"""Module for generic NeighborsRegressor."""
import numpy as np
from .approximation import Approximation
[docs]
class NeighborsRegressor(Approximation):
"""
A generic superclass for wrappers of *NeighborsRegressor from sklearn.
This class provides a common interface for neighbor-based regression methods.
:param kwargs: Arguments passed to the internal instance of
*NeighborsRegressor.
:Example:
>>> import numpy as np
>>> from ezyrb import KNeighborsRegressor
>>> x = np.random.uniform(-1, 1, size=(20, 2))
>>> y = np.sin(x[:, 0]) + np.cos(x[:, 1])
>>> knn = KNeighborsRegressor(n_neighbors=5)
>>> knn.fit(x, y)
>>> y_pred = knn.predict(x[:5])
"""
[docs]
def fit(self, points, values):
"""
Construct the interpolator given `points` and `values`.
:param array_like points: the coordinates of the points.
:param array_like values: the values in the points.
"""
points = np.array(points).reshape(len(points), -1)
values = np.array(values)
self.regressor.fit(points, values)
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def predict(self, new_point):
"""
Evaluate interpolator at given `new_points`.
:param array_like new_points: the coordinates of the given points.
:return: the interpolated values.
:rtype: numpy.ndarray
"""
if isinstance(new_point, (list, np.ndarray)):
new_point = np.array(new_point).reshape(len(new_point), -1)
else:
new_point = np.array([new_point])
return self.regressor.predict(new_point)