class_docstr: 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.
code: def __init__(self, kernel="gaussian", h=None, **kwargs):
      self.kernel = kernel
      self.h = h
      self.kwargs = kwargs
  
file_path: astroML/astroML/linear_model/kernel_regression.py
imports: import numpy
  from sklearn.base import BaseEstimator
  from sklearn.metrics import pairwise_kernels
label_desc: Use image node_id 1 to create a new NadarayaWatson object from inherited base classes: BaseEstimator with example: obj = NadarayaWatson(kernel, h)