Gradient descent: Difference between revisions

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@@ Line 209: / Line 209: @@
     # Calculate initial norm.
-    my $delG = 0;
+    my $b = $alpha / (my $delG = (map {@fi[$_]²}, ^N).sum ).sqrt;
-    for ^N { $delG += @fi[$_]² }
-    my $b = $alpha / $delG.sqrt;
     while ( $delG > $tolerance ) {   # Iterate until value is <= tolerance.
@@ Line 221: / Line 219: @@
        # Calculate next norm.
-       $delG = 0;
+       $b = $alpha / ($delG = (map {@fi[$_]²}, ^N).sum ).sqrt;
-       for ^N { $delG += @fi[$_]² }
-       $b = $alpha / $delG.sqrt;
        my $g1 = g(@x);   # Calculate next value.
@@ Line 232: / Line 228: @@
 sub gradG(@x, $h) {   # Provides a rough calculation of gradient g(x).
-    my \N = +@x ; my ( @y , @z );
+   my \N = +@x ; my @y = @x ; my $g0 = g(@x) ;
+   return map { @y[$_] += $h ; (g(@y) - $g0) / $h }, ^N
-    @y = @x;
-    my $g0 = g(@x);
-    for ^N { @y[$_] += $h ; @z[$_] = (g(@y) - $g0) / $h }
-    return @z
 }