Batch Processing

Algorithm Input

The EM for GMM initialization algorithm accepts the input described below. Pass the Input ID as a parameter to the methods that provide input for your algorithm. For more details, see Algorithms.

Input ID	Input
data	Pointer to the `n` x `p` numeric table with the data to which the EM initialization algorithm is applied. The input can be an object of any class derived from NumericTable.

Algorithm Parameters

The EM for GMM initialization algorithm has the following parameters:

Parameter	Default Value	Description
algorithmFPType	double	The floating-point type that the algorithm uses for intermediate computations. Can be float or double.
method	defaultDense	Performance-oriented computation method, the only method supported by the algorithm.
nComponents	Not applicable	The number of components in the Gaussian Mixture Model, a required parameter.
nTrials	20	The number of starts of the EM algorithm.
nIterations	10	The maximal number of iterations in each start of the EM algorithm.
seed	777	The seed value for the random number generator to get the initial means in each EM start.
accuracyThreshold	1.0e-04	The threshold for termination of the algorithm.

Algorithm Output

The EM for GMM initialization algorithm calculates the results described below. Pass the Result ID as a parameter to the methods that access the results of your algorithm. For more details, see Algorithms.

Result ID	Result
weights	Pointer to the 1 x `k` numeric table with mixture weights. By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.
means	Pointer to the `k` x `p` numeric table with each row containing the estimate of the means for the `i`-th mixture component, where `i`=0, 1, …, `k`-1. By default, this result is an object of the HomogenNumericTable class, but you can define the result as an object of any class derived from NumericTable except PackedTriangularMatrix, PackedSymmetricMatrix, and CSRNumericTable.
covariances	Pointer to the DataCollection object that contains `k` numeric tables, each with the `p` x `p` variance-covariance matrix for the `i`-th mixture component. By default, the collection contains objects of the HomogenNumericTable class, but you can define them as objects of any class derived from NumericTable except PackedTriangularMatrix and CSRNumericTable.