Developer Guide

Contents

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
float
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.
accuracyThreshold
1.0e-04
The threshold for termination of the algorithm.
covarianceStorage
full
Covariance matrix storage scheme in the Gaussian Mixture Model:
  • full
    - covariance matrices are stored as numeric tables of size
    p
    x
    p
    . All elements of the matrix are updated during the processing.
  • diagonal
    - covariance matrices are stored as numeric tables of size 1 x
    p
    . Only diagonal elements of the matrix are updated during the processing, and the rest are assumed to be zero.
engine
SharedPtr< engines:: mt19937:: Batch >()
Pointer to the random number generator engine that is used internally to get the initial means in each EM start.

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 of size:
  • p
    x
    p
    - for the full covariance matrix storage scheme
  • 1 x
    p
    - for the diagonal covariance matrix storage scheme
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
.

Product and Performance Information

1

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Notice revision #20110804