`numpy:vdot`

Signature

def vdot ( a , b , / )

Summary

Return the dot product of two vectors.

Extended Summary

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. vdot also handles multidimensional arrays differently than dot: it does not perform a matrix product, but flattens the arguments to 1-D arrays before taking a vector dot product.

Consequently, when the arguments are 2-D arrays of the same shape, this function effectively returns their Frobenius inner product (also known as the trace inner product or the standard inner product on a vector space of matrices).

Parameters

a : array_like: If a is complex the complex conjugate is taken before calculation of the dot product.
b : array_like: Second argument to the dot product.

Returns

output : ndarray: Dot product of a and b. Can be an int, float, or complex depending on the types of a and b.

Examples

import numpy as np
a = np.array([1+2j,3+4j])
b = np.array([5+6j,7+8j])

✓

np.vdot(a, b)
np.vdot(b, a)

✗

Note that higher-dimensional arrays are flattened!

a = np.array([[1, 4], [5, 6]])
b = np.array([[4, 1], [2, 2]])

✓

np.vdot(a, b)
np.vdot(b, a)

✗

1*4 + 4*1 + 5*2 + 6*2

✓

Aliases

numpy.vdot