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package org.nd4j.linalg.learning;

import lombok.Data;
import lombok.NoArgsConstructor;

import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.api.shape.Shape;
import org.nd4j.linalg.ops.transforms.Transforms;

/**
 * RMS Prop updates:
 * 
 * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
 * http://cs231n.github.io/neural-networks-3/#ada
 *
 * @author Adam Gibson
 */
@Data
@NoArgsConstructor
public class RmsProp implements GradientUpdater {
    private INDArray lastGradient;
    private double rmsDecay = 0.95;
    private double learningRate = 1e-1;
    private static final double epsilon = 1e-8;

    public RmsProp(double learningRate, double rmsDecay) {
        this.learningRate = learningRate;
        this.rmsDecay = rmsDecay;
    }

    @Override
    public int stateSizeForInputSize(int inputSize) {
        return inputSize;
    }

    @Override
    public void setStateViewArray(INDArray viewArray, int[] gradientShape, char gradientOrder, boolean initialize) {
        if (!viewArray.isRowVector()) throw new IllegalArgumentException("Invalid input: expect row vector input");
        if (initialize) viewArray.assign(epsilon);
        this.lastGradient = viewArray;

        //Reshape to match the expected shape of the input gradient arrays
        this.lastGradient = Shape.newShapeNoCopy(this.lastGradient, gradientShape, gradientOrder == 'f');
        if (lastGradient == null) throw new IllegalStateException("Could not correctly reshape gradient view array");
    }

    @Override
    public void update(Object... args) {
        if (args.length > 0) {
            learningRate = (Double) args[0];
        }
    }

    @Override
    public INDArray getGradient(INDArray gradient, int iteration) {
        if (lastGradient == null) throw new IllegalStateException("Updater has not been initialized with view state");

        lastGradient.muli(rmsDecay).addi(gradient.mul(gradient).muli(1 - rmsDecay));
        // lr * gradient / (sqrt(cache) + 1e-8)
        return gradient.muli(learningRate).divi(Transforms.sqrt(lastGradient, true).addi(epsilon));
    }

    @Override
    public GradientUpdaterAggregator getAggregator(boolean addThis) {
        RmsPropAggregator ag = new RmsPropAggregator();
        if (addThis) ag.aggregate(this);
        return ag;
    }

    public static class RmsPropAggregator implements GradientUpdaterAggregator {
        private INDArray lastGradientSum;
        private double rmsDecaySum;
        private double lrSum;
        private int count = 0;

        @Override
        public GradientUpdater getUpdater() {
            RmsProp rmsProp = new RmsProp(lrSum / count, rmsDecaySum / count);
            rmsProp.setLastGradient(lastGradientSum.div(count));
            return rmsProp;
        }

        @Override
        public void aggregate(GradientUpdater updater) {
            if (!(updater instanceof RmsProp)) throw new UnsupportedOperationException();
            RmsProp rmsProp = (RmsProp) updater;
            if (lastGradientSum == null) {
                lastGradientSum = rmsProp.lastGradient.dup();
                rmsDecaySum = rmsProp.rmsDecay;
                lrSum = rmsProp.learningRate;
            } else {
                lastGradientSum.addi(rmsProp.lastGradient);
                rmsDecaySum += rmsProp.rmsDecay;
                lrSum += rmsProp.learningRate;
            }
            count++;
        }

        @Override
        public GradientUpdaterAggregator combine(GradientUpdaterAggregator other) {
            if (!(other instanceof RmsPropAggregator))
                throw new IllegalArgumentException("Cannot combine RmsPropAggregator with aggregator: " + other);
            RmsPropAggregator aggregator = (RmsPropAggregator) other;
            lastGradientSum.addi(aggregator.lastGradientSum);
            rmsDecaySum += aggregator.rmsDecaySum;
            lrSum += aggregator.lrSum;
            count += aggregator.count;
            return this;
        }
    }
}