nums.numpy.dot

nums.numpy.dot(a, b, out=None)[source]

Dot product of two arrays.

This docstring was copied from numpy.dot.

Some inconsistencies with the NumS version may exist.

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])
```

Parameters

a (BlockArray) – First argument.
b (BlockArray) – Second argument.
out (BlockArray, 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 C-contiguous, 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 – 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 out is given, then it is returned.

Return type

BlockArray

Raises

ValueError – If the last dimension of a is not the same size as the second-to-last dimension of b.

See also

tensordot: Sum products over arbitrary axes.
matmul: ‘@’ operator as method with out parameter.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

For 2-D arrays it is the matrix product:

>>> a = nps.array([[1, 0], [0, 1]])  
>>> b = nps.array([[4, 1], [2, 2]])  
>>> nps.dot(a, b).get()  
array([[4, 1],
       [2, 2]])

nums.numpy.outer

nums.numpy.shape