Please note that, as implied in the post above, the log scale generator is actually a cheat, based on noticing that the input & output ranges specified happened to lend themselves to a simplified calculation. Ie. log( 8000 ) + 1 is very close to 10.
However, that means that genLogScaler() is basically ignoring most of its input parameters and thus won't work for the generic case.
Here's a fixed version that works for your case and (a couple of other tested examples of) the generic case:
#! perl -slw use strict; sub genScaler { my( $minout, $maxout, $minin, $maxin ) = @_; my $outRange = $maxout - $minout; my $inRange = $maxin - $minin; my $factor = $inRange / $outRange; sub { my $in = shift; return $minout + ( $in / $factor ); } } sub genLogScaler { my( $minout, $maxout, $minin, $maxin ) = @_; my $outRange = $maxout - $minout; my $inRange = log( $maxin - $minin ); my $factor = $inRange / $outRange; sub { my $in = shift; $in = 1 if $in < 1; return $minout + ( log( $in ) / $factor ); } } my $fscale = genScaler( 1, 10, 0.1, 8000 ); print "Linear:"; printf "%7.1f -> %.1f\n", $_, $fscale->( $_ ) for 0.1, 1, 10, 100, 800 +, 1000, 4000, 6000, 8000; print "\nLog"; my $lscale = genLogScaler( 1, 10, 0.1, 8000 ); printf "%7.1f -> %.1f\n", $_, $lscale->( $_ ) for 0.1, 1, 10, 100, 800 +, 1000, 4000, 6000, 8000; print "\nNother Log:"; my $lscale2 = genLogScaler( -5, +5, 0.1, 8000 ); printf "%7.1f -> %.1f\n", $_, $lscale2->( $_ ) for 0.1, 1, 10, 100, 80 +0, 1000, 4000, 6000, 8000; print "\nNother Log:"; my $lscale3 = genLogScaler( 0, +1000, 0.1, 8000 ); printf "%7.1f -> %.1f\n", $_, $lscale3->( $_ ) for 0.1, 1, 10, 100, 80 +0, 1000, 4000, 6000, 8000;
Produces:
C:\test>genScalar.pl Linear: 0.1 -> 1.0 1.0 -> 1.0 10.0 -> 1.0 100.0 -> 1.1 800.0 -> 1.9 1000.0 -> 2.1 4000.0 -> 5.5 6000.0 -> 7.8 8000.0 -> 10.0 Log 0.1 -> 1.0 1.0 -> 1.0 10.0 -> 3.3 100.0 -> 5.6 800.0 -> 7.7 1000.0 -> 7.9 4000.0 -> 9.3 6000.0 -> 9.7 8000.0 -> 10.0 Nother Log: 0.1 -> -5.0 1.0 -> -5.0 10.0 -> -2.4 100.0 -> 0.1 800.0 -> 2.4 1000.0 -> 2.7 4000.0 -> 4.2 6000.0 -> 4.7 8000.0 -> 5.0 Nother Log: 0.1 -> 0.0 1.0 -> 0.0 10.0 -> 256.2 100.0 -> 512.4 800.0 -> 743.8 1000.0 -> 768.6 4000.0 -> 922.9 6000.0 -> 968.0 8000.0 -> 1000.0
But note: it still contains the hack-y $in = 1 if $in < 1; fix.
In reply to Re^4: Data Normalization (homework?)
by BrowserUk
in thread RE: Data Normalization
by lonewolf28
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