j143 commented on a change in pull request #936:
URL: https://github.com/apache/systemml/pull/936#discussion_r435073548
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File path: dev/docs/builtins-reference.md
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@@ -415,3 +415,64 @@ y = X %*% rand(rows=ncol(X), cols=1)
y = normalize(X = X)
```
+
+## `msvm`-Function
+
+The `msvm`-function implements builtin multiclass SVM with squared slack
variables
+It learns one-against-the-rest binary-class classifiers by making a function
call to l2SVM
+
+### Usage
+---
+msvm(X,Y, icpt=False, num_classes=10, epsilon=0.001, lamda=1.0, maxiter=100,
verbose=False);
+
+---
+
+### Arguments
+| Name | Type | Default | Description |
+| :------ | :------------- | -------- | :---------- |
+| X | Double | --- | Matrix X of feature vectors.|
+| Y | Double | --- | Matrix Y of class labels. |
+| intercept | Boolean | False | No Intercept ( If set to
TRUE then a constant bias column is added to X)|
+| num_classes | Integer | 10 | Number of classes.|
+| epsilon | Double | 0.001 | Procedure terminates early
if the reduction in objective function value is less than
epsilon (tolerance) times the initial objective
function value.|
+| lamda | Double | 1.0 | Regularization parameter
(lambda) for L2 regularization|
+| maxIterations | Interger | 100 | Maximum number of conjugate
gradient iterations|
+| verbose | Boolean | False | Set to true to print while
training.|
+
+
+### Returns
+| Name | Type | Default | Description |
+| :------ | :------------- | -------- | :---------- |
+| model | Double | --- | Model matrix. |
+
+
+### Example
+```
+m_msvm = function(Matrix[Double] X, Matrix[Double] Y, Boolean intercept =
FALSE,
+ Double epsilon = 0.001, Double lambda = 1.0, Integer maxIterations = 100,
Boolean verbose = FALSE)
+ return(Matrix[Double] model)
+{
+ if(min(Y) < 0)
+ stop("MSVM: Invalid Y input, containing negative values")
+ if(verbose)
+ print("Running Multiclass-SVM")
+ num_rows_in_w = ncol(X)
+ if(intercept) {
+ num_rows_in_w = num_rows_in_w + 1
+ }
+ if(ncol(Y) > 1)
+ Y = rowMaxs(Y * t(seq(1,ncol(Y))))
+ # Assuming number of classes to be max contained in Y
+ w = matrix(0, rows=num_rows_in_w, cols=max(Y))
+ parfor(class in 1:max(Y)) {
+ Y_local = 2 * (Y == class) - 1
+ w[,class] = l2svm(X=X, Y=Y_local, intercept=intercept,
+ epsilon=epsilon, lambda=lambda, maxIterations=maxIterations,
+ verbose= verbose, columnId=class)
+ }
+
+ model = w
+}
+```
Review comment:
Can we remove these lines?. 😺
We will work with the `Example` as a different task. because
We got to do some small experiment with `Example`.
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