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In Advanced C++:Programming Styles And
Idioms (Addison-Wesley, 1992), Jim Coplien coins the term functor
which is an object whose sole purpose is to encapsulate a function (since
“functor” has a meaning in mathematics, in this book I shall use the
more explicit term function object). The point is to decouple the choice
of function to be called from the site where that function is called.
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This term is mentioned but not used in
Design Patterns. However, the theme of the function object is repeated in
a number of patterns in that book.
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This is the function object in its purest
sense: a method that’s an
object[13]. By
wrapping a method in an object, you can pass it to other methods or objects as a
parameter, to tell them to perform this particular operation in the process of
fulfilling your request.
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#: c06:CommandPattern.py
class Command:
def execute(self): pass
class Loony(Command):
def execute(self):
print "You're a loony."
class NewBrain(Command):
def execute(self):
print "You might even need a new brain."
class Afford(Command):
def execute(self):
print "I couldn't afford a whole new brain."
# An object that holds commands:
class Macro:
def __init__(self):
self.commands = []
def add(self, command):
self.commands.append(command)
def run(self):
for c in self.commands:
c.execute()
macro = Macro()
macro.add(Loony())
macro.add(NewBrain())
macro.add(Afford())
macro.run()
#:~The primary point of
Command is to allow you to hand a desired action to a method or object.
In the above example, this provides a way to queue a set of actions to be
performed collectively. In this case, it allows you to dynamically create new
behavior, something you can normally only do by writing new code but in the
above example could be done by interpreting a script (see the Interpreter
pattern if what you need to do gets very complex).
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Design Patterns says that
“Commands are an object-oriented replacement for
callbacks[14].”
However, I think that the word “back” is an essential part of the
concept of callbacks. That is, I think a callback actually reaches back to the
creator of the callback. On the other hand, with a Command object you
typically just create it and hand it to some method or object, and are not
otherwise connected over time to the Command object. That’s my take
on it, anyway. Later in this book, I combine a group of design patterns under
the heading of “callbacks.”
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Strategy appears to be a family of
Command classes, all inherited from the same base. But if you look at
Command, you’ll see that it has the same structure: a hierarchy of
function objects. The difference is in the way this hierarchy is used. As seen
in c12:DirList.py, you use Command to solve a particular
problem—in that case, selecting files from a list. The “thing that
stays the same” is the body of the method that’s being called, and
the part that varies is isolated in the function object. I would hazard to say
that Command provides flexibility while you’re writing the program,
whereas Strategy’s flexibility is at run time.
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Strategy also adds a
“Context” which can be a surrogate class that controls the selection
and use of the particular strategy object—just like State!
Here’s what it looks like:
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#: c06:StrategyPattern.py
# The strategy interface:
class FindMinima:
# Line is a sequence of points:
def algorithm(self, line) : pass
# The various strategies:
class LeastSquares(FindMinima):
def algorithm(self, line):
return [ 1.1, 2.2 ] # Dummy
class NewtonsMethod(FindMinima):
def algorithm(self, line):
return [ 3.3, 4.4 ] # Dummy
class Bisection(FindMinima):
def algorithm(self, line):
return [ 5.5, 6.6 ] # Dummy
class ConjugateGradient(FindMinima):
def algorithm(self, line):
return [ 3.3, 4.4 ] # Dummy
# The "Context" controls the strategy:
class MinimaSolver:
def __init__(self, strategy):
self.strategy = strategy
def minima(self, line):
return self.strategy.algorithm(line)
def changeAlgorithm(self, newAlgorithm):
self.strategy = newAlgorithm
solver = MinimaSolver(LeastSquares())
line = [
1.0, 2.0, 1.0, 2.0, -1.0, 3.0, 4.0, 5.0, 4.0
]
print solver.minima(line)
solver.changeAlgorithm(Bisection())
print solver.minima(line)
#:~Note similarity with template
method – TM claims distinction that it has more than one method to call,
does things piecewise. However, it’s not unlikely that strategy object
would have more than one method call; consider Shalloway’s order
fulfullment system with country information in each strategy.
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Strategy example from standard Python:
sort( ) takes a second optional argument that acts as a comparator
object; this is a strategy.
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Chain of Responsibility might be
thought of as a dynamic generalization of recursion using Strategy
objects. You make a call, and each Strategy in a linked sequence
tries to satisfy the call. The process ends when one of the strategies is
successful or the chain ends. In recursion, one method calls itself over and
over until a termination condition is reached; with Chain of
Responsibility, a method calls itself, which (by moving down the chain of
Strategies) calls a different implementation of the method, etc.,
until a termination condition is reached. The termination condition is either
the bottom of the chain is reached (in which case a default object is returned;
you may or may not be able to provide a default result so you must be able to
determine the success or failure of the chain) or one of the Strategies
is successful.
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Instead of calling a single method to
satisfy a request, multiple methods in the chain have a chance to satisfy the
request, so it has the flavor of an expert system. Since the chain is
effectively a linked list, it can be dynamically created, so you could also
think of it as a more general, dynamically-built switch statement.
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In the GoF, there’s a fair amount
of discussion of how to create the chain of
responsibility as a linked list. However, when you look at the pattern it really
shouldn’t matter how the chain is maintained; that’s an
implementation detail. Since GoF was written before the Standard Template
Library (STL) was incorporated into most C++ compilers, the reason for this is
most likely (1) there was no list and thus they had to create one and (2) data
structures are often taught as a fundamental skill in academia, and the idea
that data structures should be standard tools available with the programming
language may not have occurred to the GoF authors. I maintain that the
implementation of Chain of Responsibility as a chain (specifically, a
linked list) adds nothing to the solution and can just as easily be implemented
using a standard Python list, as shown below. Furthermore, you’ll see that
I’ve gone to some effort to separate the chain-management parts of the
implementation from the various Strategies, so that the code can be more
easily reused.
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In StrategyPattern.py, above, what
you probably want is to automatically find a solution. Chain of
Responsibility provides a way to do this by chaining the Strategy
objects together and providing a mechanism for them to automatically recurse
through each one in the chain:
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#: c06:ChainOfResponsibility.py
# Carry the information into the strategy:
class Messenger: pass
# The Result object carries the result data and
# whether the strategy was successful:
class Result:
def __init__(self):
self.succeeded = 0
def isSuccessful(self):
return self.succeeded
def setSuccessful(self, succeeded):
self.succeeded = succeeded
class Strategy:
def __call__(messenger): pass
def __str__(self):
return "Trying " + self.__class__.__name__ \
+ " algorithm"
# Manage the movement through the chain and
# find a successful result:
class ChainLink:
def __init__(self, chain, strategy):
self.strategy = strategy
self.chain = chain
self.chain.append(self)
def next(self):
# Where this link is in the chain:
location = self.chain.index(self)
if not self.end():
return self.chain[location + 1]
def end(self):
return (self.chain.index(self) + 1 >=
len(self.chain))
def __call__(self, messenger):
r = self.strategy(messenger)
if r.isSuccessful() or self.end(): return r
return self.next()(messenger)
# For this example, the Messenger
# and Result can be the same type:
class LineData(Result, Messenger):
def __init__(self, data):
self.data = data
def __str__(self): return ´self.data´
class LeastSquares(Strategy):
def __call__(self, messenger):
print self
linedata = messenger
# [ Actual test/calculation here ]
result = LineData([1.1, 2.2]) # Dummy data
result.setSuccessful(0)
return result
class NewtonsMethod(Strategy):
def __call__(self, messenger):
print self
linedata = messenger
# [ Actual test/calculation here ]
result = LineData([3.3, 4.4]) # Dummy data
result.setSuccessful(0)
return result
class Bisection(Strategy):
def __call__(self, messenger):
print self
linedata = messenger
# [ Actual test/calculation here ]
result = LineData([5.5, 6.6]) # Dummy data
result.setSuccessful(1)
return result
class ConjugateGradient(Strategy):
def __call__(self, messenger):
print self
linedata = messenger
# [ Actual test/calculation here ]
result = LineData([7.7, 8.8]) # Dummy data
result.setSuccessful(1)
return result
solutions = []
solutions = [
ChainLink(solutions, LeastSquares()),
ChainLink(solutions, NewtonsMethod()),
ChainLink(solutions, Bisection()),
ChainLink(solutions, ConjugateGradient())
]
line = LineData([
1.0, 2.0, 1.0, 2.0, -1.0,
3.0, 4.0, 5.0, 4.0
])
print solutions[0](line)
#:~[13]
In the Python language, all functions are already
objects and so the Command pattern is often redundant.
[14]
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