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        push @{ $mapped_distances[$p] }, $record->{$field_names->[6 - $p]};
    } # next $p
} # end while

unless (scalar @{ $mapped_distances[$Probability::ONE_FIFTH] } == 61) {
    die "ERROR:  The input file does not contain the expected number of "
      . "records.\n";
} # end unless

# Perform simulated annealing to optimize the coefficients for each of the
# four probabilities, and then print the results to the console:
for my $p (2..5) {
    my $cost_function = cost_function_factory($mapped_distances[$p]);
    my $optimized_coefficients;
    my @number_specs;

    push @number_specs,
      {"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)