internal double CalEstFitness(EnvironmentVar enVar)
{
DenseMatrix m = new DenseMatrix((int)enVar.pmProblem["NumOfCE"], _testSizeOfCEx);
DenseMatrix mInputVectors = new DenseMatrix(_testSizeOfCEx, _dimension);
Vector <double> vCoverPointCounts = Vector.Build.Dense((int)enVar.pmProblem["NumOfCE"]);
int estNumOfCEInSamples = 0;
int duplicateVectsInSamples = 0;
for (int i = 0; i < _testSizeOfCEx; i++)
{
Vector <double> vRandVect = GetRandVect(enVar);
if (mInputVectors.EnumerateRows().Contains(vRandVect))
{
duplicateVectsInSamples = duplicateVectsInSamples = 1;
}
else
{
mInputVectors.SetRow(i, vRandVect);
}
double[] coverPoints = null;
lock (SUT._locker)
{
coverPoints = SUT.RunSUT(enVar, vRandVect.ToArray <double>());
}
if ((int)coverPoints[ceIndex] == 1)
{
estNumOfCEInSamples = estNumOfCEInSamples + 1;
}
}
double f1 = estNumOfCEInSamples * 1.0 / (_testSizeOfCEx); //Question: Should I consider Duplicates
return(f1);
}
private void button1_Click(object sender, EventArgs e)
{
int numOfRuns = (int)numericUpDown1.Value;
chart1.Series.Remove(chart1.Series[0]);
Random rnd = new Random();
for (int i = 0; i < numOfRuns; i++)
{
chart1.Series.Add("Fitness_" + i.ToString());
chart1.Series[i].ChartType = System.Windows.Forms.DataVisualization
.Charting.SeriesChartType.Point;
chart1.Series[i].Color = Color.FromArgb(
rnd.Next(1, 255),
rnd.Next(1, 255),
rnd.Next(1, 255));
}
timer1.Start();
globalWatch.Start();
record = new Record(numOfRuns);
EnvironmentVar[] enVars = new EnvironmentVar[numOfRuns];
App frontApp = new App();
frontApp.Use <SUTInitialization>((int)numericUpDown2.Value, record);
frontApp.Use <TaskDistributor>(enVars, numOfRuns, record, Convert.ToInt16(textBox2.Text));
frontApp.Use <Monitor>(record, enVars);
frontApp.GetAppFunc().Invoke(null);
}
internal Vector <double> GetRandVect(EnvironmentVar enVar)
{
int[] selIndexArry = new int[_dimension];
double[] lowbounds = new double[_dimension];
double[] highbounds = new double[_dimension];
for (int o = 0; o < _dimension; o++)
{
lowbounds[o] = ((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[o].Item1;
highbounds[o] = ((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[o].Item2;
}
// First, Select a block
for (int i = 0; i < _dimension; i++)
{
double lowIndex = 0.0;
double sumOfWeight = weights.ColumnSums()[i];
double rndNum = enVar.rnd.NextDouble() * sumOfWeight;
for (int j = 0; j < _numOfParams; j++)
{
if (rndNum >= lowIndex && rndNum <= lowIndex + weights[j, i])
{
selIndexArry[i] = j;
break;
}
lowIndex = lowIndex + weights[j, i];
}
}
double[] result = new double[_dimension];
for (int i = 0; i < _dimension; i++)
{
if (selIndexArry[i] == 0)
{
result[i] = enVar.rnd.Next((int)lowbounds[i], (int)thetaDelta[i, 0]);
}
else if (selIndexArry[i] == _numOfParams)
{
double min = thetaDelta.Column(i).Sum()
- thetaDelta.Row(_numOfParams - 1).ElementAt(i);
result[i] = enVar.rnd.Next((int)min, (int)highbounds[i]);
}
else
{
double min = thetaDelta.Column(i)
.Select((x, k) =>
{
if (k <= selIndexArry[i] - 1)
{
return(x);
}
return(0);
}).Sum();
double max = min + thetaDelta.Column(i).ElementAt(selIndexArry[i]);
result[i] = enVar.rnd.Next((int)min, (int)max);
}
}
return(Vector.Build.Dense(result));
}
public GAEncoding(int numOfParams, int dimension, EnvironmentVar enVar)
{
_numOfParams = numOfParams;
_dimension = dimension;
_enVar = enVar;
thetaDelta = Matrix.Build.Dense(numOfParams, dimension);
weights = Matrix.Build.Dense(numOfParams + 1, dimension);
pathRecord = new Dictionary <string, double>();
}
private void webBrowser_DocumentCompleted(object sender, WebBrowserDocumentCompletedEventArgs e)
{
if (webBrowser.ReadyState == WebBrowserReadyState.Complete && e.Url.ToString() == _pageUrl)
{
Console.WriteLine(e.Url.ToString());
webBrowser.Document.InvokeScript("setExecModeForSeat");
webBrowser.Document.InvokeScript("setEnvironmentVar", new object[] { EnvironmentVar.ToJSON() });
}
}
public async Task <int> Invoke(EnvironmentVar envar)
{
enVar = envar;
dimension = (int)enVar.pmProblem["Dimension"];
await Task.Run(() => MOGA_Start());
await _next(enVar);
return(0);
}
internal Vector <double> GetRandVect(EnvironmentVar enVar)
{
int[] selIndexArry = new int[_dimension];
double[] lowbounds = new double[_dimension];
double[] highbounds = new double[_dimension];
for (int o = 0; o < _dimension; o++)
{
lowbounds[o] = ((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[o].Item1;
highbounds[o] = ((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[o].Item2;
}
// First, Select a block
for (int i = 0; i < _dimension; i++)
{
double lowIndex = 0.0;
double sumOfWeight = _numOfParams + 1;
double rndNum = enVar.rnd.NextDouble() * sumOfWeight;
for (int j = 0; j < _numOfParams; j++)
{
if (rndNum >= lowIndex && rndNum <= lowIndex + weights[j][i])
{
selIndexArry[i] = j;
break;
}
lowIndex = lowIndex + weights[j][i];
}
}
double[] result = new double[_dimension];
for (int i = 0; i < _dimension; i++)
{
if (selIndexArry[i] == 0)
{
result[i] = enVar.rnd.Next((int)lowbounds[i], (int)thetas[0][i]);
}
else if (selIndexArry[i] == _numOfParams)
{
result[i] = enVar.rnd.Next((int)thetas[_numOfParams - 1][i], (int)highbounds[i]);
}
else
{
result[i] = enVar.rnd.Next((int)thetas[selIndexArry[i] - 1][i], (int)thetas[selIndexArry[i]][i]);
}
}
return(Vector.Build.Dense(result));
}
internal void FixInputs(EnvironmentVar enVar)
{
Vector <double> low = Vector.Build.Dense(
_dimension,
(k) =>
{
return(((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[k].Item1);
}
);
Vector <double> high = Vector.Build.Dense(
_dimension,
(k) =>
{
return(((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[k].Item2);
}
);
Vector <double> tmp = (high - low).Add(1).Divide(_numOfParams);
for (int i = 0; i < _numOfParams; i++)
{
thetaDelta.SetRow(i, tmp);
}
int numOfOne = Convert.ToInt16(Math.Ceiling((_numOfParams + 1) * 0.2));
int numOfNotOne = (_numOfParams - numOfOne) / 2;
for (int i = 0; i < _numOfParams + 1; i++)
{
weights = Matrix.Build.Dense(_numOfParams + 1, _dimension, 1);
}
int[] cnt = new int[_dimension];
double[] values = new double[2] {
0, 2
};
// Initialize Weights
for (int i = 0; i < _dimension; i++)
{
for (int j = 0; j < _numOfParams + 1; j++)
{
weights[j, i] = enVar.rnd.NextDouble();
}
}
}
private void webBrowser_DocumentCompleted(object sender, WebBrowserDocumentCompletedEventArgs e)
{
if (webBrowser.ReadyState == WebBrowserReadyState.Complete && e.Url.ToString() == _pageUrl)
{
Console.WriteLine(e.Url.ToString());
webBrowser.Document.InvokeScript("setExecModeForSeat");
webBrowser.Document.InvokeScript("setEnvironmentVar", new object[] { EnvironmentVar.ToJSON() });
string strJsonStr = JsonConvert.SerializeObject(
new
{
Emergency = Common.ADDRESS_DICT[Common.ADDRESS_KEY_PROCESS_EMERGENCY],
FamilyCalls = Common.ADDRESS_DICT[Common.ADDRESS_KEY_PROCESS_FAMILYCALLS],
Life = Common.ADDRESS_DICT[Common.ADDRESS_KEY_PROCESS_LIFE],
}
);
webBrowser.Document.InvokeScript("setServiceTypeUrl", new object[] { strJsonStr });
webBrowser.Document.InvokeScript("setPageData", new object[] { JsonConvert.SerializeObject(PageData) });
}
}
internal void Randomize(EnvironmentVar enVar)
{
Vector <double> tmp = Vector.Build.Dense(
_dimension,
(k) =>
{
return(((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[k].Item1);
}
);
Vector <double> tmpW = Vector.Build.Dense(
_dimension,
(k) =>
{
return(0);
}
);
for (int i = 0; i < _numOfParams; i++)
{
Vector <double> newParam = Vector.Build.Dense(_dimension, (k) =>
{
return(enVar.rnd.Next((int)tmp[k]
, (((List <Tuple <int, int> >)enVar.pmProblem["Bound"])[k].Item2)));
});
thetas[i] = newParam;
tmp = newParam;
}
for (int i = 0; i < _numOfParams; i++)
{
Vector <double> newWeight = Vector.Build.Dense(_dimension, (k) =>
{
return(enVar.rnd.Next(0, _numOfParams + 1 - (int)tmpW[k]));
});
tmpW = newWeight + tmpW;
weights[i] = newWeight;
}
weights[_numOfParams] = tmpW.SubtractFrom(_numOfParams + 1);
}
public async Task <int> Invoke(EnvironmentVar enVar)
{
Dictionary <string, object> sutParam = SUT.SelectSUT(_selectSUT);
_sutPath = (string)sutParam["SUTPath"];
_record.sutInfo = sutParam;
_record.InitializeCurrentBest((int)sutParam["NumOfCE"]);
//04/21/2017: Since SUT data file is too large, we should use true SUT implementation.
// Comment code in below
//Task taskInitialSUT = Task.Run(() => {
// LocalFileAccess lfa = new LocalFileAccess();
// List<string> list = new List<string>();
// lfa.StoreLinesToList(_sutPath.ToString(), list);
// DataTable dt = new DataTable();
// dt.Columns.Add("Input", Type.GetType("System.String"));
// for (int i = 0; i < (int)sutParam["NumOfCE"]; i++)
// {
// dt.Columns.Add((i + 1).ToString(), Type.GetType("System.String"));
// }
// int numOfInputs = list.First().Count(x=>x == ' ');
// foreach (string s in list)
// {
// int length = dt.Rows.Count;
// if (length == 0)
// {
// int index = 0;
// for (int i = 0; i < numOfInputs; i++)
// {
// index = s.IndexOf(' ',index)+1;
// }
// int c = Convert.ToInt32(s.Substring(index))+1;
// object[] coverSeq = new object[(int)sutParam["NumOfCE"] + 1];
// for (int i = 0; i < coverSeq.Length; i++)
// {
// coverSeq[i] = 0.0;
// }
// coverSeq[c] = 1.0;
// coverSeq[0] = s.Substring(0, s.LastIndexOf(' '));
// dt.Rows.Add(
// coverSeq
// );
// }
// else if (s.Substring(0, s.IndexOf(' ', s.IndexOf(' ') + 1))
// == dt.Rows[length - 1].ItemArray[0].ToString())
// {
// int index = 0;
// for (int i = 0; i < numOfInputs; i++)
// {
// index = s.IndexOf(' ', index) + 1;
// }
// int c = Convert.ToInt32(s.Substring(index))+1;
// dt.Rows[length - 1][c] = 1.0;
// }
// else
// {
// int index = 0;
// for (int i = 0; i < numOfInputs; i++)
// {
// index = s.IndexOf(' ', index) + 1;
// }
// int c = Convert.ToInt32(s.Substring(index))+1;
// object[] coverSeq = new object[(int)sutParam["NumOfCE"] + 1];
// for (int i = 0; i < coverSeq.Length; i++)
// {
// coverSeq[i] = 0.0;
// }
// coverSeq[c] = 1.0;
// coverSeq[0] = s.Substring(0,s.LastIndexOf(' '));
// dt.Rows.Add(
// coverSeq
// );
// }
// }
// sutParam["Map"] = dt.AsEnumerable()
// .ToDictionary<DataRow, string, double[]>(
// row => row.Field<string>(0),
// row =>
// {
// double[] coverSeq = new double[(int)sutParam["NumOfCE"]];
// for (int i = 0; i < (int)sutParam["NumOfCE"]; i++)
// {
// coverSeq[i] = Convert.ToDouble(row.ItemArray[i + 1]);
// }
// return coverSeq;
// });
//});
//await taskInitialSUT;
await _next(enVar);
Console.WriteLine("All tasks are done");
return(0);
}
internal void CalEstFitness(EnvironmentVar enVar)
{
DenseMatrix m = new DenseMatrix((int)enVar.pmProblem["NumOfCE"], _enVar.testSetSize);
DenseMatrix mInputVectors = new DenseMatrix(_enVar.testSetSize, _dimension);
DenseMatrix mCoverPairs = new DenseMatrix(((int)_enVar.pmProblem["NumOfCE"] + 1) / 2, 2); //2 outgoing edges per branch
Vector <double> vIntersetSet = new DenseVector(((int)_enVar.pmProblem["NumOfCE"] + 1) / 2);
duplicateSamples = 0;
fitnessPerCE = new DenseVector((int)_enVar.pmProblem["NumOfCE"]);
pathRecord.Clear();
for (int i = 0; i < _enVar.testSetSize; i++)
{
Vector <double> vRandVect = GetRandVect(enVar);
if (mInputVectors.EnumerateRows().Contains(vRandVect))
{
duplicateSamples = duplicateSamples + 1;
}
else
{
mInputVectors.SetRow(i, vRandVect);
}
double[] coverPoints = null;
lock (SUT._locker)
{
coverPoints = SUT.RunSUT(enVar, vRandVect.ToArray <double>());
}
string tmp = null;
for (int o = 0; o < coverPoints.Length; o++)
{
if (o < (int)_enVar.pmProblem["NumOfCE"])
{
if (coverPoints[o] == 1)
{
tmp = tmp + o.ToString() + " ";
}
}
else
{
break;
}
}
if (pathRecord.Keys.Contains(tmp))
{
pathRecord[tmp] = pathRecord[tmp] + 1;
}
else
{
pathRecord.Add(tmp, 1);
}
var tm = Vector.Build.Dense(coverPoints
.Where((x, j) => j <= (int)_enVar.pmProblem["NumOfCE"] - 1).ToArray());
fitnessPerCE = fitnessPerCE.Add(tm);
List <int> indexList = coverPoints.Select((x, j) =>
{
if (x == 1)
{
return(j);
}
return(-1);
}).Where(y => y != -1).ToList();
// Construct Cover Element pair count Matrix
// |c0, c1|
// |c2, c3|
// |c4, c5|
// ........................................
double[] duplicates = new double[((int)_enVar.pmProblem["NumOfCE"] + 1) / 2];
for (int j = 0; j < indexList.Count; j++)
{
int index = indexList[j];
int rowIndex = index / 2;
int colIndex = index % 2;
Vector <double> vTmp = mCoverPairs.Row(rowIndex);
vTmp[colIndex] = vTmp[colIndex] + 1;
mCoverPairs.SetRow(rowIndex, vTmp.ToArray());
duplicates[rowIndex] = duplicates[rowIndex] + 1;
}
double[] tmpVect = duplicates.Select(x =>
{
if (x > 0)
{
return(x - 1);
}
else
{
return(0);
}
}).ToArray();
vIntersetSet = vIntersetSet.Add(Vector.Build.Dense(tmpVect));
}
for (int o = 0; o < pathRecord.Count; o++)
{
pathRecord[pathRecord.ElementAt(o).Key] /= (enVar.testSetSize * 1.0);
}
strTestSet = mInputVectors.ToString(100, _dimension)
.Replace("\r", "").Replace("\n", ";").Replace(" ", "_");
// Calculate P(In) vector
Vector <double> vPin = mCoverPairs.RowSums().Subtract(vIntersetSet) / enVar.testSetSize;
Matrix <double> mNormalizeRow = mCoverPairs.Subtract(
Matrix.Build.DenseOfColumnArrays(vIntersetSet.ToArray(), vIntersetSet.ToArray())
).NormalizeRows(1);
Matrix <double> mTmp = mNormalizeRow.Transpose().Add(0.00001).PointwisePower(-1).PointwiseLog10();
var mEye = DiagonalMatrix.Build.DiagonalOfDiagonalArray(vPin.ToArray());
var fit1Of2 = mNormalizeRow.Multiply(mTmp);
var fit2Of2 = fit1Of2.Multiply(mEye).Diagonal();
entropy = fit2Of2.Sum();
fitnessPerCE = fitnessPerCE.Divide(_enVar.testSetSize);
duplicateSamples = 1; //For Testing Only...
}