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Function vdot

numpy/core/multiarray.py:835–889  ·  view source on GitHub ↗

vdot(a, b, /) Return the dot product of two vectors. The vdot(`a`, `b`) function handles complex numbers differently than dot(`a`, `b`). If the first argument is complex the complex conjugate of the first argument is used for the calculation of the dot product. Note that

(a, b)

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833
834@array_function_from_c_func_and_dispatcher(_multiarray_umath.vdot)
835def vdot(a, b):
836 """
837 vdot(a, b, /)
838
839 Return the dot product of two vectors.
840
841 The vdot(`a`, `b`) function handles complex numbers differently than
842 dot(`a`, `b`). If the first argument is complex the complex conjugate
843 of the first argument is used for the calculation of the dot product.
844
845 Note that `vdot` handles multidimensional arrays differently than `dot`:
846 it does *not* perform a matrix product, but flattens input arguments
847 to 1-D vectors first. Consequently, it should only be used for vectors.
848
849 Parameters
850 ----------
851 a : array_like
852 If `a` is complex the complex conjugate is taken before calculation
853 of the dot product.
854 b : array_like
855 Second argument to the dot product.
856
857 Returns
858 -------
859 output : ndarray
860 Dot product of `a` and `b`. Can be an int, float, or
861 complex depending on the types of `a` and `b`.
862
863 See Also
864 --------
865 dot : Return the dot product without using the complex conjugate of the
866 first argument.
867
868 Examples
869 --------
870 >>> a = np.array([1+2j,3+4j])
871 >>> b = np.array([5+6j,7+8j])
872 >>> np.vdot(a, b)
873 (70-8j)
874 >>> np.vdot(b, a)
875 (70+8j)
876
877 Note that higher-dimensional arrays are flattened!
878
879 >>> a = np.array([[1, 4], [5, 6]])
880 >>> b = np.array([[4, 1], [2, 2]])
881 >>> np.vdot(a, b)
882 30
883 >>> np.vdot(b, a)
884 30
885 >>> 1*4 + 4*1 + 5*2 + 6*2
886 30
887
888 """
889 return (a, b)
890
891
892@array_function_from_c_func_and_dispatcher(_multiarray_umath.bincount)

Callers 1

array_vdotFunction · 0.85

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