class_docstr: Univariate Kernel Density Estimator.
  
  Parameters
  ----------
  endog : array_like
      The variable for which the density estimate is desired.
  
  Notes
  -----
  If cdf, sf, cumhazard, or entropy are computed, they are computed based on
  the definition of the kernel rather than the FFT approximation, even if
  the density is fit with FFT = True.
  
  `KDEUnivariate` is much faster than `KDEMultivariate`, due to its FFT-based
  implementation.  It should be preferred for univariate, continuous data.
  `KDEMultivariate` also supports mixed data.
  
  See Also
  --------
  KDEMultivariate
  kdensity, kdensityfft
  
  Examples
  --------
  >>> import statsmodels.api as sm
  >>> import matplotlib.pyplot as plt
  
  >>> nobs = 300
  >>> np.random.seed(1234)  # Seed random generator
  >>> dens = sm.nonparametric.KDEUnivariate(np.random.normal(size=nobs))
  >>> dens.fit()
  >>> plt.plot(dens.cdf)
  >>> plt.show()
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docstr: Evaluate density at a point or points.
  
  Parameters
  ----------
  point : {float, ndarray}
      Point(s) at which to evaluate the density.
docstr_tok: ["Evaluate","density","at","a","point","or","points",".","Parameters","--","--","--","--","--","point",":","{","float",",","ndarray","}","Point","(","s",")","at","which","to","evaluate","the","density","."]
code: def evaluate(self, point):
      """
      Evaluate density at a point or points.
  
      Parameters
      ----------
      point : {float, ndarray}
          Point(s) at which to evaluate the density.
      """
      _checkisfit(self)
      return self.kernel.density(self.endog, point)
  
file_path: statsmodels/statsmodels/nonparametric/kde.py
imports: import numpy
  from scipy import integrate, stats
  from statsmodels.sandbox.nonparametric import kernels
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.tools.validation import array_like, float_like
  from .None import bandwidths
  from .kdetools import forrt, revrt, silverman_transform
  from .linbin import fast_linbin
label_desc: Use image node_id 8 for calling the KDEUnivariate obj's underlying member method code with example usage: obj.evaluate(point) and returns: self