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/*
* 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.Linq;
using System.IO;
using System.Diagnostics;
namespace Bigtree.Algorithm.SVM
{
/// <summary>
/// Class containing the routines to perform class membership prediction using a trained SVM.
/// </summary>
public static class Prediction
{
/// <summary>
/// Predicts the class memberships of all the vectors in the problem.
/// </summary>
/// <param name="problem">The SVM Problem to solve</param>
/// <param name="outputFile">File for result output</param>
/// <param name="model">The Model to use</param>
/// <param name="predict_probability">Whether to output a distribution over the classes</param>
/// <returns>Percentage
public static double Predict(
Problem problem,
string outputFile,
Model model,
bool predict_probability)
{
int correct = 0;
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
StreamWriter output = outputFile != null ? new StreamWriter(outputFile) : null;
SvmType svm_type = Procedures.svm_get_svm_type(model);
int nr_class = Procedures.svm_get_nr_class(model);
int[] labels = new int[nr_class];
double[] prob_estimates = null;
if (predict_probability)
{
if (svm_type == SvmType.EPSILON_SVR || svm_type == SvmType.NU_SVR)
{
Console.WriteLine("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + Procedures.svm_get_svr_probability(model));
}
else
{
Procedures.svm_get_labels(model, labels);
prob_estimates = new double[nr_class];
if (output != null)
{
output.Write("labels");
for (int j = 0; j < nr_class; j++)
{
output.Write(" " + labels[j]);
}
output.Write("\n");
}
}
}
for (int i = 0; i < problem.Count; i++)
{
double target = problem.Y[i];
Node[] x = problem.X[i];
double v;
if (predict_probability && (svm_type == SvmType.C_SVC || svm_type == SvmType.NU_SVC))
{
v = Procedures.svm_predict_probability(model, x, prob_estimates);
if (output != null)
{
output.Write(v + " ");
for (int j = 0; j < nr_class; j++)
{
output.Write(prob_estimates[j] + " ");
}
output.Write("\n");
}
}
else
{
v = Procedures.svm_predict(model, x);
if(output != null)
output.Write(v + "\n");
}
if (v == target)
++correct;
error += (v - target) * (v - target);
sumv += v;
sumy += target;
sumvv += v * v;
sumyy += target * target;
sumvy += v * target;
++total;
}
if(output != null)
output.Close();
if (model.Parameter.SvmType == SvmType.EPSILON_SVR || model.Parameter.SvmType == SvmType.NU_SVR)
return error / total;
else return (double)correct / total;
}
/// <summary>
/// Predict the labels for all the data points in a problem.
/// </summary>
/// <param name="model">The model to use</param>
/// <param name="problem">The problem to solve</param>
/// <returns>The predicted labels</returns>
public static double[] PredictLabels(this Model model, Problem problem)
{
return problem.X.Select(o => model.Predict(o)).ToArray();
}
/// <summary>
/// Predict the probability distributions over all labels for each data point in a problem.
/// </summary>
/// <param name="model">The model to use</param>
/// <param name="problem">The problem to solve</param>
/// <returns>A distribution over labels for each data point</returns>
public static double[][] PredictLabelsProbability(this Model model, Problem problem)
{
return problem.X.Select(o => model.PredictProbability(o)).ToArray();
}
/// <summary>
/// Predict the class for a single input vector.
/// </summary>
/// <param name="model">The Model to use for prediction</param>
/// <param name="x">The vector for which to predict class</param>
/// <returns>The result</returns>
public static double Predict(this Model model, Node[] x)
{
return Procedures.svm_predict(model, x);
}
/// <summary>
/// Predicts a class distribution for the single input vector.
/// </summary>
/// <param name="model">Model to use for prediction</param>
/// <param name="x">The vector for which to predict the class distribution</param>
/// <returns>A probability distribtion over classes</returns>
public static double[] PredictProbability(this Model model, Node[] x)
{
SvmType svm_type = Procedures.svm_get_svm_type(model);
if (svm_type != SvmType.C_SVC && svm_type != SvmType.NU_SVC)
throw new Exception("Model type " + svm_type + " unable to predict probabilities.");
int nr_class = Procedures.svm_get_nr_class(model);
double[] probEstimates = new double[nr_class];
Procedures.svm_predict_probability(model, x, probEstimates);
return probEstimates;
}
private static void exit_with_help()
{
Debug.Write("usage: svm_predict [options] test_file model_file output_file\n" + "options:\n" + "-b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); one-class SVM not supported yet\n");
Environment.Exit(1);
}
/// <summary>
/// Legacy method, provided to allow usage as though this were the command line version of libsvm.
/// </summary>
/// <param name="args">Standard arguments passed to the svm_predict exectutable. See libsvm documentation for details.</param>
[Obsolete("Use the other version of Predict() instead")]
public static void Predict(params string[] args)
{
int i = 0;
bool predictProbability = false;
// parse options
for (i = 0; i < args.Length; i++)
{
if (args[i][0] != '-')
break;
++i;
switch (args[i - 1][1])
{
case 'b':
predictProbability = int.Parse(args[i]) == 1;
break;
default:
throw new ArgumentException("Unknown option");
}
}
if (i >= args.Length)
throw new ArgumentException("No input, model and output files provided");
Problem problem = Problem.Read(args[i]);
Model model = Model.Read(args[i + 1]);
Predict(problem, args[i + 2], model, predictProbability);
}
}
}