This is an automated email from the ASF dual-hosted git repository.
janardhan pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/systemds.git
The following commit(s) were added to refs/heads/master by this push:
new 28da55f [SYSTEMDS-2869][DOC] Built-in functions KNN and KNNBF (#1255)
28da55f is described below
commit 28da55fea4c8c1c45ba215ccb4fc29a7d4014025
Author: j143 <[email protected]>
AuthorDate: Thu May 6 02:07:25 2021 +0530
[SYSTEMDS-2869][DOC] Built-in functions KNN and KNNBF (#1255)
---
docs/site/builtins-reference.md | 50 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 50 insertions(+)
diff --git a/docs/site/builtins-reference.md b/docs/site/builtins-reference.md
index c46de37..0f37d87 100644
--- a/docs/site/builtins-reference.md
+++ b/docs/site/builtins-reference.md
@@ -43,6 +43,7 @@ limitations under the License.
* [`imputeByFD`-Function](#imputeByFD-function)
* [`intersect`-Function](#intersect-function)
* [`KMeans`-Function](#KMeans-function)
+ * [`KNN`-function](#KNN-function)
* [`lm`-Function](#lm-function)
* [`lmDS`-Function](#lmds-function)
* [`lmCG`-Function](#lmcg-function)
@@ -677,6 +678,55 @@ X = rand (rows = 3972, cols = 972)
kmeans(X = X, k = 20, runs = 10, max_iter = 5000, eps = 0.000001, is_verbose =
FALSE, avg_sample_size_per_centroid = 50, seed = -1)
```
+## `KNN`-Function
+
+The knn() implements the KNN (K Nearest Neighbor) algorithm.
+
+### Usage
+
+```r
+[NNR, PR, FI] = knn(Train, Test, CL, k_value)
+```
+
+### Arguments
+
+| Name | Type | Default | Description |
+| :--------- | :-------------- | :--------- | :---------- |
+| Train | Matrix | required | The input matrix as features |
+| Test | Matrix | required | Number of centroids |
+| CL | Matrix | Optional | The input matrix as target |
+| CL_T | Integer | `0` | The target type of matrix CL
whether columns in CL are continuous ( =1 ) or categorical ( =2 ) or not
specified ( =0 ) |
+| trans_continuous | Boolean | `FALSE` | Whether to transform continuous
features to [-1,1] |
+| k_value | int | `5` | k value for KNN, ignore if select_k enable |
+| select_k | Boolean | `FALSE` | Use k selection algorithm to estimate k (
TRUE means yes ) |
+| k_min | int | `1`| Min k value( available if select_k = 1 ) |
+| k_max | int | `100` | Max k value( available if select_k = 1 ) |
+| select_feature | Boolean | `FALSE` | Use feature selection algorithm to
select feature ( TRUE means yes ) |
+| feature_max | int | `10` | Max feature selection |
+| interval | int | `1000` | Interval value for K selecting ( available
if select_k = 1 ) |
+| feature_importance | Boolean | `FALSE` | Use feature importance algorithm to
estimate each feature ( TRUE means yes ) |
+| predict_con_tg | int | `0` | Continuous target predict function: mean(=0)
or median(=1) |
+| START_SELECTED | Matrix | Optional | feature selection initial value |
+
+### Returns
+
+| Type | Description |
+| :----- | :---------- |
+| Matrix | NNR |
+| Matrix | PR |
+| Matrix | Feature importance value |
+
+### Example
+
+```r
+X = rand(rows = 100, cols = 20)
+T = rand(rows= 3, cols = 20) # query rows, and columns
+CL = matrix(seq(1,100), 100, 1)
+k = 3
+[NNR, PR, FI] = knn(Train=X, Test=T, CL=CL, k_value=k, predict_con_tg=1)
+```
+
+
## `lm`-Function
The `lm`-function solves linear regression using either the **direct solve
method** or the **conjugate gradient algorithm**
