[DOC][1/2] imputeByFD and discoverFD builtin func.
Closes #969.
diff --git a/dev/docs/builtins-reference.md b/dev/docs/builtins-reference.md
index 8b29ee9..a9b832b 100644
--- a/dev/docs/builtins-reference.md
+++ b/dev/docs/builtins-reference.md
@@ -25,11 +25,13 @@
* [DML-Bodied Built-In functions](#dml-bodied-built-in-functions)
* [`confusionMatrix`-Function](#confusionmatrix-function)
* [`cvlm`-Function](#cvlm-function)
+ * [`discoverFD`-Function](#discoverFD-function)
* [`glm`-Function](#glm-function)
* [`gridSearch`-Function](#gridSearch-function)
* [`img_brightness`-Function](#img_brightness-function)
* [`img_crop`-Function](#img_crop-function)
* [`img_mirror`-Function](#img_mirror-function)
+ * [`imputeByFD`-Function](#imputeByFD-function)
* [`KMeans`-Function](#KMeans-function)
* [`lm`-Function](#lm-function)
* [`lmDS`-Function](#lmds-function)
@@ -196,6 +198,28 @@
[predict, beta] = cvlm(X = X, y = y, k = 4)
```
+## `discoverFD`-Function
+
+The `discoverFD`-function finds the functional dependencies.
+
+### Usage
+```r
+discoverFD(X, Mask, threshold)
+```
+
+### Arguments
+| Name | Type | Default | Description |
+| :-------- | :----- | ------- | :---------- |
+| X | Double | -- | Input Matrix X, encoded Matrix if data is categorical |
+| Mask | Double | -- | A row vector for interested features i.e. Mask =[1, 0, 1] will exclude the second column from processing |
+| threshold | Double | -- | threshold value in interval [0, 1] for robust FDs |
+
+### Returns
+| Type | Description |
+| :----- | :---------- |
+| Double | matrix of functional dependencies |
+
+
## `glm`-Function
The `glm`-function is a flexible generalization of ordinary linear regression that allows for response variables that have
@@ -356,6 +380,30 @@
B = img_mirror(img_in = A, horizontal_axis = TRUE)
```
+## `imputeByFD`-Function
+
+The `imputeByFD`-function imputes missing values from observed values (if exist)
+using robust functional dependencies.
+
+### Usage
+```r
+imputeByFD(F, sourceAttribute, targetAttribute, threshold)
+```
+
+### Arguments
+| Name | Type | Default | Description |
+| :-------- | :------ | -------- | :---------- |
+| F | String | -- | A data frame |
+| source | Integer | -- | Source attribute to use for imputation and error correction |
+| target | Integer | -- | Attribute to be fixed |
+| threshold | Double | -- | threshold value in interval [0, 1] for robust FDs |
+
+### Returns
+| Type | Description |
+| :----- | :---------- |
+| String | Frame with possible imputations |
+
+
## `KMeans`-Function
The kmeans() implements the KMeans Clustering algorithm.
