desdeo_emo.surrogatemodels.EvoNN
¶
Module Contents¶
Functions¶
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class
desdeo_emo.surrogatemodels.EvoNN.
EvoNN
(num_hidden_nodes: int = 20, p_omit: float = 0.2, w_low: float = - 5.0, w_high: float = 5.0, activation_function: str = 'sigmoid', loss_function: str = 'mse', training_algorithm: Type[desdeo_emo.EAs.BaseEA.BaseEA] = PPGA, pop_size: int = 500, model_selection_criterion: str = 'akaike_corrected', recombination_type: str = 'evonn_xover_mutation', crossover_type: str = 'standard', mutation_type: str = 'gaussian')[source]¶ Bases:
desdeo_problem.surrogatemodels.SurrogateModels.BaseRegressor
Helper class that provides a standard way to create an ABC using inheritance.
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_model_performance
(self, first_layer: numpy.ndarray = None, X: numpy.ndarray = None, y_true: numpy.ndarray = None)[source]¶
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predict
(self, X: numpy.ndarray = None, first_layer: numpy.ndarray = None, training: bool = False)[source]¶
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calculate_linear
(self, previous_layer_output)[source]¶ Calculate the final layer using LLSQ or
- Parameters
non_linear_layer (np.ndarray) – Output of the activation function
- Returns
linear_layer (np.ndarray) – The optimized weight matrix of the upper part of the network
predicted_values (np.ndarray) – The prediction of the model
training_error (float) – The model’s training error
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