parameters: (u, v, V)
return_statement: return np.sqrt(((u - v) ** 2 / V).sum())
docstring: Returns the standardized Euclidean distance between two 1-D arrays.
  
      The standardized Euclidean distance between `u` and `v`.
  
      Parameters
      ----------
      u : (N,) array_like
          Input array.
      v : (N,) array_like
          Input array.
      V : (N,) array_like
          `V` is an 1-D array of component variances. It is usually computed
          among a larger collection vectors.
  
      Returns
      -------
      seuclidean : double
          The standardized Euclidean distance between vectors `u` and `v`.
docstring_summary: Returns the standardized Euclidean distance between two 1-D arrays.
docstring_tokens: Returns the standardized Euclidean distance between two 1 - D arrays .
function: def seuclidean(u, v, V):
      """
      Returns the standardized Euclidean distance between two 1-D arrays.
  
      The standardized Euclidean distance between `u` and `v`.
  
      Parameters
      ----------
      u : (N,) array_like
          Input array.
      v : (N,) array_like
          Input array.
      V : (N,) array_like
          `V` is an 1-D array of component variances. It is usually computed
          among a larger collection vectors.
  
      Returns
      -------
      seuclidean : double
          The standardized Euclidean distance between vectors `u` and `v`.
  
      """
      u = _validate_vector(u)
      v = _validate_vector(v)
      V = _validate_vector(V, dtype=np.float64)
      if V.shape[0] != u.shape[0] or u.shape[0] != v.shape[0]:
          raise TypeError('V must be a 1-D array of the same dimension '
                          'as u and v.')
      return np.sqrt(((u - v) ** 2 / V).sum())
function_tokens: def seuclidean ( u , v , V ) : u = _validate_vector ( u ) v = _validate_vector ( v ) V = _validate_vector ( V , dtype = np . float64 ) if V . shape [ 0 ] != u . shape [ 0 ] or u . shape [ 0 ] != v . shape [ 0 ] : raise TypeError ( 'V must be a 1-D array of the same dimension ' 'as u and v.' ) return np . sqrt ( ( ( u - v ) ** 2 / V ) . sum ( ) )