Developer Guide

Contents

Distributed Processing

You can use the Naïve Bayes classifier algorithm in the distributed processing mode only at the training stage.
This computation mode assumes that the data set is split in
nblocks
blocks across computation nodes.

Training

Algorithm Parameters
At the training stage, Naïve Bayes classifier in the distributed processing mode has the following parameters:
Parameter
Default Value
Description
computeStep
Not applicable
The parameter required to initialize the algorithm. Can be:
  • step1Local
    - the first step, performed on local nodes
  • step2Master
    - the second step, performed on a master node
algorithmFPType
float
The floating-point type that the algorithm uses for intermediate computations. Can be
float
or
double
.
method
defaultDense
Available computation methods for the Naïve Bayes classifier:
  • defaultDense
    - default performance-oriented method
  • fastCSR
    - performance-oriented method for CSR numeric tables
nClasses
Not applicable
The number of classes, a required parameter.
priorClassEstimates
1/
nClasses
Vector of size
nClasses
that contains prior class estimates. The default value applies to each vector element.
alpha
1
Vector of size
p
that contains the imagined occurrences of features. The default value applies to each vector element.
Use the two-step computation schema for Naïve Bayes classifier training in the distributed processing mode, as illustrated below:
Step 1 - on Local Nodes
Naive Bayes Classifier Training, Distributed processing Workflow Step 1
In this step, Naïve Bayes classifier training 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
i
x
p
numeric table that represents the
i
-th data block on the local node. This table can be an object of any class derived from
NumericTable
.
labels
Pointer to the
n
i
x 1 numeric table with class labels associated with the
i
-th data block. This table can be an object of any class derived from
NumericTable
.
In this step, Naïve Bayes classifier training calculates the result 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
partialModel
Pointer to the partial Naïve Bayes classifier model that corresponds to the
i
-th data block. The result can only be an object of the
Model
class.
Step 2 - on Master Node
Naive Bayes Classifier Training, Distributed processing Workflow Step 2
In this step, Naïve Bayes classifier training 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
partialModels
A collection of partial models computed on local nodes in Step 1. The collection contains objects of the
Model
class.
In this step, Naïve Bayes classifier training calculates the result 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
model
Pointer to the Naïve Bayes classifier model being trained. The result can only be an object of the
Model
class.

Product and Performance Information

1

Intel's compilers may or may not optimize to the same degree for non-Intel microprocessors for optimizations that are not unique to Intel microprocessors. These optimizations include SSE2, SSE3, and SSSE3 instruction sets and other optimizations. Intel does not guarantee the availability, functionality, or effectiveness of any optimization on microprocessors not manufactured by Intel. Microprocessor-dependent optimizations in this product are intended for use with Intel microprocessors. Certain optimizations not specific to Intel microarchitecture are reserved for Intel microprocessors. Please refer to the applicable product User and Reference Guides for more information regarding the specific instruction sets covered by this notice.

Notice revision #20110804