public void getSvmModel(string modelPath, bool BuildModel = false)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.SVM)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not a DA!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
InTablePath = sr.ReadLine();
IndependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
DependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
ClassFieldNames = sr.ReadLine().Split(new char[] { ',' });
n = System.Convert.ToInt32(sr.ReadLine());
nvars = System.Convert.ToInt32(sr.ReadLine());
sserror = System.Convert.ToDouble(sr.ReadLine());
kTyp = (KernelType)Enum.Parse(typeof(KernelType), sr.ReadLine());
minValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
maxValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
sumValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
sr.Close();
}
if (BuildModel)
{
string svmMPath = modelPath.Replace(".mdl", ".svm");
MulticlassSupportVectorMachine.Load(svmMPath);
}
}
public void getDaModel(string modelPath, bool BuildModel = false)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.LDA)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not a DA!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
InTablePath = sr.ReadLine();
IndependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
DependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
ClassFieldNames = sr.ReadLine().Split(new char[] { ',' });
n = System.Convert.ToInt32(sr.ReadLine());
nvars = System.Convert.ToInt32(sr.ReadLine());
minValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
maxValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
sumValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
meanValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
stdValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
sr.Close();
}
if (BuildModel)
{
buildModel();
}
}
public void getMlrModel(string modelPath, bool BuildModel = false)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.MvlRegression)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not a Multivariate Linear Regression Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
string ln = sr.ReadLine();
List <dataPrepLinearReg> lrLst = new List <dataPrepLinearReg>();
while (ln != null)
{
dataPrepLinearReg lr = new dataPrepLinearReg();
lr.getLrModel(ln, BuildModel);
lrLst.Add(lr);
ln = sr.ReadLine();
if (IndependentFieldNames == null)
{
IndependentFieldNames = lr.IndependentFieldNames;
DependentFieldNames = lr.DependentFieldNames;
ClassFieldNames = lr.ClassFieldNames;
}
}
sr.Close();
mvr = lrLst.ToArray();
}
}
public void getPlrModel(string modelPath, bool BuildModel = false)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.PLR)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not a PLR Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
InTablePath = sr.ReadLine();
IndependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
DependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
ClassFieldNames = sr.ReadLine().Split(new char[] { ',' });
categories = sr.ReadLine().Split(new char[] { ',' });
nvars = System.Convert.ToInt32(sr.ReadLine());
ncat = System.Convert.ToInt32(sr.ReadLine());
delta = System.Convert.ToDouble(sr.ReadLine());
iteration = System.Convert.ToInt32(sr.ReadLine());
loglikelihood = System.Convert.ToDouble(sr.ReadLine());
deviance = System.Convert.ToDouble(sr.ReadLine());
x2 = System.Convert.ToDouble(sr.ReadLine());
pv = System.Convert.ToDouble(sr.ReadLine());
minValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
maxValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
sumValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
coefficients = new double[ncat - 1][];
standarderror = new double[ncat - 1][];
waldstat = new double[ncat - 1][];
waldpvalue = new double[ncat - 1][];
double[] vlArr = new double[nvars + 1];
for (int i = 0; i < ncat - 1; i++)
{
vlArr = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
coefficients[i] = vlArr;
}
for (int i = 0; i < ncat - 1; i++)
{
vlArr = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
standarderror[i] = vlArr;
}
for (int i = 0; i < ncat - 1; i++)
{
vlArr = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
waldstat[i] = vlArr;
}
for (int i = 0; i < ncat - 1; i++)
{
vlArr = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
waldpvalue[i] = vlArr;
}
sr.Close();
if (BuildModel)
{
buildModel();
}
}
}
public void getModel(string modelPath)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.CompareClassifications)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not an Accuracy Assessment!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
string fieldNames = sr.ReadLine();
string[] fieldNamesSplit = fieldNames.Split(new char[] { ',' });
IndependentFieldNames = new string[] { fieldNamesSplit[1], fieldNamesSplit[2] };
DependentFieldNames = new string[] { fieldNamesSplit[0] };
ClassFieldNames = fieldNamesSplit;
labels = sr.ReadLine().Split(new char[] { ',' }).ToList();
mctable = new int[2, 2];
g1table = new int[labels.Count, labels.Count];
g2table = new int[labels.Count, labels.Count];
g3table = new int[labels.Count, labels.Count];
string ln = sr.ReadLine();
int[] intArr = (from string s in ln.Split(new char[] { ',' }) select System.Convert.ToInt32(s)).ToArray();
int cnt = 0;
for (int i = 0; i < 2; i++)
{
for (int j = 0; j < 2; j++)
{
mctable[i, j] = intArr[cnt];
cnt++;
}
}
ln = sr.ReadLine();
int[] intArr1 = (from string s in ln.Split(new char[] { ',' }) select System.Convert.ToInt32(s)).ToArray();
ln = sr.ReadLine();
int[] intArr2 = (from string s in ln.Split(new char[] { ',' }) select System.Convert.ToInt32(s)).ToArray();
ln = sr.ReadLine();
int[] intArr3 = (from string s in ln.Split(new char[] { ',' }) select System.Convert.ToInt32(s)).ToArray();
cnt = 0;
for (int i = 0; i < labels.Count; i++)
{
for (int j = 0; j < labels.Count; j++)
{
g1table[i, j] = intArr1[cnt];
g2table[i, j] = intArr2[cnt];
g3table[i, j] = intArr3[cnt];
cnt++;
}
}
sr.Close();
}
buildModel();
}
public alglib.logitmodel getMnlModel(string modelPath)
{
double[,] coef = null;
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.SoftMax)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not a SoftMax Nnet Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return(null);
}
InTablePath = sr.ReadLine();
n = System.Convert.ToInt32(sr.ReadLine());
IndependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
DependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
ClassFieldNames = sr.ReadLine().Split(new char[] { ',' });
categories = sr.ReadLine().Split(new char[] { ',' });
nvars = System.Convert.ToInt32(sr.ReadLine());
nclasses = System.Convert.ToInt32(sr.ReadLine());
rmse = System.Convert.ToDouble(sr.ReadLine());
avgcee = System.Convert.ToDouble(sr.ReadLine());
avge = System.Convert.ToDouble(sr.ReadLine());
avgre = System.Convert.ToDouble(sr.ReadLine());
clse = System.Convert.ToDouble(sr.ReadLine());
rce = System.Convert.ToDouble(sr.ReadLine());
minValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
maxValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
sumValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
coef = new double[nclasses - 1, nvars + 1];
string ln = sr.ReadLine();
int lnCnt = 0;
while (ln != null)
{
string[] lnArr = ln.Split(new char[] { ' ' });
for (int i = 0; i < lnArr.Length; i++)
{
coef[i, lnCnt] = System.Convert.ToDouble(lnArr[i]);
}
lnCnt += 1;
ln = sr.ReadLine();
}
sr.Close();
}
alglib.mnlpack(coef, nvars, nclasses, out lm);
return(lm);
}
public void SwitchToMale(bool reset = false)
{
if (!_maleModel || reset)
{
_maleModel = Instantiate(m_MalePrefab);
_maleModel.transform.position = m_SpawnPosition;
}
if (_currentSpawnedModel)
{
_currentSpawnedModel.SetActive(false);
}
_maleModel.SetActive(true);
_currentSpawnedModel = _maleModel;
_currentModelType = modelTypes.male;
UpdateHighlightJoints();
}
public void getLrModel(string modelPath, bool BuildModel = false)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.LogisticRegression)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not a Logistic Regression Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
InTablePath = sr.ReadLine();
IndependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
DependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
ClassFieldNames = sr.ReadLine().Split(new char[] { ',' });
categories = sr.ReadLine().Split(new char[] { ',' });
nvars = System.Convert.ToInt32(sr.ReadLine());
ncat = System.Convert.ToInt32(sr.ReadLine());
loglikelihood = System.Convert.ToDouble(sr.ReadLine());
deviance = System.Convert.ToDouble(sr.ReadLine());
x2 = System.Convert.ToDouble(sr.ReadLine());
pv = System.Convert.ToDouble(sr.ReadLine());
minValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
maxValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
sumValues = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
coefficients = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
standarderror = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
waldstat = new Accord.Statistics.Testing.WaldTest[coefficients.Length];
double[] waldStatVl = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
double[] waldStatPVl = (from string s in (sr.ReadLine().Split(new char[] { ',' })) select System.Convert.ToDouble(s)).ToArray();
for (int i = 0; i < waldStatVl.Length; i++)
{
double wVl = waldStatVl[i];
Accord.Statistics.Testing.WaldTest wt = new Accord.Statistics.Testing.WaldTest(wVl);
waldstat[i] = wt;
}
sr.Close();
if (BuildModel)
{
buildModel();
}
}
}
public void getLrModel(string modelPath, bool BuildModel = false)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.LinearRegression)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not a Linear Regression Model!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
InTablePath = sr.ReadLine();
IndependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
DependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
ClassFieldNames = sr.ReadLine().Split(new char[] { ',' });
n = System.Convert.ToInt32(sr.ReadLine());
nvars = System.Convert.ToInt32(sr.ReadLine());
string vl = sr.ReadLine();
if (vl.ToLower() == "false")
{
intOrigin = false;
}
else
{
intOrigin = true;
}
rmse = System.Convert.ToDouble(sr.ReadLine());
ftest = System.Convert.ToDouble(sr.ReadLine());
pv = System.Convert.ToDouble(sr.ReadLine());
r2 = System.Convert.ToDouble(sr.ReadLine());
ar2 = System.Convert.ToDouble(sr.ReadLine());
coefficients = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
standarderrors = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
minValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
maxValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
sumValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
sr.Close();
}
if (BuildModel)
{
buildModel();
}
}
public void getXTable(string modelPath)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.Accuracy)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not an Accuracy Assessment!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
string fieldNames = sr.ReadLine();
string[] fieldNamesSplit = fieldNames.Split(new char[] { ',' });
IndependentFieldNames = new string[] { fieldNamesSplit[1] };
DependentFieldNames = new string[] { fieldNamesSplit[0] };
ClassFieldNames = fieldNamesSplit;
labels = sr.ReadLine().Split(new char[] { ',' }).ToList();
double overall = System.Convert.ToDouble(sr.ReadLine());
double k = System.Convert.ToDouble(sr.ReadLine());
double kerr = System.Convert.ToDouble(sr.ReadLine());
xtable = new int[labels.Count, labels.Count];
string ln = sr.ReadLine();
int lnCnt = 0;
while (ln != null)
{
string[] lnArr = ln.Split(new char[] { ' ' });
for (int i = 0; i < lnArr.Length; i++)
{
xtable[i, lnCnt] = System.Convert.ToInt32(lnArr[i]);
}
lnCnt += 1;
ln = sr.ReadLine();
}
sr.Close();
}
buildModel();
}
public void getGlmModel(string modelPath, bool BuildModel = false)
{
using (System.IO.StreamReader sr = new System.IO.StreamReader(modelPath))
{
string mType = sr.ReadLine();
modelTypes m = (modelTypes)Enum.Parse(typeof(modelTypes), mType);
if (m != modelTypes.GLM)
{
System.Windows.Forms.MessageBox.Show("Model file specified is not a GLM!!", "Error", System.Windows.Forms.MessageBoxButtons.OK, System.Windows.Forms.MessageBoxIcon.Error);
return;
}
InTablePath = sr.ReadLine();
IndependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
DependentFieldNames = sr.ReadLine().Split(new char[] { ',' });
ClassFieldNames = sr.ReadLine().Split(new char[] { ',' });
n = System.Convert.ToInt32(sr.ReadLine());
nvars = System.Convert.ToInt32(sr.ReadLine());
iterations = System.Convert.ToInt32(sr.ReadLine());
delta = System.Convert.ToDouble(sr.ReadLine());
loglikelihood = System.Convert.ToDouble(sr.ReadLine());
loglikelihoodratio = System.Convert.ToDouble(sr.ReadLine());
pv = System.Convert.ToDouble(sr.ReadLine());
deviance = System.Convert.ToDouble(sr.ReadLine());
chisqr = System.Convert.ToDouble(sr.ReadLine());
linkfunction = (LinkFunction)Enum.Parse(typeof(LinkFunction), sr.ReadLine());
coefficients = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
stdError = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
waldTestValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
waldTestPValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
minValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
maxValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
sumValues = (from string s in (sr.ReadLine().Split(new char[] { ' ' })) select System.Convert.ToDouble(s)).ToArray();
sr.Close();
}
if (BuildModel)
{
Accord.Statistics.Links.ILinkFunction lFunc = new Accord.Statistics.Links.IdentityLinkFunction();
switch (Link)
{
case LinkFunction.Absolute:
lFunc = new Accord.Statistics.Links.AbsoluteLinkFunction();
break;
case LinkFunction.Cauchit:
lFunc = new Accord.Statistics.Links.CauchitLinkFunction();
break;
case LinkFunction.Inverse:
lFunc = new Accord.Statistics.Links.InverseLinkFunction();
break;
case LinkFunction.InverseSquared:
lFunc = new Accord.Statistics.Links.InverseSquaredLinkFunction();
break;
case LinkFunction.Logit:
lFunc = new Accord.Statistics.Links.LogitLinkFunction();
break;
case LinkFunction.Log:
lFunc = new Accord.Statistics.Links.LogLinkFunction();
break;
case LinkFunction.LogLog:
lFunc = new Accord.Statistics.Links.LogLogLinkFunction();
break;
case LinkFunction.Probit:
lFunc = new Accord.Statistics.Links.ProbitLinkFunction();
break;
case LinkFunction.Sin:
lFunc = new Accord.Statistics.Links.SinLinkFunction();
break;
case LinkFunction.Threshold:
lFunc = new Accord.Statistics.Links.ThresholdLinkFunction();
break;
default:
break;
}
glm = new Accord.Statistics.Models.Regression.GeneralizedLinearRegression(lFunc, coefficients, stdError);
}
}
