Angus McMorland wrote:
Is there a way to specify which dimensions I want dot to work over?
Use swapaxes() on the arrays to put the desired axes in the right places.
In [2]: numpy.swapaxes?
Type: function
Base Class:
String Form:
Namespace: Interactive
File:
/Library/Frameworks/Python.framework/Versions/2.4/lib/python2.4/site-packages/numpy-0.9.7.2476-py2.4-macosx-10.4-ppc.egg/numpy/core/oldnumeric.py
Definition: numpy.swapaxes(a, axis1, axis2)
Docstring:
swapaxes(a, axis1, axis2) returns array a with axis1 and axis2
interchanged.
In [3]: numpy.dot?
Type: builtin_function_or_method
Base Class:
String Form: <built-in function dot>
Namespace: Interactive
Docstring:
matrixproduct(a,b)
Returns the dot product of a and b for arrays of floating point types.
Like the generic numpy equivalent the product sum is over
the last dimension of a and the second-to-last dimension of b.
NB: The first argument is not conjugated.
--
Robert Kern
"I have come to believe that the whole world is an enigma, a harmless enigma
that is made terrible by our own mad attempt to interpret it as though it had
an underlying truth."
-- Umberto Eco
