Click or drag to resize
Accord.NET (logo)

HiddenQuasiNewtonLearningT Class

Quasi-Newton (L-BFGS) learning algorithm for Hidden Conditional Hidden Fields.
Inheritance Hierarchy
    Accord.Statistics.Models.Fields.LearningBaseHiddenGradientOptimizationLearningT, BoundedBroydenFletcherGoldfarbShanno

Namespace:  Accord.Statistics.Models.Fields.Learning
Assembly:  Accord.Statistics (in Accord.Statistics.dll) Version: 3.6.0
public class HiddenQuasiNewtonLearning<T> : BaseHiddenGradientOptimizationLearning<T, BoundedBroydenFletcherGoldfarbShanno>, 
	ISupervisedLearning<HiddenConditionalRandomField<T>, T[], int>, IParallel, ISupportsCancellation, 
	IHiddenConditionalRandomFieldLearning<T>, IConvergenceLearning, IDisposable
Request Example View Source

Type Parameters

The type of the observations.

The HiddenQuasiNewtonLearningT type exposes the following members.

Public propertyCurrentIteration
Gets the current iteration number.
Public propertyFunction (Inherited from BaseHiddenConditionalRandomFieldLearningT.)
Public propertyHasConverged
Gets or sets whether the algorithm has converged.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public propertyMaxIterations
Gets or sets the maximum number of iterations performed by the learning algorithm.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public propertyModel
Gets or sets the model being trained.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)
Public propertyOptimizer
Gets the optimization algorithm being used.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public propertyParallelOptions
Gets or sets the parallelization options for this algorithm.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public propertyRegularization
Gets or sets the amount of the parameter weights which should be included in the objective function. Default is 0 (do not include regularization).
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public propertyToken
Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)
Public propertyTolerance
Gets or sets the tolerance value used to determine whether the algorithm has converged.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Protected methodCreate
Creates an instance of the model to be learned. Inheritors of this abstract class must define this method so new models can be created from the training data.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)
Protected methodCreateOptimizer
Inheritors of this class should create the optimization algorithm in this method, using the current MaxIterations and Tolerance settings.
(Overrides BaseHiddenGradientOptimizationLearningTData, TOptimizerCreateOptimizer.)
Public methodDispose
Performs application-defined tasks associated with freeing, releasing, or resetting unmanaged resources.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Protected methodDispose(Boolean)
Releases unmanaged and - optionally - managed resources
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public methodEquals
Determines whether the specified object is equal to the current object.
(Inherited from Object.)
Protected methodFinalize
Releases unmanaged resources and performs other cleanup operations before the HiddenQuasiNewtonLearningT is reclaimed by garbage collection.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public methodGetHashCode
Serves as the default hash function.
(Inherited from Object.)
Public methodGetType
Gets the Type of the current instance.
(Inherited from Object.)
Protected methodInnerRun
Runs the learning algorithm.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public methodLearn
Learns a model that can map the given inputs to the given outputs.
(Inherited from BaseHiddenConditionalRandomFieldLearningT.)
Protected methodMemberwiseClone
Creates a shallow copy of the current Object.
(Inherited from Object.)
Public methodRun(TData, Int32)
Online learning is not supported.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public methodRun(TData, Int32) Obsolete.
Runs the learning algorithm with the specified input training observations and corresponding output labels.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public methodRunEpoch Obsolete.
Online learning is not supported.
(Inherited from BaseHiddenGradientOptimizationLearningTData, TOptimizer.)
Public methodToString
Returns a string that represents the current object.
(Inherited from Object.)
Extension Methods
Public Extension MethodHasMethod
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)
Public Extension MethodIsEqual
Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.
(Defined by Matrix.)
Public Extension MethodToT
Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.
(Defined by ExtensionMethods.)
// Let's say we would like to do a very simple mechanism for gesture recognition. 
// In this example, we will be trying to create a classifier that can distinguish 
// between the words "hello", "car", and "wardrobe". 

// Let's say we decided to acquire some data, and we asked some people to perform 
// those words in front of a Kinect camera, and, using Microsoft's SDK, we were able 
// to captured the x and y coordinates of each hand while the word was being performed.

// Let's say we decided to represent our frames as:
//    double[] frame = { leftHandX, leftHandY, rightHandX, rightHandY }; // 4 dimensions
// Since we captured words, this means we captured sequences of frames as we described 
// above. Let's write some of those as rough examples to explain how gesture recognition 
// can be done:

double[][] hello =
    new double[] { 1.0, 0.1, 0.0, 0.0 }, // let's say the word
    new double[] { 0.0, 1.0, 0.1, 0.1 }, // hello took 6 frames
    new double[] { 0.0, 1.0, 0.1, 0.1 }, // to be recorded.
    new double[] { 0.0, 0.0, 1.0, 0.0 },
    new double[] { 0.0, 0.0, 1.0, 0.0 },
    new double[] { 0.0, 0.0, 0.1, 1.1 },

double[][] car =
    new double[] { 0.0, 0.0, 0.0, 1.0 }, // the car word
    new double[] { 0.1, 0.0, 1.0, 0.1 }, // took only 4.
    new double[] { 0.0, 0.0, 0.1, 0.0 },
    new double[] { 1.0, 0.0, 0.0, 0.0 },

double[][] wardrobe =
    new double[] { 0.0, 0.0, 1.0, 0.0 }, // same for the
    new double[] { 0.1, 0.0, 1.0, 0.1 }, // wardrobe word.
    new double[] { 0.0, 0.1, 1.0, 0.0 },
    new double[] { 0.1, 0.0, 1.0, 0.1 },

// Please note that a real-world example would involve *lots* of samples for each word. 
// Here, we are considering just one from each class which is clearly sub-optimal and 
// should _never_ be done on practice. Please keep in mind that we are doing like this
// only to simplify this example on how to create and use HCRFs.

// These are the words we have in our vocabulary:
double[][][] words = { hello, car, wardrobe };

// Now, let's associate integer labels with them. This is needed
// for the case where there are multiple samples for each word.
int[] labels = { 0, 1, 2 };

// Create a new learning algorithm to train the hidden Markov model sequence classifier
var teacher = new HiddenMarkovClassifierLearning<Independent<NormalDistribution>, double[]>()
    // Train each model until the log-likelihood changes less than 0.001
    Learner = (i) => new BaumWelchLearning<Independent<NormalDistribution>, double[]>()
        Topology = new Forward(5), // this value can be found by trial-and-error

        // We will create our classifiers assuming an independent Gaussian distribution 
        // for each component in our feature vectors (assuming a Naive Bayes assumption).
        Emissions = (s) => new Independent<NormalDistribution>(dimensions: 4), // 4 dimensions

        Tolerance = 0.001,
        Iterations = 100,

        // This is necessary so the code doesn't blow up when it realizes there is only one 
        // sample per word class. But this could also be needed in normal situations as well:
        FittingOptions = new IndependentOptions()
            InnerOption = new NormalOptions() { Regularization = 1e-5 }

// PS: In case you find exceptions trying to configure your model, you might want 
//     to try disabling parallel processing to get more descriptive error messages:
// teacher.ParallelOptions.MaxDegreeOfParallelism = 1;

// Finally, we can run the learning algorithm!
var hmm = teacher.Learn(words, labels);
double logLikelihood = teacher.LogLikelihood;

// At this point, the classifier should be successfully 
// able to distinguish between our three word classes:
int tc1 = hmm.Decide(hello);    // should be 0
int tc2 = hmm.Decide(car);      // should be 1
int tc3 = hmm.Decide(wardrobe); // should be 2
// Now, we can use the Markov classifier to initialize a HCRF
var baseline = HiddenConditionalRandomField.FromHiddenMarkov(hmm);

// We can check that both are equivalent, although they have
// formulations that can be learned with different methods:
int[] predictedLabels = baseline.Decide(words);
// Now we can learn the HCRF using one of the best learning
// algorithms available, Resilient Backpropagation learning:

// Create the Resilient Backpropagation learning algorithm
var rprop = new HiddenResilientGradientLearning<double[]>()
    Function = baseline.Function, // use the same HMM function

    Iterations = 50,
    Tolerance = 1e-5

// Run the algorithm and learn the models
var hcrf = rprop.Learn(words, labels);

// At this point, the HCRF should be successfully 
// able to distinguish between our three word classes:
int hc1 = hcrf.Decide(hello);    // should be 0
int hc2 = hcrf.Decide(car);      // should be 1
int hc3 = hcrf.Decide(wardrobe); // should be 2

The next example shows how to use the learning algorithms in a real-world dataset, including training and testing in separate sets and evaluating its performance:

// 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 template Markov classifier that we can use as a base for the HCRF
var hmmc = new HiddenMarkovClassifier<MultivariateNormalDistribution, double[]>(
    classes: pendigits.NumberOfClasses, topology: new Forward(5),
    initial: (i, j) => new MultivariateNormalDistribution(mean: priorM.Generate(), covariance: priorC.Generate()));

// Create a new learning algorithm for creating HCRFs
var teacher = 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
teacher.ParallelOptions.MaxDegreeOfParallelism = 1; // (Remove, comment, or change this line to enable full parallelism)

// Use the learning algorithm to create a classifier
var hcrf = teacher.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 ConfusionMatrix(predicted: trainPredicted, expected: trainOutputs);
double trainAcc = m1.Accuracy; // should be 0.89594053744997137

// 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 ConfusionMatrix(predicted: testPredicted, expected: testOutputs);
double testAcc = m2.Accuracy; // should be 0.89594053744997137
See Also