def pdf(self, data_predict=None):
        r"""
        Evaluate the probability density function.

        Parameters
        ----------
        data_predict: array_like, optional
            Points to evaluate at.  If unspecified, the training data is used.

        Returns
        -------
        pdf_est: array_like
            Probability density function evaluated at `data_predict`.

        Notes
        -----
        The probability density is given by the generalized product kernel
        estimator:

        .. math:: K_{h}(X_{i},X_{j}) =
            \prod_{s=1}^{q}h_{s}^{-1}k\left(\frac{X_{is}-X_{js}}{h_{s}}\right)
        """
        if data_predict is None:
            data_predict = self.data
        else:
            data_predict = _adjust_shape(data_predict, self.k_vars)

        pdf_est = []
        for i in range(np.shape(data_predict)[0]):
            pdf_est.append(gpke(self.bw, data=self.data,
                                data_predict=data_predict[i, :],
                                var_type=self.var_type) / self.nobs)

        pdf_est = np.squeeze(pdf_est)
        return pdf_est
def pdf ( self , data_predict = None ) : if data_predict is None : data_predict = self . data else : data_predict = _adjust_shape ( data_predict , self . k_vars ) pdf_est = [ ] for i in range ( np . shape ( data_predict ) [ 0 ] ) : pdf_est . append ( gpke ( self . bw , data = self . data , data_predict = data_predict [ i , : ] , var_type = self . var_type ) / self . nobs ) pdf_est = np . squeeze ( pdf_est ) return pdf_est
r"""
        Evaluate the probability density function.

        Parameters
        ----------
        data_predict: array_like, optional
            Points to evaluate at.  If unspecified, the training data is used.

        Returns
        -------
        pdf_est: array_like
            Probability density function evaluated at `data_predict`.

        Notes
        -----
        The probability density is given by the generalized product kernel
        estimator:

        .. math:: K_{h}(X_{i},X_{j}) =
            \prod_{s=1}^{q}h_{s}^{-1}k\left(\frac{X_{is}-X_{js}}{h_{s}}\right)