Diez B. Roggisch wrote:
> I fail to see where laziness has anything to do with this.
> In C++, this problem can be remedied with the so called
> temporary base class idiom.
I have seen this referred to as lazy evaluation in C++,
so I suspect that Diez and Sturia are using "Lazy evaluation"
in different contexts with different meaning.
> But this has nothing to do with laziness, which does not
> reduce the amount of code to execute, but instead defers the
> point of execution of that code.
But that is precisely what Sturia is suggesting, defer
(for a few nanoseconds) the evaluation of the multiplications
and addition until the assignment occurs. Admittedly a big
difference to the lazy evaluation implied by python's yield
statement, but still a version of lazy evaluation and (at
least sometimes) referred to as such in a C++ context.
I am a python newbie (about one month) but I think
some of what Sturia wants could be achieved by partially
following what is usually done in C++ to achieve what he
wants. It would involve a replacement array class (possibly
derived from NumPy's arrays) and a proxy class.
+ Addition, multiplication, etc of arrays and proxy
arrays does not return the result array, but returns
a proxy which stores the arguments and the
operation.
+ Array indexing of the proxy objects results in
the indexing methods of the arguments being
called and the operation being carried out and
returned. In C++ this is normally very efficient
as the operations are all defined inline and
expanded by the compiler.
+ If necessary, define an additional method to evaluate
the entire array and return it.
I think this would allow code like (if the new array type is
XArray)
a = Xarray(...)
b = Xarray(...)
c = Xarray(...)
d = Xarray(...)
y = a*b+c*d # Returns a proxy object
x = y[4] # Computes x = a[4]*b[4] + c[4]*d[4]
v = y.eval() # Evaluates all elements, returning Xarray
z = ((a+b)*(c+d)).eval() # Also evaluates all elements
Whether it would be any faster is doubtful, but it would eliminate
the temporaries.
Charles
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