Trains a Naive Bayes classifier for classifying data into one of
nClasses target classifications.
Namespace:
Imsl.DataMining
Assembly:
ImslCS (in ImslCS.dll) Version: 6.5.0.0
Syntax
C# |
---|
public void Train(
double[][] continuousData,
int[][] nominalData,
int[] classificationData
) |
Visual Basic (Declaration) |
---|
Public Sub Train ( _
continuousData As Double()(), _
nominalData As Integer()(), _
classificationData As Integer() _
) |
Visual C++ |
---|
public:
void Train(
array<array<double>^>^ continuousData,
array<array<int>^>^ nominalData,
array<int>^ classificationData
) |
Parameters
- continuousData
- Type: array<
array<
System..::.Double
>[]()[]
>[]()[]
A double matrix containing the training
values for the nContinuous continuous
attributes. The i-th row contains the input
attributes for the i-th training pattern. The
j-th column contains the values for the
j-th continuous attribute. Missing values
should be set to Double.NaN. Patterns
with both non-missing and missing values are used to
train the classifier unless the
IgnoreMissingValues method has been
set to true. A null is allowed when
nContinuous is equal to zero.
- nominalData
- Type: array<
array<
System..::.Int32
>[]()[]
>[]()[]
An int matrix containing the training
values for the nNominal nominal
attributes. The i-th row contains the input
attributes for the i-th training pattern. The
j-th column contains the classifications for the
j-th nominal attribute. The values for the
j-th nominal attribute are expected to be
encoded with integers starting from 0 to
nCategories - 1, where nCategories is
specified in the CreateNominalAttribute
method. Any value outside this range is treated as
a missing value. Patterns with both non-missing
and missing values are used to train the classifier
unless the IgnoreMissingValues
method has been set to true. A null is
allowed when nNominal is equal to zero.
- classificationData
- Type: array<
System..::.Int32
>[]()[]
An int array containing the target
classifications for the training patterns. These
must be encoded from zero to nClasses-1.
Any value outside this range is considered a
missing value. In this case, the data in that
pattern are not used to train the Naive Bayes
classifier. However, any pattern with missing
values is still classified after the classifier is
trained.
See Also