I have a matrix T:
[ 0.2 0.4 0.4]
[ 0.8 0.2 0. ]
[ 0.8 0. 0.2]
T = numpy.mat("0.2 0.4 0.4;0.8 0.2 0.0;0.8 0.0 0.2")
I have vector v, numpy.array(73543, -36772, 36772)
v = numpy.array([ 73543, -36772, 36772])
How do I multiply the array v by the matrix T correctly in python?
thanks,
Chris
use numpy.dot, which is not quite same as * operator:
In [138]: T.dot(v) #the resulting shape is (1, 3), not (3, 1) if you don't care
Out[138]: matrix([[ 14708.6, 51480. , 66188.8]])
In [139]: v.dot(T) #same with v * T
Out[139]: matrix([[ 14708.6, 22062.8, 36771.6]])
In [140]: T.dot(v[:, None]) #if you need the shape to be (3, 1) when doing T*v
Out[140]:
matrix([[ 14708.6],
[ 51480. ],
[ 66188.8]])
Simple:
v * T
numpy overload arithmetic operates in ways that make sense most of the time. In your case, since T is a matrix, it converts v to a matrix as well before doing the multiplication. That turns v into a row-vector. Therefore v*T performs matrix multiplication, but T*v throws an exception because v is the wrong shape. However you can make v the correct shape with v.reshape(3,1) or treat v as a vector of the correct orientation with T.dot(v) or numpy.dot(T,v).
