[DOC] multiLogReg and intersect builtin func.
Closes #954.
Closes #961.
diff --git a/dev/docs/builtins-reference.md b/dev/docs/builtins-reference.md
index a9b832b..ad0073b 100644
--- a/dev/docs/builtins-reference.md
+++ b/dev/docs/builtins-reference.md
@@ -32,12 +32,14 @@
* [`img_crop`-Function](#img_crop-function)
* [`img_mirror`-Function](#img_mirror-function)
* [`imputeByFD`-Function](#imputeByFD-function)
+ * [`intersect`-Function](#intersect-function)
* [`KMeans`-Function](#KMeans-function)
* [`lm`-Function](#lm-function)
* [`lmDS`-Function](#lmds-function)
* [`lmCG`-Function](#lmcg-function)
* [`lmpredict`-Function](#lmpredict-function)
* [`mice`-Function](#mice-function)
+ * [`multiLogReg`-Function](#multiLogReg-function)
* [`pnmf`-Function](#pnmf-function)
* [`scale`-Function](#scale-function)
* [`sigmoid`-Function](#sigmoid-function)
@@ -475,6 +477,27 @@
lm(X = X, y = y)
```
+## `intersect`-Function
+
+The `intersect`-function implements set intersection for numeric data.
+
+### Usage
+```r
+intersect(X, Y)
+```
+
+### Arguments
+| Name | Type | Default | Description |
+| :--- | :----- | -------- | :---------- |
+| X | Double | -- | matrix X, set A |
+| Y | Double | -- | matrix Y, set B |
+
+### Returns
+| Type | Description |
+| :----- | :---------- |
+| Double | intersection matrix, set of intersecting items |
+
+
## `lmDS`-Function
The `lmDS`-function solves linear regression by directly solving the *linear system*.
@@ -597,6 +620,40 @@
[dataset, singleSet] = mice(F, cMask, iter = 3, complete = 3, verbose = FALSE)
```
+## `multiLogReg`-Function
+
+The `multiLogReg`-function solves Multinomial Logistic Regression using Trust Region method.
+(See: Trust Region Newton Method for Logistic Regression, Lin, Weng and Keerthi, JMLR 9 (2008) 627-650)
+
+### Usage
+```r
+multiLogReg(X, Y, icpt, reg, tol, maxi, maxii, verbose)
+```
+
+### Arguments
+| Name | Type | Default | Description |
+| :---- | :----- | ------- | :---------- |
+| X | Double | -- | The matrix of feature vectors |
+| Y | Double | -- | The matrix with category labels |
+| icpt | Int | `0` | Intercept presence, shifting and rescaling X columns: 0 = no intercept, no shifting, no rescaling; 1 = add intercept, but neither shift nor rescale X; 2 = add intercept, shift & rescale X columns to mean = 0, variance = 1 |
+| reg | Double | `0` | regularization parameter (lambda = 1/C); intercept is not regularized |
+| tol | Double | `1e-6` | tolerance ("epsilon") |
+| maxi | Int | `100` | max. number of outer newton interations |
+| maxii | Int | `0` | max. number of inner (conjugate gradient) iterations |
+
+### Returns
+| Type | Description |
+| :----- | :---------- |
+| Double | Regression betas as output for prediction |
+
+### Example
+```r
+X = rand(rows = 50, cols = 30)
+Y = X %*% rand(rows = ncol(X), cols = 1)
+betas = multiLogReg(X = X, Y = Y, icpt = 2, tol = 0.000001, reg = 1.0, maxi = 100, maxii = 20, verbose = TRUE)
+```
+
+
## `pnmf`-Function
The `pnmf`-function implements Poisson Non-negative Matrix Factorization (PNMF). Matrix `X` is factorized into
