/// <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 + "\n");
output.Write("kernel_type " + param.KernelType + "\n");
if (param.KernelType == KernelType.POLY)
output.Write("degree " + param.Degree + "\n");
if (param.KernelType == KernelType.POLY || param.KernelType == KernelType.RBF || param.KernelType == KernelType.SIGMOID)
output.Write("gamma " + param.Gamma + "\n");
if (param.KernelType == KernelType.POLY || param.KernelType == KernelType.SIGMOID)
output.Write("coef0 " + param.Coefficient0 + "\n");
int nr_class = model.NumberOfClasses;
int l = model.SupportVectorCount;
output.Write("nr_class " + nr_class + "\n");
output.Write("total_sv " + l + "\n");
{
output.Write("rho");
for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
output.Write(" " + model.Rho[i]);
output.Write("\n");
}
if (model.ClassLabels != null)
{
output.Write("label");
for (int i = 0; i < nr_class; i++)
output.Write(" " + model.ClassLabels[i]);
output.Write("\n");
}
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]);
output.Write("\n");
}
if (model.PairwiseProbabilityB != null)
{
output.Write("probB");
for (int i = 0; i < nr_class * (nr_class - 1) / 2; i++)
output.Write(" " + model.PairwiseProbabilityB[i]);
output.Write("\n");
}
if (model.NumberOfSVPerClass != null)
{
output.Write("nr_sv");
for (int i = 0; i < nr_class; i++)
output.Write(" " + model.NumberOfSVPerClass[i]);
output.Write("\n");
}
output.Write("SV\n");
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] + " ");
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);
for (int j = 1; j < p.Length; j++)
output.Write(" {0}:{1}", p[j].Index, p[j].Value);
}
output.WriteLine();
}
output.Flush();
TemporaryCulture.Stop();
}
/// <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>
/// Reads a Model from the provided stream.
/// </summary>
/// <param name="stream">The stream from which to read the Model.</param>
/// <returns>the Model</returns>
public static Model Read(Stream stream)
{
TemporaryCulture.Start();
StreamReader input = new StreamReader(stream);
// 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;
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();
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>
/// 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 correctly labelled</returns>
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");
}
}
}
else
{
Procedures.svm_get_labels(model, labels);
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(target + " " + 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(target + " " + 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();
return (double)correct / total;
}
/// <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(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;
}
/// <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(Model model, Node[] x)
{
return Procedures.svm_predict(model, x);
}