one, weight is set to 1 (equal weights).

y_min : float, default=None
    Lower bound on the lowest predicted value (the minimum value may
    still be higher). If not set, defaults to -inf.

y_max : float, default=None
    Upper bound on the highest predicted value (the maximum may still be
    lower). If not set, defaults to +inf.

increasing : bool, default=True
    Whether to compute ``y_`` is increasing (if set to True) or decreasing
    (if set to False).

Returns
-------
y_ : ndarray of shape (n_samples,)
    Isotonic fit of y.

References
----------
"Active set algorithms for isotonic regression; A unifying framework"
by Michael J. Best and Nilotpal Chakravarti, section 3.

Examples
--------
>>> from sklearn.isotonic import isotonic_regression
>>> isotonic_regression([5, 3, 1, 2, 8, 10, 7, 9, 6, 4])
array([2.75   , 2.75   , 2.75   , 2.75   , 7.33...,
       7.33..., 7.33..., 7.33..., 7.33..., 7.33...])
Fr