original_docstring: 
          Compute the closed form solution of the diffusion process.
          
docstring: Compute the closed form solution of the diffusion process.
docstring_tokens: Compute the closed form solution of the diffusion process .
code: def fit(self, X):
          """
          Compute the closed form solution of the diffusion process.
          """
  
          X = _conform_x(X)
  
          if self.truncation_size is not None:
              self.truncation_size = np.minimum(self.truncation_size, X.shape[0])
  
          self._build_index(X)
          aff, ids = self._compute_neighborhood_graph(X)
          A = self._compute_affinity_matrix(aff, ids)
          D = _compute_degree_matrix(A)
          S = D.dot(A).dot(D)  # Transition matrix
          L = _compute_laplacian_matrix(S)
  
          l_inv = self._compute_inverse_laplacian(ids, L)
          l_inv = preprocessing.normalize(l_inv, norm="l2", axis=1)
          self.l_inv_ = l_inv
  
          return self
code_tokens: def fit ( self , X ) : X = _conform_x ( X ) if self . truncation_size is not None : self . truncation_size = np . minimum ( self . truncation_size , X . shape [ 0 ] ) self . _build_index ( X ) aff , ids = self . _compute_neighborhood_graph ( X ) A = self . _compute_affinity_matrix ( aff , ids ) D = _compute_degree_matrix ( A ) S = D . dot ( A ) . dot ( D ) L = _compute_laplacian_matrix ( S ) l_inv = self . _compute_inverse_laplacian ( ids , L ) l_inv = preprocessing . normalize ( l_inv , norm = "l2" , axis = 1 ) self . l_inv_ = l_inv return self
short_docstring: Compute the closed form solution of the diffusion process.
short_docstring_tokens: Compute the closed form solution of the diffusion process .
comment: """
          Compute the closed form solution of the diffusion process.
          """$SEP$# Transition matrix