Developer Guide for Intel® Data Analytics Acceleration Library 2018 Update 3
kNN classification follows the general workflow described in Usage Model: Training and Prediction.
For a description of the input and output, refer to Usage Model: Training and Prediction.
At the training stage, K-D tree based kNN classifier has the following parameters:
Parameter |
Default Value |
Description |
|
---|---|---|---|
algorithmFPType |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
|
method |
defaultDense |
The computation method used by the K-D tree based kNN classification. The only training method supported so far is the default dense method. |
|
DEPRECATED: seed |
777 |
NoteThis parameter is deprecated and will be removed in a future release.The seed for random number generators, which are used internally to perform sampling needed to choose dimensions and cut-points for the K-D tree. |
|
dataUseInModel |
doNotUse |
A parameter to enable/disable use of the input data set in the kNN model. Possible values:
The algorithm reorders feature vectors and corresponding labels in the input data set or its copy to improve performance at the prediction stage. If the value is doUse, do not deallocate the memory for input data and labels. |
|
engine |
SharePtr< engines:: mt19937:: Batch>() |
Pointer to the random number generator engine that is used internally to perform sampling needed to choose dimensions and cut-points for the K-D tree. |
For a description of the input and output, refer to Usage Model: Training and Prediction.
At the prediction stage, K-D tree based kNN classifier has the following parameters:
Parameter |
Default Value |
Description |
|
---|---|---|---|
algorithmFPType |
float |
The floating-point type that the algorithm uses for intermediate computations. Can be float or double. |
|
method |
defaultDense |
The computation method used by the K-D tree based kNN classification. The only prediction method supported so far is the default dense method. |
|
k |
1 |
The number of neighbors. |
C++: kdtree_knn_dense_batch.cpp
Java*: KDTreeKNNDenseBatch.java
Python*: kdtree_knn_dense_batch.py