mvpa2.clfs.gda.dot¶

mvpa2.clfs.gda.
dot
(a, b, out=None)¶ Dot product of two arrays.
For 2D arrays it is equivalent to matrix multiplication, and for 1D arrays to inner product of vectors (without complex conjugation). For N dimensions it is a sum product over the last axis of
a
and the secondtolast ofb
:dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
Parameters: a : array_like
First argument.
b : array_like
Second argument.
out : ndarray, optional
Output argument. This must have the exact kind that would be returned if it was not used. In particular, it must have the right type, must be Ccontiguous, and its dtype must be the dtype that would be returned for
dot(a,b)
. This is a performance feature. Therefore, if these conditions are not met, an exception is raised, instead of attempting to be flexible.Returns: output : ndarray
Returns the dot product of
a
andb
. Ifa
andb
are both scalars or both 1D arrays then a scalar is returned; otherwise an array is returned. Ifout
is given, then it is returned.Raises: ValueError
If the last dimension of
a
is not the same size as the secondtolast dimension ofb
.See also
vdot
 Complexconjugating dot product.
tensordot
 Sum products over arbitrary axes.
einsum
 Einstein summation convention.
matmul
 ‘@’ operator as method with out parameter.
Examples
>>> np.dot(3, 4) 12
Neither argument is complexconjugated:
>>> np.dot([2j, 3j], [2j, 3j]) (13+0j)
For 2D arrays it is the matrix product:
>>> a = [[1, 0], [0, 1]] >>> b = [[4, 1], [2, 2]] >>> np.dot(a, b) array([[4, 1], [2, 2]])
>>> a = np.arange(3*4*5*6).reshape((3,4,5,6)) >>> b = np.arange(3*4*5*6)[::1].reshape((5,4,6,3)) >>> np.dot(a, b)[2,3,2,1,2,2] 499128 >>> sum(a[2,3,2,:] * b[1,2,:,2]) 499128