Kernel Principal Component Analysis Action Set: Syntax

Provides actions for kernel principal component analysis

kPca Action

Performs kernel PCA training.

CASL Syntax

kernelPca.kPca <result=results> <status=rc> /

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

center=TRUE | FALSE,

centroids={

caslib="string",

compress=TRUE | FALSE,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

centroidsPC={

caslib="string",

compress=TRUE | FALSE,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

clusCCriterion=double,

clusMaxiter=integer,

clusMethod="FC" | "KMPP" | "RANDOM" | 64-bit-integer,

clusNrestart=integer,

clusRandSeed=integer,

display={

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

names={"string-1" <, "string-2", ...>},

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

eigenVal={

caslib="string",

compress=TRUE | FALSE,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

eigenVec={

casout={

caslib="string"

compress=TRUE | FALSE

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

copyVars={"variable-name-1" <, "variable-name-2", ...>}

exactScore=TRUE | FALSE,

id={"variable-name-1" <, "variable-name-2", ...>},

inputs={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

intercept=double,

kerParam=double,

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer,

mapCoeffs={

caslib="string",

compress=TRUE | FALSE,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

mapParam={

intercept=double,

kerParam=double,

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer,

lambda=double

maxClus=integer,

method="APPROXIMATE" | "EXACT" | 64-bit-integer,

output={

casout={

caslib="string"

compress=TRUE | FALSE

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

copyVars={"variable-name-1" <, "variable-name-2", ...>},

npc=integer

outputTables={

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

names={"string-1" <, "string-2", ...>} | {key-1={casouttable-1} <, key-2={casouttable-2}, ...>},

repeated=TRUE | FALSE,

replace=TRUE | FALSE

preimage=TRUE | FALSE,

preimageMethod="ITERATIVE" | "MAP" | 64-bit-integer,

preimageNPC=integer,

rankThreshold=double,

saveState={

caslib="string",

compress=TRUE | FALSE,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

scale=TRUE | FALSE,

table={

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

groupByMode="NOSORT" | "REDISTRIBUTE",

importOptions={fileType="ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters},

name="table-name",

onDemand=TRUE | FALSE,

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=TRUE | FALSE,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

dataSourceOptions={adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sforce-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

;

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies the settings for an input table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
centroids	—	specifies the output data table in which to save the centroids matrix.
centroidsPC	—	specifies the output data table in which to save the centroids matrix of the KPCA principal components.
eigenVal	—	specifies the output data table in which to save the eigenVal matrix.
eigenVec	required parametercasout	specifies the output data table in which to save the eigenVector matrix.
mapCoeffs	—	specifies the output data table in which to save the mapping pre-image coefficients matrix.
output	required parametercasout	specifies the output data table in which to save the score values of the training data.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
saveState	—	specifies the output data table in which to save the state of eigenvector matrix for future scoring.

Parameter Descriptions

attributes={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable attributes.

For more information about specifying the attributes parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	attribute

center=TRUE | FALSE

when set to True, centers the numeric variables by the mean of each column.

Alias	centering
Default	FALSE

centroids={casouttable}

specifies the output data table in which to save the centroids matrix.

For more information about specifying the centroids parameter, see the common casouttable parameter (Appendix A: Common Parameters).

centroidsPC={casouttable}

specifies the output data table in which to save the centroids matrix of the KPCA principal components.

For more information about specifying the centroidsPC parameter, see the common casouttable parameter (Appendix A: Common Parameters).

clusCCriterion=double

specifies the convergence criterion for the k-means clustering algorithm.

Alias	cc
Default	0.02

clusMaxiter=integer

specifies the maximum number of iterations for the k-means clustering algorithm.

Default	50

clusMethod="FC" | "KMPP" | "RANDOM" | 64-bit-integer

specifies the initial centroid type in k-means clustering.

Default	KMPP
FC	specifies fast clustering as the initial centroid type.
KMPP	specifies k-means plus plus as the initial centroid type.
RANDOM	specifies randomization as the initial centroid type.

clusNrestart=integer

specifies the number of restarts when the initial centroids are selected by random.

Default	5

clusRandSeed=integer

specifies the random seed to use in initial centroid selection.

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

eigenVal={casouttable}

specifies the output data table in which to save the eigenVal matrix.

For more information about specifying the eigenVal parameter, see the common casouttable parameter (Appendix A: Common Parameters).

eigenVec={eigOutput}

specifies the output data table in which to save the eigenVector matrix.

The eigOutput value can be one or more of the following:

* casout={casouttable}

specifies the output eigenvector table.

For more information about specifying the casout parameter, see the common casouttable parameter (Appendix A: Common Parameters).

copyVars={"variable-name-1" <, "variable-name-2", ...>}

copies one or more variables from the input table to the output table.

Alias	copyVar

exactScore=TRUE | FALSE

when set to False and the training method is low-rank approximation, implements the fast scoring method.

Alias	ES
Default	FALSE

id={"variable-name-1" <, "variable-name-2", ...>}

specifies the variable to use as the record identifier.

inputs={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the analysis.

For more information about specifying the inputs parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	input

intercept=double

specifies the constant term in the polynomial kernel.

Alias	coefficient0
Default	1

kerParam=double

specifies the kernel parameter.

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer

specifies the kernel type to use for kernel principal component analysis. "RBF" indicates the radial basis function type.

Aliases	kernel
Aliases	kernelType

mapCoeffs={casouttable}

specifies the output data table in which to save the mapping pre-image coefficients matrix.

For more information about specifying the mapCoeffs parameter, see the common casouttable parameter (Appendix A: Common Parameters).

mapParam={mapParm}

specifies the parameter list in the mapping pre-image method.

The mapParm value can be one or more of the following:

intercept=double

specifies the intercept parameter of the polynomial kernel for kernel ridge regression to use in the mapping pre-image method.

Default	1

kerParam=double

specifies the kernel parameter for kernel ridge regression to use in the mapping method.

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer

specifies the kernel type for kernel ridge regression to use in the mapping method.

lambda=double

specifies the lambda parameter for the L2 regularization term for kernel ridge regression to use in the mapping pre-image method.

Alias	L2
Default	1

maxClus=integer

specifies the maximum number of clusters to use in k-means clustering.

Alias	maxc
Default	100

method="APPROXIMATE" | "EXACT" | 64-bit-integer

specifies the computation method to use for kernel principal component analysis.

output={kpcaOutputStatement}

specifies the output data table in which to save the score values of the training data.

The kpcaOutputStatement value can be one or more of the following:

* casout={casouttable}

specifies the output scoring table for training data.

For more information about specifying the casout parameter, see the common casouttable parameter (Appendix A: Common Parameters).

copyVars={"variable-name-1" <, "variable-name-2", ...>}

copies one or more variables from the input table to the output table.

Alias	copyVar

npc=integer

specifies the number of principal components to use in scoring the training data.

Default	4

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias	DISPLAYOUT

preimage=TRUE | FALSE

when set to True, implements pre-image training.

Alias	pre
Default	FALSE

preimageMethod="ITERATIVE" | "MAP" | 64-bit-integer

specifies the method to use in pre-image training.

Alias	preMethod

Default	ITERATIVE
ITERATIVE	uses the iterative method to calculate the pre-image.
MAP	uses the mapping method to calculate the pre-image.

preimageNPC=integer

specifies the number of principal components to use in pre-image training. For the mapping method, this is also the number of principal components to use in pre-image scoring.

Alias	preNPC
Default	4

rankThreshold=double

specifies the eigenvalue threshold to use for determining the kernel matrix rank.

Alias	threshold
Default	1E-08

saveState={casouttable}

specifies the output data table in which to save the state of eigenvector matrix for future scoring.

For more information about specifying the saveState parameter, see the common casouttable parameter (Appendix A: Common Parameters).

scale=TRUE | FALSE

when set to True, scales the numeric variables by the standard deviation of each column.

Alias	scaling
Default	FALSE

* table={castable}

specifies the settings for an input table.

For more information about specifying the table parameter, see the common castable parameter (Appendix A: Common Parameters).

kPca Action

Performs kernel PCA training.

Lua Syntax

results, info = s:kernelPca_kPca{

attributes={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

center=true | false,

centroids={

caslib="string",

compress=true | false,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=true | false,

promote=true | false,

replace=true | false,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

centroidsPC={

caslib="string",

compress=true | false,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=true | false,

promote=true | false,

replace=true | false,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

clusCCriterion=double,

clusMaxiter=integer,

clusMethod="FC" | "KMPP" | "RANDOM" | 64-bit-integer,

clusNrestart=integer,

clusRandSeed=integer,

display={

caseSensitive=true | false,

exclude=true | false,

excludeAll=true | false,

keyIsPath=true | false,

names={"string-1" <, "string-2", ...>},

pathType="LABEL" | "NAME",

traceNames=true | false

eigenVal={

caslib="string",

compress=true | false,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=true | false,

promote=true | false,

replace=true | false,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

eigenVec={

casout={

caslib="string"

compress=true | false

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

onDemand=true | false

promote=true | false

replace=true | false

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

copyVars={"variable-name-1" <, "variable-name-2", ...>}

exactScore=true | false,

id={"variable-name-1" <, "variable-name-2", ...>},

inputs={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

intercept=double,

kerParam=double,

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer,

mapCoeffs={

caslib="string",

compress=true | false,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=true | false,

promote=true | false,

replace=true | false,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

mapParam={

intercept=double,

kerParam=double,

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer,

lambda=double

maxClus=integer,

method="APPROXIMATE" | "EXACT" | 64-bit-integer,

output={

casout={

caslib="string"

compress=true | false

indexVars={"variable-name-1" <, "variable-name-2", ...>}

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

onDemand=true | false

promote=true | false

replace=true | false

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

where={"string-1" <, "string-2", ...>}

copyVars={"variable-name-1" <, "variable-name-2", ...>},

npc=integer

outputTables={

groupByVarsRaw=true | false,

includeAll=true | false,

names={"string-1" <, "string-2", ...>} | {key-1={casouttable-1} <, key-2={casouttable-2}, ...>},

repeated=true | false,

replace=true | false

preimage=true | false,

preimageMethod="ITERATIVE" | "MAP" | 64-bit-integer,

preimageNPC=integer,

rankThreshold=double,

saveState={

caslib="string",

compress=true | false,

indexVars={"variable-name-1" <, "variable-name-2", ...>},

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=true | false,

promote=true | false,

replace=true | false,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where={"string-1" <, "string-2", ...>}

scale=true | false,

table={

caslib="string",

computedOnDemand=true | false,

computedVars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

computedVarsProgram="string",

dataSourceOptions={key-1=any-list-or-data-type-1 <, key-2=any-list-or-data-type-2, ...>},

groupBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

onDemand=true | false,

orderBy={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

singlePass=true | false,

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}},

where="where-expression",

whereTable={

casLib="string"

name="table-name"

vars={{

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

}, {...}}

where="where-expression"

}

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies the settings for an input table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
centroids	—	specifies the output data table in which to save the centroids matrix.
centroidsPC	—	specifies the output data table in which to save the centroids matrix of the KPCA principal components.
eigenVal	—	specifies the output data table in which to save the eigenVal matrix.
eigenVec	required parametercasout	specifies the output data table in which to save the eigenVector matrix.
mapCoeffs	—	specifies the output data table in which to save the mapping pre-image coefficients matrix.
output	required parametercasout	specifies the output data table in which to save the score values of the training data.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
saveState	—	specifies the output data table in which to save the state of eigenvector matrix for future scoring.

Parameter Descriptions

attributes={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variable attributes.

For more information about specifying the attributes parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	attribute

center=true | false

when set to True, centers the numeric variables by the mean of each column.

Alias	centering
Default	false

centroids={casouttable}

specifies the output data table in which to save the centroids matrix.

For more information about specifying the centroids parameter, see the common casouttable parameter (Appendix A: Common Parameters).

centroidsPC={casouttable}

specifies the output data table in which to save the centroids matrix of the KPCA principal components.

For more information about specifying the centroidsPC parameter, see the common casouttable parameter (Appendix A: Common Parameters).

clusCCriterion=double

specifies the convergence criterion for the k-means clustering algorithm.

Alias	cc
Default	0.02

clusMaxiter=integer

specifies the maximum number of iterations for the k-means clustering algorithm.

Default	50

clusMethod="FC" | "KMPP" | "RANDOM" | 64-bit-integer

specifies the initial centroid type in k-means clustering.

Default	KMPP
FC	specifies fast clustering as the initial centroid type.
KMPP	specifies k-means plus plus as the initial centroid type.
RANDOM	specifies randomization as the initial centroid type.

clusNrestart=integer

specifies the number of restarts when the initial centroids are selected by random.

Default	5

clusRandSeed=integer

specifies the random seed to use in initial centroid selection.

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

eigenVal={casouttable}

specifies the output data table in which to save the eigenVal matrix.

For more information about specifying the eigenVal parameter, see the common casouttable parameter (Appendix A: Common Parameters).

eigenVec={eigOutput}

specifies the output data table in which to save the eigenVector matrix.

The eigOutput value can be one or more of the following:

* casout={casouttable}

specifies the output eigenvector table.

For more information about specifying the casout parameter, see the common casouttable parameter (Appendix A: Common Parameters).

copyVars={"variable-name-1" <, "variable-name-2", ...>}

copies one or more variables from the input table to the output table.

Alias	copyVar

exactScore=true | false

when set to False and the training method is low-rank approximation, implements the fast scoring method.

Alias	ES
Default	false

id={"variable-name-1" <, "variable-name-2", ...>}

specifies the variable to use as the record identifier.

inputs={{casinvardesc-1} <, {casinvardesc-2}, ...>}

specifies the variables to use in the analysis.

For more information about specifying the inputs parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	input

intercept=double

specifies the constant term in the polynomial kernel.

Alias	coefficient0
Default	1

kerParam=double

specifies the kernel parameter.

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer

specifies the kernel type to use for kernel principal component analysis. "RBF" indicates the radial basis function type.

Aliases	kernel
Aliases	kernelType

mapCoeffs={casouttable}

specifies the output data table in which to save the mapping pre-image coefficients matrix.

For more information about specifying the mapCoeffs parameter, see the common casouttable parameter (Appendix A: Common Parameters).

mapParam={mapParm}

specifies the parameter list in the mapping pre-image method.

The mapParm value can be one or more of the following:

intercept=double

specifies the intercept parameter of the polynomial kernel for kernel ridge regression to use in the mapping pre-image method.

Default	1

kerParam=double

specifies the kernel parameter for kernel ridge regression to use in the mapping method.

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer

specifies the kernel type for kernel ridge regression to use in the mapping method.

lambda=double

specifies the lambda parameter for the L2 regularization term for kernel ridge regression to use in the mapping pre-image method.

Alias	L2
Default	1

maxClus=integer

specifies the maximum number of clusters to use in k-means clustering.

Alias	maxc
Default	100

method="APPROXIMATE" | "EXACT" | 64-bit-integer

specifies the computation method to use for kernel principal component analysis.

output={kpcaOutputStatement}

specifies the output data table in which to save the score values of the training data.

The kpcaOutputStatement value can be one or more of the following:

* casout={casouttable}

specifies the output scoring table for training data.

For more information about specifying the casout parameter, see the common casouttable parameter (Appendix A: Common Parameters).

copyVars={"variable-name-1" <, "variable-name-2", ...>}

copies one or more variables from the input table to the output table.

Alias	copyVar

npc=integer

specifies the number of principal components to use in scoring the training data.

Default	4

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias	DISPLAYOUT

preimage=true | false

when set to True, implements pre-image training.

Alias	pre
Default	false

preimageMethod="ITERATIVE" | "MAP" | 64-bit-integer

specifies the method to use in pre-image training.

Alias	preMethod

Default	ITERATIVE
ITERATIVE	uses the iterative method to calculate the pre-image.
MAP	uses the mapping method to calculate the pre-image.

preimageNPC=integer

specifies the number of principal components to use in pre-image training. For the mapping method, this is also the number of principal components to use in pre-image scoring.

Alias	preNPC
Default	4

rankThreshold=double

specifies the eigenvalue threshold to use for determining the kernel matrix rank.

Alias	threshold
Default	1E-08

saveState={casouttable}

specifies the output data table in which to save the state of eigenvector matrix for future scoring.

For more information about specifying the saveState parameter, see the common casouttable parameter (Appendix A: Common Parameters).

scale=true | false

when set to True, scales the numeric variables by the standard deviation of each column.

Alias	scaling
Default	false

* table={castable}

specifies the settings for an input table.

For more information about specifying the table parameter, see the common castable parameter (Appendix A: Common Parameters).

kPca Action

Performs kernel PCA training.

Python Syntax

results= s.kernelPca.kPca(

attributes=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

center=True | False,

centroids={

"caslib":"string",

"compress":True | False,

"indexVars":["variable-name-1" <, "variable-name-2", ...>],

"label":"string",

"lifetime":64-bit-integer,

"maxMemSize":64-bit-integer,

"memoryFormat":"DVR" | "INHERIT" | "STANDARD",

"name":"table-name",

"onDemand":True | False,

"promote":True | False,

"replace":True | False,

"replication":integer,

"threadBlockSize":64-bit-integer,

"timeStamp":"string",

"where":["string-1" <, "string-2", ...>]

centroidsPC={

"caslib":"string",

"compress":True | False,

"indexVars":["variable-name-1" <, "variable-name-2", ...>],

"label":"string",

"lifetime":64-bit-integer,

"maxMemSize":64-bit-integer,

"memoryFormat":"DVR" | "INHERIT" | "STANDARD",

"name":"table-name",

"onDemand":True | False,

"promote":True | False,

"replace":True | False,

"replication":integer,

"threadBlockSize":64-bit-integer,

"timeStamp":"string",

"where":["string-1" <, "string-2", ...>]

clusCCriterion=double,

clusMaxiter=integer,

clusMethod="FC" | "KMPP" | "RANDOM" | 64-bit-integer,

clusNrestart=integer,

clusRandSeed=integer,

display={

"caseSensitive":True | False,

"exclude":True | False,

"excludeAll":True | False,

"keyIsPath":True | False,

"names":["string-1" <, "string-2", ...>],

"pathType":"LABEL" | "NAME",

"traceNames":True | False

eigenVal={

"caslib":"string",

"compress":True | False,

"indexVars":["variable-name-1" <, "variable-name-2", ...>],

"label":"string",

"lifetime":64-bit-integer,

"maxMemSize":64-bit-integer,

"memoryFormat":"DVR" | "INHERIT" | "STANDARD",

"name":"table-name",

"onDemand":True | False,

"promote":True | False,

"replace":True | False,

"replication":integer,

"threadBlockSize":64-bit-integer,

"timeStamp":"string",

"where":["string-1" <, "string-2", ...>]

eigenVec={

"casout":{

"caslib":"string"

"compress":True | False

"indexVars":["variable-name-1" <, "variable-name-2", ...>]

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

"name":"table-name"

"onDemand":True | False

"promote":True | False

"replace":True | False

"replication":integer

"threadBlockSize":64-bit-integer

"timeStamp":"string"

"where":["string-1" <, "string-2", ...>]

"copyVars":["variable-name-1" <, "variable-name-2", ...>]

exactScore=True | False,

id=["variable-name-1" <, "variable-name-2", ...>],

inputs=[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

intercept=double,

kerParam=double,

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer,

mapCoeffs={

"caslib":"string",

"compress":True | False,

"indexVars":["variable-name-1" <, "variable-name-2", ...>],

"label":"string",

"lifetime":64-bit-integer,

"maxMemSize":64-bit-integer,

"memoryFormat":"DVR" | "INHERIT" | "STANDARD",

"name":"table-name",

"onDemand":True | False,

"promote":True | False,

"replace":True | False,

"replication":integer,

"threadBlockSize":64-bit-integer,

"timeStamp":"string",

"where":["string-1" <, "string-2", ...>]

mapParam={

"intercept":double,

"kerParam":double,

"kerType":"LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer,

"lambda_":double

maxClus=integer,

method="APPROXIMATE" | "EXACT" | 64-bit-integer,

output={

"casout":{

"caslib":"string"

"compress":True | False

"indexVars":["variable-name-1" <, "variable-name-2", ...>]

"label":"string"

"lifetime":64-bit-integer

"maxMemSize":64-bit-integer

"memoryFormat":"DVR" | "INHERIT" | "STANDARD"

"name":"table-name"

"onDemand":True | False

"promote":True | False

"replace":True | False

"replication":integer

"threadBlockSize":64-bit-integer

"timeStamp":"string"

"where":["string-1" <, "string-2", ...>]

"copyVars":["variable-name-1" <, "variable-name-2", ...>],

"npc":integer

outputTables={

"groupByVarsRaw":True | False,

"includeAll":True | False,

"names":["string-1" <, "string-2", ...>] | {"key-1":{casouttable-1} <, "key-2":{casouttable-2}, ...>},

"repeated":True | False,

"replace":True | False

preimage=True | False,

preimageMethod="ITERATIVE" | "MAP" | 64-bit-integer,

preimageNPC=integer,

rankThreshold=double,

saveState={

"caslib":"string",

"compress":True | False,

"indexVars":["variable-name-1" <, "variable-name-2", ...>],

"label":"string",

"lifetime":64-bit-integer,

"maxMemSize":64-bit-integer,

"memoryFormat":"DVR" | "INHERIT" | "STANDARD",

"name":"table-name",

"onDemand":True | False,

"promote":True | False,

"replace":True | False,

"replication":integer,

"threadBlockSize":64-bit-integer,

"timeStamp":"string",

"where":["string-1" <, "string-2", ...>]

scale=True | False,

table={

"caslib":"string",

"computedOnDemand":True | False,

"computedVars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"computedVarsProgram":"string",

"dataSourceOptions":{"key-1":{any-list-or-data-type-1} <, "key-2":{any-list-or-data-type-2}, ...>},

"groupBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"groupByMode":"NOSORT" | "REDISTRIBUTE",

"importOptions":{"fileType":"ANY" | "AUDIO" | "AUTO" | "BASESAS" | "CSV" | "DELIMITED" | "DOCUMENT" | "DTA" | "ESP" | "EXCEL" | "FMT" | "HDAT" | "IMAGE" | "JMP" | "LASR" | "PARQUET" | "SOUND" | "SPSS" | "VIDEO" | "XLS", fileType-specific-parameters},

"name":"table-name",

"onDemand":True | False,

"orderBy":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"singlePass":True | False,

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>],

"where":"where-expression",

"whereTable":{

"casLib":"string"

"dataSourceOptions":{adls_noreq-parameters | bigquery-parameters | cas_noreq-parameters | clouddex-parameters | db2-parameters | dnfs-parameters | esp-parameters | fedsvr-parameters | gcs_noreq-parameters | hadoop-parameters | hana-parameters | impala-parameters | jdbc-parameters | mongodb-parameters | mysql-parameters | odbc-parameters | oracle-parameters | path-parameters | postgres-parameters | redshift-parameters | s3-parameters | sforce-parameters | snowflake-parameters | spark-parameters | spde-parameters | sqlserver-parameters | ss_noreq-parameters | teradata-parameters | vertica-parameters | yellowbrick-parameters}

"name":"table-name"

"vars":[{

"format":"string",

"formattedLength":integer,

"label":"string",

"name":"variable-name",

"nfd":integer,

"nfl":integer

}<, {...}>]

"where":"where-expression"

}

)

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies the settings for an input table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
centroids	—	specifies the output data table in which to save the centroids matrix.
centroidsPC	—	specifies the output data table in which to save the centroids matrix of the KPCA principal components.
eigenVal	—	specifies the output data table in which to save the eigenVal matrix.
eigenVec	required parametercasout	specifies the output data table in which to save the eigenVector matrix.
mapCoeffs	—	specifies the output data table in which to save the mapping pre-image coefficients matrix.
output	required parametercasout	specifies the output data table in which to save the score values of the training data.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
saveState	—	specifies the output data table in which to save the state of eigenvector matrix for future scoring.

Parameter Descriptions

attributes=[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variable attributes.

For more information about specifying the attributes parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	attribute

center=True | False

when set to True, centers the numeric variables by the mean of each column.

Alias	centering
Default	False

centroids={casouttable}

specifies the output data table in which to save the centroids matrix.

For more information about specifying the centroids parameter, see the common casouttable parameter (Appendix A: Common Parameters).

centroidsPC={casouttable}

specifies the output data table in which to save the centroids matrix of the KPCA principal components.

For more information about specifying the centroidsPC parameter, see the common casouttable parameter (Appendix A: Common Parameters).

clusCCriterion=double

specifies the convergence criterion for the k-means clustering algorithm.

Alias	cc
Default	0.02

clusMaxiter=integer

specifies the maximum number of iterations for the k-means clustering algorithm.

Default	50

clusMethod="FC" | "KMPP" | "RANDOM" | 64-bit-integer

specifies the initial centroid type in k-means clustering.

Default	KMPP
FC	specifies fast clustering as the initial centroid type.
KMPP	specifies k-means plus plus as the initial centroid type.
RANDOM	specifies randomization as the initial centroid type.

clusNrestart=integer

specifies the number of restarts when the initial centroids are selected by random.

Default	5

clusRandSeed=integer

specifies the random seed to use in initial centroid selection.

display={displayTables}

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

eigenVal={casouttable}

specifies the output data table in which to save the eigenVal matrix.

For more information about specifying the eigenVal parameter, see the common casouttable parameter (Appendix A: Common Parameters).

eigenVec={eigOutput}

specifies the output data table in which to save the eigenVector matrix.

The eigOutput value can be one or more of the following:

* "casout":{casouttable}

specifies the output eigenvector table.

For more information about specifying the casout parameter, see the common casouttable parameter (Appendix A: Common Parameters).

"copyVars":["variable-name-1" <, "variable-name-2", ...>]

copies one or more variables from the input table to the output table.

Alias	copyVar

exactScore=True | False

when set to False and the training method is low-rank approximation, implements the fast scoring method.

Alias	ES
Default	False

id=["variable-name-1" <, "variable-name-2", ...>]

specifies the variable to use as the record identifier.

inputs=[{casinvardesc-1} <, {casinvardesc-2}, ...>]

specifies the variables to use in the analysis.

For more information about specifying the inputs parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	input

intercept=double

specifies the constant term in the polynomial kernel.

Alias	coefficient0
Default	1

kerParam=double

specifies the kernel parameter.

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer

specifies the kernel type to use for kernel principal component analysis. "RBF" indicates the radial basis function type.

Aliases	kernel
Aliases	kernelType

mapCoeffs={casouttable}

specifies the output data table in which to save the mapping pre-image coefficients matrix.

For more information about specifying the mapCoeffs parameter, see the common casouttable parameter (Appendix A: Common Parameters).

mapParam={mapParm}

specifies the parameter list in the mapping pre-image method.

The mapParm value can be one or more of the following:

"intercept":double

specifies the intercept parameter of the polynomial kernel for kernel ridge regression to use in the mapping pre-image method.

Default	1

"kerParam":double

specifies the kernel parameter for kernel ridge regression to use in the mapping method.

"kerType":"LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer

specifies the kernel type for kernel ridge regression to use in the mapping method.

"lambda_":double

specifies the lambda parameter for the L2 regularization term for kernel ridge regression to use in the mapping pre-image method.

Alias	L2
Default	1

maxClus=integer

specifies the maximum number of clusters to use in k-means clustering.

Alias	maxc
Default	100

method="APPROXIMATE" | "EXACT" | 64-bit-integer

specifies the computation method to use for kernel principal component analysis.

output={kpcaOutputStatement}

specifies the output data table in which to save the score values of the training data.

The kpcaOutputStatement value can be one or more of the following:

* "casout":{casouttable}

specifies the output scoring table for training data.

For more information about specifying the casout parameter, see the common casouttable parameter (Appendix A: Common Parameters).

"copyVars":["variable-name-1" <, "variable-name-2", ...>]

copies one or more variables from the input table to the output table.

Alias	copyVar

"npc":integer

specifies the number of principal components to use in scoring the training data.

Default	4

outputTables={outputTables}

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias	DISPLAYOUT

preimage=True | False

when set to True, implements pre-image training.

Alias	pre
Default	False

preimageMethod="ITERATIVE" | "MAP" | 64-bit-integer

specifies the method to use in pre-image training.

Alias	preMethod

Default	ITERATIVE
ITERATIVE	uses the iterative method to calculate the pre-image.
MAP	uses the mapping method to calculate the pre-image.

preimageNPC=integer

specifies the number of principal components to use in pre-image training. For the mapping method, this is also the number of principal components to use in pre-image scoring.

Alias	preNPC
Default	4

rankThreshold=double

specifies the eigenvalue threshold to use for determining the kernel matrix rank.

Alias	threshold
Default	1E-08

saveState={casouttable}

specifies the output data table in which to save the state of eigenvector matrix for future scoring.

For more information about specifying the saveState parameter, see the common casouttable parameter (Appendix A: Common Parameters).

scale=True | False

when set to True, scales the numeric variables by the standard deviation of each column.

Alias	scaling
Default	False

* table={castable}

specifies the settings for an input table.

For more information about specifying the table parameter, see the common castable parameter (Appendix A: Common Parameters).

kPca Action

Performs kernel PCA training.

R Syntax

results <– cas.kernelPca.kPca(s,

attributes=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

center=TRUE | FALSE,

centroids=list(

caslib="string",

compress=TRUE | FALSE,

indexVars=list("variable-name-1" <, "variable-name-2", ...>),

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where=list("string-1" <, "string-2", ...>)

centroidsPC=list(

caslib="string",

compress=TRUE | FALSE,

indexVars=list("variable-name-1" <, "variable-name-2", ...>),

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where=list("string-1" <, "string-2", ...>)

clusCCriterion=double,

clusMaxiter=integer,

clusMethod="FC" | "KMPP" | "RANDOM" | 64-bit-integer,

clusNrestart=integer,

clusRandSeed=integer,

display=list(

caseSensitive=TRUE | FALSE,

exclude=TRUE | FALSE,

excludeAll=TRUE | FALSE,

keyIsPath=TRUE | FALSE,

names=list("string-1" <, "string-2", ...>),

pathType="LABEL" | "NAME",

traceNames=TRUE | FALSE

eigenVal=list(

caslib="string",

compress=TRUE | FALSE,

indexVars=list("variable-name-1" <, "variable-name-2", ...>),

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where=list("string-1" <, "string-2", ...>)

eigenVec=list(

casout=list(

caslib="string"

compress=TRUE | FALSE

indexVars=list("variable-name-1" <, "variable-name-2", ...>)

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

where=list("string-1" <, "string-2", ...>)

copyVars=list("variable-name-1" <, "variable-name-2", ...>)

exactScore=TRUE | FALSE,

id=list("variable-name-1" <, "variable-name-2", ...>),

inputs=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

intercept=double,

kerParam=double,

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer,

mapCoeffs=list(

caslib="string",

compress=TRUE | FALSE,

indexVars=list("variable-name-1" <, "variable-name-2", ...>),

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where=list("string-1" <, "string-2", ...>)

mapParam=list(

intercept=double,

kerParam=double,

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer,

lambda=double

maxClus=integer,

method="APPROXIMATE" | "EXACT" | 64-bit-integer,

output=list(

casout=list(

caslib="string"

compress=TRUE | FALSE

indexVars=list("variable-name-1" <, "variable-name-2", ...>)

label="string"

lifetime=64-bit-integer

maxMemSize=64-bit-integer

memoryFormat="DVR" | "INHERIT" | "STANDARD"

name="table-name"

onDemand=TRUE | FALSE

promote=TRUE | FALSE

replace=TRUE | FALSE

replication=integer

threadBlockSize=64-bit-integer

timeStamp="string"

where=list("string-1" <, "string-2", ...>)

copyVars=list("variable-name-1" <, "variable-name-2", ...>),

npc=integer

outputTables=list(

groupByVarsRaw=TRUE | FALSE,

includeAll=TRUE | FALSE,

names=list("string-1" <, "string-2", ...>) | list(key-1=list(casouttable-1) <, key-2=list(casouttable-2), ...>),

repeated=TRUE | FALSE,

replace=TRUE | FALSE

preimage=TRUE | FALSE,

preimageMethod="ITERATIVE" | "MAP" | 64-bit-integer,

preimageNPC=integer,

rankThreshold=double,

saveState=list(

caslib="string",

compress=TRUE | FALSE,

indexVars=list("variable-name-1" <, "variable-name-2", ...>),

label="string",

lifetime=64-bit-integer,

maxMemSize=64-bit-integer,

memoryFormat="DVR" | "INHERIT" | "STANDARD",

name="table-name",

onDemand=TRUE | FALSE,

promote=TRUE | FALSE,

replace=TRUE | FALSE,

replication=integer,

threadBlockSize=64-bit-integer,

timeStamp="string",

where=list("string-1" <, "string-2", ...>)

scale=TRUE | FALSE,

table=list(

caslib="string",

computedOnDemand=TRUE | FALSE,

computedVars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

computedVarsProgram="string",

dataSourceOptions=list(key-1=list(any-list-or-data-type-1) <, key-2=list(any-list-or-data-type-2), ...>),

groupBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

groupByMode="NOSORT" | "REDISTRIBUTE",

name="table-name",

onDemand=TRUE | FALSE,

orderBy=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

singlePass=TRUE | FALSE,

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>),

where="where-expression",

whereTable=list(

casLib="string"

name="table-name"

vars=list( list(

format="string",

formattedLength=integer,

label="string",

name="variable-name",

nfd=integer,

nfl=integer

) <, list(...)>)

where="where-expression"

)

indicates a required parameter

Summary: Input and Output Tables

If a row includes a subparameter, you can specify the name, caslib, and so on in the subparameter. Otherwise, you can specify the name, caslib, and so on in the parameter.

Parameters for Reading Input Tables
Parameter	Subparameter	Description
required parametertable	—	specifies the settings for an input table.

Parameters for Creating Output Tables
Parameter	Subparameter	Description
centroids	—	specifies the output data table in which to save the centroids matrix.
centroidsPC	—	specifies the output data table in which to save the centroids matrix of the KPCA principal components.
eigenVal	—	specifies the output data table in which to save the eigenVal matrix.
eigenVec	required parametercasout	specifies the output data table in which to save the eigenVector matrix.
mapCoeffs	—	specifies the output data table in which to save the mapping pre-image coefficients matrix.
output	required parametercasout	specifies the output data table in which to save the score values of the training data.
outputTables	names	lists the names of results tables to save as CAS tables on the server.
saveState	—	specifies the output data table in which to save the state of eigenvector matrix for future scoring.

Parameter Descriptions

attributes=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the variable attributes.

For more information about specifying the attributes parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	attribute

center=TRUE | FALSE

when set to True, centers the numeric variables by the mean of each column.

Alias	centering
Default	FALSE

centroids=list(casouttable)

specifies the output data table in which to save the centroids matrix.

For more information about specifying the centroids parameter, see the common casouttable parameter (Appendix A: Common Parameters).

centroidsPC=list(casouttable)

specifies the output data table in which to save the centroids matrix of the KPCA principal components.

For more information about specifying the centroidsPC parameter, see the common casouttable parameter (Appendix A: Common Parameters).

clusCCriterion=double

specifies the convergence criterion for the k-means clustering algorithm.

Alias	cc
Default	0.02

clusMaxiter=integer

specifies the maximum number of iterations for the k-means clustering algorithm.

Default	50

clusMethod="FC" | "KMPP" | "RANDOM" | 64-bit-integer

specifies the initial centroid type in k-means clustering.

Default	KMPP
FC	specifies fast clustering as the initial centroid type.
KMPP	specifies k-means plus plus as the initial centroid type.
RANDOM	specifies randomization as the initial centroid type.

clusNrestart=integer

specifies the number of restarts when the initial centroids are selected by random.

Default	5

clusRandSeed=integer

specifies the random seed to use in initial centroid selection.

display=list(displayTables)

specifies a list of results tables to send to the client for display.

For more information about specifying the display parameter, see the common displayTables parameter (Appendix A: Common Parameters).

eigenVal=list(casouttable)

specifies the output data table in which to save the eigenVal matrix.

For more information about specifying the eigenVal parameter, see the common casouttable parameter (Appendix A: Common Parameters).

eigenVec=list(eigOutput)

specifies the output data table in which to save the eigenVector matrix.

The eigOutput value can be one or more of the following:

* casout=list(casouttable)

specifies the output eigenvector table.

For more information about specifying the casout parameter, see the common casouttable parameter (Appendix A: Common Parameters).

copyVars=list("variable-name-1" <, "variable-name-2", ...>)

copies one or more variables from the input table to the output table.

Alias	copyVar

exactScore=TRUE | FALSE

when set to False and the training method is low-rank approximation, implements the fast scoring method.

Alias	ES
Default	FALSE

id=list("variable-name-1" <, "variable-name-2", ...>)

specifies the variable to use as the record identifier.

inputs=list( list(casinvardesc-1) <, list(casinvardesc-2), ...>)

specifies the variables to use in the analysis.

For more information about specifying the inputs parameter, see the common casinvardesc parameter (Appendix A: Common Parameters).

Alias	input

intercept=double

specifies the constant term in the polynomial kernel.

Alias	coefficient0
Default	1

kerParam=double

specifies the kernel parameter.

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer

specifies the kernel type to use for kernel principal component analysis. "RBF" indicates the radial basis function type.

Aliases	kernel
Aliases	kernelType

mapCoeffs=list(casouttable)

specifies the output data table in which to save the mapping pre-image coefficients matrix.

For more information about specifying the mapCoeffs parameter, see the common casouttable parameter (Appendix A: Common Parameters).

mapParam=list(mapParm)

specifies the parameter list in the mapping pre-image method.

The mapParm value can be one or more of the following:

intercept=double

specifies the intercept parameter of the polynomial kernel for kernel ridge regression to use in the mapping pre-image method.

Default	1

kerParam=double

specifies the kernel parameter for kernel ridge regression to use in the mapping method.

kerType="LINEAR" | "POLYNOMIAL" | "RBF" | 64-bit-integer

specifies the kernel type for kernel ridge regression to use in the mapping method.

lambda=double

specifies the lambda parameter for the L2 regularization term for kernel ridge regression to use in the mapping pre-image method.

Alias	L2
Default	1

maxClus=integer

specifies the maximum number of clusters to use in k-means clustering.

Alias	maxc
Default	100

method="APPROXIMATE" | "EXACT" | 64-bit-integer

specifies the computation method to use for kernel principal component analysis.

output=list(kpcaOutputStatement)

specifies the output data table in which to save the score values of the training data.

The kpcaOutputStatement value can be one or more of the following:

* casout=list(casouttable)

specifies the output scoring table for training data.

For more information about specifying the casout parameter, see the common casouttable parameter (Appendix A: Common Parameters).

copyVars=list("variable-name-1" <, "variable-name-2", ...>)

copies one or more variables from the input table to the output table.

Alias	copyVar

npc=integer

specifies the number of principal components to use in scoring the training data.

Default	4

outputTables=list(outputTables)

lists the names of results tables to save as CAS tables on the server.

For more information about specifying the outputTables parameter, see the common outputTables parameter (Appendix A: Common Parameters).

Alias	DISPLAYOUT

preimage=TRUE | FALSE

when set to True, implements pre-image training.

Alias	pre
Default	FALSE

preimageMethod="ITERATIVE" | "MAP" | 64-bit-integer

specifies the method to use in pre-image training.

Alias	preMethod

Default	ITERATIVE
ITERATIVE	uses the iterative method to calculate the pre-image.
MAP	uses the mapping method to calculate the pre-image.

preimageNPC=integer

specifies the number of principal components to use in pre-image training. For the mapping method, this is also the number of principal components to use in pre-image scoring.

Alias	preNPC
Default	4

rankThreshold=double

specifies the eigenvalue threshold to use for determining the kernel matrix rank.

Alias	threshold
Default	1E-08

saveState=list(casouttable)

specifies the output data table in which to save the state of eigenvector matrix for future scoring.

For more information about specifying the saveState parameter, see the common casouttable parameter (Appendix A: Common Parameters).

scale=TRUE | FALSE

when set to True, scales the numeric variables by the standard deviation of each column.

Alias	scaling
Default	FALSE

* table=list(castable)

specifies the settings for an input table.

For more information about specifying the table parameter, see the common castable parameter (Appendix A: Common Parameters).

Last updated: September 10, 2021