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