dot(a, b, out=None)
Some inconsistencies with the Dask version may exist.
Dot product of two arrays. Specifically,
If both a and b are 1-D arrays, it is inner product of vectors (without complex conjugation).
If both a and b are 2-D arrays, it is matrix multiplication, but using matmul
or a @ b
is preferred.
If either a or b is 0-D (scalar), it is equivalent to multiply
and using numpy.multiply(a, b)
or a * b
is preferred.
If a is an N-D array and b is a 1-D array, it is a sum product over the last axis of a and b.
If a is an N-D array and b is an M-D array (where M>=2
), it is a sum product over the last axis of a and the second-to-last axis of b:
dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
First argument.
Second argument.
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 C-contiguous, and its dtype must be the dtype that would be returned for :None:None:`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 the dot product of a and b. If a and b are both scalars or both 1-D arrays then a scalar is returned; otherwise an array is returned. If :None:None:`out` is given, then it is returned.
This docstring was copied from numpy.dot.
einsum
Einstein summation convention.
linalg.multi_dot
Chained dot product.
matmul
'@' operator as method with out parameter.
tensordot
Sum products over arbitrary axes.
vdot
Complex-conjugating dot product.
>>> np.dot(3, 4) # doctest: +SKIP 12
Neither argument is complex-conjugated:
This example is valid syntax, but we were not able to check execution>>> np.dot([2j, 3j], [2j, 3j]) # doctest: +SKIP (-13+0j)
For 2-D arrays it is the matrix product:
This example is valid syntax, but we were not able to check execution>>> a = [[1, 0], [0, 1]] # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... b = [[4, 1], [2, 2]] # doctest: +SKIP
... np.dot(a, b) # doctest: +SKIP array([[4, 1], [2, 2]])
>>> a = np.arange(3*4*5*6).reshape((3,4,5,6)) # doctest: +SKIPThis example is valid syntax, but we were not able to check execution
... b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3)) # doctest: +SKIP
... np.dot(a, b)[2,3,2,1,2,2] # doctest: +SKIP 499128
>>> sum(a[2,3,2,:] * b[1,2,:,2]) # doctest: +SKIP 499128See :
The following pages refer to to this document either explicitly or contain code examples using this.
dask.array.routines.vdot
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