/*
 * 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.IO;

namespace SVM {
  /// <summary>
  /// Encapsulates an SVM Model.
  /// </summary>
  [Serializable]
  public class Model {
    private Parameter _parameter;
    private int _numberOfClasses;
    private int _supportVectorCount;
    private int[] _supportVectorIndizes;
    private Node[][] _supportVectors;
    private double[][] _supportVectorCoefficients;
    private double[] _rho;
    private double[] _pairwiseProbabilityA;
    private double[] _pairwiseProbabilityB;

    private int[] _classLabels;
    private int[] _numberOfSVPerClass;

    internal Model() {
    }

    /// <summary>
    /// Parameter object.
    /// </summary>
    public Parameter Parameter {
      get {
        return _parameter;
      }
      set {
        _parameter = value;
      }
    }

    /// <summary>
    /// Number of classes in the model.
    /// </summary>
    public int NumberOfClasses {
      get {
        return _numberOfClasses;
      }
      set {
        _numberOfClasses = value;
      }
    }

    /// <summary>
    /// Total number of support vectors.
    /// </summary>
    public int SupportVectorCount {
      get {
        return _supportVectorCount;
      }
      set {
        _supportVectorCount = value;
      }
    }

    /// <summary>
    /// Indizes of support vectors identified in the training.
    /// </summary>
    public int[] SupportVectorIndizes {
      get {
        return _supportVectorIndizes;
      }
      set {
        _supportVectorIndizes = value;
      }
    }

    /// <summary>
    /// The support vectors.
    /// </summary>
    public Node[][] SupportVectors {
      get {
        return _supportVectors;
      }
      set {
        _supportVectors = value;
      }
    }

    /// <summary>
    /// The coefficients for the support vectors.
    /// </summary>
    public double[][] SupportVectorCoefficients {
      get {
        return _supportVectorCoefficients;
      }
      set {
        _supportVectorCoefficients = value;
      }
    }

    /// <summary>
    /// Rho values.
    /// </summary>
    public double[] Rho {
      get {
        return _rho;
      }
      set {
        _rho = value;
      }
    }

    /// <summary>
    /// First pairwise probability.
    /// </summary>
    public double[] PairwiseProbabilityA {
      get {
        return _pairwiseProbabilityA;
      }
      set {
        _pairwiseProbabilityA = value;
      }
    }

    /// <summary>
    /// Second pairwise probability.
    /// </summary>
    public double[] PairwiseProbabilityB {
      get {
        return _pairwiseProbabilityB;
      }
      set {
        _pairwiseProbabilityB = value;
      }
    }

    // for classification only

    /// <summary>
    /// Class labels.
    /// </summary>
    public int[] ClassLabels {
      get {
        return _classLabels;
      }
      set {
        _classLabels = value;
      }
    }

    /// <summary>
    /// Number of support vectors per class.
    /// </summary>
    public int[] NumberOfSVPerClass {
      get {
        return _numberOfSVPerClass;
      }
      set {
        _numberOfSVPerClass = value;
      }
    }

    /// <summary>
    /// Reads a Model from the provided file.
    /// </summary>
    /// <param name="filename">The name of the file containing the Model</param>
    /// <returns>the Model</returns>
    public static Model Read(string filename) {
      FileStream input = File.OpenRead(filename);
      try {
        return Read(input);
      }
      finally {
        input.Close();
      }
    }

    public static Model Read(Stream stream) {
      return Read(new StreamReader(stream));
    }

    /// <summary>
    /// Reads a Model from the provided stream.
    /// </summary>
    /// <param name="input">The stream from which to read the Model.</param>
    /// <returns>the Model</returns>
    public static Model Read(TextReader input) {
      TemporaryCulture.Start();

      // read parameters

      Model model = new Model();
      Parameter param = new Parameter();
      model.Parameter = param;
      model.Rho = null;
      model.PairwiseProbabilityA = null;
      model.PairwiseProbabilityB = null;
      model.ClassLabels = null;
      model.NumberOfSVPerClass = null;
      model.SupportVectorIndizes = new int[0];

      bool headerFinished = false;
      while (!headerFinished) {
        string line = input.ReadLine();
        string cmd, arg;
        int splitIndex = line.IndexOf(' ');
        if (splitIndex >= 0) {
          cmd = line.Substring(0, splitIndex);
          arg = line.Substring(splitIndex + 1);
        } else {
          cmd = line;
          arg = "";
        }
        // arg = arg.ToLower(); (transforms double NaN or Infinity values to incorrect format [gkronber])

        int i, n;
        switch (cmd) {
          case "svm_type":
            param.SvmType = (SvmType)Enum.Parse(typeof(SvmType), arg.ToUpper());
            break;

          case "kernel_type":
            param.KernelType = (KernelType)Enum.Parse(typeof(KernelType), arg.ToUpper());
            break;

          case "degree":
            param.Degree = int.Parse(arg);
            break;

          case "gamma":
            param.Gamma = double.Parse(arg);
            break;

          case "coef0":
            param.Coefficient0 = double.Parse(arg);
            break;

          case "nr_class":
            model.NumberOfClasses = int.Parse(arg);
            break;

          case "total_sv":
            model.SupportVectorCount = int.Parse(arg);
            break;

          case "rho":
            n = model.NumberOfClasses * (model.NumberOfClasses - 1) / 2;
            model.Rho = new double[n];
            string[] rhoParts = arg.Split();
            for (i = 0; i < n; i++)
              model.Rho[i] = double.Parse(rhoParts[i]);
            break;

          case "label":
            n = model.NumberOfClasses;
            model.ClassLabels = new int[n];
            string[] labelParts = arg.Split();
            for (i = 0; i < n; i++)
              model.ClassLabels[i] = int.Parse(labelParts[i]);
            break;

          case "probA":
            n = model.NumberOfClasses * (model.NumberOfClasses - 1) / 2;
            model.PairwiseProbabilityA = new double[n];
            string[] probAParts = arg.Split();
            for (i = 0; i < n; i++)
              model.PairwiseProbabilityA[i] = double.Parse(probAParts[i]);
            break;

          case "probB":
            n = model.NumberOfClasses * (model.NumberOfClasses - 1) / 2;
            model.PairwiseProbabilityB = new double[n];
            string[] probBParts = arg.Split();
            for (i = 0; i < n; i++)
              model.PairwiseProbabilityB[i] = double.Parse(probBParts[i]);
            break;

          case "nr_sv":
            n = model.NumberOfClasses;
            model.NumberOfSVPerClass = new int[n];
            string[] nrsvParts = arg.Split();
            for (i = 0; i < n; i++)
              model.NumberOfSVPerClass[i] = int.Parse(nrsvParts[i]);
            break;

          case "SV":
            headerFinished = true;
            break;

          default:
            throw new Exception("Unknown text in model file");
        }
      }

      // read sv_coef and SV

      int m = model.NumberOfClasses - 1;
      int l = model.SupportVectorCount;
      model.SupportVectorCoefficients = new double[m][];
      for (int i = 0; i < m; i++) {
        model.SupportVectorCoefficients[i] = new double[l];
      }
      model.SupportVectors = new Node[l][];

      for (int i = 0; i < l; i++) {
        string[] parts = input.ReadLine().Trim().Split();

        for (int k = 0; k < m; k++)
          model.SupportVectorCoefficients[k][i] = double.Parse(parts[k]);
        int n = parts.Length - m;
        model.SupportVectors[i] = new Node[n];
        for (int j = 0; j < n; j++) {
          string[] nodeParts = parts[m + j].Split(':');
          model.SupportVectors[i][j] = new Node();
          model.SupportVectors[i][j].Index = int.Parse(nodeParts[0]);
          model.SupportVectors[i][j].Value = double.Parse(nodeParts[1]);
        }
      }

      TemporaryCulture.Stop();

      return model;
    }

    /// <summary>
    /// Writes a model to the provided filename.  This will overwrite any previous data in the file.
    /// </summary>
    /// <param name="filename">The desired file</param>
    /// <param name="model">The Model to write</param>
    public static void Write(string filename, Model model) {
      FileStream stream = File.Open(filename, FileMode.Create);
      try {
        Write(stream, model);
      }
      finally {
        stream.Close();
      }
    }

    /// <summary>
    /// Writes a model to the provided stream.
    /// </summary>
    /// <param name="stream">The output stream</param>
    /// <param name="model">The model to write</param>
    public static void Write(Stream stream, Model model) {
      TemporaryCulture.Start();

      StreamWriter output = new StreamWriter(stream);

      Parameter param = model.Parameter;

      output.Write("svm_type " + param.SvmType + Environment.NewLine);
      output.Write("kernel_type " + param.KernelType + Environment.NewLine);

      if (param.KernelType == KernelType.POLY)
        output.Write("degree " + param.Degree.ToString("r") + Environment.NewLine);

      if (param.KernelType == KernelType.POLY || param.KernelType == KernelType.RBF || param.KernelType == KernelType.SIGMOID)
        output.Write("gamma " + param.Gamma.ToString("r") + Environment.NewLine);

      if (param.KernelType == KernelType.POLY || param.KernelType == KernelType.SIGMOID)
        output.Write("coef0 " + param.Coefficient0.ToString("r") + Environment.NewLine);

      int nr_class = model.NumberOfClasses;
      int l = model.SupportVectorCount;
      output.Write("nr_class " + nr_class + Environment.NewLine);
      output.Write("total_sv " + l + Environment.NewLine);

      {
        output.Write("rho");
        for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
          output.Write(" " + model.Rho[i].ToString("r"));
        output.Write(Environment.NewLine);
      }

      if (model.ClassLabels != null) {
        output.Write("label");
        for (int i = 0; i < nr_class; i++)
          output.Write(" " + model.ClassLabels[i]);
        output.Write(Environment.NewLine);
      }

      if (model.PairwiseProbabilityA != null)
      // regression has probA only
            {
        output.Write("probA");
        for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
          output.Write(" " + model.PairwiseProbabilityA[i].ToString("r"));
        output.Write(Environment.NewLine);
      }
      if (model.PairwiseProbabilityB != null) {
        output.Write("probB");
        for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
          output.Write(" " + model.PairwiseProbabilityB[i].ToString("r"));
        output.Write(Environment.NewLine);
      }

      if (model.NumberOfSVPerClass != null) {
        output.Write("nr_sv");
        for (int i = 0; i < nr_class; i++)
          output.Write(" " + model.NumberOfSVPerClass[i].ToString("r"));
        output.Write(Environment.NewLine);
      }

      output.WriteLine("SV");
      double[][] sv_coef = model.SupportVectorCoefficients;
      Node[][] SV = model.SupportVectors;

      for (int i = 0; i < l; i++) {
        for (int j = 0; j < nr_class - 1; j++)
          output.Write(sv_coef[j][i].ToString("r") + " ");

        Node[] p = SV[i];
        if (p.Length == 0) {
          output.WriteLine();
          continue;
        }
        if (param.KernelType == KernelType.PRECOMPUTED)
          output.Write("0:{0}", (int)p[0].Value);
        else {
          output.Write("{0}:{1}", p[0].Index, p[0].Value.ToString("r"));
          for (int j = 1; j < p.Length; j++)
            output.Write(" {0}:{1}", p[j].Index, p[j].Value.ToString("r"));
        }
        output.WriteLine();
      }

      output.Flush();

      TemporaryCulture.Stop();
    }
  }
}