class_docstr: Base class for multivariate kernels.
  
  An instance of MultivariateKernel implements a `call` method having
  signature `call(x, loc)`, returning the kernel weights comparing `x`
  (a 1d ndarray) to each row of `loc` (a 2d ndarray).
docstr: Set default bandwiths based on domain values.
  
  Parameters
  ----------
  loc : array_like
      Values from the domain to which the kernel will
      be applied.
  bwm : scalar, optional
      A non-negative scalar that is used to multiply
      the default bandwidth.
docstr_tok: ["Set","default","bandwiths","based","on","domain","values",".","Parameters","--","--","--","--","--","loc",":","array_like","Values","from","the","domain","to","which","the","kernel","will","be","applied",".","bwm",":","scalar",",","optional","A","non-negative","scalar","that","is","used","to","multiply","the","default","bandwidth","."]
code: def set_default_bw(self, loc, bwm=None):
      """
      Set default bandwiths based on domain values.
  
      Parameters
      ----------
      loc : array_like
          Values from the domain to which the kernel will
          be applied.
      bwm : scalar, optional
          A non-negative scalar that is used to multiply
          the default bandwidth.
      """
  
      sd = loc.std(0)
      q25, q75 = np.percentile(loc, [25, 75], axis=0)
      iqr = (q75 - q25) / 1.349
      bw = np.where(iqr < sd, iqr, sd)
      bw *= 0.9 / loc.shape[0] ** 0.2
  
      if bwm is not None:
          bw *= bwm
  
      # The final bandwidths
      self.bw = np.asarray(bw, dtype=np.float64)
  
      self._setup()
  
imports: import numpy
  import scipy.sparse
  from scipy.sparse.linalg import svds
  from scipy.optimize import fminbound
  import warnings
  from statsmodels.tools.tools import Bunch
  from statsmodels.tools.sm_exceptions import IterationLimitWarning, iteration_limit_doc
label_desc: Use image node_id 4 for calling the MultivariateKernel obj's underlying member method code with example usage: obj.set_default_bw(loc, bwm) without return types