openmethods 1.3.2
fast, open multimethods for D
To use this package, run the following command in your project's root directory:
This package provides sub packages which can be used individually:
openmethods:acceptnovisitors  replace awful visitor with neat open method
openmethods:adventure  example of a method with three virtual arguments
openmethods:matrix  unary and binary matrix operations with two types of matrices
openmethods:next  example of 'next', equivalent to calling super
openmethods:rolex  Role and Expense example from the yomm11 article on Code Project
openmethods:synopsis  synopsis
openmethods:whytheunderscore  the reason for the underscore in front of method specializations
openmethods:runtimemetrics  test against large code base
openmethods:tests  tests for openmethods
openmethods.d
Open methods are a simple, expressive, and efficient mechanism to dynamically dispatch functions depending on the type of one or more objects. They combine the advantages of virtual methods with those of the Visitor pattern while avoiding their disadvantages (such as tight coupling, intricate dependencies, awkward setup, and extensive boilerplate requirements). Open methods scale up naturally to multiple dispatch in a payasyougo manner.
TL;DR
If you are familiar with the concept of open multimethods, or prefer to learn by reading code, go directly to the synopsis
Introduction
This library implements fast, open multimethods for D.
Imagine that you are writing a matrix math library. Matrices come in different subtypes  dense (i.e. ordinary), diagonal, tridiagonal, etc. Some operations can be implemented more efficiently for certain types  for example, transposing a diagonal matrix is a noop; adding two diagonal matrices does not require adding the zeroes that are outside of the diagonals; etc.
At one point you want to write a demo program, which needs to output matrices to the console in a format that clearly identifies which category they belong to. For example, just print the diagonal for a diagonal matrix, or just the non zero elements for a sparse matrix. You need a polymorphic print function.
At this point you don't have any good option in traditional OO languages like D, Java, C++ and any others that follow the message passing metaphor, as initiated by Simula and popularized for the first time by Smalltalk.
The most obvious approach is to stick a virtual print
function in the
Matrix
base class and override it in the subclasses. It will work but it has
severe drawbacks. Different application may want to display matrices in
different ways. Some applications may not need to display matrices at all  but
they will still pull the print
functions from your library, because of the
way virtual functions are implemented.
Or you may resort on a "type switch": have the application test for each category and print accordingly. This is tedious, error prone and, above all, not extensible. Adding a new matrix subclass requires updating all the type switches.
Then there is the binary operation problem. To implement, say, matrix addition efficiently, you need to take the type of two objects into account. In addition to the problems described above, you will have to resort on hacks like double dispatch or  again  type switches.
This library neatly solves this problem. It brings you the flexibility and the power of open multimethods, as found in languages like Lisp, Clojure, Dylan or TADS. They are fast too, comparable to ordinary virtual function calls. And, because they use compressed dispatch tables, the memory footprint remains reasonable even in presence of multiple virtual arguments. Hey, you can even add methods to Object if you need to.
Example
The full code for the examples can be found in the following files:
 matrix.d, densematrix.d and diagonalmatrix.d  the matrix "library"
 app.d  the "application"
matrix.d defines the Matrix interface:
interface Matrix
{
@property int rows() const;
@property int cols() const;
@property double at(int i, int j) const;
}
The DenseMatrix subclass stores the elements in a single array. So does DiagonalMatrix, but it only stores the elements on the diagonal.
The print
method in app.d is declared as follows:
import openmethods;
void print(virtual!Matrix m);
The virtual!
qualifier indicates that the right version of print
will be
selected depending on the runtime type of its argument. In effect, print
is a
virtual function  except that it's defined outside of the matrix
class.
This is just a declaration. Here is a catchall implementation that works for all matrix types:
@method
void _print(Matrix m)
{
const int nr = m.rows;
const int nc = m.cols;
for (int i = 0; i < nr; ++i) {
for (int j = 0; j < nc; ++j) {
writef("%3g", m.at(i, j));
}
writeln();
}
}
Note two things:
 the
@method
attribute marks the function as a method implementation  the method name is prefixed by an underscore
Here is an implementation for DiagonalMatrix:
@method
void _print(DiagonalMatrix m)
{
import std.algorithm;
import std.format;
import std.array;
writeln("diag(" ~ m.elems.map!(x => format("%g", x)).join(" ") ~ ")");
}
Every module that declares methods or define implementations must include the following line:
mixin(registerMethods);
I like to place it just after the import methods
directive.
To recap:
import std.stdio;
import matrix;
import diagonalmatrix;
import densematrix;
import openmethods;
mixin(registerMethods);
void print(virtual!Matrix m);
@method
void _print(Matrix m)
{
const int nr = m.rows;
const int nc = m.cols;
for (int i = 0; i < nr; ++i) {
for (int j = 0; j < nc; ++j) {
writef("%3g", m.at(i, j));
}
writeln();
}
}
@method
void _print(DiagonalMatrix m)
{
import std.algorithm;
import std.format;
import std.array;
writeln("diag(" ~ m.elems.map!(x => format("%g", x)).join(" ") ~ ")");
}
void main()
{
Matrix dense1 =
new DenseMatrix(2, 3,
[ 1, 2, 3,
4, 5, 6 ]);
writeln("dense1 =");
print(dense1);
writeln();
Matrix diag1 = new DiagonalMatrix([ 7, 8, 9]);
write("diag1 = ");
print(diag1);
writeln();
// ...
}
Multiple Dispatch
Let's now look at matrix addition...
In matrix.d, a plus
method is declared with two virtual parameters:
Matrix plus(virtual!Matrix, virtual!Matrix);
densematrix.d contains a "catchall" implementations that uses the Matrix
interface to access the elements of the operands, and returns a DenseMatrix
:
@method
Matrix _plus(Matrix m1, Matrix m2)
{
const int nr = m1.rows;
const int nc = m1.cols;
assert(nr == m2.rows);
assert(nc == m2.cols);
double[] result;
result.length = nr * nc;
int o = 0;
for (int j = 0; j < nc; ++j) {
for (int i = 0; i < nr; ++i) {
result[o++] = m1.at(i, j) + m2.at(i, j);
}
}
return new DenseMatrix(nr, nc, result);
}
While this override returns the correct result if both operands are dense matrices, it is inefficient. Thus a better override is also provided:
@method
Matrix _plus(DenseMatrix a, DenseMatrix b)
{
const int nr = a.rows;
const int nc = a.cols;
assert(a.nr == b.nr);
assert(a.nc == b.nc);
auto result = new DenseMatrix;
result.nr = nr;
result.nc = nc;
result.elems.length = a.elems.length;
result.elems[] = a.elems[] + b.elems[];
return result;
}
diagonalmatrix.d provides an override that just adds the diagonals  and returns a new DiagonalMatrix
.
@method
Matrix _plus(DiagonalMatrix a, DiagonalMatrix b)
{
assert(a.rows == b.rows);
double[] sum;
sum.length = a.elems.length;
sum[] = a.elems[] + b.elems[];
return new DiagonalMatrix(sum);
}
Conclusion
Because open methods live outside class hierarchies, they make it possible to extend libraries to a degree that virtual member functions cannot provide.
If a function is missing from the matrix library  say, transpose
, it can
be added without changing the library's code. I can vary the implementation of
transpose
depending on its exact type  transposing a diagonal matrix is a
noop.
The library can also be extended with new classes. I can add a
TriDiagonalMatrix class and specialize plus
to handle cases like the addition
of a diagonal and a tridiagonal matrix.
Behavior that belongs in application code need not be forced into classes anymore, therefore avoiding the God Object problem.
Tying polymorphism to membership is a mistake that most OOP languages have made in the wake of Simula and Smalltalk. OOP has been under a lot of criticism during the last decade. Indeed, OOP promised a lot, especially in terms of modularity and extensibility  but failed to deliver. Open methods rectify this mistake  to the extent that some would not even consider the open method approach OOP. Indeed, with methods algorithms take the front stage again, and method calls look more like "rule matching" than "message passing".
 1.3.2 released 4 months ago
 jll63/openmethods.d
 Boost Software License 1.0
 Copyright © 20172020, JeanLouis Leroy
 Authors:
 Sub packages:
 openmethods:acceptnovisitors, openmethods:adventure, openmethods:matrix, openmethods:next, openmethods:rolex, openmethods:synopsis, openmethods:whytheunderscore, openmethods:runtimemetrics, openmethods:tests
 Dependencies:
 none
 Versions:

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