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Is there a difference between
import numpy as np
np.dot(a,b)
and
a.dot(b)
internally? I wasn't able to find any documentation on the latter method.
689
numpy.dot(vector_a, vector_b, out = None) returns the dot product of vectors a and b. It can handle 2D arrays but considers them as matrix and will perform matrix multiplication. For N dimensions it is a sum-product over the last axis of a and the second-to-last of b : dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
The matmul() function broadcasts the array like a stack of matrices as elements residing in the last two indexes, respectively. The numpy. dot() function, on the other hand, performs multiplication as the sum of products over the last axis of the first array and the second-to-last of the second.
ndarray. dot() function return the dot product of two arrays.
Because np. dot executes the actual arithmetic operations and the enclosing loop in compiled code, which is much faster than the Python interpreter.
If a is an array, they're equivalent. The docs you couldn't find for the dot method are here, and they boil down to "see numpy.dot".
If type(a) is not numpy.ndarray, then numpy.dot will convert a to an array and use the array for the multiplication, while a.dot will do whatever a's type says it does, or raise an AttributeError if a doesn't have a dot method.
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