On 3/24/07, Anne Archibald <[EMAIL PROTECTED]> wrote: > On 24/03/07, Bill Baxter <[EMAIL PROTECTED]> wrote: > > I mentioned in another thread Travis started on the scipy list that I > > would find it useful if there were a function like dot() that could > > multiply more than just two things. > > > > Here's a sample implementation called 'mdot'. > > > > mdot(a,b,c,d) ==> dot(dot(dot(a,b),c),d) > > mdot(a,(b,c),d) ==> dot(dot(a,dot(b,c),d) > > mdot(a,(b,(c,d))) ==> dot(a,dot(b,dot(c,d)) > > > > --- > > def mdot(*args): > > """Multiply all the arguments using matrix product rules. > > The output is equivalent to multiplying the arguments one by one > > from left to right using dot(). > > Precedence can be controlled by creating tuples of arguments, > > for instance mdot(a,((b,c),d)) multiplies a (a*((b*c)*d)). > > Note that this means the output of dot(a,b) and mdot(a,b) will differ if > > a or b is a pure tuple of numbers. > > """ > > if len(args)==1: > > return args[0] > > elif len(args)==2: > > return _mdot_r(args[0],args[1]) > > else: > > return _mdot_r(args[:-1],args[-1]) > > > > def _mdot_r(a,b): > > """Recursive helper for mdot""" > > if type(a)==types.TupleType: > > if len(a)>1: > > a = mdot(*a) > > else: > > a = a[0] > > if type(b)==types.TupleType: > > if len(b)>1: > > b = mdot(*b) > > else: > > b = b[0] > > return numpy.dot(a,b) > > You can do better: > In [1]: from numpy import * > > In [2]: a = array([[0,-1],[1,0]]) > > In [3]: reduce(dot,(a,a,a,a)) > Out[3]: > array([[1, 0], > [0, 1]]) > > In [4]: def mdot(*args): > ...: return reduce(dot,args) > ...: > > In [5]: mdot(a,a,a,a) > Out[5]: > array([[1, 0], > [0, 1]]) >
Nice, but how does that fare on things like mdot(a,(b,c),d) ? I'm pretty sure it doesn't handle it. I think an mdot that can only multiply things left to right comes up short compared to an infix operator that can easily use parentheses to control order. --bb _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
