parameters: (data, axis=None, out=None)
docstring: Return length, i.e. Euclidean 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))
      >>> 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])
      1.0
docstring_summary: Return length, i.e. Euclidean norm, of ndarray along axis.
docstring_tokens: Return length i . e . Euclidean norm of ndarray along axis .
function: def vector_norm(data, axis=None, out=None):
      """Return length, i.e. Euclidean 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))
      >>> 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])
      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)
function_tokens: def vector_norm ( data , axis = None , out = None ) : 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 )