Developer Guide for Intel® Data Analytics Acceleration Library 2019 Update 5
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:
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. |
|
nClasses |
2 |
The number of classes. |
|
k |
1 |
The number of neighbors. |
C++: kdtree_knn_dense_batch.cpp
Java*: KDTreeKNNDenseBatch.java
Python*: kdtree_knn_dense_batch.py