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sklearn.isotonic.isotonic_regression(y, sample_weight=None, y_min=None, y_max=None, increasing=True)[source] -
Solve the isotonic regression model:
min sum w[i] (y[i] - y_[i]) ** 2 subject to y_min = y_[1] <= y_[2] ... <= y_[n] = y_max
- where:
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- y[i] are inputs (real numbers)
- y_[i] are fitted
- w[i] are optional strictly positive weights (default to 1.0)
Read more in the User Guide.
Parameters: y : iterable of floating-point values
The data.
sample_weight : iterable of floating-point values, optional, default: None
Weights on each point of the regression. If None, weight is set to 1 (equal weights).
y_min : optional, default: None
If not None, set the lowest value of the fit to y_min.
y_max : optional, default: None
If not None, set the highest value of the fit to y_max.
increasing : boolean, optional, default: True
Whether to compute
y_is increasing (if set to True) or decreasing (if set to False)Returns: y_ : list of floating-point values
Isotonic fit of y.
References
?Active set algorithms for isotonic regression; A unifying framework? by Michael J. Best and Nilotpal Chakravarti, section 3.
sklearn.isotonic.isotonic_regression()
2017-01-15 04:26:09
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