/// <summary>
/// Classify our data using naive bias classifer and save the model.
/// </summary>
/// <param name="train_data">Frame objects that we will use to train classifers.</param>
/// <param name="test_data">Frame objects that we will use to test classifers.</param>
/// <param name="train_label">Labels of the train data.</param>
/// <param name="test_label">Labels of the test data.</param>
/// <param name="Classifier_Path">Path where we want to save the classifer on the disk.</param>
/// <param name="Classifier_Name">Name of the classifer we wnat to save.</param>
/// <returns></returns>
public void Naive_Bias(double[][] train_data, double[][] test_data, int[] train_label, int[] test_label, String Classifier_Path, String Classifier_Name)
{
Accord.Math.Random.Generator.Seed = 0;
int[][] tr_da = new int[train_data.Length][];
for (int i = 0; i < train_data.Length; i++)
{
int[] temp = new int[2];
temp[0] = (int)train_data[i][0];
temp[1] = (int)train_data[i][1];
tr_da[i] = temp;
}
int[][] te_da = new int[test_data.Length][];
for (int i = 0; i < test_data.Length; i++)
{
int[] temp = new int[2];
temp[0] = (int)test_data[i][0];
temp[1] = (int)test_data[i][1];
te_da[i] = temp;
}
// Let us create a learning algorithm
var learner = new NaiveBayesLearning();
// and teach a model on the data examples
NaiveBayes nb = learner.Learn(tr_da, train_label);
// Now, let's test the model output for the first input sample:
//int answer = nb.Decide(new int[] {20,10000}); // should be 1
double[] scores = nb.Probability(te_da);
nb.Save(Path.Combine(Classifier_Path, Classifier_Name));
}
public void laplace_smoothing_missing_sample()
{
#region doc_laplace
// To test the effectiveness of the Laplace rule for when
// an example of a symbol is not present in the training set,
// lets create dataset where the second column could contain
// values 0, 1 or 2 but only actually contains examples with
// containing 1 and 2:
int[][] inputs =
{
// input output
new [] { 0, 1 }, // 0
new [] { 0, 2 }, // 0
new [] { 0, 1 }, // 0
new [] { 1, 2 }, // 1
new [] { 0, 2 }, // 1
new [] { 0, 2 }, // 1
new [] { 1, 1 }, // 2
new [] { 0, 1 }, // 2
new [] { 1, 1 }, // 2
};
int[] outputs = // those are the class labels
{
0, 0, 0, 1, 1, 1, 2, 2, 2,
};
// Since the data is not enough to determine which symbols we are
// expecting in our model, we will have to specify the model by
// hand. The first column can assume 2 different values, whereas
// the third column can assume 3:
var bayes = new NaiveBayes(classes: 3, symbols: new[] { 2, 3 });
// Now we can create a learning algorithm
var learning = new NaiveBayesLearning()
{
Model = bayes
};
// Enable the use of the Laplace rule
learning.Options.InnerOption.UseLaplaceRule = true;
// Learn the Naive Bayes model
learning.Learn(inputs, outputs);
// Estimate a sample with 0 in the second col
int answer = bayes.Decide(new int[] { 0, 1 });
#endregion
Assert.AreEqual(0, answer);
double prob = bayes.Probability(new int[] { 0, 1 }, out answer);
Assert.AreEqual(0, answer);
Assert.AreEqual(0.52173913043478259, prob, 1e-10);
double error = new ZeroOneLoss(outputs)
{
Mean = true
}.Loss(bayes.Decide(inputs));
Assert.AreEqual(2 / 9.0, error);
}
public override double Probabilty(FeatureVector feature)
{
return(_bayes.Probability(Array.ConvertAll(feature.BandIntensities, s => (double)s / ushort.MaxValue)));
}
