numpy.dot# numpy. dot (a, b, out = None) # 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.

Jun 22, 2021 · numpy.ndarray.dot¶ method. ndarray. dot (b, out = None) ¶ Dot product of two arrays. Refer to numpy.dot for full documentation.

Jun 22, 2021 · numpy.dot¶ numpy. dot (a, b, out = None) ¶ 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.

May 24, 2020 · numpy.dot¶ numpy.dot (a, b, out=None) ¶ 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.

Return the dot product of two vectors. The vdot function handles complex numbers differently than dot: if the first argument is complex, it is replaced by its complex conjugate in the dot product calculation.

Choose version . GitHub; numpy.ndarray.dot

Jan 31, 2021 · numpy.ndarray.dot¶ method. ndarray.dot (b, out=None) ¶ Dot product of two arrays. Refer to numpy.dot for full documentation.

Compute the dot product of two or more arrays in a single function call, while automatically selecting the fastest evaluation order. multi_dot chains numpy.dot and uses optimal parenthesization of the matrices [1] [2] .

ma. dot (a, b, strict = False, out = None) [source] # Return the dot product of two arrays. This function is the equivalent of numpy.dot that takes masked values into account.

Compute tensor dot product along specified axes. Given two tensors, a and b, and an array_like object containing two array_like objects, (a_axes, b_axes), sum the products of a’s and b’s elements (components) over the axes specified by a_axes and b_axes.