wizard-420 commented on a change in pull request #943:
URL: https://github.com/apache/systemml/pull/943#discussion_r435209789
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File path: dev/docs/builtins-reference.md
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@@ -291,34 +291,95 @@ y = X %*% rand(rows=ncol(X), 1)
[C, S] = steplm(X = X, y = y, icpt = 1);
```
-## `slicefinder`-Function
+## `outlier-Function
-The `slicefinder`-function returns top-k worst performing subsets according to
a model calculation.
+An outlier in a probability distribution function is a number that is more
than 1.5 times the length of the data set away from either the lower or upper
quartiles.
+Specifically, if a number is less than Q1−1.5×IQR or greater than Q3+1.5×IQR,
then it is an outlier.
### Usage
```r
-slicefinder(X,W, y, k, paq, S);
+outlier(X,opposite);
```
### Arguments
| Name | Type | Default | Description |
| :------ | :------------- | -------- | :---------- |
| X | Matrix[Double] | required | Recoded dataset into Matrix |
-| W | Matrix[Double] | required | Trained model |
-| y | Matrix[Double] | required | 1-column matrix of response values. |
-| k | Integer | 1 | Number of subsets required |
-| paq | Integer | 1 | amount of values wanted for each col,
if paq = 1 then its off |
-| S | Integer | 2 | amount of subsets to combine (for now
supported only 1 and 2) |
+|opposite| Boolean | required | Used for xor gate evaluation |
### Returns
| Type | Description |
| :------------- | :---------- |
-| Matrix[Double] | Matrix containing the information of top_K slices (relative
error, standart error, value0, value1, col_number(sort), rows,
cols,range_row,range_cols, value00, value01,col_number2(sort), rows2,
cols2,range_row2,range_cols2) |
+| Matrix[Double] | 1-column matrix of weights. |
-### Usage
+### Example
```r
X = rand (rows = 50, cols = 10)
-y = X %*% rand(rows=ncol(X), 1)
-w = lm(X = X, y = y)
-ress = slicefinder(X = X,W = w, Y = y, k = 5, paq = 1, S = 2);
+opposite = 1
+outlier(X=X,opposite=opposite)
```
+## outlierByIQR - Function
+
+Builtin function for detecting and repairing outliers using Interquartile
Range.
+A commonly used rule says that a data point is an outlier if it is more than
1.5 IQR
+above the third quartile or below the first quartile.
+outlierByIQR function computes the matrix and set's a lower-bound quartile
range and upper-bound quartile range
+and the number which is less then the lower-bound or higher then the
upper-bound is treated as a outlier, hence
+removed from the matrix.
+
+
+### Usage
+```r
+outlierByIQR(X,k,repair_method,max_iterations,verbose)
+`
+### Arguments
+| Name | Type | Default | Description |
+| :------ | :------------- | -------- | :---------- |
+| X | Matrix[Double] | required | matrix with outliers |
+|k | Double | 1.5 | a constant used to discern
outliers k*IQR
+ |isIterative| Boolean | TRUE |iterative repair or single repair
+ |repairMethod| Integer| 1 | values: 0 = delete rows having
outliers,
+ 1 = replace
outliers with zeros
+ 2 = replace
outliers as missing values
+ |max_iterations| Integer | 0 | values: 0 = arbitrary number of
iteraition until all outliers are removed,
+ n = any constant
defined by user
+### Returns
+| Type | Description |
+| :------------- | :---------- |
+| Matrix[Double] | matrix without any outlier. |
+
+### Example
+```r
+X = rand (rows=10,cols=10)
+opposite = 1
+Y = outlier(X = X, opposite = opposite)
+Z = outlierByIQR(X=Y,k=1.5,repairMethod=0,max_iterations=3,verbose=1)
+print("\n"+toString(Z))
+`
+###outlierBySd - function
+Builtin function for detecting and repairing outliers using standard deviation
+
Review comment:
thanks for your help.
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