示例#1
0
        } /* 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 */
示例#2
0
        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);
        }
示例#3
0
        } /* 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 */
示例#4
0
        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 */