numpy.linalg.vector_norm#
- linalg.vector_norm(x, /, *, axis=None, keepdims=False, ord=2)[source]#
Computes the vector norm of a vector (or batch of vectors)
x
.This function is Array API compatible.
- Parameters:
- xarray_like
Input array.
- axis{None, int, 2-tuple of ints}, optional
If an integer,
axis
specifies the axis (dimension) along which to compute vector norms. If an n-tuple,axis
specifies the axes (dimensions) along which to compute batched vector norms. IfNone
, the vector norm must be computed over all array values (i.e., equivalent to computing the vector norm of a flattened array). Default:None
.- keepdimsbool, optional
If this is set to True, the axes which are normed over are left in the result as dimensions with size one. Default: False.
- ord{1, -1, 2, -2, inf, -inf, ‘fro’, ‘nuc’}, optional
The order of the norm. For details see the table under
Notes
innumpy.linalg.norm
.
See also
numpy.linalg.norm
Generic norm function
Examples
>>> from numpy import linalg as LA >>> a = np.arange(9) + 1 >>> a array([1, 2, 3, 4, 5, 6, 7, 8, 9]) >>> b = a.reshape((3, 3)) >>> b array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
>>> LA.vector_norm(b) 16.881943016134134 >>> LA.vector_norm(b, ord=np.inf) 9.0 >>> LA.vector_norm(b, ord=-np.inf) 1.0
>>> LA.vector_norm(b, ord=1) 45.0 >>> LA.vector_norm(b, ord=-1) 0.3534857623790153 >>> LA.vector_norm(b, ord=2) 16.881943016134134 >>> LA.vector_norm(b, ord=-2) 0.8058837395885292