Description
The SVMDensePredict (td_svm_dense_predict_mle
) function takes the
model output by the function SVMDense (td_svm_dense_mle
) and a set
of test samples in dense format and outputs a prediction for each sample.
Usage
td_svm_dense_predict_mle ( object = NULL, newdata = NULL, attribute.columns = NULL, sample.id.column = NULL, accumulate.label = NULL, output.class.num = NULL, output.response.probdist = TRUE, output.responses = NULL, newdata.sequence.column = NULL, object.sequence.column = NULL, newdata.order.column = NULL, object.order.column = NULL ) ## S3 method for class 'td_svm_dense_mle' predict( object = NULL, newdata = NULL, attribute.columns = NULL, sample.id.column = NULL, accumulate.label = NULL, output.class.num = NULL, output.response.probdist = TRUE, output.responses = NULL, newdata.sequence.column = NULL, object.sequence.column = NULL, newdata.order.column = NULL, object.order.column = NULL )
Arguments
object |
Required Argument. |
object.order.column |
Optional Argument. |
newdata |
Required Argument. |
newdata.order.column |
Optional Argument. |
attribute.columns |
Required Argument. |
sample.id.column |
Required Argument. |
accumulate.label |
Optional Argument. |
output.class.num |
Optional Argument.
Types: numeric |
output.response.probdist |
Optional Argument. |
output.responses |
Optional Argument.
Types: character OR vector of characters |
newdata.sequence.column |
Optional Argument. |
object.sequence.column |
Optional Argument. |
Value
Function returns an object of class "td_svm_dense_predict_mle" which is a named list containing Teradata tbl object. Named list member can be referenced directly with the "$" operator using name: result.
Examples
# Get the current context/connection con <- td_get_context()$connection # Load example data. loadExampleData("svmdense_example", "svm_iris_train") loadExampleData("svmdensepredict_example", "svm_iris_test") # Create remote tibble objects. svm_iris_train <- tbl(con, "svm_iris_train") svm_iris_test <- tbl(con, "svm_iris_test") # Example 1 - Linear Model # Create the Model td_svm_dense_linear <- td_svm_dense_mle(data = svm_iris_train, sample.id.column = "id", attribute.columns = c('sepal_length', 'sepal_width' , 'petal_length' , 'petal_width'), kernel.function = "linear", label.column = "species", cost = 1, bias = 0, max.step = 100, seed = 1 ) # Run predict on the test data using the model generated td_svm_dense_predict_mle_out1 <- td_svm_dense_predict_mle(object = td_svm_dense_linear, newdata = svm_iris_test, attribute.columns=c('sepal_length','sepal_width','petal_length','petal_width'), sample.id.column = "id", accumulate.label = c("id","species"), output.class.num = 2 ) # Example 2 - Polynomial Model # Create the Model td_svm_dense_polynomial <- td_svm_dense_mle(data = svm_iris_train, sample.id.column = "id", attribute.columns = c('sepal_length', 'sepal_width' , 'petal_length' , 'petal_width'), kernel.function = "polynomial", gamma = 0.1, degree = 2, subspace.dimension = 120, hash.bits = 512, label.column = "species", cost = 1, bias = 0, max.step = 100, seed = 1 ) # Run predict on the test data using the model generated td_svm_dense_predict_mle_out2 <- td_svm_dense_predict_mle(object = td_svm_dense_polynomial, newdata = svm_iris_test, attribute.columns=c('sepal_length','sepal_width','petal_length','petal_width'), sample.id.column = "id", accumulate.label = c("id","species") ) # Example 3 - Radial Basis Model (RBF) Model # Create the Model td_svm_dense_rbf <- td_svm_dense_mle(data = svm_iris_train, sample.id.column = "id", attribute.columns = c('sepal_length', 'sepal_width' , 'petal_length' , 'petal_width'), kernel.function = "rbf", gamma = 0.1, subspace.dimension = 120, hash.bits = 512, label.column = "species", cost = 1, bias = 0, max.step = 100, seed = 1 ) # Run predict on the test data using the model generated td_svm_dense_predict_mle_out3 <- td_svm_dense_predict_mle(object = td_svm_dense_rbf, newdata = svm_iris_test, attribute.columns=c('sepal_length','sepal_width','petal_length','petal_width'), sample.id.column = "id", accumulate.label = c("id","species"), output.responses = c("setosa","virginica","versicolor") ) # Example 4 - Sigmoid Model # Create the Model td_svm_dense_sigmoid <- td_svm_dense_mle(data = svm_iris_train, sample.id.column = "id", attribute.columns = c('sepal_length', 'sepal_width' , 'petal_length' , 'petal_width'), kernel.function = "sigmoid", gamma = 0.1, subspace.dimension = 120, hash.bits = 512, label.column = "species", cost = 1, bias = 0, max.step = 30, seed = 1 ) # Run predict on the test data using the model generated td_svm_dense_predict_mle_out4 <- td_svm_dense_predict_mle(object = td_svm_dense_sigmoid$model.table, newdata = svm_iris_test, attribute.columns=c('sepal_length','sepal_width','petal_length','petal_width'), sample.id.column = "id", accumulate.label = c("id","species"), output.responses = c("virginica") ) # Example 5 - Alternatively use the predict S3 method for prediction predict_out_linear <- predict(td_svm_dense_linear, newdata = svm_iris_test, attribute.columns=c('sepal_length','sepal_width','petal_length','petal_width'), sample.id.column = "id", accumulate.label = c("id","species"), output.class.num = 2 )