code: def _compute_input_activations(self, X):
          
  
          n_samples = X.shape[0]
  
          mlp_acts = np.zeros((n_samples, self.n_hidden))
          if (self._use_mlp_input):
              b = self.components_['biases']
              w = self.components_['weights']
              mlp_acts = self.alpha * (safe_sparse_dot(X, w) + b)
  
          rbf_acts = np.zeros((n_samples, self.n_hidden))
          if (self._use_rbf_input):
              radii = self.components_['radii']
              centers = self.components_['centers']
              scale = self.rbf_width * (1.0 - self.alpha)
              rbf_acts = scale * cdist(X, centers)/radii
  
          self.input_activations_ = mlp_acts + rbf_acts
docs: Compute input activations given X