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################################################################################
# Copyright 2016 Indiana University
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
################################################################################
#' This class specifies a dense matrix microbenchmark.
#'
#' @name DenseMatrixMicrobenchmark
#' @field active a logical indicating whether the microbenchmark is to be
#' executed (TRUE) or not (FALSE).
#' @field benchmarkName a character string that is the name of the
#' microbenchmark.
#' @field benchmarkDescription a character string describing the microbenchmark.
#' @field dimensionParameters an integer vector specifying the dimension
#' parameters the microbenchmark uses to define the matrix dimensions to be
#' tested with.
#' @field numberOfTrials an integer vector specifying the number of performance
#' trials conducted for each matrix to be tested. Must be the same length as
#' \code{dimensionParameters}.
#' @field numberOfWarmupTrials an integer vector specifying the number of warmup
#' trials to be performed for each matrix to be tested.
#' @field allocatorFunction the function that allocates and initializes input to
#' the benchmark function. The function takes a
#' \code{DenseMatrixMicrobenchmark} object and an integer index indicating
#' which matrix dimension parameter from \code{dimensionParameters} should
#' be used to generate the matrix.
#' @field benchmarkFunction the benchmark function which executes the
#' functionality to be timed. The function takes a
#' \code{DenseMatrixMicrobenchmark} and a list of kernel parameters
#' returned by the allocator function.
methods::setRefClass(
"DenseMatrixMicrobenchmark",
fields = list(
active = "logical",
benchmarkName = "character",
benchmarkDescription = "character",
dimensionParameters = "integer",
numberOfTrials = "integer",
numberOfWarmupTrials = "integer",
allocatorFunction = "function",
benchmarkFunction = "function"
)
)
#' This class specifies a sparse matrix microbenchmark.
#'
#' @name SparseMatrixMicrobenchmark
#' @field active a logical indicating whether the microbenchmark is to be
#' executed (TRUE) or not (FALSE).
#' @field benchmarkName a character string that is the name of the
#' microbenchmark.
#' @field benchmarkDescription a character string describing the microbenchmark.
#' @field matrixObjectName a character string specifying the name of the sparse
#' matrix object that is input to the benchmark; the object must be stored in
#' the R data file with name \code{matrixObjectName}.RData
#' Setting the field to NA_character_ indicates that the test data will
#' be generated dynamically by the function given in the
#' \code{allocatorFunction} field instead of read from a data file.
#' @field numberOfRows an integer specifying the expected number of rows in the
#' input sparse matrix.
#' @field numberOfColumns an integer specifying the expected number of columns
#' in the input sparse matrix.
#' @field numberOfNonzeros an integer specifying the expected number of nonzeros
#' in the input sparse matrix.
#' @field numberOfTrials an integer vector specifying the number of performance
#' trials conducted for each matrix to be tested.
#' @field numberOfWarmupTrials an integer vector specifying the number of warmup
#' trials to be performed for each matrix to be tested.
#' @field allocatorFunction the function that allocates and initializes input
#' to the benchmark function. The function takes a
#' \code{SparseMatrixMicrobenchmark} object and an integer index indicating
#' which matrix parameter from \code{numberOfRows}, \code{numberOfColumns},
#' and \code{numberOfNonzeros} should be used to generate the matrix.
#' @field benchmarkFunction the benchmark function which executes the
#' functionality to be timed. The function takes a
#' \code{SparseMatrixMicrobenchmark} and a list of kernel parameters
#' returned by the allocator function.
methods::setRefClass(
"SparseMatrixMicrobenchmark",
fields = list(
active = "logical",
benchmarkName = "character",
benchmarkDescription = "character",
matrixObjectName = "character",
numberOfRows = "integer",
numberOfColumns = "integer",
numberOfNonzeros = "integer",
numberOfTrials = "integer",
numberOfWarmupTrials = "integer",
allocatorFunction = "function",
benchmarkFunction = "function"
)
)
#' This class specifies a clustering for machine learning microbenchmark.
#'
#' @name ClusteringMicrobenchmark
#' @field active a logical indicating whether the microbenchmark is to be
#' executed (TRUE) or not (FALSE).
#' @field benchmarkName a character string that is the name of the
#' microbenchmark.
#' @field benchmarkDescription a character string describing the microbenchmark.
#' @field 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 \code{.RData} extension.
#' Setting the field to \code{NA_character_} indicates that the test data will
#' be dynamically generated by the function given in the
#' \code{allocatorFunction} field instead of read from a data file.
#' @field numberOfFeatures the number features; this value must match the
#' number of features in the data set given by the field \code{dataObjectName}
#' unless the field is populated with \code{NA_character_}.
#' @field 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
#' \code{dataObjectName} unless the field is populated with
#' \code{NA_character_}.
#' @field numberOfFeatureVectorsPerCluster the number of feature vectors per
#' cluster; this value must match the number of clusters in the data set given
#' by the field \code{dataObjectName} unless the field is populated with
#' \code{NA_character_}.
#' @field numberOfTrials an integer specifying the number of performance
#' trials conducted on the data set to be tested.
#' @field numberOfWarmupTrials an integer specifying the number of warmup
#' trials to be conducted on the data set.
#' @field allocatorFunction the function that allocates and initializes input
#' to the benchmark function. The function takes a
#' \code{ClusteringMicrobenchmark} object. For clustering benchmarks, the
#' allocator function should return a list containing the following items:
#' \describe{
#' \item{featureVectors}{a matrix, the rows of which are the feature
#' vectors}
#' \item{numberOfFeatures}{an integer indicating the number of features}
#' \item{numberOfFeatureVectors}{an integer indicating the number of feature
#' vectors}
#' \item{numberOfClusters}{an integer indicating the number of clusters in
#' the data set}
#' }
#' @field benchmarkFunction the benchmark function which executes the
#' functionality to be timed. The function takes a
#' \code{SparseMatrixMicrobenchmark} and a list of kernel parameters
#' returned by the allocator function.
methods::setRefClass(
"ClusteringMicrobenchmark",
fields = list(
active = "logical",
benchmarkName = "character",
benchmarkDescription = "character",
dataObjectName = "character",
numberOfFeatures = "integer",
numberOfClusters = "integer",
numberOfFeatureVectorsPerCluster = "integer",
numberOfTrials = "integer",
numberOfWarmupTrials = "integer",
allocatorFunction = "function",
benchmarkFunction = "function"
)
)
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