docs: def normalize_std_array(vector):
    """Applies a unit mean and variance normalization to an arrayset"""
  
    # Initializes variables
    length = 1
    n_samples = len(vector)
    mean = numpy.ndarray((length,), 'float64')
    std = numpy.ndarray((length,), 'float64')
    mean.fill(0)
    std.fill(0)
  
    # Computes mean and variance
    for array in vector:
      x = array.astype('float64')
      mean += x
      std += (x ** 2)
  
    mean /= n_samples
    std /= n_samples
    std -= (mean ** 2)
    std = std ** 0.5
    arrayset = numpy.ndarray(shape=(n_samples,mean.shape[0]), dtype=numpy.float64)
  
    for i in range (0, n_samples):
      arrayset[i,:] = (vector[i]-mean) / std
    return arrayset