/*
 * 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
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package org.apache.commons.math3.ml.neuralnet.twod;

import java.util.List;
import java.util.ArrayList;
import java.util.Iterator;
import java.io.Serializable;
import java.io.ObjectInputStream;
import org.apache.commons.math3.ml.neuralnet.Neuron;
import org.apache.commons.math3.ml.neuralnet.Network;
import org.apache.commons.math3.ml.neuralnet.FeatureInitializer;
import org.apache.commons.math3.ml.neuralnet.SquareNeighbourhood;
import org.apache.commons.math3.exception.NumberIsTooSmallException;
import org.apache.commons.math3.exception.OutOfRangeException;
import org.apache.commons.math3.exception.MathInternalError;

Neural network with the topology of a two-dimensional surface.
Each neuron defines one surface element.


This network is primarily intended to represent a

 Self Organizing Feature Map.
See Also:
sofm
Since:
3.3/**
 * Neural network with the topology of a two-dimensional surface.
 * Each neuron defines one surface element.
 * <br/>
 * This network is primarily intended to represent a
 * <a href="http://en.wikipedia.org/wiki/Kohonen">
 *  Self Organizing Feature Map</a>.
 *
 * @see org.apache.commons.math3.ml.neuralnet.sofm
 * @since 3.3
 */
public class NeuronSquareMesh2D
    implements Iterable<Neuron>,
               Serializable {
    
Serial version ID /** Serial version ID */
    private static final long serialVersionUID = 1L;
    
Underlying network. /** Underlying network. */
    private final Network network;
    
Number of rows. /** Number of rows. */
    private final int numberOfRows;
    
Number of columns. /** Number of columns. */
    private final int numberOfColumns;
    
Wrap. /** Wrap. */
    private final boolean wrapRows;
    
Wrap. /** Wrap. */
    private final boolean wrapColumns;
    
Neighbourhood type. /** Neighbourhood type. */
    private final SquareNeighbourhood neighbourhood;
    
Mapping of the 2D coordinates (in the rectangular mesh) to the neuron identifiers (attributed by the network instance). /**
     * Mapping of the 2D coordinates (in the rectangular mesh) to
     * the neuron identifiers (attributed by the {@link #network}
     * instance).
     */
    private final long[][] identifiers;

    
Horizontal (along row) direction.
Since:
3.6/**
     * Horizontal (along row) direction.
     * @since 3.6
     */
    public enum HorizontalDirection {
        
Column at the right of the current column. /** Column at the right of the current column. */
       RIGHT,
       
Current column. /** Current column. */
       CENTER,
       
Column at the left of the current column. /** Column at the left of the current column. */
       LEFT,
    }
    
Vertical (along column) direction.
Since:
3.6/**
     * Vertical (along column) direction.
     * @since 3.6
     */
    public enum VerticalDirection {
        
Row above the current row. /** Row above the current row. */
        UP,
        
Current row. /** Current row. */
        CENTER,
        
Row below the current row. /** Row below the current row. */
        DOWN,
    }

    
Constructor with restricted access, solely used for deserialization.
Params:
wrapRowDim – Whether to wrap the first dimension (i.e the first
and last neurons will be linked together).
wrapColDim – Whether to wrap the second dimension (i.e the first
and last neurons will be linked together).
neighbourhoodType – Neighbourhood type.
featuresList – Arrays that will initialize the features sets of
the network's neurons.
Throws:
NumberIsTooSmallException – if numRows < 2 or numCols < 2./**
     * Constructor with restricted access, solely used for deserialization.
     *
     * @param wrapRowDim Whether to wrap the first dimension (i.e the first
     * and last neurons will be linked together).
     * @param wrapColDim Whether to wrap the second dimension (i.e the first
     * and last neurons will be linked together).
     * @param neighbourhoodType Neighbourhood type.
     * @param featuresList Arrays that will initialize the features sets of
     * the network's neurons.
     * @throws NumberIsTooSmallException if {@code numRows < 2} or
     * {@code numCols < 2}.
     */
    NeuronSquareMesh2D(boolean wrapRowDim,
                       boolean wrapColDim,
                       SquareNeighbourhood neighbourhoodType,
                       double[][][] featuresList) {
        numberOfRows = featuresList.length;
        numberOfColumns = featuresList[0].length;

        if (numberOfRows < 2) {
            throw new NumberIsTooSmallException(numberOfRows, 2, true);
        }
        if (numberOfColumns < 2) {
            throw new NumberIsTooSmallException(numberOfColumns, 2, true);
        }

        wrapRows = wrapRowDim;
        wrapColumns = wrapColDim;
        neighbourhood = neighbourhoodType;

        final int fLen = featuresList[0][0].length;
        network = new Network(0, fLen);
        identifiers = new long[numberOfRows][numberOfColumns];

        // Add neurons.
        for (int i = 0; i < numberOfRows; i++) {
            for (int j = 0; j < numberOfColumns; j++) {
                identifiers[i][j] = network.createNeuron(featuresList[i][j]);
            }
        }

        // Add links.
        createLinks();
    }

    
Creates a two-dimensional network composed of square cells:
Each neuron not located on the border of the mesh has four
neurons linked to it.


The links are bi-directional.


The topology of the network can also be a cylinder (if one
of the dimensions is wrapped) or a torus (if both dimensions
are wrapped).
Params:
numRows – Number of neurons in the first dimension.
wrapRowDim – Whether to wrap the first dimension (i.e the first
and last neurons will be linked together).
numCols – Number of neurons in the second dimension.
wrapColDim – Whether to wrap the second dimension (i.e the first
and last neurons will be linked together).
neighbourhoodType – Neighbourhood type.
featureInit – Array of functions that will initialize the
corresponding element of the features set of each newly created
neuron. In particular, the size of this array defines the size of
feature set.
Throws:
NumberIsTooSmallException – if numRows < 2 or numCols < 2./**
     * Creates a two-dimensional network composed of square cells:
     * Each neuron not located on the border of the mesh has four
     * neurons linked to it.
     * <br/>
     * The links are bi-directional.
     * <br/>
     * The topology of the network can also be a cylinder (if one
     * of the dimensions is wrapped) or a torus (if both dimensions
     * are wrapped).
     *
     * @param numRows Number of neurons in the first dimension.
     * @param wrapRowDim Whether to wrap the first dimension (i.e the first
     * and last neurons will be linked together).
     * @param numCols Number of neurons in the second dimension.
     * @param wrapColDim Whether to wrap the second dimension (i.e the first
     * and last neurons will be linked together).
     * @param neighbourhoodType Neighbourhood type.
     * @param featureInit Array of functions that will initialize the
     * corresponding element of the features set of each newly created
     * neuron. In particular, the size of this array defines the size of
     * feature set.
     * @throws NumberIsTooSmallException if {@code numRows < 2} or
     * {@code numCols < 2}.
     */
    public NeuronSquareMesh2D(int numRows,
                              boolean wrapRowDim,
                              int numCols,
                              boolean wrapColDim,
                              SquareNeighbourhood neighbourhoodType,
                              FeatureInitializer[] featureInit) {
        if (numRows < 2) {
            throw new NumberIsTooSmallException(numRows, 2, true);
        }
        if (numCols < 2) {
            throw new NumberIsTooSmallException(numCols, 2, true);
        }

        numberOfRows = numRows;
        wrapRows = wrapRowDim;
        numberOfColumns = numCols;
        wrapColumns = wrapColDim;
        neighbourhood = neighbourhoodType;
        identifiers = new long[numberOfRows][numberOfColumns];

        final int fLen = featureInit.length;
        network = new Network(0, fLen);

        // Add neurons.
        for (int i = 0; i < numRows; i++) {
            for (int j = 0; j < numCols; j++) {
                final double[] features = new double[fLen];
                for (int fIndex = 0; fIndex < fLen; fIndex++) {
                    features[fIndex] = featureInit[fIndex].value();
                }
                identifiers[i][j] = network.createNeuron(features);
            }
        }

        // Add links.
        createLinks();
    }

    
Constructor with restricted access, solely used for making a deep copy. 
Params:
wrapRowDim – Whether to wrap the first dimension (i.e the first
and last neurons will be linked together).
wrapColDim – Whether to wrap the second dimension (i.e the first
and last neurons will be linked together).
neighbourhoodType – Neighbourhood type.
net – Underlying network.
idGrid – Neuron identifiers./**
     * Constructor with restricted access, solely used for making a
     * {@link #copy() deep copy}.
     *
     * @param wrapRowDim Whether to wrap the first dimension (i.e the first
     * and last neurons will be linked together).
     * @param wrapColDim Whether to wrap the second dimension (i.e the first
     * and last neurons will be linked together).
     * @param neighbourhoodType Neighbourhood type.
     * @param net Underlying network.
     * @param idGrid Neuron identifiers.
     */
    private NeuronSquareMesh2D(boolean wrapRowDim,
                               boolean wrapColDim,
                               SquareNeighbourhood neighbourhoodType,
                               Network net,
                               long[][] idGrid) {
        numberOfRows = idGrid.length;
        numberOfColumns = idGrid[0].length;
        wrapRows = wrapRowDim;
        wrapColumns = wrapColDim;
        neighbourhood = neighbourhoodType;
        network = net;
        identifiers = idGrid;
    }

    
Performs a deep copy of this instance.
Upon return, the copied and original instances will be independent:
Updating one will not affect the other.
Returns:
a new instance with the same state as this instance.
Since:
3.6/**
     * Performs a deep copy of this instance.
     * Upon return, the copied and original instances will be independent:
     * Updating one will not affect the other.
     *
     * @return a new instance with the same state as this instance.
     * @since 3.6
     */
    public synchronized NeuronSquareMesh2D copy() {
        final long[][] idGrid = new long[numberOfRows][numberOfColumns];
        for (int r = 0; r < numberOfRows; r++) {
            for (int c = 0; c < numberOfColumns; c++) {
                idGrid[r][c] = identifiers[r][c];
            }
        }

        return new NeuronSquareMesh2D(wrapRows,
                                      wrapColumns,
                                      neighbourhood,
                                      network.copy(),
                                      idGrid);
    }

    
{@inheritDoc}
 @since 3.6
/**
     * {@inheritDoc}
     *  @since 3.6
     */
    public Iterator<Neuron> iterator() {
        return network.iterator();
    }

    
Retrieves the underlying network. A reference is returned (enabling, for example, the network to be trained). This also implies that calling methods that modify the Network topology may cause this class to become inconsistent. 
Returns:
the network./**
     * Retrieves the underlying network.
     * A reference is returned (enabling, for example, the network to be
     * trained).
     * This also implies that calling methods that modify the {@link Network}
     * topology may cause this class to become inconsistent.
     *
     * @return the network.
     */
    public Network getNetwork() {
        return network;
    }

    
Gets the number of neurons in each row of this map.
Returns:
the number of rows./**
     * Gets the number of neurons in each row of this map.
     *
     * @return the number of rows.
     */
    public int getNumberOfRows() {
        return numberOfRows;
    }

    
Gets the number of neurons in each column of this map.
Returns:
the number of column./**
     * Gets the number of neurons in each column of this map.
     *
     * @return the number of column.
     */
    public int getNumberOfColumns() {
        return numberOfColumns;
    }

    
Retrieves the neuron at location (i, j) in the map. The neuron at position (0, 0) is located at the upper-left corner of the map. 
Params:
i – Row index.
j – Column index.
Throws:
OutOfRangeException – if i or j is out of range.
See Also:
getNeuron(int, int, HorizontalDirection, VerticalDirection)
Returns:
the neuron at (i, j)./**
     * Retrieves the neuron at location {@code (i, j)} in the map.
     * The neuron at position {@code (0, 0)} is located at the upper-left
     * corner of the map.
     *
     * @param i Row index.
     * @param j Column index.
     * @return the neuron at {@code (i, j)}.
     * @throws OutOfRangeException if {@code i} or {@code j} is
     * out of range.
     *
     * @see #getNeuron(int,int,HorizontalDirection,VerticalDirection)
     */
    public Neuron getNeuron(int i,
                            int j) {
        if (i < 0 ||
            i >= numberOfRows) {
            throw new OutOfRangeException(i, 0, numberOfRows - 1);
        }
        if (j < 0 ||
            j >= numberOfColumns) {
            throw new OutOfRangeException(j, 0, numberOfColumns - 1);
        }

        return network.getNeuron(identifiers[i][j]);
    }

    
Retrieves the neuron at (location[0], location[1]) in the map. The neuron at position (0, 0) is located at the upper-left corner of the map. 
Params:
row – Row index.
col – Column index.
alongRowDir – Direction along the given row (i.e. an offset will be added to the given column index.
alongColDir – Direction along the given col (i.e. an offset will be added to the given row index.
See Also:
getNeuron(int, int)
Returns:
the neuron at the requested location, or null if the location is not on the map./**
     * Retrieves the neuron at {@code (location[0], location[1])} in the map.
     * The neuron at position {@code (0, 0)} is located at the upper-left
     * corner of the map.
     *
     * @param row Row index.
     * @param col Column index.
     * @param alongRowDir Direction along the given {@code row} (i.e. an
     * offset will be added to the given <em>column</em> index.
     * @param alongColDir Direction along the given {@code col} (i.e. an
     * offset will be added to the given <em>row</em> index.
     * @return the neuron at the requested location, or {@code null} if
     * the location is not on the map.
     *
     * @see #getNeuron(int,int)
     */
    public Neuron getNeuron(int row,
                            int col,
                            HorizontalDirection alongRowDir,
                            VerticalDirection alongColDir) {
        final int[] location = getLocation(row, col, alongRowDir, alongColDir);

        return location == null ? null : getNeuron(location[0], location[1]);
    }

    
Computes the location of a neighbouring neuron. It will return null if the resulting location is not part of the map. Position (0, 0) is at the upper-left corner of the map. 
Params:
row – Row index.
col – Column index.
alongRowDir – Direction along the given row (i.e. an offset will be added to the given column index.
alongColDir – Direction along the given col (i.e. an offset will be added to the given row index.
See Also:
getNeuron(int, int)
Returns:
an array of length 2 containing the indices of the requested location, or null if that location is not part of the map./**
     * Computes the location of a neighbouring neuron.
     * It will return {@code null} if the resulting location is not part
     * of the map.
     * Position {@code (0, 0)} is at the upper-left corner of the map.
     *
     * @param row Row index.
     * @param col Column index.
     * @param alongRowDir Direction along the given {@code row} (i.e. an
     * offset will be added to the given <em>column</em> index.
     * @param alongColDir Direction along the given {@code col} (i.e. an
     * offset will be added to the given <em>row</em> index.
     * @return an array of length 2 containing the indices of the requested
     * location, or {@code null} if that location is not part of the map.
     *
     * @see #getNeuron(int,int)
     */
    private int[] getLocation(int row,
                              int col,
                              HorizontalDirection alongRowDir,
                              VerticalDirection alongColDir) {
        final int colOffset;
        switch (alongRowDir) {
        case LEFT:
            colOffset = -1;
            break;
        case RIGHT:
            colOffset = 1;
            break;
        case CENTER:
            colOffset = 0;
            break;
        default:
            // Should never happen.
            throw new MathInternalError();
        }
        int colIndex = col + colOffset;
        if (wrapColumns) {
            if (colIndex < 0) {
                colIndex += numberOfColumns;
            } else {
                colIndex %= numberOfColumns;
            }
        }

        final int rowOffset;
        switch (alongColDir) {
        case UP:
            rowOffset = -1;
            break;
        case DOWN:
            rowOffset = 1;
            break;
        case CENTER:
            rowOffset = 0;
            break;
        default:
            // Should never happen.
            throw new MathInternalError();
        }
        int rowIndex = row + rowOffset;
        if (wrapRows) {
            if (rowIndex < 0) {
                rowIndex += numberOfRows;
            } else {
                rowIndex %= numberOfRows;
            }
        }

        if (rowIndex < 0 ||
            rowIndex >= numberOfRows ||
            colIndex < 0 ||
            colIndex >= numberOfColumns) {
            return null;
        } else {
            return new int[] { rowIndex, colIndex };
        }
    }

    
Creates the neighbour relationships between neurons.
/**
     * Creates the neighbour relationships between neurons.
     */
    private void createLinks() {
        // "linkEnd" will store the identifiers of the "neighbours".
        final List<Long> linkEnd = new ArrayList<Long>();
        final int iLast = numberOfRows - 1;
        final int jLast = numberOfColumns - 1;
        for (int i = 0; i < numberOfRows; i++) {
            for (int j = 0; j < numberOfColumns; j++) {
                linkEnd.clear();

                switch (neighbourhood) {

                case MOORE:
                    // Add links to "diagonal" neighbours.
                    if (i > 0) {
                        if (j > 0) {
                            linkEnd.add(identifiers[i - 1][j - 1]);
                        }
                        if (j < jLast) {
                            linkEnd.add(identifiers[i - 1][j + 1]);
                        }
                    }
                    if (i < iLast) {
                        if (j > 0) {
                            linkEnd.add(identifiers[i + 1][j - 1]);
                        }
                        if (j < jLast) {
                            linkEnd.add(identifiers[i + 1][j + 1]);
                        }
                    }
                    if (wrapRows) {
                        if (i == 0) {
                            if (j > 0) {
                                linkEnd.add(identifiers[iLast][j - 1]);
                            }
                            if (j < jLast) {
                                linkEnd.add(identifiers[iLast][j + 1]);
                            }
                        } else if (i == iLast) {
                            if (j > 0) {
                                linkEnd.add(identifiers[0][j - 1]);
                            }
                            if (j < jLast) {
                                linkEnd.add(identifiers[0][j + 1]);
                            }
                        }
                    }
                    if (wrapColumns) {
                        if (j == 0) {
                            if (i > 0) {
                                linkEnd.add(identifiers[i - 1][jLast]);
                            }
                            if (i < iLast) {
                                linkEnd.add(identifiers[i + 1][jLast]);
                            }
                        } else if (j == jLast) {
                             if (i > 0) {
                                 linkEnd.add(identifiers[i - 1][0]);
                             }
                             if (i < iLast) {
                                 linkEnd.add(identifiers[i + 1][0]);
                             }
                        }
                    }
                    if (wrapRows &&
                        wrapColumns) {
                        if (i == 0 &&
                            j == 0) {
                            linkEnd.add(identifiers[iLast][jLast]);
                        } else if (i == 0 &&
                                   j == jLast) {
                            linkEnd.add(identifiers[iLast][0]);
                        } else if (i == iLast &&
                                   j == 0) {
                            linkEnd.add(identifiers[0][jLast]);
                        } else if (i == iLast &&
                                   j == jLast) {
                            linkEnd.add(identifiers[0][0]);
                        }
                    }

                    // Case falls through since the "Moore" neighbourhood
                    // also contains the neurons that belong to the "Von
                    // Neumann" neighbourhood.

                    // fallthru (CheckStyle)
                case VON_NEUMANN:
                    // Links to preceding and following "row".
                    if (i > 0) {
                        linkEnd.add(identifiers[i - 1][j]);
                    }
                    if (i < iLast) {
                        linkEnd.add(identifiers[i + 1][j]);
                    }
                    if (wrapRows) {
                        if (i == 0) {
                            linkEnd.add(identifiers[iLast][j]);
                        } else if (i == iLast) {
                            linkEnd.add(identifiers[0][j]);
                        }
                    }

                    // Links to preceding and following "column".
                    if (j > 0) {
                        linkEnd.add(identifiers[i][j - 1]);
                    }
                    if (j < jLast) {
                        linkEnd.add(identifiers[i][j + 1]);
                    }
                    if (wrapColumns) {
                        if (j == 0) {
                            linkEnd.add(identifiers[i][jLast]);
                        } else if (j == jLast) {
                            linkEnd.add(identifiers[i][0]);
                        }
                    }
                    break;

                default:
                    throw new MathInternalError(); // Cannot happen.
                }

                final Neuron aNeuron = network.getNeuron(identifiers[i][j]);
                for (long b : linkEnd) {
                    final Neuron bNeuron = network.getNeuron(b);
                    // Link to all neighbours.
                    // The reverse links will be added as the loop proceeds.
                    network.addLink(aNeuron, bNeuron);
                }
            }
        }
    }

    
Prevents proxy bypass.
Params:
in – Input stream./**
     * Prevents proxy bypass.
     *
     * @param in Input stream.
     */
    private void readObject(ObjectInputStream in) {
        throw new IllegalStateException();
    }

    
Custom serialization.
Returns:
the proxy instance that will be actually serialized./**
     * Custom serialization.
     *
     * @return the proxy instance that will be actually serialized.
     */
    private Object writeReplace() {
        final double[][][] featuresList = new double[numberOfRows][numberOfColumns][];
        for (int i = 0; i < numberOfRows; i++) {
            for (int j = 0; j < numberOfColumns; j++) {
                featuresList[i][j] = getNeuron(i, j).getFeatures();
            }
        }

        return new SerializationProxy(wrapRows,
                                      wrapColumns,
                                      neighbourhood,
                                      featuresList);
    }

    
Serialization.
/**
     * Serialization.
     */
    private static class SerializationProxy implements Serializable {
        
Serializable. /** Serializable. */
        private static final long serialVersionUID = 20130226L;
        
Wrap. /** Wrap. */
        private final boolean wrapRows;
        
Wrap. /** Wrap. */
        private final boolean wrapColumns;
        
Neighbourhood type. /** Neighbourhood type. */
        private final SquareNeighbourhood neighbourhood;
        
Neurons' features. /** Neurons' features. */
        private final double[][][] featuresList;

        
Params:
wrapRows – Whether the row dimension is wrapped.
wrapColumns – Whether the column dimension is wrapped.
neighbourhood – Neighbourhood type.
featuresList – List of neurons features. neuronList./**
         * @param wrapRows Whether the row dimension is wrapped.
         * @param wrapColumns Whether the column dimension is wrapped.
         * @param neighbourhood Neighbourhood type.
         * @param featuresList List of neurons features.
         * {@code neuronList}.
         */
        SerializationProxy(boolean wrapRows,
                           boolean wrapColumns,
                           SquareNeighbourhood neighbourhood,
                           double[][][] featuresList) {
            this.wrapRows = wrapRows;
            this.wrapColumns = wrapColumns;
            this.neighbourhood = neighbourhood;
            this.featuresList = featuresList;
        }

        
Custom serialization.
Returns:
the Neuron for which this instance is the proxy./**
         * Custom serialization.
         *
         * @return the {@link Neuron} for which this instance is the proxy.
         */
        private Object readResolve() {
            return new NeuronSquareMesh2D(wrapRows,
                                          wrapColumns,
                                          neighbourhood,
                                          featuresList);
        }
    }
}