This class specifies a clustering for machine learning microbenchmark.
active
a logical indicating whether the microbenchmark is to be executed (TRUE) or not (FALSE).
benchmarkName
a character string that is the name of the microbenchmark.
benchmarkDescription
a character string describing the microbenchmark.
dataObjectName
a character string specifying the name of the data
object that is input to the benchmark; the object must be stored in
the R data file with the same base name and a .RData
extension.
Setting the field to NA_character_
indicates that the test data will
be dynamically generated by the function given in the
allocatorFunction
field instead of read from a data file.
numberOfFeatures
the number features; this value must match the
number of features in the data set given by the field dataObjectName
unless the field is populated with NA_character_
.
numberOfClusters
the number of clusters in the data set; this value
must match the number of clusters in the data set given by the field
dataObjectName
unless the field is populated with
NA_character_
.
numberOfFeatureVectorsPerCluster
the number of feature vectors per
cluster; this value must match the number of clusters in the data set given
by the field dataObjectName
unless the field is populated with
NA_character_
.
numberOfTrials
an integer specifying the number of performance trials conducted on the data set to be tested.
numberOfWarmupTrials
an integer specifying the number of warmup trials to be conducted on the data set.
allocatorFunction
the function that allocates and initializes input
to the benchmark function. The function takes a
ClusteringMicrobenchmark
object. For clustering benchmarks, the
allocator function should return a list containing the following items:
a matrix, the rows of which are the feature vectors
an integer indicating the number of features
an integer indicating the number of feature vectors
an integer indicating the number of clusters in the data set
benchmarkFunction
the benchmark function which executes the
functionality to be timed. The function takes a
SparseMatrixMicrobenchmark
and a list of kernel parameters
returned by the allocator function.
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