K];
for (int t = 0; t < T - t0; t++) {
// System.out.println("compute gradient 1, t = " + t);
double delta_t = delta_s.get(t);
double[][] G_t = GS.get(t);
double[][] h_prime_t = h_prime_s.get(t);
double[][] mu_hat_t = mu_hat_s.get(t);
if (t != 0) {
double[][] mu_hat_pre_t = mu_hat_s.get(t - 1);
Matrix a = new Matrix(AS.get(t - 1));
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1));
Matrix ave_neighbors = a.times(hprime_pre_t);
/* TODO: check whether we can save computation by comparing s and t */
for (int s = 0; s < T - t0; s++) {
double[][] grad_hat_t = grad_mu_hat_s.get(t * (T - t0) + s);
double[][] grad_hat_pre_t = grad_mu_hat_s.get((t - 1) * (T - t0) + s);
double[] hp2delta2 = new double[n];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
hp2delta2[i] += 0.5 * h_prime_t[i][k] * h_prime_t[i][k] * delta_t * delta_t;
}
for (int i = 0; i < n; i++) {
/* first term */
double[] weighted_exp_num = new double[K];
double weighted_exp_den = 0;
for (int l = 0; l < n; l++) {
double hp_muh = Operations.inner_product(h_prime_t[l], mu_hat_t[i], K);
double e = Math.exp(hp_muh + hp2delta2[l]);
if (Double.isNaN(e)) {
/* check if e explodes */
System.out.println("ERROR2");
Scanner sc = new Scanner(System.in);
int gu;
gu = sc.nextInt();
}
for (int k = 0; k < K; k++) {
weighted_exp_num[k] += h_prime_t[l][k] * e;
weighted_exp_den += e;
}
}
for (int j = 0; j < n; j++)
for (int k = 0; k < K; k++) {
double weighted_exp = weighted_exp_num[k] / weighted_exp_den;
double gi1 = G_t[i][j] * grad_hat_t[i][k] * (h_prime_t[j][k] - weighted_exp);
tmp_grad_h_hat_s[s][i][k] += gi1;
}
/* second term */
for (int k = 0; k < K; k++) {
double gi2 =
-(mu_hat_t[i][k]
- (1 - lambda) * mu_hat_pre_t[i][k]
- lambda * ave_neighbors.get(i, k))
* (grad_hat_t[i][k] - (1 - lambda) * grad_hat_pre_t[i][k])
/ (sigma * sigma);
tmp_grad_h_hat_s[s][i][k] += gi2;
}
}
}
} else {
/* no such term (t=0) in ELBO */
/*
for (int s = 0; s < T-t0; s++) {
double[] grad_hat_t = grad_mu_hat_s.get(t * (T-t0) + s);
for (int i = 0; i < n; i++) {
double n_it = 0;
for (int j = 0; j < n; j++) n_it += G_t[i][j];
// first term
double gi1 = -mu_hat_t[i] * grad_hat_t[i] / (sigma * sigma);
tmp_grad_h_hat_s[s][i] += gi1;
// second term
double gi2 = 0;
double weighted_exp_num = 0, weighted_exp_den = 0;
for (int j = 0; j < NEG; j++) {
int l = neg_samples.get(t)[i][j];
double hpl = h_prime_t[l][0];
double muit = mu_hat_t[i];
double e = Math.exp(hpl * muit + 0.5 * hpl * hpl * delta_t * delta_t);
// TODO: check if e explodes
if (Double.isNaN(e)) {
System.out.println("ERROR3");
Scanner sc = new Scanner(System.in);
int gu; gu = sc.nextInt();
}
weighted_exp_num += hpl * e;
weighted_exp_den += e;
}
double weighted_exp = weighted_exp_num / weighted_exp_den;
for (int j = 0; j < n; j++) {
gi2 += G_t[i][j] * grad_hat_t[i] * (h_prime_t[j][0] - weighted_exp);
}
tmp_grad_h_hat_s[s][i] += gi2;
}
}
*/
}
/* end if-else */
}
/* update global gradient */
for (int t = 0; t < T - t0; t++) {
double[][] grad = new double[n][K];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
grad[i][k] = tmp_grad_h_hat_s[t][i][k];
}
grad_h_hat_s.set(t, grad);
}
FileParser.output_2d(grad_h_hat_s, "./grad/grad_" + iteration + ".txt");
return;
}
/** compute_objective1: return the lower bound when h' is fixed */
public static double compute_objective1() {
double res = 0;
for (int t = 0; t < T - t0; t++) {
if (t != 0) {
double[][] G_t = GS.get(t);
double[][] h_prime_t = h_prime_s.get(t);
double[][] h_prime_pre_t = h_prime_s.get(t - 1);
double[][] mu_hat_t = mu_hat_s.get(t);
double[][] mu_hat_pre_t = mu_hat_s.get(t - 1);
double delta_t = delta_s.get(t);
Matrix a = new Matrix(AS.get(t - 1));
Matrix hprime_pre_t = new Matrix(h_prime_s.get(t - 1));
Matrix ave_neighbors = a.times(hprime_pre_t);
double[] hp2delta2 = new double[n];
for (int i = 0; i < n; i++)
for (int k = 0; k < K; k++) {
hp2delta2[i] += 0.5 * h_prime_t[i][k] * h_prime_t[i][k] * delta_t * delta_t;
}
for (int i = 0; i < n; i++) {
/* first term */
List<Double> powers = new ArrayList<Double>();
for (int l = 0; l < n; l++) {
double hp_muh = Operations.inner_product(h_prime_t[l], mu_hat_t[i], K);
powers.add(hp_muh + hp2delta2[l]);
}
double lse = log_sum_exp(powers);
for (int j = 0; j < n; j++)
if (G_t[i][j] != 0) {
double hp_muh = Operations.inner_product(h_prime_t[j], mu_hat_t[i], K);
res += G_t[i][j] * (hp_muh - lse);
}
/* second term */
for (int k = 0; k < K; k++) {
double diff =
mu_hat_t[i][k]
- (1 - lambda) * mu_hat_pre_t[i][k]
- lambda * ave_neighbors.get(i, k);
res -= 0.5 * diff * diff / (sigma * sigma);
}
}
} else {
/*
double[][] G_t = GS.get(t);
double[][] h_prime_t = h_prime_s.get(t);
double[] mu_hat_t = mu_hat_s.get(t);
double delta_t = delta_s.get(t);
int[][] neg_sam_t = neg_samples.get(t);
for (int i = 0; i < n; i++) {
// first term
for (int j = 0; j < n; j++) if (G_t[i][j] != 0) {
List<Double> powers = new ArrayList<Double>();
for (int _l = 0; _l < NEG; _l++) {
int l = neg_sam_t[i][_l];
powers.add(h_prime_t[l][0] * mu_hat_t[i]
+ 0.5 * h_prime_t[l][0] * h_prime_t[l][0] * delta_t * delta_t);
}
double lse = log_sum_exp(powers);
res += G_t[i][j] * (h_prime_t[j][0] * mu_hat_t[i] - lse);
}
}
*/
}
}
return res;
}
