/*
* /// <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>Adds training units to a set from a file</summary>
/// <param name="filename">File containing features</param>
/// <param name="TrSet">Training set to be populated</param>
private void FillTrainingSet(string filename, TrainingSet TrSet)
{
string sepdec = (1.5).ToString().Substring(1, 1);
using (StreamReader sr = new StreamReader(filename))
{
string line;
line = sr.ReadLine();
int n = int.Parse(line);
line = sr.ReadLine();
int dim = (int)Math.Sqrt(double.Parse(line));
for (int i = 0; i < n; i++)
{
line = sr.ReadLine().Replace(".", sepdec);
string[] s = line.Split(new string[] { " " }, StringSplitOptions.RemoveEmptyEntries);
float[] x = new float[364];
float y;
for (int j = 0; j < s.Length - 1; j++)
{
x[j] = float.Parse(s[j]);
}
y = float.Parse(s[s.Length - 1]);
TrSet.addTrainingUnit(new TrainingUnit(x, y));
/*
* Features Haar
* [04:05:53] Edmundo ITA05(FOX.Howler): 1,1
* [04:05:57] Edmundo ITA05(FOX.Howler): 7,4
* [04:06:55] Edmundo ITA05(FOX.Howler): new Rectangle(1, 1, 7, 4)
* [04:07:06] Edmundo ITA05(FOX.Howler): 7 x 4
* [04:08:15] Edmundo ITA05(FOX.Howler): new Rectangle(11, 4, 7, 4)
* [04:08:24] Edmundo ITA05(FOX.Howler): divide por 7*4
* [04:09:04] Edmundo ITA05(FOX.Howler): subtrai da área do Rect(8, 1, 3, 4)
* [04:09:13] Edmundo ITA05(FOX.Howler): dividido pela 3 *4
*
*
*
* [04:05:53] Edmundo ITA05(FOX.Howler): 1,1
* [04:05:57] Edmundo ITA05(FOX.Howler): 7,4
* [04:06:55] Edmundo ITA05(FOX.Howler): new Rectangle(1, 1, 7, 4)
* [04:07:06] Edmundo ITA05(FOX.Howler): 7 x 4
* [04:08:15] Edmundo ITA05(FOX.Howler): new Rectangle(11, 4, 7, 4)
* [04:08:24] Edmundo ITA05(FOX.Howler): divide por 7*4
* [04:09:04] Edmundo ITA05(FOX.Howler): subtrai da área do Rect(8, 1, 3, 4)
* [04:09:13] Edmundo ITA05(FOX.Howler): dividido pela 3 *4
* [04:13:38] Edmundo ITA05(FOX.Howler): ((1,6,5,5) + (13,6,5,5)) / 2 - (7,6,5,6)
*
*
* [04:17:15] Edmundo ITA05(FOX.Howler): (1,1,17,5) - (1,6,17,8) dividir pela area
*/
}
}
}
/// <summary>Extracts a cross validation set from a given set</summary>
/// <param name="Set">Set to extract cross validation from</param>
/// <param name="CrossValidationSetPercent">Percentage of elements to extract</param>
public static TrainingSet GetCrossValidationSet(TrainingSet Set, float CrossValidationSetPercent)
{
TrainingSet CrossValidationSet = new TrainingSet();
int nCrossSet = (int)(CrossValidationSetPercent * (float)Set.getN);
Random rnd = new Random();
for (int i = 0; i < nCrossSet; i++)
{
int ind = rnd.Next(0, Set.trainingArray.Count - 1);
TrainingUnit u = Set.trainingArray[ind];
Set.trainingArray.Remove(u);
CrossValidationSet.addTrainingUnit(u);
}
return CrossValidationSet;
}
/// <summary>Extracts a cross validation set from a given set</summary>
/// <param name="Set">Set to extract cross validation from</param>
/// <param name="CrossValidationSetPercent">Percentage of elements to extract</param>
public static TrainingSet GetCrossValidationSet(TrainingSet Set, float CrossValidationSetPercent)
{
TrainingSet CrossValidationSet = new TrainingSet();
int nCrossSet = (int)(CrossValidationSetPercent * (float)Set.getN);
Random rnd = new Random();
for (int i = 0; i < nCrossSet; i++)
{
int ind = rnd.Next(0, Set.trainingArray.Count - 1);
TrainingUnit u = Set.trainingArray[ind];
Set.trainingArray.Remove(u);
CrossValidationSet.addTrainingUnit(u);
}
return(CrossValidationSet);
}
/// <summary>Adds a new self training example</summary>
public void AddSelfTraining(int[] sbFrames, int faceIndex, Bitmap bmp)
{
if (SelfTSet == null)
{
SelfTSet = new TrainingSet();
}
float[] subF = new float[(sbFrames.Length / 3) * 364];
ExtractFeatures(sbFrames, subF, bmp);
for (int i = 0; i < sbFrames.Length / 3; i++)
{
float[] x = new float[364];
for (int k = 0; k < 364; k++)
{
x[k] = subF[k + i * 364];
}
TrainingUnit tu = new TrainingUnit(x, i == faceIndex ? 1.0f : -1.0f);
SelfTSet.addTrainingUnit(tu);
}
}
/// <summary>Adds training units to a set from a file</summary>
/// <param name="filename">File containing features</param>
/// <param name="TrSet">Training set to be populated</param>
private void FillTrainingSet(string filename, TrainingSet TrSet)
{
string sepdec = (1.5).ToString().Substring(1, 1);
using (StreamReader sr = new StreamReader(filename))
{
string line;
line = sr.ReadLine();
int n = int.Parse(line);
line = sr.ReadLine();
int dim = (int)Math.Sqrt(double.Parse(line));
for (int i = 0; i < n; i++)
{
line = sr.ReadLine().Replace(".", sepdec);
string[] s = line.Split(new string[] { " " }, StringSplitOptions.RemoveEmptyEntries);
float[] x = new float[364];
float y;
for (int j = 0; j < s.Length - 1; j++) x[j] = float.Parse(s[j]);
y = float.Parse(s[s.Length - 1]);
TrSet.addTrainingUnit(new TrainingUnit(x, y));
/*
* Features Haar
* [04:05:53] Edmundo ITA05(FOX.Howler): 1,1
[04:05:57] Edmundo ITA05(FOX.Howler): 7,4
[04:06:55] Edmundo ITA05(FOX.Howler): new Rectangle(1, 1, 7, 4)
[04:07:06] Edmundo ITA05(FOX.Howler): 7 x 4
[04:08:15] Edmundo ITA05(FOX.Howler): new Rectangle(11, 4, 7, 4)
[04:08:24] Edmundo ITA05(FOX.Howler): divide por 7*4
[04:09:04] Edmundo ITA05(FOX.Howler): subtrai da área do Rect(8, 1, 3, 4)
[04:09:13] Edmundo ITA05(FOX.Howler): dividido pela 3 *4
*
*
*
* [04:05:53] Edmundo ITA05(FOX.Howler): 1,1
[04:05:57] Edmundo ITA05(FOX.Howler): 7,4
[04:06:55] Edmundo ITA05(FOX.Howler): new Rectangle(1, 1, 7, 4)
[04:07:06] Edmundo ITA05(FOX.Howler): 7 x 4
[04:08:15] Edmundo ITA05(FOX.Howler): new Rectangle(11, 4, 7, 4)
[04:08:24] Edmundo ITA05(FOX.Howler): divide por 7*4
[04:09:04] Edmundo ITA05(FOX.Howler): subtrai da área do Rect(8, 1, 3, 4)
[04:09:13] Edmundo ITA05(FOX.Howler): dividido pela 3 *4
[04:13:38] Edmundo ITA05(FOX.Howler): ((1,6,5,5) + (13,6,5,5)) / 2 - (7,6,5,6)
*
*
* [04:17:15] Edmundo ITA05(FOX.Howler): (1,1,17,5) - (1,6,17,8) dividir pela area
*/
}
}
}
/// <summary>Trains current SVM with cross-validation, adjusting kernel parameter lambda and box parameter C. Returns best performance so far</summary>
/// <param name="CrossValidationSetPercent">Percentage of training examples that should be used as cross validation set</param>
/// <param name="lambdaSet">Values of lambda to try</param>
/// <param name="CSet">Values of c to try</param>
public float TrainWithCrossValidation(float CrossValidationSetPercent, float[] lambdaSet, float[] CSet)
{
if (alphaList == null || alphaList.Count != TrainingSet.getN)
{
//Problem changed, previous values dont make sense
initializeWithZeros();
CrossValParams = null;
}
#region Constructs cross validation set
TrainingSet CrossValidationSet = new TrainingSet();
int nCrossSet = (int)(CrossValidationSetPercent * (float)this.TrainingSet.getN);
Random rnd = new Random();
for (int i = 0; i < nCrossSet; i++)
{
int ind = rnd.Next(0, this.TrainingSet.trainingArray.Count - 1);
TrainingUnit u = this.TrainingSet.trainingArray[ind];
this.TrainingSet.trainingArray.Remove(u);
CrossValidationSet.addTrainingUnit(u);
}
#endregion
#region Loops through lambdas and Cs and finds maximum crossvalidation
foreach (float _lambda in lambdaSet)
{
this.ProblemCfg.lambda = _lambda;
this.initializeWithZeros();
PreComputeKernels();
foreach (float _c in CSet)
{
this.ProblemCfg.c = _c;
//ProblemSolver.solveSMOStartingFromPreviousSolution(this);
ProblemSolver.solveSMOStartingFromZero(this);
float performance = this.GetHitRate(CrossValidationSet);
if (CrossValParams == null) CrossValParams = new float[3];
if (performance > CrossValParams[0])
{
CrossValParams[0] = performance;
CrossValParams[1] = _lambda;
CrossValParams[2] = _c;
}
}
}
#endregion
#region Trains with best parameters so far
this.ProblemCfg.lambda = CrossValParams[1];
this.ProblemCfg.c = CrossValParams[2];
this.Train();
#endregion
return CrossValParams[0];
}
/// <summary>Attempts to pre-calibrate configuration parameters.
/// Finds an alpha that enhances similarities between positive examples
/// and reduces similarities between positive and negative examples.
/// Assumes that decreasing lambda increases kernel match.
/// </summary>
/// <param name="tolPositive">Positive kernels average should be greater than tolPositive</param>
/// <param name="tolNegative">Negative kernels average should be lesser than tolNegative</param>
public void PreCalibrateCfg(float tolPositive, float tolNegative)
{
#region Checks if there are positive and negative examples
bool posSamples = false; bool negSamples = false;
for (int i = 0; i < TrainingSet.trainingArray.Count; i++)
{
if (TrainingSet.trainingArray[i].y > 0) posSamples = true;
if (TrainingSet.trainingArray[i].y < 0) negSamples = true;
if (posSamples && negSamples) i = TrainingSet.trainingArray.Count;
}
if ((!posSamples) || (!negSamples)) throw new Exception("Training set must contain positive and negative samples");
#endregion
Random rnd = new Random();
int nSet = (int)(20 * Math.Log(TrainingSet.getN, 2));
TrainingSet PositiveExamples1 = new TrainingSet();
TrainingSet PositiveExamples2 = new TrainingSet();
TrainingSet NegativeExamples = new TrainingSet();
//Kernel average for positive and negative samples
float positiveAvg = 0, negativeAvg = 0;
float invN = 1 / (float)nSet;
int count = 0;
float bestLambda = ProblemCfg.lambda;
float maxPosNegAvg = -1.0f;
while ((positiveAvg <= tolPositive || negativeAvg >= tolNegative) && count < nSet)
{
//Populates training sets
PositiveExamples1.trainingArray.Clear();
PositiveExamples2.trainingArray.Clear();
NegativeExamples.trainingArray.Clear();
while (PositiveExamples1.getN < nSet || PositiveExamples2.getN < nSet || NegativeExamples.getN < nSet)
{
TrainingUnit tu = TrainingSet.trainingArray[rnd.Next(TrainingSet.trainingArray.Count - 1)];
if (tu.y > 0 && PositiveExamples1.getN < nSet)
PositiveExamples1.addTrainingUnit(tu);
else if (tu.y > 0 && PositiveExamples2.getN < nSet)
PositiveExamples2.addTrainingUnit(tu);
if (tu.y < 0 && NegativeExamples.getN < nSet) NegativeExamples.addTrainingUnit(tu);
}
count++;
positiveAvg = 0;
negativeAvg = 0;
for (int i = 0; i < nSet; i++)
{
positiveAvg += ProblemSolver.calculateSingleKernel(PositiveExamples1.trainingArray[i], PositiveExamples2.trainingArray[i], this);
negativeAvg += ProblemSolver.calculateSingleKernel(PositiveExamples1.trainingArray[i], NegativeExamples.trainingArray[i], this);
}
positiveAvg *= invN;
negativeAvg *= invN;
if (maxPosNegAvg < positiveAvg - negativeAvg)
{
bestLambda = ProblemCfg.lambda;
maxPosNegAvg = positiveAvg - negativeAvg;
}
//Desired: positiveAvg=1, negativeAvg = 0
if (positiveAvg <= tolPositive) this.ProblemCfg.lambda *= 0.15f;
else if (negativeAvg >= tolNegative) this.ProblemCfg.lambda *= 1.2f;
}
ProblemCfg.lambda = bestLambda;
}
/*
/// <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>Attempts to pre-calibrate configuration parameters.
/// Finds an alpha that enhances similarities between positive examples
/// and reduces similarities between positive and negative examples.
/// Assumes that decreasing lambda increases kernel match.
/// </summary>
/// <param name="tolPositive">Positive kernels average should be greater than tolPositive</param>
/// <param name="tolNegative">Negative kernels average should be lesser than tolNegative</param>
public void PreCalibrateCfg(float tolPositive, float tolNegative)
{
#region Checks if there are positive and negative examples
bool posSamples = false; bool negSamples = false;
for (int i = 0; i < TrainingSet.trainingArray.Count; i++)
{
if (TrainingSet.trainingArray[i].y > 0)
{
posSamples = true;
}
if (TrainingSet.trainingArray[i].y < 0)
{
negSamples = true;
}
if (posSamples && negSamples)
{
i = TrainingSet.trainingArray.Count;
}
}
if ((!posSamples) || (!negSamples))
{
throw new Exception("Training set must contain positive and negative samples");
}
#endregion
Random rnd = new Random();
int nSet = (int)(20 * Math.Log(TrainingSet.getN, 2));
TrainingSet PositiveExamples1 = new TrainingSet();
TrainingSet PositiveExamples2 = new TrainingSet();
TrainingSet NegativeExamples = new TrainingSet();
//Kernel average for positive and negative samples
float positiveAvg = 0, negativeAvg = 0;
float invN = 1 / (float)nSet;
int count = 0;
float bestLambda = ProblemCfg.lambda;
float maxPosNegAvg = -1.0f;
while ((positiveAvg <= tolPositive || negativeAvg >= tolNegative) && count < nSet)
{
//Populates training sets
PositiveExamples1.trainingArray.Clear();
PositiveExamples2.trainingArray.Clear();
NegativeExamples.trainingArray.Clear();
while (PositiveExamples1.getN < nSet || PositiveExamples2.getN < nSet || NegativeExamples.getN < nSet)
{
TrainingUnit tu = TrainingSet.trainingArray[rnd.Next(TrainingSet.trainingArray.Count - 1)];
if (tu.y > 0 && PositiveExamples1.getN < nSet)
{
PositiveExamples1.addTrainingUnit(tu);
}
else if (tu.y > 0 && PositiveExamples2.getN < nSet)
{
PositiveExamples2.addTrainingUnit(tu);
}
if (tu.y < 0 && NegativeExamples.getN < nSet)
{
NegativeExamples.addTrainingUnit(tu);
}
}
count++;
positiveAvg = 0;
negativeAvg = 0;
for (int i = 0; i < nSet; i++)
{
positiveAvg += ProblemSolver.calculateSingleKernel(PositiveExamples1.trainingArray[i], PositiveExamples2.trainingArray[i], this);
negativeAvg += ProblemSolver.calculateSingleKernel(PositiveExamples1.trainingArray[i], NegativeExamples.trainingArray[i], this);
}
positiveAvg *= invN;
negativeAvg *= invN;
if (maxPosNegAvg < positiveAvg - negativeAvg)
{
bestLambda = ProblemCfg.lambda;
maxPosNegAvg = positiveAvg - negativeAvg;
}
//Desired: positiveAvg=1, negativeAvg = 0
if (positiveAvg <= tolPositive)
{
this.ProblemCfg.lambda *= 0.15f;
}
else if (negativeAvg >= tolNegative)
{
this.ProblemCfg.lambda *= 1.2f;
}
}
ProblemCfg.lambda = bestLambda;
}
/// <summary>Trains current SVM with cross-validation, adjusting kernel parameter lambda and box parameter C. Returns best performance so far</summary>
/// <param name="CrossValidationSetPercent">Percentage of training examples that should be used as cross validation set</param>
/// <param name="lambdaSet">Values of lambda to try</param>
/// <param name="CSet">Values of c to try</param>
public float TrainWithCrossValidation(float CrossValidationSetPercent, float[] lambdaSet, float[] CSet)
{
if (alphaList == null || alphaList.Count != TrainingSet.getN)
{
//Problem changed, previous values dont make sense
initializeWithZeros();
CrossValParams = null;
}
#region Constructs cross validation set
TrainingSet CrossValidationSet = new TrainingSet();
int nCrossSet = (int)(CrossValidationSetPercent * (float)this.TrainingSet.getN);
Random rnd = new Random();
for (int i = 0; i < nCrossSet; i++)
{
int ind = rnd.Next(0, this.TrainingSet.trainingArray.Count - 1);
TrainingUnit u = this.TrainingSet.trainingArray[ind];
this.TrainingSet.trainingArray.Remove(u);
CrossValidationSet.addTrainingUnit(u);
}
#endregion
#region Loops through lambdas and Cs and finds maximum crossvalidation
foreach (float _lambda in lambdaSet)
{
this.ProblemCfg.lambda = _lambda;
this.initializeWithZeros();
PreComputeKernels();
foreach (float _c in CSet)
{
this.ProblemCfg.c = _c;
//ProblemSolver.solveSMOStartingFromPreviousSolution(this);
ProblemSolver.solveSMOStartingFromZero(this);
float performance = this.GetHitRate(CrossValidationSet);
if (CrossValParams == null)
{
CrossValParams = new float[3];
}
if (performance > CrossValParams[0])
{
CrossValParams[0] = performance;
CrossValParams[1] = _lambda;
CrossValParams[2] = _c;
}
}
}
#endregion
#region Trains with best parameters so far
this.ProblemCfg.lambda = CrossValParams[1];
this.ProblemCfg.c = CrossValParams[2];
this.Train();
#endregion
return(CrossValParams[0]);
}