public double buildSVMTestCorpus(string filename)
{
double total = 0, tp = 0;
string trainDataPath = filename + "SimpleTrainSVM.txt";
if (File.Exists(trainDataPath))
{
_test = ProblemHelper.ReadProblem(trainDataPath);
_test = ProblemHelper.ScaleProblem(_test);
svm_node[][] sn = _test.x;
total = sn.Length;
double[] lbls = _test.y;
for (int i = 0; i < sn.Length; i++)
{
if (_test.y[i] == svm.Predict(sn[i]))
{
tp++;
}
}
fileExistance = true;
//ProblemHelper.WriteProblem(filename+"TestSVM.txt", _test);
}
else
{
SVMScale readyData = new SVMScale();
readyData.buildSVMCorpus(filename);
readyData.scaleSVMData(filename);
buildSVMTestCorpus(filename);
}
return((tp / total) * 100);
}
/// <summary>
///Test pour ScaleProblem
///</summary>
//[TestMethod()]
public void ScaleProblemTest()
{
string full_path = System.IO.Path.Combine(base_path, TEST_FILE);
var prob = ProblemHelper.ScaleProblem(ProblemHelper.ReadProblem(full_path));
Assert.IsNotNull(prob);
Assert.IsTrue(prob.x.Max(v => v.Max(n => n.value)) == 1.0);
Assert.IsTrue(prob.x.Min(v => v.Min(n => n.value)) == -1.0);
}
public bool buildSVMCorpus(string filename)
{
string trainDataPath = filename + "TrainSVM.txt";
if (File.Exists(trainDataPath))
{
_prob = ProblemHelper.ReadProblem(trainDataPath);
_test = ProblemHelper.ScaleProblem(_prob);
svm = new C_SVC(_test, KernelHelper.LinearKernel(), C);
ProblemHelper.WriteProblem(filename + "output.txt", _test);
fileExistance = true;
}
return(fileExistance);
}
public string svmRealTimeTest(double[] testData)
{
int len = 0;
for (int i = 0; i < testData.Length; i++)
{
if (testData[i] < lowPass)
{
len++;
}
else
{
i += escape;
}
}
svm_problem tempProb = _prob;
tempProb.x[0] = new svm_node[len];
//testData=scaleData(testData);
/*List<List<double>> testD = new List<List<double>>();
* testD.Add(testData);
* testData = new List<double>();
* double[] data1 = new double[] { 4, 13.465019915, 221.931854818, 34.448097045, 51.47996222,41.137614759, 15.230779949, 22.01443672, 32.395593998, 21.310546988, 0.988700891, 6.74993337, 5.037963203, 1.074775069, 0.615915165, 0.920866746, 6.755586104, 5.014666624, 2.568192279, 12.08015653, 03.931508695, 500, 500, 500, 1269.375212, 185.55572135 };
* for (int i = 0; i < data1.Length; i++)
* testData.Add(data1[i]);
* testD.Add(testData);
*/
for (int i = 0, j = 0; i < len && j < testData.Length; j++)
{
if (testData[j] < lowPass)
{
tempProb.x[0][i] = new svm_node();
tempProb.x[0][i].value = testData[j];
tempProb.x[0][i].index = j + 1;
i++;
}
else
{
j += escape;
}
}
if (len > 0)
{
tempProb = ProblemHelper.ScaleProblem(tempProb);
}
var predictY = svm.Predict(tempProb.x[0]);
return(predictionDictionary[(int)predictY]);
}
