code: def _joint_probabilities_nn(distances, neighbors, desired_perplexity, verbose):
      
      t0 = time()
      # Compute conditional probabilities such that they approximately match
      # the desired perplexity
      n_samples, k = neighbors.shape
      distances = distances.astype(np.float32, copy=False)
      neighbors = neighbors.astype(np.int64, copy=False)
      conditional_P = _utils._binary_search_perplexity(
          distances, neighbors, desired_perplexity, verbose)
      assert np.all(np.isfinite(conditional_P)), \
          "All probabilities should be finite"
  
      # Symmetrize the joint probability distribution using sparse operations
      P = csr_matrix((conditional_P.ravel(), neighbors.ravel(),
                      range(0, n_samples * k + 1, k)),
                     shape=(n_samples, n_samples))
      P = P + P.T
  
      # Normalize the joint probability distribution
      sum_P = np.maximum(P.sum(), MACHINE_EPSILON)
      P /= sum_P
  
      assert np.all(np.abs(P.data) <= 1.0)
      if verbose >= 2:
          duration = time() - t0
          print("[t-SNE] Computed conditional probabilities in {:.3f}s"
                .format(duration))
      return P
docs: Compute joint probabilities p_ij from distances using just nearest
      neighbors.
  
      This method is approximately equal to _joint_probabilities. The latter
      is O(N), but limiting the joint probability to nearest neighbors improves
      this substantially to O(uN).
  
      Parameters
      ----------
      distances : array, shape (n_samples, k)
          Distances of samples to its k nearest neighbors.
  
      neighbors : array, shape (n_samples, k)
          Indices of the k nearest-neighbors for each samples.
  
      desired_perplexity : float
          Desired perplexity of the joint probability distributions.
  
      verbose : int
          Verbosity level.
  
      Returns
      -------
      P : csr sparse matrix, shape (n_samples, n_samples)
          Condensed joint probability matrix with only nearest neighbors.