Source code for astroML.linear_model.kernel_regression

import numpy as np
from sklearn.base import BaseEstimator
from sklearn.metrics import pairwise_kernels

[docs]class NadarayaWatson(BaseEstimator): """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. """
[docs] 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)