/*
 * SVM.NET Library
 * Copyright (C) 2008 Matthew Johnson
 * 
 * This program is free software: you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation, either version 3 of the License, or
 * (at your option) any later version.
 * 
 * This program is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 * GNU General Public License for more details.
 * 
 * You should have received a copy of the GNU General Public License
 * along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */


using System;
using System.Globalization;
using System.IO;

namespace SVM {
  /// <summary>
  /// A transform which learns the mean and variance of a sample set and uses these to transform new data
  /// so that it has zero mean and unit variance.
  /// </summary>
  public class GaussianTransform : IRangeTransform {
    private double[] _means;
    private double[] _stddevs;

    /// <summary>
    /// Determines the Gaussian transform for the provided problem.
    /// </summary>
    /// <param name="prob">The Problem to analyze</param>
    /// <returns>The Gaussian transform for the problem</returns>
    public static GaussianTransform Compute(Problem prob) {
      int[] counts = new int[prob.MaxIndex];
      double[] means = new double[prob.MaxIndex];
      foreach (Node[] sample in prob.X) {
        for (int i = 0; i < sample.Length; i++) {
          means[sample[i].Index - 1] += sample[i].Value;
          counts[sample[i].Index - 1]++;
        }
      }
      for (int i = 0; i < prob.MaxIndex; i++) {
        if (counts[i] == 0)
          counts[i] = 2;
        means[i] /= counts[i];
      }

      double[] stddevs = new double[prob.MaxIndex];
      foreach (Node[] sample in prob.X) {
        for (int i = 0; i < sample.Length; i++) {
          double diff = sample[i].Value - means[sample[i].Index - 1];
          stddevs[sample[i].Index - 1] += diff * diff;
        }
      }
      for (int i = 0; i < prob.MaxIndex; i++) {
        if (stddevs[i] == 0)
          continue;
        stddevs[i] /= (counts[i] - 1);
        stddevs[i] = Math.Sqrt(stddevs[i]);
      }

      return new GaussianTransform(means, stddevs);
    }

    /// <summary>
    /// Constructor.
    /// </summary>
    /// <param name="means">Means in each dimension</param>
    /// <param name="stddevs">Standard deviation in each dimension</param>
    public GaussianTransform(double[] means, double[] stddevs) {
      _means = means;
      _stddevs = stddevs;
    }

    /// <summary>
    /// Saves the transform to the disk.  The samples are not stored, only the 
    /// statistics.
    /// </summary>
    /// <param name="stream">The destination stream</param>
    /// <param name="transform">The transform</param>
    public static void Write(Stream stream, GaussianTransform transform) {
      TemporaryCulture.Start();

      StreamWriter output = new StreamWriter(stream);
      output.WriteLine(transform._means.Length);
      for (int i = 0; i < transform._means.Length; i++)
        output.WriteLine("{0} {1}", transform._means[i], transform._stddevs[i]);
      output.Flush();

      TemporaryCulture.Stop();
    }

    /// <summary>
    /// Reads a GaussianTransform from the provided stream.
    /// </summary>
    /// <param name="stream">The source stream</param>
    /// <returns>The transform</returns>
    public static GaussianTransform Read(Stream stream) {
      TemporaryCulture.Start();

      StreamReader input = new StreamReader(stream);
      int length = int.Parse(input.ReadLine(), CultureInfo.InvariantCulture);
      double[] means = new double[length];
      double[] stddevs = new double[length];
      for (int i = 0; i < length; i++) {
        string[] parts = input.ReadLine().Split();
        means[i] = double.Parse(parts[0], CultureInfo.InvariantCulture);
        stddevs[i] = double.Parse(parts[1], CultureInfo.InvariantCulture);
      }

      TemporaryCulture.Stop();

      return new GaussianTransform(means, stddevs);
    }

    /// <summary>
    /// Saves the transform to the disk.  The samples are not stored, only the 
    /// statistics.
    /// </summary>
    /// <param name="filename">The destination filename</param>
    /// <param name="transform">The transform</param>
    public static void Write(string filename, GaussianTransform transform) {
      FileStream output = File.Open(filename, FileMode.Create);
      try {
        Write(output, transform);
      }
      finally {
        output.Close();
      }
    }

    /// <summary>
    /// Reads a GaussianTransform from the provided stream.
    /// </summary>
    /// <param name="filename">The source filename</param>
    /// <returns>The transform</returns>
    public static GaussianTransform Read(string filename) {
      FileStream input = File.Open(filename, FileMode.Open);
      try {
        return Read(input);
      }
      finally {
        input.Close();
      }
    }

    #region IRangeTransform Members

    /// <summary>
    /// Transform the input value using the transform stored for the provided index.
    /// </summary>
    /// <param name="input">Input value</param>
    /// <param name="index">Index of the transform to use</param>
    /// <returns>The transformed value</returns>
    public double Transform(double input, int index) {
      index--;
      if (_stddevs[index] == 0)
        return 0;
      double diff = input - _means[index];
      diff /= _stddevs[index];
      return diff;
    }
    /// <summary>
    /// Transforms the input array.
    /// </summary>
    /// <param name="input">The array to transform</param>
    /// <returns>The transformed array</returns>
    public Node[] Transform(Node[] input) {
      Node[] output = new Node[input.Length];
      for (int i = 0; i < output.Length; i++) {
        int index = input[i].Index;
        double value = input[i].Value;
        output[i] = new Node(index, Transform(value, index));
      }
      return output;
    }

    #endregion
  }
}