original_docstring: Computes conditioned probabilities p(z|x) using Gaussian Mixtures.
  
          See :class:`uwb.map.NoiseMap` and :class:`uwb.map.NoiseMapNormal` for more information.
          This is generally to slow for most applications. TODO C++/Numpy reimplementation.
          
docstring: Computes conditioned probabilities p(z|x) using Gaussian Mixtures.
docstring_tokens: Computes conditioned probabilities p ( z|x ) using Gaussian Mixtures .
code: def conditioned_probability(self, z, particles):
          """Computes conditioned probabilities p(z|x) using Gaussian Mixtures.
  
          See :class:`uwb.map.NoiseMap` and :class:`uwb.map.NoiseMapNormal` for more information.
          This is generally to slow for most applications. TODO C++/Numpy reimplementation.
          """
          pos_coords, pos = self.generator.get_closest_position(particles)
          prob = np.zeros((len(z)))
          for i, p in enumerate(pos_coords):
              weights, means, covs = self[p]
              for j, w in enumerate(weights):
                  prob[i] += w * multivariate_normal.pdf(
                      z[i], mean=means[j] + pos[i], cov=covs[j]
                  )
          return prob
code_tokens: def conditioned_probability ( self , z , particles ) : pos_coords , pos = self . generator . get_closest_position ( particles ) prob = np . zeros ( ( len ( z ) ) ) for i , p in enumerate ( pos_coords ) : weights , means , covs = self [ p ] for j , w in enumerate ( weights ) : prob [ i ] += w * multivariate_normal . pdf ( z [ i ] , mean = means [ j ] + pos [ i ] , cov = covs [ j ] ) return prob
short_docstring: Computes conditioned probabilities p(z|x) using Gaussian Mixtures.
short_docstring_tokens: Computes conditioned probabilities p ( z|x ) using Gaussian Mixtures .
comment: """Computes conditioned probabilities p(z|x) using Gaussian Mixtures.
  
          See :class:`uwb.map.NoiseMap` and :class:`uwb.map.NoiseMapNormal` for more information.
          This is generally to slow for most applications. TODO C++/Numpy reimplementation.
          """