public static float calculateSingleKernel(TrainingUnit xi, TrainingUnit xj, SVM ProblemSolution)
{
ProblemConfig problemConfig = ProblemSolution.ProblemCfg;
// Vectors size check
//if (xi.getDimension() != xj.getDimension()) return 0;
// Linear: u'*v (inner product)
if (problemConfig.kernelType == ProblemConfig.KernelType.Linear)
{
float sum = 0;
for (int i = 0; i < xi.getDimension(); i++)
{
sum += xi.xVector[i] * xj.xVector[i];
}
return(sum);
}
// Radial basis function: exp(-gamma*|u-v|^2)
if (problemConfig.kernelType == ProblemConfig.KernelType.RBF)
{
// Gamma is, by choice, 1 / (number of features).
float sum = 0, temp;
for (int i = 0; i < xi.getDimension(); i++)
{
temp = xi.xVector[i] - xj.xVector[i];
sum += temp * temp;
}
return((float)Math.Exp(-ProblemSolution.ProblemCfg.lambda * sum));
}
return(0);
}
public static void calculateAllKernels(SVM problemSolution)
{
TrainingSet trainingSet = problemSolution.TrainingSet;
ProblemConfig problemConfig = problemSolution.ProblemCfg;
trainingSet.errors = new float[trainingSet.getN];
trainingSet.kernels = new float[trainingSet.getN][];
for (int i = 0; i < trainingSet.getN; i++)
{
trainingSet.kernels[i] = new float[trainingSet.getN];
}
trainingSet.IsKernelCalculated = new bool[trainingSet.getN];
// Caching kernels
for (int i = 0; i < trainingSet.getN; i++)
{
if (problemSolution.alphaList[i] != 0)
{
trainingSet.IsKernelCalculated[i] = true;
for (int j = i; j < trainingSet.getN; j++)
{
trainingSet.kernels[i][j] = calculateSingleKernel(trainingSet.trainingArray[i], trainingSet.trainingArray[j], problemSolution);
if (j != i)
{
trainingSet.kernels[j][i] = trainingSet.kernels[i][j];
}
}
}
}
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
/// <param name="SVMCfg">Configuration parameters</param>
private void initMultiSVM(TrainingSet TSet, ProblemConfig SVMCfg)
{
//Determines how many different classifications are there
Classifications = new List <float>();
foreach (TrainingUnit tu in TSet.trainingArray)
{
if (Classifications.IndexOf(tu.y) < 0 && tu.y != -1.0f)
{
Classifications.Add(tu.y);
}
}
//For each different possible classification, create a different SVM
SVMs = new List <SVM>();
foreach (float c in Classifications)
{
SVM svm = new SVM();
svm.TrainingSet = new TrainingSet();
svm.ProblemCfg = SVMCfg.Clone();
SVMs.Add(svm);
foreach (TrainingUnit tu in TSet.trainingArray)
{
TrainingUnit newTu = tu.Clone();
newTu.y = tu.y == c ? 1 : -1;
svm.TrainingSet.addTrainingUnit(newTu);
}
//Train svm
svm.PreCalibrateCfg(0.8f / (float)Math.Sqrt(svm.TrainingSet.getN), 0.3f / (float)Math.Sqrt(svm.TrainingSet.getN));
svm.Train();
svm.RemoveNonSupportVectors();
}
}
/*
* /// <summary>
* /// Copy all values from another solution
* /// </summary>
* /// <param name="sourceSolution">The source to copy from</param>
* public void Load(SVM sourceSolution)
* {
* dimension = sourceSolution.dimension;
* alphaList = new float[dimension];
* for (int i = 0; i < dimension; i++)
* {
* alphaList[i] = sourceSolution.alphaList[i];
* }
* b = sourceSolution.b;
* }
*/
/// <summary>
/// Copy all values from another solution
/// </summary>
/// <param name="FileName">File containing alpha's data</param>
public void Load(string FileName)
{
DataSet d = new DataSet();
d.ReadXml(FileName);
DataTable t = d.Tables["Solution"];
dimension = t.Rows.Count;
//Configuration
DataTable TblCfg = d.Tables["Config"];
float valC, valTol; int valKernel, valMaxP;
valC = (float)((double)TblCfg.Rows[0]["dblValues"]);
valKernel = (int)((double)TblCfg.Rows[1]["dblValues"]);
valTol = (float)((double)TblCfg.Rows[2]["dblValues"]);
valMaxP = (int)((double)TblCfg.Rows[3]["dblValues"]);
this.b = (float)((double)TblCfg.Rows[4]["dblValues"]);
float Lambda = (float)((double)TblCfg.Rows[5]["dblValues"]);
int xDim = (int)((double)TblCfg.Rows[6]["dblValues"]);
//Reads classifications
DataTable TblClassif = d.Tables["Classifications"];
alphaList = new List <float>();
TrainingSet = new TrainingSet();
for (int i = 0; i < dimension; i++)
{
TrainingSet.addTrainingUnit(new TrainingUnit(new float[xDim], -1));
}
for (int i = 0; i < dimension; i++)
{
alphaList.Add((float)((double)t.Rows[i]["dblValues"]));
TrainingSet.trainingArray[i].y = (float)((double)TblClassif.Rows[i]["dblValues"]) > 0 ? 1 : -1;
}
//Reads training set
//Creates datatables for training examples
DataTable Tbl = d.Tables["Examples"];
for (int i = 0; i < dimension; i++)
{
for (int j = 0; j < xDim; j++)
{
TrainingSet.trainingArray[i].xVector[j] = (float)((double)Tbl.Rows[j + i * xDim]["dblValues"]);
}
}
this.ProblemCfg = new ProblemConfig(Lambda, valC, valTol, valMaxP, (ProblemConfig.KernelType)valKernel);
if (OpenCLTemplate.CLCalc.CLAcceleration == OpenCLTemplate.CLCalc.CLAccelerationType.UsingCL)
{
this.WriteToDevice();
}
}
/// <summary>
/// Predicts the output of a single entry, given a previous problem, solution and correspondent training set
/// </summary>
/// <param name="problemSolution">Correspondent problem solution</param>
/// <param name="untrainedUnit">Input features from which the output will be predicted</param>
/// <returns>The y classification (true/false = positive/negative)</returns>
public static float CLpredictOutput(SVM problemSolution, TrainingUnit untrainedUnit)
{
TrainingSet trainingSet = problemSolution.TrainingSet;
ProblemConfig problemConfig = problemSolution.ProblemCfg;
#region Compute kernel
float[] K = new float[problemSolution.TrainingSet.getN];
CLCalc.Program.MemoryObject[] args = new CLCalc.Program.MemoryObject[]
{
problemSolution.CLTrainingFeatures,
problemSolution.CLXVecLen,
problemSolution.CLSample,
problemSolution.CLKernelValues,
problemSolution.CLLambda
};
for (int j = 0; j < untrainedUnit.xVector.Length; j++)
{
problemSolution.HostSample[j] = untrainedUnit.xVector[j];
}
problemSolution.CLSample.WriteToDevice(problemSolution.HostSample);
lock (CLResource)
{
kernelComputeKernelRBF.Execute(args, problemSolution.TrainingSet.getN);
problemSolution.CLKernelValues.ReadFromDeviceTo(K);
}
#endregion
// F(x) = sum + b
// sum = summation of alpha_i * y_i * kernel(untrained unit, i) for all i in the training set
float sum = 0;
for (int i = 0; i < trainingSet.getN; i++)
{
if (trainingSet.trainingArray[i].y > 0)
{
sum += problemSolution.alphaList[i] * K[i];
}
else
{
sum -= problemSolution.alphaList[i] * K[i];
}
}
return(sum + problemSolution.b);
}
/// <summary>Computes All kernels and errors accelerating with OpenCL</summary>
/// <param name="problemSolution">Problem solution SVM</param>
public static void CLcalculateAllKernels(SVM problemSolution)
{
TrainingSet trainingSet = problemSolution.TrainingSet;
ProblemConfig problemConfig = problemSolution.ProblemCfg;
trainingSet.errors = new float[trainingSet.getN];
trainingSet.kernels = new float[trainingSet.getN][];
trainingSet.IsKernelCalculated = new bool[trainingSet.getN];
// Caching kernels
for (int i = 0; i < trainingSet.getN; i++)
{
if (problemSolution.alphaList[i] != 0)
{
CLComputeKernels(problemSolution, i);
}
}
}
private static float calculateFx(int indexX, SVM currentSolution)
{
TrainingSet trainingSet = currentSolution.TrainingSet;
ProblemConfig problemConfig = currentSolution.ProblemCfg;
float sum = 0;
for (int i = 0; i < trainingSet.getN; i++)
{
if (trainingSet.trainingArray[i].y > 0)
{
sum += currentSolution.alphaList[i] * trainingSet.kernels[i][indexX];
}
else
{
sum -= currentSolution.alphaList[i] * trainingSet.kernels[i][indexX];
}
}
return(sum + currentSolution.b);
}
/// <summary>
/// Predicts the output of a single entry, given a previous problem, solution and correspondent training set
/// </summary>
/// <param name="problemSolution">Correspondent problem solution</param>
/// <param name="untrainedUnit">Input features from which the output will be predicted</param>
/// <returns>The y classification (true/false = positive/negative)</returns>
public static float predictOutput(SVM problemSolution, TrainingUnit untrainedUnit)
{
TrainingSet trainingSet = problemSolution.TrainingSet;
ProblemConfig problemConfig = problemSolution.ProblemCfg;
// F(x) = sum + b
// sum = summation of alpha_i * y_i * kernel(untrained unit, i) for all i in the training set
float sum = 0;
for (int i = 0; i < trainingSet.getN; i++)
{
if (trainingSet.trainingArray[i].y > 0)
{
sum += problemSolution.alphaList[i] * calculateSingleKernel(trainingSet.trainingArray[i], untrainedUnit, problemSolution);
}
else
{
sum -= problemSolution.alphaList[i] * calculateSingleKernel(trainingSet.trainingArray[i], untrainedUnit, problemSolution);
}
}
return(sum + problemSolution.b);
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
/// <param name="SVMCfg">Configuration parameters</param>
private void initMultiSVM(TrainingSet TSet, ProblemConfig SVMCfg)
{
//Determines how many different classifications are there
Classifications = new List<float>();
foreach (TrainingUnit tu in TSet.trainingArray)
{
if (Classifications.IndexOf(tu.y) < 0 && tu.y != -1.0f) Classifications.Add(tu.y);
}
//For each different possible classification, create a different SVM
SVMs = new List<SVM>();
foreach (float c in Classifications)
{
SVM svm = new SVM();
svm.TrainingSet = new TrainingSet();
svm.ProblemCfg = SVMCfg.Clone();
SVMs.Add(svm);
foreach (TrainingUnit tu in TSet.trainingArray)
{
TrainingUnit newTu = tu.Clone();
newTu.y = tu.y == c ? 1 : -1;
svm.TrainingSet.addTrainingUnit(newTu);
}
//Train svm
svm.PreCalibrateCfg(0.8f / (float)Math.Sqrt(svm.TrainingSet.getN), 0.3f / (float)Math.Sqrt(svm.TrainingSet.getN));
svm.Train();
svm.RemoveNonSupportVectors();
}
}
/*
/// <summary>
/// Copy all values from another solution
/// </summary>
/// <param name="sourceSolution">The source to copy from</param>
public void Load(SVM sourceSolution)
{
dimension = sourceSolution.dimension;
alphaList = new float[dimension];
for (int i = 0; i < dimension; i++)
{
alphaList[i] = sourceSolution.alphaList[i];
}
b = sourceSolution.b;
}
*/
/// <summary>
/// Copy all values from another solution
/// </summary>
/// <param name="FileName">File containing alpha's data</param>
public void Load(string FileName)
{
DataSet d = new DataSet();
d.ReadXml(FileName);
DataTable t = d.Tables["Solution"];
dimension = t.Rows.Count;
//Configuration
DataTable TblCfg = d.Tables["Config"];
float valC, valTol; int valKernel, valMaxP;
valC = (float)((double)TblCfg.Rows[0]["dblValues"]);
valKernel = (int)((double)TblCfg.Rows[1]["dblValues"]);
valTol = (float)((double)TblCfg.Rows[2]["dblValues"]);
valMaxP = (int)((double)TblCfg.Rows[3]["dblValues"]);
this.b = (float)((double)TblCfg.Rows[4]["dblValues"]);
float Lambda = (float)((double)TblCfg.Rows[5]["dblValues"]);
int xDim = (int)((double)TblCfg.Rows[6]["dblValues"]);
//Reads classifications
DataTable TblClassif = d.Tables["Classifications"];
alphaList = new List<float>();
TrainingSet = new TrainingSet();
for (int i = 0; i < dimension; i++)
{
TrainingSet.addTrainingUnit(new TrainingUnit(new float[xDim], -1));
}
for (int i = 0; i < dimension; i++)
{
alphaList.Add((float)((double)t.Rows[i]["dblValues"]));
TrainingSet.trainingArray[i].y = (float)((double)TblClassif.Rows[i]["dblValues"]) > 0 ? 1 : -1;
}
//Reads training set
//Creates datatables for training examples
DataTable Tbl = d.Tables["Examples"];
for (int i = 0; i < dimension; i ++)
{
for (int j = 0; j < xDim; j++)
{
TrainingSet.trainingArray[i].xVector[j] = (float)((double)Tbl.Rows[j + i*xDim]["dblValues"]);
}
}
this.ProblemCfg = new ProblemConfig(Lambda, valC, valTol, valMaxP, (ProblemConfig.KernelType)valKernel);
if (OpenCLTemplate.CLCalc.CLAcceleration == OpenCLTemplate.CLCalc.CLAccelerationType.UsingCL)
{
this.WriteToDevice();
}
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
/// <param name="SVMCfg">Configuration parameters</param>
/// <param name="PreCalibrate">Precalibrate RBF parameter lambda? This will ignore the given value</param>
public MultiClassSVM(TrainingSet TSet, ProblemConfig SVMCfg, bool PreCalibrate)
{
initMultiSVM(TSet, SVMCfg, PreCalibrate);
}
/// <summary>
/// Solves the SMO considering no previous knowledge about the problem
/// </summary>
/// <param name="problemSolution">Known solution</param>
/// <returns>Solution of the problem with alphas and threshold</returns>
public static SVM solveSMOStartingFromPreviousSolution(SVM problemSolution)
{
System.Diagnostics.Stopwatch swTotalTime = new System.Diagnostics.Stopwatch();
System.Diagnostics.Stopwatch swHeuristica = new System.Diagnostics.Stopwatch();
System.Diagnostics.Stopwatch swComputeKernel = new System.Diagnostics.Stopwatch();
System.Diagnostics.Stopwatch swUpdateError = new System.Diagnostics.Stopwatch();
swTotalTime.Start();
ProblemConfig problemConfig = problemSolution.ProblemCfg;
if (problemSolution.alphaList == null)
{
problemSolution.initializeWithZeros();
}
ProblemSolver.calculateErrors(problemSolution);
//Initializes GPU error vector
if (OpenCLTemplate.CLCalc.CLAcceleration == OpenCLTemplate.CLCalc.CLAccelerationType.UsingCL)
{
WriteCLErr(problemSolution);
}
TrainingSet trainingSet = problemSolution.TrainingSet;
int passes = 0;
int m = trainingSet.getN;
while (passes < problemConfig.maxPasses)
{
int changedAlphas = 0;
for (int i = 0; i < m; i++)
{
float yi = trainingSet.trainingArray[i].y;
float alpha_i = problemSolution.alphaList[i];
// Error between the SVM output on the ith training unit and the true ith output
float ei = trainingSet.errors[i];
// KKT conditions for ith element
if (
((yi * ei < -problemConfig.tol && alpha_i < problemConfig.c) || (yi * ei > problemConfig.tol && alpha_i > 0))
)
{
swHeuristica.Start();
#region Computes J using maximum variation heuristics
// Get a number from 0 to m - 1 not equal to i
int j = 0;
if (trainingSet.errors[i] >= 0)
{
if (OpenCLTemplate.CLCalc.CLAcceleration == OpenCLTemplate.CLCalc.CLAccelerationType.UsingCL)
{
j = CLFindMinError(problemSolution);
}
else
{
float minError = trainingSet.errors[0];
for (int k = 1; k < trainingSet.getN; k++)
{
if (minError > trainingSet.errors[k])
{
minError = trainingSet.errors[k];
j = k;
}
}
}
}
else
{
if (OpenCLTemplate.CLCalc.CLAcceleration == OpenCLTemplate.CLCalc.CLAccelerationType.UsingCL)
{
j = CLFindMaxError(problemSolution);
}
else
{
float maxError = trainingSet.errors[0];
for (int k = 1; k < trainingSet.getN; k++)
{
if (maxError < trainingSet.errors[k])
{
maxError = trainingSet.errors[k];
j = k;
}
}
}
}
#endregion
swHeuristica.Stop();
float yj = trainingSet.trainingArray[j].y;
float alpha_j = problemSolution.alphaList[j];
// Error between the SVM output on the jth training unit and the true jth output
float ej = trainingSet.errors[j];
// Save old alphas
float oldAlpha_i = problemSolution.alphaList[i];
float oldAlpha_j = problemSolution.alphaList[j];
#region Compute lower and higher bounds of alpha_j
float lowerBound;
float higherBound;
if (yi != yj)
{
lowerBound = Math.Max(0, alpha_j - alpha_i);
higherBound = Math.Min(problemConfig.c, problemConfig.c + alpha_j - alpha_i);
}
else
{
lowerBound = Math.Max(0, alpha_j + alpha_i - problemConfig.c);
higherBound = Math.Min(problemConfig.c, alpha_j + alpha_i);
}
#endregion
// Nothing to adjust if we can't set any value between those bounds
if (lowerBound == higherBound)
{
continue;
}
#region Compute eta
float kernel_xi_xj;
float kernel_xi_xi;
float kernel_xj_xj;
if (trainingSet.IsKernelCalculated[i])
{
kernel_xi_xj = trainingSet.kernels[i][j];
}
else if (trainingSet.IsKernelCalculated[j])
{
kernel_xi_xj = trainingSet.kernels[j][i];
}
else
{
kernel_xi_xj = calculateSingleKernel(trainingSet.trainingArray[i], trainingSet.trainingArray[j], problemSolution); //trainingSet.kernels[i][j];
}
if (trainingSet.IsKernelCalculated[i])
{
kernel_xi_xi = trainingSet.kernels[i][i];
}
else
{
kernel_xi_xi = calculateSingleKernel(trainingSet.trainingArray[i], trainingSet.trainingArray[i], problemSolution); //trainingSet.kernels[i][i];
}
if (trainingSet.IsKernelCalculated[j])
{
kernel_xj_xj = trainingSet.kernels[j][j];
}
else
{
kernel_xj_xj = calculateSingleKernel(trainingSet.trainingArray[j], trainingSet.trainingArray[j], problemSolution); //trainingSet.kernels[j][j];
}
float eta = 2 * kernel_xi_xj - kernel_xi_xi - kernel_xj_xj;
#endregion
if (eta >= 0)
{
continue;
}
// Compute new alpha_j
alpha_j = alpha_j - yj * (ei - ej) / eta;
// Clip alpha_j if necessary
if (alpha_j > higherBound)
{
alpha_j = higherBound;
}
else if (alpha_j < lowerBound)
{
alpha_j = lowerBound;
}
// If the changes are not big enough, just continue
if (Math.Abs(oldAlpha_j - alpha_j) < MIN_ALPHA_CHANGE)
{
continue;
}
swComputeKernel.Start();
//Needs to compute lines K[i][] and K[j][] since the alphas will change
if (OpenCLTemplate.CLCalc.CLAcceleration == OpenCLTemplate.CLCalc.CLAccelerationType.UsingCL)
{
CLComputeKernels(problemSolution, i);
CLComputeKernels(problemSolution, j);
}
else
{
ComputeKernels(problemSolution, i);
ComputeKernels(problemSolution, j);
}
swComputeKernel.Stop();
// Compute value for alpha_i
alpha_i = alpha_i + yi * yj * (oldAlpha_j - alpha_j);
// Compute b1, b2 and new b (threshold)
float oldB = problemSolution.b;
if (0 < alpha_i && alpha_i < problemConfig.c)
{
// b1 is enough in this case
float b1 = problemSolution.b - ei - yi * (alpha_i - oldAlpha_i) * kernel_xi_xi - yj * (alpha_j - oldAlpha_j) * kernel_xi_xj;
problemSolution.b = b1;
}
else if (0 < alpha_j && alpha_j < problemConfig.c)
{
// b2 is enough in this case
float b2 = problemSolution.b - ej - yi * (alpha_i - oldAlpha_i) * kernel_xi_xj - yj * (alpha_j - oldAlpha_j) * kernel_xj_xj;
problemSolution.b = b2;
}
else
{
// b is the average between b1 and b2
float b1 = problemSolution.b - ei - yi * (alpha_i - oldAlpha_i) * kernel_xi_xi - yj * (alpha_j - oldAlpha_j) * kernel_xi_xj;
float b2 = problemSolution.b - ej - yi * (alpha_i - oldAlpha_i) * kernel_xi_xj - yj * (alpha_j - oldAlpha_j) * kernel_xj_xj;
problemSolution.b = (b1 + b2) * 0.5f;
}
// Update the changed alphas in the solution
problemSolution.alphaList[i] = alpha_i;
problemSolution.alphaList[j] = alpha_j;
// Update errors cache
swUpdateError.Start();
if (OpenCLTemplate.CLCalc.CLAcceleration == OpenCLTemplate.CLCalc.CLAccelerationType.UsingCL)
{
CLupdateErrorsCache(trainingSet, problemSolution, oldAlpha_i, alpha_i, i, oldAlpha_j, alpha_j, j, oldB, problemSolution.b);
}
else
{
updateErrorsCache(trainingSet, problemSolution, oldAlpha_i, alpha_i, i, oldAlpha_j, alpha_j, j, oldB, problemSolution.b);
}
swUpdateError.Stop();
changedAlphas++;
}
}
if (changedAlphas == 0)
{
passes++;
}
else
{
passes = 0;
}
}
return(problemSolution);
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
public MultiClassSVM(TrainingSet TSet)
{
ProblemConfig cfg = new ProblemConfig(2.529822E-8f * (float)Math.Sqrt(TSet.getN), 127.922182f, 1e-3f, 1, ProblemConfig.KernelType.RBF);
initMultiSVM(TSet, cfg);
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
/// <param name="SVMCfg">Configuration parameters</param>
public MultiClassSVM(TrainingSet TSet, ProblemConfig SVMCfg)
{
initMultiSVM(TSet, SVMCfg);
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
public MultiClassSVM(TrainingSet TSet)
{
ProblemConfig cfg = new ProblemConfig(2.529822E-8f * (float)Math.Sqrt(TSet.getN), 127.922182f, 1e-3f, 1, ProblemConfig.KernelType.RBF);
initMultiSVM(TSet, cfg);
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
/// <param name="SVMCfg">Configuration parameters</param>
public MultiClassSVM(TrainingSet TSet, ProblemConfig SVMCfg)
{
initMultiSVM(TSet, SVMCfg);
}
/// <summary>Creates a new multiclass SVM using desired outputs from training set. Classifications -1.0f are negative for all sets</summary>
/// <param name="TSet">Training set</param>
/// <param name="SVMCfg">Configuration parameters</param>
/// <param name="PreCalibrate">Precalibrate RBF parameter lambda? This will ignore the given value</param>
public MultiClassSVM(TrainingSet TSet, ProblemConfig SVMCfg, bool PreCalibrate)
{
initMultiSVM(TSet, SVMCfg, PreCalibrate);
}