90787dc646305306274fb85fefbfa49d7458cda8,cde/density_estimator/LSCDE.py,LSConditionalDensityEstimation,pdf,#LSConditionalDensityEstimation#Any#Any#,93

Before Change

    X_normalized, Y_normalized = self._normalize(X, Y)

    p = np.dot(self.alpha.T, self._gaussian_kernel(X_normalized, Y_normalized).T)
    p_normalization = (np.sqrt(2*np.pi) * self.bw) ** self.ndim_y * np.dot(self.alpha.T, self._gaussian_kernel(X_normalized).T)

    return np.squeeze(p / p_normalization / np.product(self.y_std))

  def predict_density(self, X, Y=None, resolution=50):
     Computes conditional density p(y|x) over a predefined grid of y target values

After Change

    if n_samples > MULTIPROC_THRESHOLD:
      return execute_batch_async_pdf(self._pdf, X, Y, n_jobs=self.n_jobs)
    else:
      return self._pdf(X, Y)

  def predict_density(self, X, Y=None, resolution=50):
     Computes conditional density p(y|x) over a predefined grid of y target values

Instances