/**
 * Licensed to the Apache Software Foundation (ASF) under one
 * or more contributor license agreements.  See the NOTICE file
 * distributed with this work for additional information
 * regarding copyright ownership.  The ASF licenses this file
 * to you 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.apache.horn.core;

import java.io.IOException;

import org.apache.hama.HamaConfiguration;
import org.apache.hama.bsp.BSPJob;
import org.apache.hama.commons.math.Function;
import org.apache.horn.core.Constants.LearningStyle;
import org.apache.horn.core.Constants.TrainingMethod;
import org.apache.horn.funcs.FunctionFactory;

public class HornJob extends BSPJob {

  AbstractLayeredNeuralNetwork neuralNetwork;

  @Deprecated
  public HornJob(HamaConfiguration conf, Class<?> exampleClass)
      throws IOException {
    super(conf);
    this.setJarByClass(exampleClass);

    // default local file block size 10mb
    this.getConfiguration().set("fs.local.block.size", "10358951");
    neuralNetwork = new LayeredNeuralNetwork();
  }

  public HornJob(HamaConfiguration conf,
      Class<? extends AbstractLayeredNeuralNetwork> neuralNetworkClass,
      Class<?> exampleClass)
      throws IOException, InstantiationException, IllegalAccessException {
    this.setJarByClass(exampleClass);

    // default local file block size 10mb
    this.getConfiguration().set("fs.local.block.size", "10358951");
    neuralNetwork = neuralNetworkClass.newInstance();
  }

  public void inputLayer(int featureDimension) {
    addLayer(featureDimension, null, null);
    neuralNetwork.setDropRateOfInputLayer(1);
  }

  public void inputLayer(int featureDimension, float dropRate) {
    addLayer(featureDimension, null, null);
    neuralNetwork.setDropRateOfInputLayer(dropRate);
  }

  public void inputLayer(int featureDimension, float dropRate, Class<? extends Neuron<?>> neuronClass) {
    addLayer(featureDimension, null, neuronClass);
    neuralNetwork.setDropRateOfInputLayer(dropRate);
  }

  public void addLayer(int featureDimension, Class<? extends Function> func,
      Class<? extends Neuron<?>> neuronClass) {
    neuralNetwork.addLayer(
        featureDimension,
        false,
        (func != null) ? FunctionFactory.createFloatFunction(func
            .getSimpleName()) : null, neuronClass);
  }

  /**
   * TODO: Adds comments
   * @param featureDimension
   * @param class1
   * @param neuronClass
   */
  public void addLayer(int featureDimension, Class<? extends Function> func,
      Class<? extends Neuron<?>> neuronClass, boolean isRecurrent) {
    if (neuralNetwork instanceof RecurrentLayeredNeuralNetwork) {
        ((RecurrentLayeredNeuralNetwork)neuralNetwork).addLayer(
            featureDimension,
            false,
            (func != null) ? FunctionFactory.createFloatFunction(func
                .getSimpleName()) : null, neuronClass, null, isRecurrent);
    } else {
      this.addLayer(featureDimension, func, neuronClass);
    }
  }

  public void outputLayer(int labels, Class<? extends Function> func,
      Class<? extends Neuron<?>> neuronClass) {
    neuralNetwork.addLayer(labels, true,
        FunctionFactory.createFloatFunction(func.getSimpleName()), neuronClass);
  }

  public void outputLayer(int labels, Class<? extends Function> func,
      Class<? extends Neuron<?>> neuronClass, int numOutCells) {
    ((RecurrentLayeredNeuralNetwork)neuralNetwork).addLayer(labels, true,
        FunctionFactory.createFloatFunction(func.getSimpleName()), neuronClass, numOutCells);
  }

  public void setCostFunction(Class<? extends Function> func) {
    neuralNetwork.setCostFunction(FunctionFactory.createFloatFloatFunction(func
        .getSimpleName()));
  }

  public void setDouble(String name, double value) {
    conf.setDouble(name, value);
  }

  public void setMaxIteration(int maxIteration) {
    this.conf.setInt("training.max.iterations", maxIteration);
  }

  public void setBatchSize(int batchSize) {
    this.conf.setInt("training.batch.size", batchSize);
  }

  public void setRecurrentStepSize(int stepSize) {
    ((RecurrentLayeredNeuralNetwork) neuralNetwork).setRecurrentStepSize(stepSize);
    this.conf.setInt("training.recurrent.step.size", stepSize);
  }

  public void setTrainingMethod(TrainingMethod method) {
    this.neuralNetwork.setTrainingMethod(method);
  }

  public void setLearningStyle(LearningStyle style) {
    this.neuralNetwork.setLearningStyle(style);
  }

  public void setLearningRate(float learningRate) {
    this.neuralNetwork.setLearningRate(learningRate);
  }

  public void setConvergenceCheckInterval(int n) {
    this.conf.setInt("convergence.check.interval", n);
  }

  public void setMomentumWeight(float momentumWeight) {
    this.neuralNetwork.setMomemtumWeight(momentumWeight);
  }

  public void setRegularizationWeight(float regularizationWeight) {
    this.neuralNetwork.setRegularizationWeight(regularizationWeight);
  }

  public AbstractLayeredNeuralNetwork getNeuralNetwork() {
    return neuralNetwork;
  }

  public boolean waitForCompletion(boolean verbose) throws IOException,
      InterruptedException, ClassNotFoundException {
    BSPJob job = neuralNetwork.train((HamaConfiguration) this.conf);
    if (verbose) {
      return job.waitForCompletion(true);
    } else {
      return job.waitForCompletion(false);
    }
  }

  public void setModelPath(String modelPath) {
    this.conf.set("model.path", modelPath);
    neuralNetwork.setModelPath(modelPath);
  }

  public void setTrainingSetPath(String inputPath) {
    this.conf.set("training.input.path", inputPath);
  }

}