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t/annealing_tests.t  view on Meta::CPAN

eval {
    $bsv_file_reader = Text::BSV::BsvFileReader->new($bsv_file_path);
};

if ($EVAL_ERROR) {
    my $exception = $EVAL_ERROR;

    given ($exception->get_type()) {
        when ($Text::BSV::Exception::FILE_NOT_FOUND) {
            say STDERR "$DQ$bsv_file_path$DQ is not a valid file path.";
            exit(1);
        }
        when ($Text::BSV::Exception::IO_ERROR) {
            say STDERR "Couldn't open $DQ$bsv_file_path$DQ for reading.";
            exit(1);
        }
        when ($Text::BSV::Exception::INVALID_DATA_FORMAT) {
            say STDERR "Invalid BSV data:  " . $exception->get_message();
            exit(1);
        }
        default {
            say STDERR $exception->get_message();
            exit(1);
        } # end when
    } # end given
} # end if

# Generate a list of distances for each probability from the data in the
# BSV file:
my $field_names = $bsv_file_reader->get_field_names();
my @mapped_distances; # indexes 2-5 = Probability constants;
                      # values = references to number arrays

t/annealing_tests.t  view on Meta::CPAN

      {"LowerBound" =>  0.0, "UpperBound" => 3.0, "Precision" => 3};
    push @number_specs,
      {"LowerBound" => -1.0, "UpperBound" => 5.0, "Precision" => 3};
    push @number_specs,
      {"LowerBound" => -4.0, "UpperBound" => 0.0, "Precision" => 3};

    $optimized_coefficients = anneal(
      \@number_specs, $cost_function, $CYCLES_PER_TEMPERATURE);

    # Print the results for this probability to the console:
    say "\nProbability:  1/$p";
    printf("Coefficients:  a = %1.3f; b = %1.3f; c= %1.3f\n",
      $optimized_coefficients->[0],
      $optimized_coefficients->[1],
      $optimized_coefficients->[2]);
    say "Cost:  " . $cost_function->($optimized_coefficients);
} # next $p

# Perform an annealing test with integers that triggers brute-force analysis
# and uses an anonymous cost function that minimizes this sum:
#
#     (10 * abs(23 - val)) + (the total range of a, b, and c)
#
# where "val" is the result of following expression:
#
#     (a * (x ** 2)) + bx + c

t/annealing_tests.t  view on Meta::CPAN

$abc = anneal(\@number_specs,
  sub {
    my $nums = $_[0];
    my $range = max(@{ $nums }) - min(@{ $nums });
    my $val = ($nums->[0] * 9) + ($nums->[1] * 3) + $nums->[2];
    my $cost = $range + (10 * abs(23 - $val));

    return $cost;
  }, 120);

say "\nHere are a, b, and c:  " . $abc->[0] . ", "
  . $abc->[1] . ", " . $abc->[2];

# Helper functions:

# The cost_function_factory() takes a reference to an array containing
# real-world market distances and returns a reference to a cost function.
# The cost function takes a reference to an array of three coefficients,
# and returns the mean absolute percentage deviation of the calculated
# results from the real-world results based on this formula:
#