BinarySplit Class 
Namespace: Accord.MachineLearning
The BinarySplit type exposes the following members.
Name  Description  

BinarySplit(Int32) 
Initializes a new instance of the Binary Split algorithm
 
BinarySplit(Int32, IDistanceDouble) 
Initializes a new instance of the Binary Split algorithm

Name  Description  

Clusters 
Gets the clusters found by Kmeans.
(Inherited from KMeans.)  
ComputeCovariances 
Gets or sets whether covariance matrices for the clusters should
be computed at the end of an iteration. Default is true.
(Inherited from KMeans.)  
ComputeError 
Gets or sets whether the clustering distortion error (the
average distance between all data points and the cluster
centroids) should be computed at the end of the algorithm.
The result will be stored in Error. Default is true.
(Inherited from KMeans.)  
Dimension 
Gets the dimensionality of the data space.
(Inherited from KMeans.)  
Distance 
Gets or sets the distance function used
as a distance metric between data points.
(Inherited from KMeans.)  
Error 
Gets the cluster distortion error after the
last call to this class' Compute methods.
(Inherited from KMeans.)  
Iterations 
Gets the number of iterations performed in the
last call to this class' Compute methods.
(Inherited from KMeans.)  
K 
Gets the number of clusters.
(Inherited from KMeans.)  
MaxIterations 
Gets or sets the maximum number of iterations to
be performed by the method. If set to zero, no
iteration limit will be imposed. Default is 0.
(Inherited from KMeans.)  
ParallelOptions 
Gets or sets the parallelization options for this algorithm.
(Inherited from ParallelLearningBase.)  
Token 
Gets or sets a cancellation token that can be used
to cancel the algorithm while it is running.
(Inherited from ParallelLearningBase.)  
Tolerance 
Gets or sets the relative convergence threshold
for stopping the algorithm. Default is 1e5.
(Inherited from KMeans.)  
UseSeeding 
Gets or sets the strategy used to initialize the
centroids of the clustering algorithm. Default is
KMeansPlusPlus.
(Inherited from KMeans.) 
Name  Description  

Compute(Double)  Obsolete.
Divides the input data into K clusters.
(Inherited from KMeans.)  
Compute(Double, Double)  Obsolete.
Divides the input data into K clusters.
(Inherited from KMeans.)  
Compute(Double, Double)  Obsolete.
Divides the input data into K clusters.
(Inherited from KMeans.)  
Equals  Determines whether the specified object is equal to the current object. (Inherited from Object.)  
Finalize  Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)  
GetHashCode  Serves as the default hash function. (Inherited from Object.)  
GetType  Gets the Type of the current instance. (Inherited from Object.)  
Learn 
Learns a model that can map the given inputs to the desired outputs.
(Overrides KMeansLearn(Double, Double).)  
MemberwiseClone  Creates a shallow copy of the current Object. (Inherited from Object.)  
Randomize 
Randomizes the clusters inside a dataset.
(Inherited from KMeans.)  
ToString  Returns a string that represents the current object. (Inherited from Object.) 
Name  Description  

HasMethod 
Checks whether an object implements a method with the given name.
(Defined by ExtensionMethods.)  
ToT  Overloaded.
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.)  
ToT  Overloaded.
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 Matrix.) 
// Use a fixed seed for reproducibility Accord.Math.Random.Generator.Seed = 0; // Declare some data to be clustered double[][] input = { new double[] { 5, 2, 1 }, new double[] { 5, 5, 6 }, new double[] { 2, 1, 1 }, new double[] { 1, 1, 2 }, new double[] { 1, 2, 2 }, new double[] { 3, 1, 2 }, new double[] { 11, 5, 4 }, new double[] { 15, 5, 6 }, new double[] { 10, 5, 6 }, }; // Create a new binary split with 3 clusters BinarySplit binarySplit = new BinarySplit(3); // Learn a data partitioning using the Binary Split algorithm KMeansClusterCollection clustering = binarySplit.Learn(input); // Predict group labels for each point int[] output = clustering.Decide(input); // As a result, the first two observations should belong to the // same cluster (thus having the same label). The same should // happen to the next four observations and to the last three.