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.
