Function vector_norm

utils/utils_poses/ATE/transformations.py:1804–1840

Return length, i.e. eucledian norm, of ndarray along axis. >>> v = numpy.random.random(3) >>> n = vector_norm(v) >>> numpy.allclose(n, numpy.linalg.norm(v)) True >>> v = numpy.random.rand(6, 5, 3) >>> n = vector_norm(v, axis=-1) >>> numpy.allclose(n, numpy.sqrt(numpy.sum

(data, axis=None, out=None)

def vector_norm(data, axis=None, out=None):
    """Return length, i.e. eucledian norm, of ndarray along axis.

    >>> v = numpy.random.random(3)
    >>> n = vector_norm(v)
    >>> numpy.allclose(n, numpy.linalg.norm(v))
    True
    >>> v = numpy.random.rand(6, 5, 3)
    >>> n = vector_norm(v, axis=-1)
    >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=2)))
    True
    >>> n = vector_norm(v, axis=1)
    >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1)))
    True
    >>> v = numpy.random.rand(5, 4, 3)
    >>> n = numpy.empty((5, 3), dtype=numpy.float64)
    >>> vector_norm(v, axis=1, out=n)
    >>> numpy.allclose(n, numpy.sqrt(numpy.sum(v*v, axis=1)))
    True
    >>> vector_norm([])
    0.0
    >>> vector_norm([1.0])
    1.0

    """
    data = numpy.array(data, dtype=numpy.float64, copy=True)
    if out is None:
        if data.ndim == 1:
            return math.sqrt(numpy.dot(data, data))
        data *= data
        out = numpy.atleast_1d(numpy.sum(data, axis=axis))
        numpy.sqrt(out, out)
        return out
    else:
        data *= data
        numpy.sum(data, axis=axis, out=out)
        numpy.sqrt(out, out)


def unit_vector(data, axis=None, out=None):