smile.feature

Class SparseOneHotEncoder

java.lang.Object

smile.feature.SparseOneHotEncoder

public class SparseOneHotEncoder
extends java.lang.Object

Encode categorical integer features using sparse one-hot scheme. All variables should be nominal attributes and will be converted to binary dummy variables in a compact representation in which only indices of nonzero elements are stored in an integer array. In Maximum Entropy Classifier, the data are expected to store in this format.

Constructor Summary

Constructors

Constructor and Description
SparseOneHotEncoder(smile.data.type.StructType schema) Constructor.

Method Summary

Modifier and Type	Method and Description
int[][]	apply(smile.data.DataFrame data) Generates the compact representation of sparse binary features for a data frame.
int[]	apply(smile.data.Tuple x) Generates the compact representation of sparse binary features for given object.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

SparseOneHotEncoder

public SparseOneHotEncoder(smile.data.type.StructType schema)

Constructor.

Parameters:

schema - the variable attributes. All of them have to be nominal attributes.

Method Detail

apply

public int[] apply(smile.data.Tuple x)

Generates the compact representation of sparse binary features for given object.

Parameters:

x - an object of interest.

Returns:

an integer array of nonzero binary features.

apply

public int[][] apply(smile.data.DataFrame data)

Generates the compact representation of sparse binary features for a data frame.

Parameters:

data - a data frame.