Check in the pristine copy of ICU 4.6...

icu46/source/test/perf/perldriver/Dataset.pm

+#!/usr/local/bin/perl
+#  ********************************************************************
+#  * COPYRIGHT:
+#  * Copyright (c) 2002, International Business Machines Corporation and
+#  * others. All Rights Reserved.
+#  ********************************************************************
+
+package Dataset;
+use Statistics::Descriptive;
+use Statistics::Distributions;
+use strict;
+
+# Create a new Dataset with the given data.
+sub new {
+    my ($class) = shift;
+    my $self = bless {
+        _data => \@_,
+        _scale => 1.0,
+        _mean => 0.0,
+        _error => 0.0,
+    }, $class;
+
+    my $n = @_;
+    
+    if ($n >= 1) {
+        my $stats = Statistics::Descriptive::Full->new();
+        $stats->add_data(@{$self->{_data}});
+        $self->{_mean} = $stats->mean();
+
+        if ($n >= 2) {
+            # Use a t distribution rather than Gaussian because (a) we
+            # assume an underlying normal dist, (b) we do not know the
+            # standard deviation -- we estimate it from the data, and (c)
+            # we MAY have a small sample size (also works for large n).
+            my $t = Statistics::Distributions::tdistr($n-1, 0.005);
+            $self->{_error} = $t * $stats->standard_deviation();
+        }
+    }
+
+    $self;
+}
+
+# Set a scaling factor for all data; 1.0 means no scaling.
+# Scale must be > 0.
+sub setScale {
+    my ($self, $scale) = @_;
+    $self->{_scale} = $scale;
+}
+
+# Multiply the scaling factor by a value.
+sub scaleBy {
+    my ($self, $a) = @_;
+    $self->{_scale} *= $a;
+}
+
+# Return the mean.
+sub getMean {
+    my $self = shift;
+    return $self->{_mean} * $self->{_scale};
+}
+
+# Return a 99% error based on the t distribution.  The dataset
+# is desribed as getMean() +/- getError().
+sub getError {
+    my $self = shift;
+    return $self->{_error} * $self->{_scale};
+}
+
+# Divide two Datasets and return a new one, maintaining the
+# mean+/-error.  The new Dataset has no data points.
+sub divide {
+    my $self = shift;
+    my $rhs = shift;
+    
+    my $minratio = ($self->{_mean} - $self->{_error}) /
+                   ($rhs->{_mean} + $rhs->{_error});
+    my $maxratio = ($self->{_mean} + $self->{_error}) /
+                   ($rhs->{_mean} - $rhs->{_error});
+
+    my $result = Dataset->new();
+    $result->{_mean} = ($minratio + $maxratio) / 2;
+    $result->{_error} = $result->{_mean} - $minratio;
+    $result->{_scale} = $self->{_scale} / $rhs->{_scale};
+    $result;
+}
+
+# subtracts two Datasets and return a new one, maintaining the
+# mean+/-error.  The new Dataset has no data points.
+sub subtract {
+    my $self = shift;
+    my $rhs = shift;
+    
+    my $result = Dataset->new();
+    $result->{_mean} = $self->{_mean} - $rhs->{_mean};
+    $result->{_error} = $self->{_error} + $rhs->{_error};
+    $result->{_scale} = $self->{_scale};
+    $result;
+}
+
+# adds two Datasets and return a new one, maintaining the
+# mean+/-error.  The new Dataset has no data points.
+sub add {
+    my $self = shift;
+    my $rhs = shift;
+    
+    my $result = Dataset->new();
+    $result->{_mean} = $self->{_mean} + $rhs->{_mean};
+    $result->{_error} = $self->{_error} + $rhs->{_error};
+    $result->{_scale} = $self->{_scale};
+    $result;
+}
+
+# Divides a dataset by a scalar.
+# The new Dataset has no data points.
+sub divideByScalar {
+    my $self = shift;
+    my $s = shift;
+    
+    my $result = Dataset->new();
+    $result->{_mean} = $self->{_mean}/$s;
+    $result->{_error} = $self->{_error}/$s;
+    $result->{_scale} = $self->{_scale};
+    $result;
+}
+
+# Divides a dataset by a scalar.
+# The new Dataset has no data points.
+sub multiplyByScalar {
+    my $self = shift;
+    my $s = shift;
+    
+    my $result = Dataset->new();
+    $result->{_mean} = $self->{_mean}*$s;
+    $result->{_error} = $self->{_error}*$s;
+    $result->{_scale} = $self->{_scale};
+    $result;
+}
+
+1;