ALBD

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t/test.t  view on Meta::CPAN

#!/usr/local/bin/perl -w

# Before `make install' is performed this script should be runnable with
# `make test'. After `make install' it should work as `perl t/lch.t'

use strict;
use warnings;
use Test::Simple tests => 10;

#error tolerance for exact numerical matches due to precision issues 
# and sort issues (again due to precision) there may be small
# differences between runs. The precision at K difference is 
# larger due to small differences in ranking making big differences
# in scores when the K < 10. See Rank::rankDescending for more
# details as to why the ranking imprecision occurrs
my $precRecallErrorTol = 0.0001;
my $atKErrorTol = 1.0;

#######################################################
# test script to run the sample code and compare its 

t/test.t  view on Meta::CPAN

#read in new time slicing output
(my $newAPScoresRef, my $newMAP, my $newPAtKScoresRef, my $newFAtKScoresRef)
    = &readTimeSlicingData('./samples/sampleTimeSliceOutput');

#check that the correct number of values are read for all the 
# time slicing metrics
ok (scalar @{$newAPScoresRef} == 11, "Correct Count of Average Precisions");
ok (scalar @{$newPAtKScoresRef} == 19, "Correct Count of Precision at K's");
ok (scalar @{$newFAtKScoresRef} == 19, "Correct Count of Freq at K's");

#check that each of the AP scores match the gold (within error tolerance)
my $apSame = 1;
for (my $i = 0; $i < scalar @{$goldAPScoresRef}; $i++) {
    
    #check both comma seperated values (precision and recall)
    my @goldScores = split(',',${$goldAPScoresRef}[$i]);
    my @newScores = split(',',${$newAPScoresRef}[$i]);

    if ((abs($goldScores[0]-$newScores[0]) > $precRecallErrorTol)
	&& (abs($goldScores[1]-$newScores[1]) > $precRecallErrorTol)) {
	$apSame = 0;
	last;
    }
}
ok($apSame == 1, "Average Precisions Match");

#check MAP is the same (within error tolerance)
ok (abs($goldMAP - $newMAP) > $precRecallErrorTol, "Mean Average Precision Matches");

#check that each of Precision at K scores match the gold
# (within error tolerance)
my $pAtKSame = 1;
for (my $i = 0; $i < scalar @{$goldPAtKScoresRef}; $i++) {
    if (abs(${$goldPAtKScoresRef}[$i] - ${$newPAtKScoresRef}[$i]) > $atKErrorTol) {
	$pAtKSame = 0;
	last;
    }
}
ok($pAtKSame == 1, "Precision at K Matches");

#check that each of the Freq at K scores match the gold 
# (within error tolerance)
my $fAtKSame = 1;
for (my $i = 0; $i < scalar @{$goldFAtKScoresRef}; $i++) {
    if (abs(${$goldFAtKScoresRef}[$i] - ${$newFAtKScoresRef}[$i]) > $atKErrorTol) {
	$fAtKSame = 0;
	last;
    }
}
ok($fAtKSame == 1, "Frequency at K Matches");

print "Done with Time Slicing Tests\n";

utils/datasetCreator/squaring/squareMatrix.m  view on Meta::CPAN

%nohup matlab -nojvm -r squareMatrix -logfile squareMatrix_matlab.out </dev/null &

clear all;
close all;

sparseSquare('/home/henryst/lbdData/squaring/1975_1999_window8_noOrder','/home/henryst/lbdData/squaring/1975_1999_window8_noOrder_squared');

error('DONE!');


function sparseSquare(fileIn, fileOut)

    %load the data
    data = load(fileIn);
    disp('   loaded data');

    %convert to sparse
    vals = max(data);

utils/datasetCreator/squaring/squareMatrix_partial.m  view on Meta::CPAN

%nohup matlab -nojvm -r squareMatrix_partial -logfile squareMatrix_matlab.out </dev/null &

clear all;
close all;

%The number of rows/cols to square at a time. Larger numbers will run faster
% but will require more ram. Lower numbers will run slower but require less ram.
% You want this number to be as high as possible without MATLAB crashing
increment = 40000;
sparseSquare_sectioned('/home/henryst/lbdData/squaring/1975_1999_window8_noOrder','/home/henryst/lbdData/squaring/1975_1999_window8_noOrder_squared_secondTry',increment);
error('DONE!');

function sparseSquare_sectioned(fileIn, fileOut, increment)
  disp(fileIn);

  %open, close, and clear the output file
  fid = fopen(fileOut,'w');
  fclose(fid);

  %load the data
  data = load(fileIn);