Description
The SparseSVMPredictor function takes the model generated by the
function SparseSVMTrainer (td_svm_sparse_mle
) and a set of test samples
(in sparse format) and outputs a prediction for each sample.
Usage
td_svm_sparse_predict_sqle ( object = NULL, newdata = NULL, sample.id.column = NULL, attribute.column = NULL, value.column = NULL, accumulate.label = NULL, output.class.num = 1, newdata.partition.column = NULL, newdata.order.column = NULL, object.order.column = NULL ) ## S3 method for class 'td_svm_sparse_mle' predict( object = NULL, newdata = NULL, sample.id.column = NULL, attribute.column = NULL, value.column = NULL, accumulate.label = NULL, output.class.num = 1, newdata.partition.column = NULL, newdata.order.column = NULL, object.order.column = NULL)
Arguments
object |
Required Argument. |
object.order.column |
Optional Argument. |
newdata |
Required Argument. |
newdata.partition.column |
Required Argument. |
newdata.order.column |
Optional Argument. |
sample.id.column |
Required Argument. |
attribute.column |
Required Argument. |
value.column |
Optional Argument. |
accumulate.label |
Optional Argument. |
output.class.num |
Optional Argument. |
Value
Function returns an object of class "td_svm_sparse_predict_sqle"
which is a named list containing object of class "tbl_teradata".
Named list member can be referenced directly with the "$" operator
using the name: result.
Examples
# Get the current context/connection con <- td_get_context()$connection # Load example data. loadExampleData("svmsparsepredict_example", "svm_iris_input_test", "svm_iris_input_train") # Create object(s) of class "tbl_teradata". svm_iris_input_train <- tbl(con, "svm_iris_input_train") svm_iris_input_test <- tbl(con, "svm_iris_input_test") # Example - # Create the Sparse SVM model. svm_train <- td_svm_sparse_mle(data = svm_iris_input_train, sample.id.column = 'id', attribute.column = 'attribute', value.column = 'value1', label.column = 'species', max.step = 150, seed = 0 ) # Run predict on the output of td_svm_sparse_mle() function. svm_sparse_predict_result <- td_svm_sparse_predict_sqle(newdata = svm_iris_input_test, newdata.partition.column = c("id"), object = svm_train, sample.id.column = "id", attribute.column = "attribute", value.column = "value1", accumulate.label = c("species") ) # Alternatively use S3 predict on the output of td_svm_sparse_mle() to find prediction. predict_out <- predict(svm_train, newdata = svm_iris_input_test, newdata.partition.column = c("id"), sample.id.column = "id", attribute.column = "attribute", value.column = "value1", accumulate.label = c("species") )