コード例 #1
0
ファイル: Form1.cs プロジェクト: AriFreyr/Arti-CV
        public void TestKNN(double[][] inputs, int[] outputs, int kValue)
        {
            var crossValidation = new CrossValidation(inputs.Length, KNNCVFolds);

            crossValidation.Fitting = delegate(int k, int[] indicesTrain, int[] indicesValidation)
            {
                var trainingInputs  = inputs.Submatrix(indicesTrain);
                var trainingOutputs = outputs.Submatrix(indicesTrain);

                // And now the validation data:
                var validationInputs  = inputs.Submatrix(indicesValidation);
                var validationOutputs = outputs.Submatrix(indicesValidation);

                var sw  = Stopwatch.StartNew();
                var knn = new KNN();
                knn.TrainKNN(trainingInputs, trainingOutputs, kValue);
                sw.Stop();

                //addToTestResultBox("Training for: " + sw.ElapsedMilliseconds + "ms");

                var error = knn.ComputeError(validationInputs, validationOutputs);

                return(new CrossValidationValues(knn, 0, error));
            };

            // Compute the cross-validation
            var result = crossValidation.Compute();

            // Finally, access the measured performance.
            var trainingErrors   = result.Training.Mean;
            var validationErrors = result.Validation.Mean;

            textBoxTestResults.AppendText("Finished with " + trainingErrors + " training errors and " + validationErrors + " validation errors" + Environment.NewLine);
        }
コード例 #2
0
ファイル: Form1.cs プロジェクト: AriFreyr/Arti-CV
 private void buttonClassify_Click(object sender, EventArgs e)
 {
     if (selectedAlgorithm.Equals(Algorithms.KNN))
     {
         textBoxResults.AppendText("Starting classification with kNN where k is " + classK + Environment.NewLine);
         if (img == null)
         {
             textBoxResults.AppendText("ERROR: Please choose an image first" + Environment.NewLine);
         }
         else
         {
             var    knn = new KNN();
             string result;
             textBoxResults.AppendText("Running training function..." + Environment.NewLine);
             knn.TrainKNN(inputs, outputs, classK);
             result = knn.Classify(imageProcessor.ProcessImages(img));
             textBoxResults.AppendText("Result: " + result + Environment.NewLine);
         }
     }
     else if (selectedAlgorithm.Equals(Algorithms.SVM))
     {
         textBoxResults.AppendText("Starting classification with SVM" + Environment.NewLine);
         if (img == null)
         {
             textBoxResults.AppendText("ERROR: Please choose an image first" + Environment.NewLine);
         }
         else
         {
             var    svm = new SVM();
             string result;
             textBoxResults.AppendText("Running training function..." + Environment.NewLine);
             svm.TrainSVM(new RationalQuadratic(1), 3, inputs, outputs);
             result = svm.Classify(imageProcessor.ProcessImages(img));
             textBoxResults.AppendText("Result: " + result + Environment.NewLine);
         }
     }
 }