public void learn_pendigits_normalization()
{
Console.WriteLine("Starting NormalQuasiNewtonHiddenLearningTest.learn_pendigits_normalization");
using (var travis = new KeepTravisAlive())
{
#region doc_learn_pendigits
// Ensure we get reproducible results
Accord.Math.Random.Generator.Seed = 0;
// Download the PENDIGITS dataset from UCI ML repository
var pendigits = new Pendigits(path: Path.GetTempPath());
// Get and pre-process the training set
double[][][] trainInputs = pendigits.Training.Item1;
int[] trainOutputs = pendigits.Training.Item2;
// Pre-process the digits so each of them is centered and scaled
trainInputs = trainInputs.Apply(Accord.Statistics.Tools.ZScores);
trainInputs = trainInputs.Apply((x) => x.Subtract(x.Min())); // make them positive
// Create some prior distributions to help initialize our parameters
var priorC = new WishartDistribution(dimension: 2, degreesOfFreedom: 5);
var priorM = new MultivariateNormalDistribution(dimension: 2);
// Create a new learning algorithm for creating continuous hidden Markov model classifiers
var teacher1 = new HiddenMarkovClassifierLearning <MultivariateNormalDistribution, double[]>()
{
// This tells the generative algorithm how to train each of the component models. Note: The learning
// algorithm is more efficient if all generic parameters are specified, including the fitting options
Learner = (i) => new BaumWelchLearning <MultivariateNormalDistribution, double[], NormalOptions>()
{
Topology = new Forward(5), // Each model will have a forward topology with 5 states
// Their emissions will be multivariate Normal distributions initialized using the prior distributions
Emissions = (j) => new MultivariateNormalDistribution(mean: priorM.Generate(), covariance: priorC.Generate()),
// We will train until the relative change in the average log-likelihood is less than 1e-6 between iterations
Tolerance = 1e-6,
MaxIterations = 1000, // or until we perform 1000 iterations (which is unlikely for this dataset)
// We will prevent our covariance matrices from becoming degenerate by adding a small
// regularization value to their diagonal until they become positive-definite again:
FittingOptions = new NormalOptions()
{
Regularization = 1e-6
}
}
};
// The following line is only needed to ensure reproducible results. Please remove it to enable full parallelization
teacher1.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)
// Use the learning algorithm to create a classifier
var hmmc = teacher1.Learn(trainInputs, trainOutputs);
// Create a new learning algorithm for creating HCRFs
var teacher2 = new HiddenQuasiNewtonLearning <double[]>()
{
Function = new MarkovMultivariateFunction(hmmc),
MaxIterations = 10
};
// The following line is only needed to ensure reproducible results. Please remove it to enable full parallelization
teacher2.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)
// Use the learning algorithm to create a classifier
var hcrf = teacher2.Learn(trainInputs, trainOutputs);
// Compute predictions for the training set
int[] trainPredicted = hcrf.Decide(trainInputs);
// Check the performance of the classifier by comparing with the ground-truth:
var m1 = new GeneralConfusionMatrix(predicted: trainPredicted, expected: trainOutputs);
double trainAcc = m1.Accuracy; // should be 0.66523727844482561
// Prepare the testing set
double[][][] testInputs = pendigits.Testing.Item1;
int[] testOutputs = pendigits.Testing.Item2;
// Apply the same normalizations
testInputs = testInputs.Apply(Accord.Statistics.Tools.ZScores);
testInputs = testInputs.Apply((x) => x.Subtract(x.Min())); // make them positive
// Compute predictions for the test set
int[] testPredicted = hcrf.Decide(testInputs);
// Check the performance of the classifier by comparing with the ground-truth:
var m2 = new GeneralConfusionMatrix(predicted: testPredicted, expected: testOutputs);
double testAcc = m2.Accuracy; // should be 0.66506538564184681
#endregion
Assert.AreEqual(0.66523727844482561, trainAcc, 1e-10);
Assert.AreEqual(0.66506538564184681, testAcc, 1e-10);
}
}
public void learn_pendigits_normalization()
{
Console.WriteLine("Starting BagOfWordsTest.learn_pendigits_normalization");
using (var travis = new KeepTravisAlive())
{
#region doc_learn_pendigits
// The Bag-Of-Words model can be used to extract finite-length feature
// vectors from sequences of arbitrary length, like handwritten digits
// Ensure we get reproducible results
Accord.Math.Random.Generator.Seed = 0;
// Download the PENDIGITS dataset from UCI ML repository
var pendigits = new Pendigits(path: Path.GetTempPath());
// Get and pre-process the training set
double[][][] trainInputs = pendigits.Training.Item1;
int[] trainOutputs = pendigits.Training.Item2;
// Pre-process the digits so each of them is centered and scaled
trainInputs = trainInputs.Apply(Accord.Statistics.Tools.ZScores);
// Create a Bag-of-Words learning algorithm
var bow = new BagOfWords <double[], KMeans>()
{
Clustering = new KMeans(5),
};
// Use the BoW to create a quantizer
var quantizer = bow.Learn(trainInputs);
// Extract vector representations from the pen sequences
double[][] trainVectors = quantizer.Transform(trainInputs);
// Create a new learning algorithm for support vector machines
var teacher = new MulticlassSupportVectorLearning <ChiSquare, double[]>
{
Learner = (p) => new SequentialMinimalOptimization <ChiSquare, double[]>()
{
Complexity = 1
}
};
// Use the learning algorithm to create a classifier
var svm = teacher.Learn(trainVectors, trainOutputs);
// Compute predictions for the training set
int[] trainPredicted = svm.Decide(trainVectors);
// Check the performance of the classifier by comparing with the ground-truth:
var m1 = new GeneralConfusionMatrix(predicted: trainPredicted, expected: trainOutputs);
double trainAcc = m1.Accuracy; // should be 0.690
// Prepare the testing set
double[][][] testInputs = pendigits.Testing.Item1;
int[] testOutputs = pendigits.Testing.Item2;
// Apply the same normalizations
testInputs = testInputs.Apply(Accord.Statistics.Tools.ZScores);
double[][] testVectors = quantizer.Transform(testInputs);
// Compute predictions for the test set
int[] testPredicted = svm.Decide(testVectors);
// Check the performance of the classifier by comparing with the ground-truth:
var m2 = new GeneralConfusionMatrix(predicted: testPredicted, expected: testOutputs);
double testAcc = m2.Accuracy; // should be 0.600
#endregion
#if NET35
Assert.AreEqual(0.89594053744997137d, trainAcc, 1e-10);
Assert.AreEqual(0.89605017347211102d, testAcc, 1e-10);
#else
Assert.AreEqual(0.69039451114922812, trainAcc, 1e-10);
Assert.AreEqual(0.600880704563651, testAcc, 1e-10);
#endif
}
}
