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/*-
 *
 *  * Copyright 2016 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.conf.graph;


import org.deeplearning4j.nn.conf.CacheMode;
import org.deeplearning4j.nn.conf.inputs.InputType;
import org.deeplearning4j.nn.conf.inputs.InvalidInputTypeException;
import org.deeplearning4j.nn.conf.memory.LayerMemoryReport;
import org.deeplearning4j.nn.conf.memory.MemoryReport;
import org.deeplearning4j.nn.graph.ComputationGraph;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.shade.jackson.annotation.JsonProperty;

import java.util.HashMap;
import java.util.Map;

/**
 * L2Vertex calculates the L2 least squares error of two inputs.
 *
 * For example, in Triplet Embedding you can input an anchor and a pos/neg class and use two parallel
 * L2 vertices to calculate two real numbers which can be fed into a LossLayer to calculate TripletLoss.
 *
 * @author Justin Long (crockpotveggies)
 */
public class L2Vertex extends GraphVertex {
    protected double eps;

    public L2Vertex() {
        this.eps = 1e-8;
    }

    public L2Vertex(double eps) {
        this.eps = eps;
    }

    @Override
    public L2Vertex clone() {
        return new L2Vertex();
    }

    @Override
    public boolean equals(Object o) {
        return o instanceof L2Vertex;
    }

    @Override
    public int numParams(boolean backprop) {
        return 0;
    }

    @Override
    public int minVertexInputs() {
        return 2;
    }

    @Override
    public int maxVertexInputs() {
        return 2;
    }

    @Override
    public int hashCode() {
        return 433682566;
    }

    @Override
    public org.deeplearning4j.nn.graph.vertex.GraphVertex instantiate(ComputationGraph graph, String name, int idx,
                    INDArray paramsView, boolean initializeParams) {
        return new org.deeplearning4j.nn.graph.vertex.impl.L2Vertex(graph, name, idx, null, null, eps);
    }

    @Override
    public InputType getOutputType(int layerIndex, InputType... vertexInputs) throws InvalidInputTypeException {
        return InputType.feedForward(1);
    }

    @Override
    public MemoryReport getMemoryReport(InputType... inputTypes) {
        InputType outputType = getOutputType(-1, inputTypes);

        //Inference: only calculation is for output activations; no working memory
        //Working memory for training:
        //1 for each example (fwd pass) + output size (1 per ex) + input size + output size... in addition to the returned eps arrays
        //output size == input size here
        int trainWorkingSizePerEx = 3 + 2 * inputTypes[0].arrayElementsPerExample();

        return new LayerMemoryReport.Builder(null, L2Vertex.class, inputTypes[0], outputType).standardMemory(0, 0) //No params
                        .workingMemory(0, 0, 0, trainWorkingSizePerEx).cacheMemory(0, 0) //No caching
                        .build();
    }
}