/usr/lib/python3/dist-packages/astroML/linear_model/kernel_regression.py is in python3-astroml 0.3-6.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import numpy as np
from .linear_regression import gaussian_basis
from sklearn.metrics import pairwise_kernels
class NadarayaWatson(object):
"""Nadaraya-Watson Kernel Regression
This is basically a gaussian-weighted moving average of points
Parameters
----------
kernel : string
kernel is either "gaussian", or one of the kernels available in
sklearn.metrics.pairwise.
h : float or array_like
width of kernel. If array, its length must be the number of
dimensions in the training data
Additional keyword arguments are passed to the kernel.
"""
def __init__(self, kernel='gaussian', h=None, **kwargs):
self.kernel = kernel
self.h = h
self.kwargs = kwargs
def fit(self, X, y, dy=1):
self.X = np.asarray(X)
self.y = np.asarray(y)
self.dy = np.atleast_1d(dy)
return self
def predict(self, X):
X = np.asarray(X)
if X.ndim != 2:
raise ValueError('X must be two-dimensional')
if X.shape[1] != self.X.shape[1]:
raise ValueError('dimensions of X do not match training dimension')
if self.kernel == 'gaussian':
# wrangle gaussian into scikit-learn's 'rbf' kernel
h = np.asarray(self.h)
gamma = 0.5 / h / h
K = pairwise_kernels(X, self.X, metric='rbf', gamma=gamma)
else:
K = pairwise_kernels(X, self.X, metric=self.kernel, **self.kwargs)
K /= self.dy ** 2
return (K * self.y).sum(1) / K.sum(1)
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