numpy.vecdot#
- numpy.vecdot(x1,x2,/,out=None,*,casting='same_kind',order='K',dtype=None,subok=True[,signature,axes,axis])=<ufunc'vecdot'>#
Vector dot product of two arrays.
Let\(\mathbf{a}\) be a vector inx1 and\(\mathbf{b}\) bea corresponding vector inx2. The dot product is defined as:
\[\mathbf{a} \cdot \mathbf{b} = \sum_{i=0}^{n-1} \overline{a_i}b_i\]where the sum is over the last dimension (unlessaxis is specified) andwhere\(\overline{a_i}\) denotes the complex conjugate if\(a_i\)is complex and the identity otherwise.
New in version 2.0.0.
- Parameters:
- x1, x2array_like
Input arrays, scalars not allowed.
- outndarray, optional
A location into which the result is stored. If provided, it must havethe broadcasted shape ofx1 andx2 with the last axis removed.If not provided or None, a freshly-allocated array is used.
- **kwargs
For other keyword-only arguments, see theufunc docs.
- Returns:
- yndarray
The vector dot product of the inputs.This is a scalar only when both x1, x2 are 1-d vectors.
- Raises:
- ValueError
If the last dimension ofx1 is not the same size asthe last dimension ofx2.
If a scalar value is passed in.
See also
Examples
>>>importnumpyasnp
Get the projected size along a given normal for an array of vectors.
>>>v=np.array([[0.,5.,0.],[0.,0.,10.],[0.,6.,8.]])>>>n=np.array([0.,0.6,0.8])>>>np.vecdot(v,n)array([ 3., 8., 10.])