public void Test_ClassificationWith_BackwardsEliminationKnnModel()
{
// Given
var randomizer = new Random(55);
var data = TestDataBuilder.ReadIrisData();
var trainingDataPercentage = 0.8;
int trainingDataCount = (int)(data.RowCount * trainingDataPercentage);
var shuffledIndices = data.RowIndices.Shuffle();
var trainingIndices = shuffledIndices.Take(trainingDataCount).ToList();
var testIndices =
shuffledIndices.Skip(trainingDataCount).Take(shuffledIndices.Count - trainingDataCount).ToList();
var trainingData = data.GetSubsetByRows(trainingIndices);
var testData = data.GetSubsetByRows(testIndices);
var weightingFunction = new GaussianFunction(0.07);
var predictor = new SimpleKnnClassifier<string>(
new EuclideanDistanceMeasure(),
new MinMaxNormalizer(),
weightingFunction.GetValue);
var modelBuilder = new BackwardsEliminationKnnModelBuilder<string>(
new MinMaxNormalizer(),
predictor,
new ClassificationAccuracyError<string>()
);
var modelParams = new KnnAdditionalParams(3, true);
var errorMeasure = new MeanSquareError();
var subject = new BackwardsEliminationKnnClassifier<string>(
new EuclideanDistanceMeasure(),
new MinMaxNormalizer(),
weightingFunction.GetValue);
// When
var model = modelBuilder.BuildModel(trainingData, "iris_class", modelParams);
var actualResults = subject.Predict(testData, model, "iris_class");
var confusionMatrix = new ConfusionMatrix<string>(testData.GetColumnVector<string>("iris_class"),
actualResults);
// Then
Assert.IsTrue(confusionMatrix.Accuracy >= 0.95);
}
public void Test_ClassificationWith_BackwardsEliminationKnnModel()
{
// Given
var randomizer = new Random(55);
var data = TestDataBuilder.ReadIrisData();
var trainingDataPercentage = 0.8;
int trainingDataCount = (int)(data.RowCount * trainingDataPercentage);
var shuffledIndices = data.RowIndices.Shuffle();
var trainingIndices = shuffledIndices.Take(trainingDataCount).ToList();
var testIndices =
shuffledIndices.Skip(trainingDataCount).Take(shuffledIndices.Count - trainingDataCount).ToList();
var trainingData = data.GetSubsetByRows(trainingIndices);
var testData = data.GetSubsetByRows(testIndices);
var weightingFunction = new GaussianFunction(0.07);
var predictor = new SimpleKnnClassifier <string>(
new EuclideanDistanceMeasure(),
new MinMaxNormalizer(),
weightingFunction.GetValue);
var modelBuilder = new BackwardsEliminationKnnModelBuilder <string>(
new MinMaxNormalizer(),
predictor,
new ClassificationAccuracyError <string>()
);
var modelParams = new KnnAdditionalParams(3, true);
var errorMeasure = new MeanSquareError();
var subject = new BackwardsEliminationKnnClassifier <string>(
new EuclideanDistanceMeasure(),
new MinMaxNormalizer(),
weightingFunction.GetValue);
// When
var model = modelBuilder.BuildModel(trainingData, "iris_class", modelParams);
var actualResults = subject.Predict(testData, model, "iris_class");
var confusionMatrix = new ConfusionMatrix <string>(testData.GetColumnVector <string>("iris_class"),
actualResults);
// Then
Assert.IsTrue(confusionMatrix.Accuracy >= 0.95);
}
