Baunsgaard commented on a change in pull request #1124:
URL: https://github.com/apache/systemds/pull/1124#discussion_r541622750
##########
File path: scripts/builtin/scale.dml
##########
@@ -19,29 +19,48 @@
#
#-------------------------------------------------------------
-# Scale and center individual features in the input matrix
-# (column-wise) using z-score to scale the values.
-# -----------------------------------------------------------------------------
-# NAME TYPE DEFAULT MEANING
-# -----------------------------------------------------------------------------
-# X Matrix --- Input feature matrix
-# center Boolean TRUE Indicates whether or not to center the feature matrix
-# scale Boolean TRUE Indicates whether or not to scale the feature matrix
-# -----------------------------------------------------------------------------
-# Y Matrix --- Output feature matrix with K columns
-# -----------------------------------------------------------------------------
+# Scale and center individual features in the input matrix (column wise.)
using z-score to scale the values.
+#
---------------------------------------------------------------------------------------------
+# NAME TYPE DEFAULT MEANING
+#
---------------------------------------------------------------------------------------------
+# X Matrix --- Input feature matrix
+# Center Boolean TRUE Indicates whether or not to center the
feature matrix
+# Scale Boolean TRUE Indicates whether or not to scale the
feature matrix
+#
---------------------------------------------------------------------------------------------
+# Y Matrix --- Output feature matrix with K columns
+# ColMean Matrix --- The column means of the input, subtracted if
Center was TRUE
+# ScaleFactor Matrix --- The Scaling of the values, to make each
dimension have similar value ranges
+#
---------------------------------------------------------------------------------------------
-m_scale = function(Matrix[Double] X, Boolean center, Boolean scale) return
(Matrix[Double] Y) {
- if( center )
- X = X - colMeans(X);
+m_scale = function(Matrix[Double] X, Boolean center, Boolean scale)
+ return (Matrix[Double] Y, Matrix[Double] ColMean, Matrix[Double]
ScaleFactor)
+{
+ if(center){
+ ColMean = colMeans(X)
+ X = X - ColMean
+ }
+ else {
+ ColMean = matrix(0,rows=0,cols=0)
+ }
if (scale) {
- cvars = colSums(X^2)/(nrow(X)-1);
+ N = nrow(X);
+
+ ScaleFactor = sqrt(colSums(X^2)/(N-1))
- #scale by std-dev and replace NaNs with 0's
- X = replace(target=X/sqrt(cvars),
- pattern=NaN, replacement=0);
+ # Replace entries in the scale factor that are 0 with an small eps
+ # To avoid division by 0, and thereby introducing NaN to the ouput.
+ ScaleFactor = replace(target=ScaleFactor,
+ pattern=NaN, replacement=1e-16);
Review comment:
Alternatively we could replace the values with 1, that would maybe be
better since that would remove the scaling of that dimension.
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