Teradata Package for R Function Reference | 17.20 - RandomProjectionTransform - Teradata Package for R - Look here for syntax, methods and examples for the functions included in the Teradata Package for R.

Teradata® Package for R Function Reference

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for R
Release Number
17.20
Published
March 2024
Language
English (United States)
Last Update
2024-05-03
dita:id
TeradataR_FxRef_Enterprise_1720
Product Category
Teradata Vantage

RandomProjectionTransform

Description

The td_random_projection_transform_sqle() function converts the high-dimensional input data to a low-dimensional space using the td_random_projection_fit_sqle() function output.

Usage

  td_random_projection_transform_sqle (
      object = NULL,
      data = NULL,
      accumulate = NULL,
      ...
  )

Arguments

object

Required Argument.
Specifies the tbl_teradata containing the output generated by
td_random_projection_fit_sqle() function or the instance of td_random_projection_fit_sqle.
Types: tbl_teradata or td_random_projection_fit_sqle

data

Required Argument.
Specifies the input tbl_teradata.
Types: tbl_teradata

accumulate

Optional Argument.
Specifies the name(s) of input tbl_teradata column(s) to copy to the output. By default, only transformed columns are present in the output.
Types: character OR vector of Strings (character)

...

Specifies the generic keyword arguments SQLE functions accept. Below are the generic keyword arguments:

persist:
Optional Argument.
Specifies whether to persist the results of the
function in a table or not. When set to TRUE, results are persisted in a table; otherwise, results are garbage collected at the end of the session.
Default Value: FALSE
Types: logical

volatile:
Optional Argument.
Specifies whether to put the results of the function in a volatile table or not. When set to TRUE, results are stored in a volatile table, otherwise not.
Default Value: FALSE
Types: logical

Function allows the user to partition, hash, order or local order the input data. These generic arguments are available for each argument that accepts tbl_teradata as input and can be accessed as:

  • "<input.data.arg.name>.partition.column" accepts character or vector of character (Strings)

  • "<input.data.arg.name>.hash.column" accepts character or vector of character (Strings)

  • "<input.data.arg.name>.order.column" accepts character or vector of character (Strings)

  • "local.order.<input.data.arg.name>" accepts logical

Note:
These generic arguments are supported by tdplyr if the underlying SQL Engine function supports, else an exception is raised.

Value

Function returns an object of class "td_random_projection_transform_sqle" which is a named list containing object of class "tbl_teradata".
Named list member(s) can be referenced directly with the "$" operator using the name(s):result

Examples

  
    
    # Get the current context/connection.
    con <- td_get_context()$connection
    
    # Load the example data.
    loadExampleData("tdplyr_example", "stock_movement")
    
    # Create tbl_teradata object.
    stock_movement <- tbl(con, "stock_movement")
    
    # Check the list of available analytic functions.
    display_analytic_functions()
    
    # Example 1 : Get random projection matrix for
    #             stock_movement tbl_teradata.
    fit_obj <- td_random_projection_fit_sqle(data=stock_movement,
                                             target.columns="1:",
                                             epsilon=0.9,
                                             num.components=343)
    
    # Generate feature matrix. Note that tbl_teradata representing
    # the model is passed as input to "object".
    RandomProjectionTransform_out <- td_random_projection_transform_sqle(
                                                              data=stock_movement,
                                                              object=fit_obj$result,
                                                              )
    
    # Print the result.
    print(RandomProjectionTransform_out$result)
    
    # Example 2 : Generate feature matrix. Note that model is passed as instance of
    #             td_random_projection_fit_sqle to "object".
    RandomProjectionTransform_out1 <- td_random_projection_transform_sqle(
                                                               data=stock_movement,
                                                               object=fit_obj,
                                                               )
    
    # Print the result.
    print(RandomProjectionTransform_out1$result)
    
    
    # Alternatively use S3 transform function to run transform on the output of
    # td_random_projection_fit_sqle() function.
    
    obj1 <- transform(fit_obj,
                      data=stock_movement)
    
    # Print the result.
    print(obj1$result)