public bool LoadModelFromFile()
{
var filePath = Environment.GetFolderPath(Environment.SpecialFolder.Desktop);
if (!File.Exists($"{filePath}//ModelMean.csv"))
{
return(false);
}
if (!File.Exists($"{filePath}//ModelCov.csv"))
{
return(false);
}
ModelMean.Clear();
ModelCov.Clear();
StreamReader fileReader_mean = new StreamReader($"{filePath}//ModelMean.csv");
string strLine = "";
while (strLine != null)
{
strLine = fileReader_mean.ReadLine();
if (!string.IsNullOrEmpty(strLine))
{
List <double> tempDoubles = new List <double>();
var temp = strLine.Split(',');
foreach (var str in temp)
{
double tempdouble = 0f;
double.TryParse(str, out tempdouble);
if (Math.Abs(tempdouble) > 0)
{
tempDoubles.Add(tempdouble);
}
}
ModelMean.Add(tempDoubles);
}
}
fileReader_mean.Close();
StreamReader fileReader_cov = new StreamReader($"{filePath}//ModelCov.csv");
string strLine_cov = "";
while (strLine_cov != null)
{
strLine_cov = fileReader_cov.ReadLine();
if (!string.IsNullOrEmpty(strLine_cov))
{
List <double> tempDoubles = new List <double>();
var temp = strLine_cov.Split(',');
foreach (var str in temp)
{
double tempdouble = 0f;
double.TryParse(str, out tempdouble);
if (Math.Abs(tempdouble) > 0)
{
tempDoubles.Add(tempdouble);
}
}
ModelCov.Add(tempDoubles);
}
}
fileReader_cov.Close();
for (var i = 0; i < ModelMean.Count; i++)
{
_mClassLabel.Add(i);
}
return(true);
}
private bool BayesTrain(List <List <double> > feature, List <int> label)
{
var featNum = feature.Count;
var featDim = feature[0].Count;
var labNum = label.Count;
if (labNum != featNum)
{
return(false);
}
_mClassLabel.Clear();
for (var i = 0; i < GestureNumber; i++)
{
_mClassLabel.Add(i);
}
Matrix <double> featMat = new DenseMatrix(featNum, featDim);
for (var i = 0; i < featNum; i++)
{
for (var j = 0; j < featDim; j++)
{
featMat[i, j] = feature[i][j];
}
}
var cNum = _mClassLabel.Count;
Matrix <double> meanMat = new DenseMatrix(cNum, featDim);
Matrix <double> covMat = new DenseMatrix(featDim * cNum, featDim);
Matrix <double> poolCovMat = new DenseMatrix(featDim, featDim);
Vector <double> numPerClass = new DenseVector(cNum);
// compute the mean vector for each class
for (var i = 0; i < featNum; i++)
{
for (var j = 0; j < cNum; j++)
{
if (label[i] != _mClassLabel[j])
{
continue;
}
meanMat.SetRow(j, meanMat.Row(j) + featMat.Row(i));
numPerClass.At(j, numPerClass.At(j) + 1);
}
}
for (var i = 0; i < cNum; i++)
{
meanMat.SetRow(i, meanMat.Row(i) / numPerClass.At(i));
}
//compute the covariance matrix for each class and pool covariance matrix
for (var i = 0; i < featNum; i++)
{
for (var j = 0; j < cNum; j++)
{
if (label[i] == _mClassLabel[j])
{
covMat.SetSubMatrix(j * featDim, featDim, 0, featDim,
covMat.SubMatrix(j * featDim, featDim, 0, featDim) +
(featMat.Row(i) - meanMat.Row(j)).OuterProduct(featMat.Row(i) - meanMat.Row(j)));
}
}
}
for (var i = 0; i < cNum; i++)
{
poolCovMat += covMat.SubMatrix(i * featDim, featDim, 0, featDim);
//PoolCovMat += CovMat.block(i* feat_dim,0,feat_dim,feat_dim);
covMat.SetSubMatrix(i * featDim, featDim, 0, featDim,
covMat.SubMatrix(i * featDim, featDim, 0, featDim) / (numPerClass.At(i) - 1));
//CovMat.block(i* feat_dim,0,feat_dim,feat_dim)=CovMat.block(i* feat_dim,0,feat_dim,feat_dim)/(feat_num_perclass(i)-1);
}
poolCovMat /= featNum - cNum;
poolCovMat = poolCovMat.Inverse();
//transform the data format from Eigen to member vectors
ModelMean?.Clear();
ModelCov?.Clear();
List <double> temp;
for (var i = 0; i < cNum; i++)
{
temp = new List <double>();
for (var j = 0; j < featDim; j++)
{
temp.Add(meanMat[i, j]);
}
ModelMean?.Add(temp);
}
for (var i = 0; i < featDim; i++)
{
temp = new List <double>();
for (var j = 0; j < featDim; j++)
{
temp.Add(poolCovMat[i, j]);
}
ModelCov?.Add(temp);
}
return(true);
}