guess results from the CPAN

=head2 home

    #/where/is/your_app/rootdir
    $app->home;

Returns the root directory into which $app is installed.  The guessing order is
the following:

=over

=item 1. If C<$ENV{MOJO_HOME}> is defined, it is honored.

view all matches for this distribution

Advanced-Config

2 results

lib/Advanced/Config/Date.pm view on Meta::CPAN

      foreach my $k ( sort keys %Date::Manip::Lang::index::Lang ) {
         my $mod = $Date::Manip::Lang::index::Lang{$k};
         my $lang = ( $k eq $mod ) ? ucfirst ($mod) : $mod;
         my $module = "Date::Manip::Lang::${mod}";

         my %data = ( Language => $lang,    # A guess that's wrong sometimes
                      Module   => $module );
         $date_manip_installed_languages{lc ($k)} = \%data;
      }
   };

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Affix

6 results

eg/benchmark.pl view on Meta::CPAN

        };
        bless $thing, $package;
    }

    sub load ( $path, $version = () ) {
        dlLoadLibrary( guess_library_name( $path, $version ) );
    }
    sub func ( $lib, $symbol ) { dlFindSymbol( $lib, $symbol ); }
    our $OS = $^O;

    sub guess_library_name ( $name = (), $version = () ) {
        ( $name, $version ) = @$name if ref $name eq 'ARRAY';
        $name // return ();    # NULL
        return $name if -f $name;
        CORE::state $_lib_cache;
        my @retval;

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Algorithm-AM

1 match

                    print "It may very well be supported all the way back to ",
                          format_version(5.003_07), ".\n";
                }
                else {
                    print "But given the things $f depends on, it's a good",
                          " guess that it isn't\n",
                          "supported prior to ", format_version($todo), ".\n";
                }
            }
        }
    }


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-AdaBoost

1 match

t/01_basic.t view on Meta::CPAN

  ++$correct if $answer == $test_data->{label};
}
my $accuracy = $correct / @test_set;
cmp_ok(
  $accuracy, '>', 0.65,
  'The constructed classifier should sagnificantly accurate rather than random guess'
);

done_testing;

# Generates simple linear classifier randomly.

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Algorithm-AhoCorasick-XS

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-BIT-XS

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-BaumWelch

1 match

lib/Algorithm/BaumWelch.pm view on Meta::CPAN


    # Feed in the transition and emission matrices and the starting probabilities.
    $ba->feed_values($trans, $emis, $start);

    # Alternatively you can randomly initialise the values - pass it the number of hidden states - 
    # i.e. to determine the parameters we need to make a first guess).
    # $ba->random_initialise(2);
     
    # Perform the algorithm.
    $ba->baum_welch;

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Algorithm-BestChoice

1 match

    if ( @Missing and not( $CheckOnly or $UnderCPAN ) ) {
        require Config;
        print
"*** Dependencies will be installed the next time you type '$Config::Config{make}'.\n";

        # make an educated guess of whether we'll need root permission.
        print "    (You may need to do that as the 'root' user.)\n"
          if eval '$>';
    }
    print "*** $class configuration finished.\n";

              or _load('CPANPLUS::Shell::Default')
        )
    );
}

# make guesses on whether we're under the CPAN installation directory
sub _under_cpan {
    require Cwd;
    require File::Spec;

    my $cwd  = File::Spec->canonpath( Cwd::cwd() );

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Algorithm-BloomFilter

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-BreakOverlappingRectangles

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-CP-IZ

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-Closest-NetworkAddress

1 match

    if ( @Missing and not( $CheckOnly or $UnderCPAN ) ) {
        require Config;
        print
"*** Dependencies will be installed the next time you type '$Config::Config{make}'.\n";

        # make an educated guess of whether we'll need root permission.
        print "    (You may need to do that as the 'root' user.)\n"
          if eval '$>';
    }
    print "*** $class configuration finished.\n";

              or _load('CPANPLUS::Shell::Default')
        )
    );
}

# make guesses on whether we're under the CPAN installation directory
sub _under_cpan {
    require Cwd;
    require File::Spec;

    my $cwd  = File::Spec->canonpath( Cwd::cwd() );

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Algorithm-ConsistentHash-CHash

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-ConsistentHash-JumpHash

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-CurveFit

2 results

lib/Algorithm/CurveFit.pm view on Meta::CPAN

                $deriv = $deriv->new; # better safe than sorry
                foreach my $x ( 0 .. $#xdata ) {
                    my $xv = $xdata[$x];
                    my $value = $deriv->value(
                      $variable => $xv,
                      map { ( @{$_}[ 0, 1 ] ) } @parameters # a, guess
                    );
                    if (not defined $value) { # fall back to numeric five-point stencil
                        my $h = SQRT_EPS*$xv; my $t = $xv + $h; $h = $t-$xv; # numerics. Cf. NR
                        $value = $formula_sub->($xv, map { $_->[1] } @parameters)
                    }

lib/Algorithm/CurveFit.pm view on Meta::CPAN


=over 2

=item

A good guess improves the probability of convergence and the quality
of the fit.

=item

Increasing the number of free parameters decreases the quality and

lib/Algorithm/CurveFit.pm view on Meta::CPAN

one or more parameters, but please keep in mind that the number of parameters
not only increases processing time, but also decreases the quality of the fit.

The value of this options should be an anonymous array. This array should
hold one anonymous array for each parameter. That array should hold (in order)
a parameter name, an initial guess, and optionally an accuracy measure.

Example:

  $params = [
    ['parameter1', 5,  0.00001],

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Algorithm-Diff-XS

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-Evolutionary-Simple

1 match

README.md view on Meta::CPAN


or

	sudo cpan Algorithm::Evolutionary::Simple

This module is apparently also available at the OpenSUSE repos. To install it, I guess, you should use:

	zypper install perl-Algorithm-Evolutionary-Simple

or via YaST

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Algorithm-Evolutionary

1 match

lib/Algorithm/Evolutionary/Op/Base.pm view on Meta::CPAN

	    $str .= $i->asXML()."\n";
	  }
	} elsif ( ref $self->{$_} eq 'CODE' ) {
	  my $deparse = B::Deparse->new;
	  $str .="<code type='eval' language='perl'>\n<src><![CDATA[".$deparse->coderef2text($self->{$_})."]]>\n </src>\n</code>";
	} elsif ( (ref $self->{$_} ) =~ 'Algorithm::Evolutionary' ) { #Composite object, I guess...
	  $str .= $self->{$_}->asXML( $_ );
	}
      }
    }
    $str .= "\n</op>";

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Algorithm-ExpectationMaximization

1 match

lib/Algorithm/ExpectationMaximization.pm view on Meta::CPAN

data point, whose values dictate the selection of the Gaussian for that data point.
This point is explained in great detail in my on-line tutorial at
L<https://engineering.purdue.edu/kak/Tutorials/ExpectationMaximization.pdf>.

The EM algorithm in our context reduces to an iterative invocation of the following
steps: (1) Given the current guess for the means and the covariances of the different
Gaussians in our mixture model, use Bayes' Rule to update the posterior class
probabilities at each of the data points; (2) Using the updated posterior class
probabilities, first update the class priors; (3) Using the updated class priors,
update the class means and the class covariances; and go back to Step (1).  Ideally,
the iterations should terminate when the expected log-likelihood of the observed data

lib/Algorithm/ExpectationMaximization.pm view on Meta::CPAN

iterations in the values calculated for the priors.

This module provides three different choices for seeding the clusters: (1) random,
(2) kmeans, and (3) manual.  When random seeding is chosen, the algorithm randomly
selects C<K> data elements as cluster seeds.  That is, the data vectors associated
with these seeds are treated as initial guesses for the means of the Gaussian
distributions.  The covariances are then set to the values calculated from the entire
dataset with respect to the means corresponding to the seeds. With kmeans seeding, on
the other hand, the means and the covariances are set to whatever values are returned
by the kmeans algorithm.  And, when seeding is set to manual, you are allowed to
choose C<K> data elements --- by specifying their tag names --- for the seeds.  The

lib/Algorithm/ExpectationMaximization.pm view on Meta::CPAN


=item I<canned_example3.pl>

Like the first example, this example again involves three Gaussians, but now their
means are not co-located.  Additionally, we now seed the clusters manually by
specifying three selected data points as the initial guesses for the cluster means.
The datafile used for this example is C<mydatafile3.dat>.  The EM based clustering
for this data is shown in the files C<save_example_3_cluster_plot.png> and
C<save_example_3_posterior_prob_plot.png>, the former displaying the hard clusters
obtained by using the naive Bayes' classifier and the latter showing the soft
clusters obtained on the basis of the posterior class probabilities at the data

lib/Algorithm/ExpectationMaximization.pm view on Meta::CPAN

=head1 CAVEATS

When you run the scripts in the C<examples> directory, your results will NOT always
look like what I have shown in the PNG image files in the directory.  As mentioned
earlier in Description, the EM algorithm starting from randomly chosen initial
guesses for the cluster means can get stuck in a local maximum.

That raises an interesting question of how one judges the correctness of clustering
results when dealing with real experimental data.  For real data, the best approach
is to try the EM algorithm multiple times with all of the seeding options included in
this module.  It would be safe to say that, at least in low dimensional spaces and

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Algorithm-FloodControl

1 match

    if ( @Missing and not( $CheckOnly or $UnderCPAN ) ) {
        require Config;
        print
"*** Dependencies will be installed the next time you type '$Config::Config{make}'.\n";

        # make an educated guess of whether we'll need root permission.
        print "    (You may need to do that as the 'root' user.)\n"
          if eval '$>';
    }
    print "*** $class configuration finished.\n";

              or _load('CPANPLUS::Shell::Default')
        )
    );
}

# make guesses on whether we're under the CPAN installation directory
sub _under_cpan {
    require Cwd;
    require File::Spec;

    my $cwd  = File::Spec->canonpath( Cwd::cwd() );

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Algorithm-Heapify-XS

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-IncludeExclude

1 match

    if ( @Missing and not( $CheckOnly or $UnderCPAN ) ) {
        require Config;
        print
"*** Dependencies will be installed the next time you type '$Config::Config{make}'.\n";

        # make an educated guess of whether we'll need root permission.
        print "    (You may need to do that as the 'root' user.)\n"
          if eval '$>';
    }
    print "*** $class configuration finished.\n";

              or _load('CPANPLUS::Shell::Default')
        )
    );
}

# make guesses on whether we're under the CPAN installation directory
sub _under_cpan {
    require Cwd;
    require File::Spec;

    my $cwd  = File::Spec->canonpath( Cwd::cwd() );

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Algorithm-KMeans

1 match

lib/Algorithm/KMeans.pm view on Meta::CPAN

Version 1.40 includes a C<smart> option for seeding the clusters.  This option,
supplied through the constructor parameter C<cluster_seeding>, means that the
clusterer will (1) Subject the data to principal components analysis in order to
determine the maximum variance direction; (2) Project the data onto this direction;
(3) Find peaks in a smoothed histogram of the projected points; and (4) Use the
locations of the highest peaks as initial guesses for the cluster centers.  If you
don't want to use this option, set C<cluster_seeding> to C<random>. That should work
as in the previous version of the module.

Version 1.30 includes a bug fix for the case when the datafile contains empty lines,
that is, lines with no data records.  Another bug fix in Version 1.30 deals with the

lib/Algorithm/KMeans.pm view on Meta::CPAN

module.  Working with Version 1.30, Nadeem noticed that occasionally the module would
produce variable clustering results on the same dataset.  I believe that this
variability was caused (at least partly) by the purely random mode that was used in
Version 1.30 for the seeding of the cluster centers.  Version 1.40 now includes a
C<smart> mode. With the new mode the clusterer uses a PCA (Principal Components
Analysis) of the data to make good guesses for the cluster centers.  However,
depending on how the data is jumbled up, it is possible that the new mode will not
produce uniformly good results in all cases.  So you can still use the old mode by
setting C<cluster_seeding> to C<random> in the constructor.  Thanks Nadeem for your
feedback!

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Algorithm-KNN-XS

1 match


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-LBFGS

2 results

    if ( @Missing and not( $CheckOnly or $UnderCPAN ) ) {
        require Config;
        print
"*** Dependencies will be installed the next time you type '$Config::Config{make}'.\n";

        # make an educated guess of whether we'll need root permission.
        print "    (You may need to do that as the 'root' user.)\n"
          if eval '$>';
    }
    print "*** $class configuration finished.\n";

              or _load('CPANPLUS::Shell::Default')
        )
    );
}

# make guesses on whether we're under the CPAN installation directory
sub _under_cpan {
    require Cwd;
    require File::Spec;

    my $cwd  = File::Spec->canonpath( Cwd::cwd() );

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Algorithm-Line-Lerp

1 match

                    print "It may very well be supported all the way back to ",
                          format_version(5.003_07), ".\n";
                }
                else {
                    print "But given the things $f depends on, it's a good",
                          " guess that it isn't\n",
                          "supported prior to ", format_version($todo), ".\n";
                }
            }
        }
    }


#ifndef IVSIZE
#  ifdef LONGSIZE
#    define IVSIZE LONGSIZE
#  else
#    define IVSIZE 4 /* A bold guess, but the best we can make. */
#  endif
#endif
#ifndef UVTYPE
#  define UVTYPE                         unsigned IVTYPE
#endif

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Algorithm-LinearManifoldDataClusterer

1 match

lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN

need to have already clustered the data in order to construct the subspaces at
different places on the manifold so that you can figure out which cluster to place a
data element in.

Such problems, when they do possess a solution, are best tackled through iterative
algorithms in which you start with a guess for the final solution, you rearrange the
measured data on the basis of the guess, and you then use the new arrangement of the
data to refine the guess.  Subsequently, you iterate through the second and the third
steps until you do not see any discernible changes in the new arrangements of the
data.  This forms the basis of the clustering algorithm that is described under
B<Phase 1> in the section that follows.  This algorithm was first proposed in the
article "Dimension Reduction by Local Principal Component Analysis" by Kambhatla and
Leen that appeared in the journal Neural Computation in 1997.

lib/Algorithm/LinearManifoldDataClusterer.pm view on Meta::CPAN


=item B<get_data_from_csv()>:

    $clusterer->get_data_from_csv();

As you can guess from its name, the method extracts the data from the CSV file named
in the constructor.

=item B<linear_manifold_clusterer()>:

    $clusterer->linear_manifold_clusterer();

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Algorithm-MasterMind

10 results

lib/Algorithm/MasterMind.pm view on Meta::CPAN


=head2 feedback()

Obtain the result to the last combination played

=head2 guesses()

Total number of guesses

=head2 evaluated()

Total number of combinations checked to issue result

lib/Algorithm/MasterMind.pm view on Meta::CPAN

Returns the letters from the alphabet that are _not_ in this
combination. Might be useful for certain strategies.

=head2 random_combination

Combines randomly the alphabet, issuing, you guessed it, a random
combination. 

=head2 partitions

From a set of combinations, returns the "partitions", that is, the

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Algorithm-MedianSelect-XS

1 match