moocore.normalise#

moocore.normalise(data, /, to_range=[0.0, 1.0], *, lower=nan, upper=nan, maximise=False)[source]#

Normalise points per coordinate to a range, e.g., to_range = [1,2], where the minimum value will correspond to 1 and the maximum to 2.

Parameters:

  • data (ArrayLike) – Numpy array of numerical values, where each row gives the coordinates of a point in objective space. See normalise_sets() to normalise data that includes set numbers (Multiple sets)

  • to_range (ArrayLike, default: [0.0, 1.0]) – Range composed of two numerical values. Normalise values to this range. If the objective is maximised, it is normalised to (to_range[1], to_range[0]) instead.

  • upper (ArrayLike, default: nan) – Bounds on the values. If numpy.nan, the maximum and minimum values of each coordinate are used.

  • lower (ArrayLike, default: nan) – Bounds on the values. If numpy.nan, the maximum and minimum values of each coordinate are used.

  • maximise (single bool, or list of booleans) – Whether the objectives must be maximised instead of minimised. Either a single boolean value that applies to all objectives or a list of booleans, with one value per objective. Also accepts a 1D numpy array with values 0 or 1 for each objective

Returns:

ndarray – Returns the data normalised as requested.

Examples

>>> dat = np.array([[3.5,5.5], [3.6,4.1], [4.1,3.2], [5.5,1.5]])
>>> moocore.normalise(dat)
array([[0.   , 1.   ],
       [0.05 , 0.65 ],
       [0.3  , 0.425],
       [1.   , 0.   ]])

>>> moocore.normalise(dat, to_range = [1,2], lower = [3.5, 3.5], upper = 5.5)
array([[1.  , 2.  ],
       [1.05, 1.3 ],
       [1.3 , 0.85],
       [2.  , 0.  ]])