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/*
 *
 *  * Copyright 2015 Skymind,Inc.
 *  *
 *  *    Licensed under the Apache License, Version 2.0 (the "License");
 *  *    you may not use this file except in compliance with the License.
 *  *    You may obtain a copy of the License at
 *  *
 *  *        http://www.apache.org/licenses/LICENSE-2.0
 *  *
 *  *    Unless required by applicable law or agreed to in writing, software
 *  *    distributed under the License is distributed on an "AS IS" BASIS,
 *  *    WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  *    See the License for the specific language governing permissions and
 *  *    limitations under the License.
 *
 */

package org.deeplearning4j.nn.layers;


import org.deeplearning4j.berkeley.Pair;
import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
import org.deeplearning4j.nn.gradient.DefaultGradient;
import org.deeplearning4j.nn.gradient.Gradient;
import org.deeplearning4j.nn.params.DefaultParamInitializer;
import org.deeplearning4j.nn.params.PretrainParamInitializer;
import org.deeplearning4j.optimize.api.IterationListener;
import org.deeplearning4j.optimize.api.TrainingListener;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.factory.Nd4j;
import org.nd4j.linalg.indexing.NDArrayIndex;
import org.nd4j.linalg.lossfunctions.ILossFunction;

import java.util.*;


/**
 * Baseline class for any Neural Network used
 * as a layer in a deep network *
 * @author Adam Gibson
 *
 */
public abstract class BasePretrainNetwork
        extends BaseLayer {

    protected Collection trainingListeners = null;

    public BasePretrainNetwork(NeuralNetConfiguration conf) {
        super(conf);
    }

    public BasePretrainNetwork(NeuralNetConfiguration conf, INDArray input) {
        super(conf, input);
    }


    @Override
    public void setListeners(Collection listeners) {
        if (iterationListeners == null) iterationListeners = new ArrayList<>();
        else iterationListeners.clear();
        if (trainingListeners == null) trainingListeners = new ArrayList<>();
        else trainingListeners.clear();

        if(listeners != null && listeners.size() > 0) {
            iterationListeners.addAll(listeners);
            for (IterationListener il : listeners) {
                if (il instanceof TrainingListener) {
                    trainingListeners.add((TrainingListener) il);
                }
            }
        }
    }

    @Override
    public void setListeners(IterationListener... listeners) {
        setListeners(Arrays.asList(listeners));
    }

    /**
     * Corrupts the given input by doing a binomial sampling
     * given the corruption level
     * @param x the input to corrupt
     * @param corruptionLevel the corruption value
     * @return the binomial sampled corrupted input
     */
    public INDArray getCorruptedInput(INDArray x, double corruptionLevel) {
        INDArray corrupted = Nd4j.getDistributions().createBinomial(1,1 - corruptionLevel).sample(x.shape());
        corrupted.muli(x);
        return corrupted;
    }


    protected Gradient createGradient(INDArray wGradient,INDArray vBiasGradient,INDArray hBiasGradient) {
        Gradient ret = new DefaultGradient();
        ret.gradientForVariable().put(PretrainParamInitializer.VISIBLE_BIAS_KEY,vBiasGradient);
        ret.gradientForVariable().put(PretrainParamInitializer.BIAS_KEY,hBiasGradient);
        ret.gradientForVariable().put(PretrainParamInitializer.WEIGHT_KEY, wGradient);
        return ret;
    }

    @Override
    public int numParams(boolean backwards) {
        return super.numParams(backwards);
    }

    /**
     * Sample the hidden distribution given the visible
     * @param v the visible to sample from
     * @return the hidden mean and sample
     */
    public abstract Pair sampleHiddenGivenVisible(INDArray v);

    /**
     * Sample the visible distribution given the hidden
     * @param h the hidden to sample from
     * @return the mean and sample
     */
    public abstract Pair sampleVisibleGivenHidden(INDArray h);

    @Override
    protected void setScoreWithZ(INDArray z) {
        if( input == null || z == null)
            throw new IllegalStateException("Cannot calculate score without input and labels");
        ILossFunction lossFunction = layerConf().getLossFunction().getILossFunction();

        //double score = lossFunction.computeScore(input, z, layerConf().getActivationFunction(), maskArray, false);
        double score = lossFunction.computeScore(input, z, layerConf().getActivationFn(), maskArray, false);
        score += calcL1(false) + calcL2(false);
        score /= getInputMiniBatchSize();

        this.score = score;
    }

    @Override
    public Map paramTable(boolean backpropParamsOnly){
        if(!backpropParamsOnly) return params;
        Map map = new LinkedHashMap<>();
        map.put(PretrainParamInitializer.WEIGHT_KEY, params.get(PretrainParamInitializer.WEIGHT_KEY));
        map.put(PretrainParamInitializer.BIAS_KEY, params.get(PretrainParamInitializer.BIAS_KEY));
        return map;
    }

    public INDArray params() {
        List list = new ArrayList<>(2);
        for(Map.Entry entry : params.entrySet()){
            list.add(entry.getValue());
        }
        return Nd4j.toFlattened('f', list);
    }

    /**The number of parameters for the model, for backprop (i.e., excluding visible bias)
     * @return the number of parameters for the model (ex. visible bias)
     */
    public int numParams() {
        int ret = 0;
        for(Map.Entry entry : params.entrySet()){
            ret += entry.getValue().length();
        }
        return ret;
    }

    @Override
    public void setParams(INDArray params) {
        if(params == paramsFlattened) return;   //No op

        //SetParams has two different uses: during pretrain vs. backprop.
        //pretrain = 3 sets of params (inc. visible bias); backprop = 2

        List parameterList = conf.variables();
        int paramLength = 0;
        for(String s : parameterList) {
            int len = getParam(s).length();
            paramLength += len;
        }

        if(params.length() != paramLength) {
            throw new IllegalArgumentException("Unable to set parameters: must be of length " + paramLength);
        }

        // Set for backprop and only W & hb
        paramsFlattened.assign(params);

    }

    public Pair backpropGradient(INDArray epsilon) {
        Pair result = super.backpropGradient(epsilon);
        INDArray vBiasGradient = gradientViews.get(PretrainParamInitializer.VISIBLE_BIAS_KEY);
        result.getFirst().gradientForVariable().put(PretrainParamInitializer.VISIBLE_BIAS_KEY, vBiasGradient);
        return result;
    }


    @Override
    public double calcL2(boolean backpropParamsOnly) {
        if(!conf.isUseRegularization() || conf.getLayer().getL2() <= 0.0 ) return 0.0;
        double l2Sum = super.calcL2(true);
        if(backpropParamsOnly) return l2Sum;
        if(conf.getL2ByParam(PretrainParamInitializer.VISIBLE_BIAS_KEY) > 0){
            double l2Norm = getParam(PretrainParamInitializer.VISIBLE_BIAS_KEY).norm2Number().doubleValue();
            l2Sum += 0.5 * conf.getL2ByParam(PretrainParamInitializer.VISIBLE_BIAS_KEY) * l2Norm * l2Norm;
        }
        return l2Sum;
    }

    @Override
    public double calcL1(boolean backpropParamsOnly) {
        if(!conf.isUseRegularization() || conf.getLayer().getL1()  <= 0.0 ) return 0.0;
        double l1Sum = super.calcL1(true);
        if(conf.getL1ByParam(PretrainParamInitializer.VISIBLE_BIAS_KEY) > 0){
            l1Sum += conf.getLayer().getL1() * getParam(PretrainParamInitializer.VISIBLE_BIAS_KEY).norm1Number().doubleValue();
        }
        return l1Sum;
    }

}