} /* ExploreMatrixCell */
public void MakePrediction(PicesClass knownClass,
PicesClass predictedClass,
double predProb,
PicesFeatureVector fv,
TrainingModel2 trainModel
)
{
bool partOfOriginalClassifier = ClassInClassifierClasses(knownClass);
if (!partOfOriginalClassifier)
{
String notPartClassName = "(V)" + knownClass.Name;
knownClass = PicesClassList.GetUniqueClass(notPartClassName, "");
}
int x = classes.LookUpIndex(knownClass);
if (x < 0)
{
classes.Add(knownClass);
}
x = classes.LookUpIndex(predictedClass);
if (x < 0)
{
classes.Add(predictedClass);
}
Prediction p = new Prediction(knownClass, predictedClass, predProb, fv, trainModel, partOfOriginalClassifier);
predictions.Add(p);
} /* featureVector */
private PicesClassList SortIntoDisplayOrder(PicesClassList l)
{
PicesClassList knownClasses = new PicesClassList();
PicesClassList unKnownClasses = new PicesClassList();
foreach (PicesClass pc in l)
{
if (ClassInClassifierClasses(pc))
{
knownClasses.Add(pc);
}
else
{
unKnownClasses.Add(pc);
}
}
knownClasses.SortByName();
unKnownClasses.SortByName();
foreach (PicesClass pc in unKnownClasses)
{
knownClasses.Add(pc);
}
return(knownClasses);
}
} /* ReportByClassCollumns */
private PicesClassList BuildMLClassList()
{
PicesClassList mlClasses = new PicesClassList();
int cIDX;
//for (cIDX = 0; cIDX < totals->end (); cIDX++)
for (cIDX = 0; cIDX < totals.Count; cIDX++)
{
ClassTotals ct = totals[cIDX];
mlClasses.Add(PicesClassList.GetUniqueClass(ct.Name, ""));
}
mlClasses.SortByName();
return(mlClasses);
} /* BuildMLClassList */
private void PopulateMatrix()
{
int x, y;
Font normFont = new Font("Courier", 8, FontStyle.Regular);
Font boldFont = new Font("Courier", 10, FontStyle.Bold);
Matrix.Items.Clear();
Matrix.Columns.Clear();
Matrix.Columns.Add("ClassName", 135, HorizontalAlignment.Left);
Matrix.Columns.Add("Count", 60, HorizontalAlignment.Center);
matrixRowClassAssigned = new PicesClassList();
foreach (PicesClass c in classesPredLevel)
{
Size textSize = TextRenderer.MeasureText(c.Name, Matrix.Font);
int width = Math.Max(65, (textSize.Width + 6));
Matrix.Columns.Add(c.Name, width, HorizontalAlignment.Center);
}
for (x = 0; x < numPredClasses; x++)
{
PicesClass c = classesPredLevel[x];
bool okToDisplayThisRow = false;
if (knownClassCounts[x] != 0)
{
okToDisplayThisRow = true;
}
else
{
for (y = 0; y < numPredClasses; y++)
{
if (predictionCounts[x][y] != 0)
{
okToDisplayThisRow = true;
break;
}
}
}
if (okToDisplayThisRow)
{
// Matrix.I
ListViewItem lvi = new ListViewItem(c.Name);
//lvi.SubItems.Add (c.Name);
lvi.SubItems.Add(MatrixFieldStr(knownClassCounts[x], totalCount));
for (y = 0; y < numPredClasses; y++)
{
String field = MatrixFieldStr(predictionCounts[x][y], knownClassCounts[x]);
if (x == y)
{
if (ClassInClassifierClasses(c))
{
lvi.SubItems.Add(field, Color.Green, Color.White, boldFont);
}
else
{
lvi.SubItems.Add(field + 'v', Color.Olive, Color.White, boldFont);
}
}
else
{
if (predictionCounts[x][y] == 0)
{
lvi.SubItems.Add("", Color.Black, Color.White, normFont);
}
else
{
lvi.SubItems.Add(field, Color.Black, Color.White, normFont);
}
}
}
lvi.UseItemStyleForSubItems = false;
Matrix.Items.Add(lvi);
matrixRowClassAssigned.Add(c);
}
}
{
// Lets print a Total Line.
// Matrix.I
ListViewItem lvi = new ListViewItem("Totals");
//lvi.SubItems.Add (c.Name);
lvi.SubItems.Add(MatrixFieldStr(totalCount, totalCount));
for (y = 0; y < numPredClasses; y++)
{
String field = MatrixFieldStr(predClassCounts [y], totalCount);
lvi.SubItems.Add(field, Color.Black, Color.White, boldFont);
}
lvi.UseItemStyleForSubItems = false;
Matrix.Items.Add(lvi);
}
ComputeSumaryAccuracies();
TotalAll.Text = totalCount.ToString("###,##0");
CorrectAll.Text = totalCorrect.ToString("###,##0.00");
AccuracyAll.Text = accuracyAll.ToString("P2");
AccuracyAll_NPC.Text = accuracyAllNPC.ToString("P2");
TotalPartOfClassifier.Text = totalPartOfClassifier.ToString("###,##0");
CorrectPartOfClassifier.Text = totalPartOfClassifierCorrect.ToString("###,##0.00");
AccuracyPartOfClassifier.Text = accuracyPartOfClass.ToString("P2");
AccuracyPartOfClassifier_NPC.Text = accuracyPartOfClassNPC.ToString("P2");
TotalNoise2.Text = totalNoise.ToString("###,##0");
CorrectNoise2.Text = totalNoiseCorrect.ToString("###,##0.00");
AccuracyNoise2.Text = accuracyNoise.ToString("P2");
AccuracyNoise2_NPC.Text = accuracyNoiseNPC.ToString("P2");
TotalNonNoise2.Text = totalNonNoise.ToString("###,##0");
CorrectNonNoise.Text = totalNonNoiseCorrect.ToString("###,##0.00");
AccuracyNonNoise.Text = accuracyNonNoise.ToString("P2");
AccuracyNonNoise_NPC.Text = accuracyNonNoiseNPC.ToString("P2");
} /* PopulateMatrix */