public static void Main()
{
modshogun.init_shogun_with_defaults();
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
double[] trainlab = Load.load_labels("../data/label_train_multiclass.dat");
RealFeatures feats_train = new RealFeatures();
feats_train.set_feature_matrix(traindata_real);
RealFeatures feats_test = new RealFeatures();
feats_test.set_feature_matrix(testdata_real);
MulticlassLabels labels = new MulticlassLabels(trainlab);
GaussianNaiveBayes gnb = new GaussianNaiveBayes(feats_train, labels);
gnb.train();
double[] out_labels = MulticlassLabels.obtain_from_generic(gnb.apply(feats_test)).get_labels();
foreach (double item in out_labels)
{
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
double[] trainlab = Load.load_labels("../data/label_train_multiclass.dat");
RealFeatures feats_train = new RealFeatures();
feats_train.set_feature_matrix(traindata_real);
RealFeatures feats_test = new RealFeatures();
feats_test.set_feature_matrix(testdata_real);
Labels labels = new Labels(trainlab);
GaussianNaiveBayes gnb = new GaussianNaiveBayes(feats_train, labels);
gnb.train();
double[] out_labels = gnb.apply(feats_test).get_labels();
foreach(double item in out_labels) {
Console.Write(item);
}
modshogun.exit_shogun();
}
static void Main(string[] args)
{
FarOutGenerator generator = new FarOutGenerator();
generator.Generate("FarOutData.json");
var data = JsonConvert.DeserializeObject <double[][]>(File.ReadAllText("FarOutData.json"));
// split data
int takeAmount = ( int )Math.Floor(data.Length * 0.9);
var training = data.Take(takeAmount);
var trainingData = training.Select(dataPoint => dataPoint.Take(dataPoint.Length - 1).ToArray()).ToArray();
var trainingClasses = training.Select(dataPoint => dataPoint.Last()).ToArray();
var testing = data.Skip(takeAmount);
var testingData = testing.Select(dataPoint => dataPoint.Take(dataPoint.Length - 1).ToArray()).ToArray();
var testingClasses = testing.Select(dataPoint => dataPoint.Last()).ToArray();
GaussianNaiveBayes model = new GaussianNaiveBayes();
model.Fit(trainingData, trainingClasses);
var predictions = model.Predict(testingData);
var results = predictions.Select((prediction, index) => testingData[index].Concat(new double[] { testingClasses[index], prediction }).ToArray()).ToArray();
var accuracy = results.Count(result => result[2] == result[3]) * 100.0 / results.Length;
File.WriteAllText("FarOutData-Results.json", JsonConvert.SerializeObject(results));
}
public void When_Calculate_Gaussian_Naive_Bayes_Distribution_Number()
{
double[][] inputs =
{
new double [] { 0, 1 },
new double [] { 0, 2 },
new double [] { 0, 1 },
new double [] { 1, 2 },
new double [] { 0, 2 },
new double [] { 0, 2 },
new double [] { 1, 1 },
new double [] { 0, 1 },
new double [] { 1, 1 }
};
double[][] predict =
{
new double[] { 0, 1 }
};
double[] outputs = // those are the class labels
{
0, 0, 0, 1, 1, 1, 2, 2, 2,
};
var bayes = new GaussianNaiveBayes();
bayes.Estimate(inputs, outputs);
var result = bayes.PredictProbability(predict);
var prediction = bayes.Predict(predict);
var firstRow = result.GetRowVector(0);
Assert.Equal(0.68, System.Math.Round(firstRow.Values[0].GetNumber(), 2));
Assert.Equal(0, firstRow.Values[1].GetNumber());
Assert.Equal(0.32, System.Math.Round(firstRow.Values[2].GetNumber(), 2));
Assert.Equal(1, prediction.Length);
Assert.Equal(0, prediction.Values[0].GetNumber());
}
public IActionResult GetGaussianNaiveBayesResult([FromBody] GetGaussianNaiveBayesRequest request)
{
var bayes = new GaussianNaiveBayes();
bayes.Estimate(request.Inputs, request.Outputs);
var result = bayes.PredictProbability(request.Predict);
var prediction = bayes.Predict(request.Predict);
return(new OkObjectResult(new GaussianNaiveBayesResult
{
Classes = prediction.GetNumbers(),
Probabilities = result.DoubleValues
}));
}
static void Main(string[] argv)
{
modshogun.init_shogun_with_defaults();
DoubleMatrix traindata_real = Load.load_numbers("../data/fm_train_real.dat");
DoubleMatrix testdata_real = Load.load_numbers("../data/fm_test_real.dat");
DoubleMatrix trainlab = Load.load_labels("../data/label_train_multiclass.dat");
RealFeatures feats_train = new RealFeatures();
feats_train.set_feature_matrix(traindata_real);
RealFeatures feats_test = new RealFeatures();
feats_test.set_feature_matrix(testdata_real);
Labels labels = new Labels(trainlab);
GaussianNaiveBayes gnb = new GaussianNaiveBayes(feats_train, labels);
gnb.train();
DoubleMatrix out_labels = gnb.apply(feats_test).get_labels();
Console.WriteLine(out_labels.ToString());
modshogun.exit_shogun();
}
internal static HandleRef getCPtr(GaussianNaiveBayes obj)
{
return((obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr);
}
internal static HandleRef getCPtr(GaussianNaiveBayes obj) {
return (obj == null) ? new HandleRef(null, IntPtr.Zero) : obj.swigCPtr;
}
