140 lines
3.7 KiB
Perl
140 lines
3.7 KiB
Perl
#!/usr/local/bin/perl
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# ********************************************************************
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# * COPYRIGHT:
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# * Copyright (c) 2006, International Business Machines Corporation and
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# * others. All Rights Reserved.
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# ********************************************************************
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package Dataset;
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use Statistics::Descriptive;
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use Statistics::Distributions;
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use strict;
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# Create a new Dataset with the given data.
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sub new {
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my ($class) = shift;
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my $self = bless {
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_data => \@_,
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_scale => 1.0,
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_mean => 0.0,
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_error => 0.0,
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}, $class;
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my $n = @_;
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if ($n >= 1) {
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my $stats = Statistics::Descriptive::Full->new();
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$stats->add_data(@{$self->{_data}});
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$self->{_mean} = $stats->mean();
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if ($n >= 2) {
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# Use a t distribution rather than Gaussian because (a) we
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# assume an underlying normal dist, (b) we do not know the
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# standard deviation -- we estimate it from the data, and (c)
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# we MAY have a small sample size (also works for large n).
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my $t = Statistics::Distributions::tdistr($n-1, 0.005);
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$self->{_error} = $t * $stats->standard_deviation();
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}
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}
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$self;
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}
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# Set a scaling factor for all data; 1.0 means no scaling.
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# Scale must be > 0.
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sub setScale {
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my ($self, $scale) = @_;
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$self->{_scale} = $scale;
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}
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# Multiply the scaling factor by a value.
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sub scaleBy {
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my ($self, $a) = @_;
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$self->{_scale} *= $a;
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}
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# Return the mean.
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sub getMean {
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my $self = shift;
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return $self->{_mean} * $self->{_scale};
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}
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# Return a 99% error based on the t distribution. The dataset
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# is desribed as getMean() +/- getError().
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sub getError {
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my $self = shift;
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return $self->{_error} * $self->{_scale};
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}
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# Divide two Datasets and return a new one, maintaining the
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# mean+/-error. The new Dataset has no data points.
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sub divide {
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my $self = shift;
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my $rhs = shift;
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my $minratio = ($self->{_mean} - $self->{_error}) /
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($rhs->{_mean} + $rhs->{_error});
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my $maxratio = ($self->{_mean} + $self->{_error}) /
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($rhs->{_mean} - $rhs->{_error});
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my $result = Dataset->new();
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$result->{_mean} = ($minratio + $maxratio) / 2;
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$result->{_error} = $result->{_mean} - $minratio;
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$result->{_scale} = $self->{_scale} / $rhs->{_scale};
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$result;
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}
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# subtracts two Datasets and return a new one, maintaining the
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# mean+/-error. The new Dataset has no data points.
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sub subtract {
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my $self = shift;
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my $rhs = shift;
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my $result = Dataset->new();
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$result->{_mean} = $self->{_mean} - $rhs->{_mean};
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$result->{_error} = $self->{_error} + $rhs->{_error};
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$result->{_scale} = $self->{_scale};
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$result;
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}
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# adds two Datasets and return a new one, maintaining the
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# mean+/-error. The new Dataset has no data points.
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sub add {
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my $self = shift;
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my $rhs = shift;
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my $result = Dataset->new();
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$result->{_mean} = $self->{_mean} + $rhs->{_mean};
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$result->{_error} = $self->{_error} + $rhs->{_error};
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$result->{_scale} = $self->{_scale};
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$result;
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}
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# Divides a dataset by a scalar.
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# The new Dataset has no data points.
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sub divideByScalar {
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my $self = shift;
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my $s = shift;
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my $result = Dataset->new();
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$result->{_mean} = $self->{_mean}/$s;
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$result->{_error} = $self->{_error}/$s;
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$result->{_scale} = $self->{_scale};
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$result;
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}
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# Divides a dataset by a scalar.
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# The new Dataset has no data points.
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sub multiplyByScalar {
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my $self = shift;
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my $s = shift;
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my $result = Dataset->new();
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$result->{_mean} = $self->{_mean}*$s;
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$result->{_error} = $self->{_error}*$s;
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$result->{_scale} = $self->{_scale};
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$result;
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}
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1;
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