Batch Processing
Algorithm Input
Input ID
 Input
 

data  Pointer to the
n x
p numeric table with the data to which the EM algorithm is applied. The input can be an object of any class derived from
NumericTable .
 
inputWeights  Pointer to the 1 x
k numeric table with initial mixture weights. This input can be an object of any class derived from
NumericTable .
 
inputMeans  Pointer to a
k x
p numeric table. Each row in this table contains the initial value of the means for the
i th mixture component, where
i =0, 1, …,
k 1. This input can be an object of any class derived from
NumericTable .
 
inputCovariances  Pointer to the
DataCollection object that contains
k numeric tables, each with the
p x
p variancecovariance matrix for the
i th mixture component of size:
The collection can contain objects of any class derived from
NumericTable .
 
inputValues  Pointer to the result of the EM for GMM initialization algorithm. The result of initialization contains weights, means, and a collection of covariances. You can use this input to set the initial values for the EM for GMM algorithm instead of explicitly specifying the weights, means, and covariance collection.

Algorithm Parameters
Parameter
 Default Value
 Description
 

algorithmFPType  float  The floatingpoint type that the algorithm uses for intermediate computations. Can be
float or
double .
 
method  defaultDense  Performanceoriented computation method, the only method supported by the algorithm.
 
nComponents  Not applicable
 The number of components in the Gaussian Mixture Model, a required parameter.
 
maxIterations  10
 The maximal number of iterations in the algorithm.
 
accuracyThreshold  1.0e04
 The threshold for termination of the algorithm.
 
covariance  Pointer to an object of the
BatchIface class
 Pointer to the algorithm that computes the covariance matrix. By default, the respective Intel DAAL algorithm is used, implemented in the class derived from
BatchIface .
 
regularization Factor  0.01
 Factor for covariance regularization in the case of illconditional data.
 
covarianceStorage  full  Covariance matrix storage scheme in the Gaussian Mixture Model:

Algorithm Output
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 variancecovariance matrix for the
i th mixture component of size:
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 .
 
goalFunction  Pointer to the 1 x 1 numeric table with the value of the logarithm of the likelihood function after the last iteration. By default, this result is an object of the
HomogenNumericTable class.
 
nIterations  Pointer to the 1 x 1 numeric table with the number of iterations computed after completion of the algorithm. By default, this result is an object of the
HomogenNumericTable class.
