class_docstr: Base class for density estimation and regression KDE classes.
class_docstr_tok: ["Base","class","for","density","estimation","and","regression","KDE","classes","."]
docstr: Sets bandwidth lower bound to effectively zero )1e-10), and for
  discrete values upper bound to 1.
docstr_tok: ["Sets","bandwidth","lower","bound","to","effectively","zero",")","1e-10",")",",","and","for","discrete","values","upper","bound","to","1","."]
code: def _set_bw_bounds(self, bw):
      """
      Sets bandwidth lower bound to effectively zero )1e-10), and for
      discrete values upper bound to 1.
      """
      bw[bw < 0] = 1e-10
      _, ix_ord, ix_unord = _get_type_pos(self.data_type)
      bw[ix_ord] = np.minimum(bw[ix_ord], 1.0)
      bw[ix_unord] = np.minimum(bw[ix_unord], 1.0)
  
      return bw
  
imports: import copy
  import numpy
  from scipy import optimize
  from scipy.stats.mstats import mquantiles
  from .None import kernels
label_desc: Use image node_id 7 for calling the GenericKDE obj's underlying member method code with example usage: obj._set_bw_bounds(bw) and returns: bw