# This file was automatically generated by SWIG (https://www.swig.org). # Version 4.2.1 # # Do not make changes to this file unless you know what you are doing - modify # the SWIG interface file instead. from sys import version_info as _swig_python_version_info # Import the low-level C/C++ module if __package__ or "." in __name__: from . import _libfann else: import _libfann try: import builtins as __builtin__ except ImportError: import __builtin__ def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except __builtin__.Exception: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) def _swig_setattr_nondynamic_instance_variable(set): def set_instance_attr(self, name, value): if name == "this": set(self, name, value) elif name == "thisown": self.this.own(value) elif hasattr(self, name) and isinstance(getattr(type(self), name), property): set(self, name, value) else: raise AttributeError("You cannot add instance attributes to %s" % self) return set_instance_attr def _swig_setattr_nondynamic_class_variable(set): def set_class_attr(cls, name, value): if hasattr(cls, name) and not isinstance(getattr(cls, name), property): set(cls, name, value) else: raise AttributeError("You cannot add class attributes to %s" % cls) return set_class_attr def _swig_add_metaclass(metaclass): """Class decorator for adding a metaclass to a SWIG wrapped class - a slimmed down version of six.add_metaclass""" def wrapper(cls): return metaclass(cls.__name__, cls.__bases__, cls.__dict__.copy()) return wrapper class _SwigNonDynamicMeta(type): """Meta class to enforce nondynamic attributes (no new attributes) for a class""" __setattr__ = _swig_setattr_nondynamic_class_variable(type.__setattr__) class SwigPyIterator(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr __swig_destroy__ = _libfann.delete_SwigPyIterator def value(self): return _libfann.SwigPyIterator_value(self) def incr(self, n=1): return _libfann.SwigPyIterator_incr(self, n) def decr(self, n=1): return _libfann.SwigPyIterator_decr(self, n) def distance(self, x): return _libfann.SwigPyIterator_distance(self, x) def equal(self, x): return _libfann.SwigPyIterator_equal(self, x) def copy(self): return _libfann.SwigPyIterator_copy(self) def next(self): return _libfann.SwigPyIterator_next(self) def __next__(self): return _libfann.SwigPyIterator___next__(self) def previous(self): return _libfann.SwigPyIterator_previous(self) def advance(self, n): return _libfann.SwigPyIterator_advance(self, n) def __eq__(self, x): return _libfann.SwigPyIterator___eq__(self, x) def __ne__(self, x): return _libfann.SwigPyIterator___ne__(self, x) def __iadd__(self, n): return _libfann.SwigPyIterator___iadd__(self, n) def __isub__(self, n): return _libfann.SwigPyIterator___isub__(self, n) def __add__(self, n): return _libfann.SwigPyIterator___add__(self, n) def __sub__(self, *args): return _libfann.SwigPyIterator___sub__(self, *args) def __iter__(self): return self # Register SwigPyIterator in _libfann: _libfann.SwigPyIterator_swigregister(SwigPyIterator) ERRORFUNC_LINEAR = _libfann.ERRORFUNC_LINEAR ERRORFUNC_TANH = _libfann.ERRORFUNC_TANH STOPFUNC_MSE = _libfann.STOPFUNC_MSE STOPFUNC_BIT = _libfann.STOPFUNC_BIT TRAIN_INCREMENTAL = _libfann.TRAIN_INCREMENTAL TRAIN_BATCH = _libfann.TRAIN_BATCH TRAIN_RPROP = _libfann.TRAIN_RPROP TRAIN_QUICKPROP = _libfann.TRAIN_QUICKPROP TRAIN_SARPROP = _libfann.TRAIN_SARPROP LINEAR = _libfann.LINEAR THRESHOLD = _libfann.THRESHOLD THRESHOLD_SYMMETRIC = _libfann.THRESHOLD_SYMMETRIC SIGMOID = _libfann.SIGMOID SIGMOID_STEPWISE = _libfann.SIGMOID_STEPWISE SIGMOID_SYMMETRIC = _libfann.SIGMOID_SYMMETRIC SIGMOID_SYMMETRIC_STEPWISE = _libfann.SIGMOID_SYMMETRIC_STEPWISE GAUSSIAN = _libfann.GAUSSIAN GAUSSIAN_SYMMETRIC = _libfann.GAUSSIAN_SYMMETRIC GAUSSIAN_STEPWISE = _libfann.GAUSSIAN_STEPWISE ELLIOT = _libfann.ELLIOT ELLIOT_SYMMETRIC = _libfann.ELLIOT_SYMMETRIC LINEAR_PIECE = _libfann.LINEAR_PIECE LINEAR_PIECE_SYMMETRIC = _libfann.LINEAR_PIECE_SYMMETRIC SIN_SYMMETRIC = _libfann.SIN_SYMMETRIC COS_SYMMETRIC = _libfann.COS_SYMMETRIC LAYER = _libfann.LAYER SHORTCUT = _libfann.SHORTCUT class training_data_parent(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, *args): _libfann.training_data_parent_swiginit(self, _libfann.new_training_data_parent(*args)) __swig_destroy__ = _libfann.delete_training_data_parent def destroy_train(self): return _libfann.training_data_parent_destroy_train(self) def read_train_from_file(self, filename): return _libfann.training_data_parent_read_train_from_file(self, filename) def save_train(self, filename): return _libfann.training_data_parent_save_train(self, filename) def save_train_to_fixed(self, filename, decimal_point): return _libfann.training_data_parent_save_train_to_fixed(self, filename, decimal_point) def shuffle_train_data(self): return _libfann.training_data_parent_shuffle_train_data(self) def merge_train_data(self, data): return _libfann.training_data_parent_merge_train_data(self, data) def length_train_data(self): return _libfann.training_data_parent_length_train_data(self) def num_input_train_data(self): return _libfann.training_data_parent_num_input_train_data(self) def num_output_train_data(self): return _libfann.training_data_parent_num_output_train_data(self) def get_input(self): return _libfann.training_data_parent_get_input(self) def get_output(self): return _libfann.training_data_parent_get_output(self) def set_train_data(self, num_data, num_input, input, num_output, output): return _libfann.training_data_parent_set_train_data(self, num_data, num_input, input, num_output, output) def create_train_from_callback(self, num_data, num_input, num_output, user_function): return _libfann.training_data_parent_create_train_from_callback(self, num_data, num_input, num_output, user_function) def scale_input_train_data(self, new_min, new_max): return _libfann.training_data_parent_scale_input_train_data(self, new_min, new_max) def scale_output_train_data(self, new_min, new_max): return _libfann.training_data_parent_scale_output_train_data(self, new_min, new_max) def scale_train_data(self, new_min, new_max): return _libfann.training_data_parent_scale_train_data(self, new_min, new_max) def subset_train_data(self, pos, length): return _libfann.training_data_parent_subset_train_data(self, pos, length) # Register training_data_parent in _libfann: _libfann.training_data_parent_swigregister(training_data_parent) class neural_net_parent(object): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, *args): _libfann.neural_net_parent_swiginit(self, _libfann.new_neural_net_parent(*args)) def copy_from_struct_fann(self, other): return _libfann.neural_net_parent_copy_from_struct_fann(self, other) __swig_destroy__ = _libfann.delete_neural_net_parent def destroy(self): return _libfann.neural_net_parent_destroy(self) def create_standard(self, num_layers): return _libfann.neural_net_parent_create_standard(self, num_layers) def create_standard_array(self, num_layers, layers): return _libfann.neural_net_parent_create_standard_array(self, num_layers, layers) def create_sparse(self, connection_rate, num_layers): return _libfann.neural_net_parent_create_sparse(self, connection_rate, num_layers) def create_sparse_array(self, connection_rate, num_layers, layers): return _libfann.neural_net_parent_create_sparse_array(self, connection_rate, num_layers, layers) def create_shortcut(self, num_layers): return _libfann.neural_net_parent_create_shortcut(self, num_layers) def create_shortcut_array(self, num_layers, layers): return _libfann.neural_net_parent_create_shortcut_array(self, num_layers, layers) def run(self, input): return _libfann.neural_net_parent_run(self, input) def randomize_weights(self, min_weight, max_weight): return _libfann.neural_net_parent_randomize_weights(self, min_weight, max_weight) def init_weights(self, data): return _libfann.neural_net_parent_init_weights(self, data) def print_connections(self): return _libfann.neural_net_parent_print_connections(self) def create_from_file(self, configuration_file): return _libfann.neural_net_parent_create_from_file(self, configuration_file) def save(self, configuration_file): return _libfann.neural_net_parent_save(self, configuration_file) def save_to_fixed(self, configuration_file): return _libfann.neural_net_parent_save_to_fixed(self, configuration_file) def train(self, input, desired_output): return _libfann.neural_net_parent_train(self, input, desired_output) def train_epoch(self, data): return _libfann.neural_net_parent_train_epoch(self, data) def train_on_data(self, data, max_epochs, epochs_between_reports, desired_error): return _libfann.neural_net_parent_train_on_data(self, data, max_epochs, epochs_between_reports, desired_error) def train_on_file(self, filename, max_epochs, epochs_between_reports, desired_error): return _libfann.neural_net_parent_train_on_file(self, filename, max_epochs, epochs_between_reports, desired_error) def test(self, input, desired_output): return _libfann.neural_net_parent_test(self, input, desired_output) def test_data(self, data): return _libfann.neural_net_parent_test_data(self, data) def get_MSE(self): return _libfann.neural_net_parent_get_MSE(self) def reset_MSE(self): return _libfann.neural_net_parent_reset_MSE(self) def set_callback(self, callback, user_data): return _libfann.neural_net_parent_set_callback(self, callback, user_data) def print_parameters(self): return _libfann.neural_net_parent_print_parameters(self) def get_training_algorithm(self): return _libfann.neural_net_parent_get_training_algorithm(self) def set_training_algorithm(self, training_algorithm): return _libfann.neural_net_parent_set_training_algorithm(self, training_algorithm) def get_learning_rate(self): return _libfann.neural_net_parent_get_learning_rate(self) def set_learning_rate(self, learning_rate): return _libfann.neural_net_parent_set_learning_rate(self, learning_rate) def get_activation_function(self, layer, neuron): return _libfann.neural_net_parent_get_activation_function(self, layer, neuron) def set_activation_function(self, activation_function, layer, neuron): return _libfann.neural_net_parent_set_activation_function(self, activation_function, layer, neuron) def set_activation_function_layer(self, activation_function, layer): return _libfann.neural_net_parent_set_activation_function_layer(self, activation_function, layer) def set_activation_function_hidden(self, activation_function): return _libfann.neural_net_parent_set_activation_function_hidden(self, activation_function) def set_activation_function_output(self, activation_function): return _libfann.neural_net_parent_set_activation_function_output(self, activation_function) def get_activation_steepness(self, layer, neuron): return _libfann.neural_net_parent_get_activation_steepness(self, layer, neuron) def set_activation_steepness(self, steepness, layer, neuron): return _libfann.neural_net_parent_set_activation_steepness(self, steepness, layer, neuron) def set_activation_steepness_layer(self, steepness, layer): return _libfann.neural_net_parent_set_activation_steepness_layer(self, steepness, layer) def set_activation_steepness_hidden(self, steepness): return _libfann.neural_net_parent_set_activation_steepness_hidden(self, steepness) def set_activation_steepness_output(self, steepness): return _libfann.neural_net_parent_set_activation_steepness_output(self, steepness) def get_train_error_function(self): return _libfann.neural_net_parent_get_train_error_function(self) def set_train_error_function(self, train_error_function): return _libfann.neural_net_parent_set_train_error_function(self, train_error_function) def get_quickprop_decay(self): return _libfann.neural_net_parent_get_quickprop_decay(self) def set_quickprop_decay(self, quickprop_decay): return _libfann.neural_net_parent_set_quickprop_decay(self, quickprop_decay) def get_quickprop_mu(self): return _libfann.neural_net_parent_get_quickprop_mu(self) def set_quickprop_mu(self, quickprop_mu): return _libfann.neural_net_parent_set_quickprop_mu(self, quickprop_mu) def get_rprop_increase_factor(self): return _libfann.neural_net_parent_get_rprop_increase_factor(self) def set_rprop_increase_factor(self, rprop_increase_factor): return _libfann.neural_net_parent_set_rprop_increase_factor(self, rprop_increase_factor) def get_rprop_decrease_factor(self): return _libfann.neural_net_parent_get_rprop_decrease_factor(self) def set_rprop_decrease_factor(self, rprop_decrease_factor): return _libfann.neural_net_parent_set_rprop_decrease_factor(self, rprop_decrease_factor) def get_rprop_delta_zero(self): return _libfann.neural_net_parent_get_rprop_delta_zero(self) def set_rprop_delta_zero(self, rprop_delta_zero): return _libfann.neural_net_parent_set_rprop_delta_zero(self, rprop_delta_zero) def get_rprop_delta_min(self): return _libfann.neural_net_parent_get_rprop_delta_min(self) def set_rprop_delta_min(self, rprop_delta_min): return _libfann.neural_net_parent_set_rprop_delta_min(self, rprop_delta_min) def get_rprop_delta_max(self): return _libfann.neural_net_parent_get_rprop_delta_max(self) def set_rprop_delta_max(self, rprop_delta_max): return _libfann.neural_net_parent_set_rprop_delta_max(self, rprop_delta_max) def get_sarprop_weight_decay_shift(self): return _libfann.neural_net_parent_get_sarprop_weight_decay_shift(self) def set_sarprop_weight_decay_shift(self, sarprop_weight_decay_shift): return _libfann.neural_net_parent_set_sarprop_weight_decay_shift(self, sarprop_weight_decay_shift) def get_sarprop_step_error_threshold_factor(self): return _libfann.neural_net_parent_get_sarprop_step_error_threshold_factor(self) def set_sarprop_step_error_threshold_factor(self, sarprop_step_error_threshold_factor): return _libfann.neural_net_parent_set_sarprop_step_error_threshold_factor(self, sarprop_step_error_threshold_factor) def get_sarprop_step_error_shift(self): return _libfann.neural_net_parent_get_sarprop_step_error_shift(self) def set_sarprop_step_error_shift(self, sarprop_step_error_shift): return _libfann.neural_net_parent_set_sarprop_step_error_shift(self, sarprop_step_error_shift) def get_sarprop_temperature(self): return _libfann.neural_net_parent_get_sarprop_temperature(self) def set_sarprop_temperature(self, sarprop_temperature): return _libfann.neural_net_parent_set_sarprop_temperature(self, sarprop_temperature) def get_num_input(self): return _libfann.neural_net_parent_get_num_input(self) def get_num_output(self): return _libfann.neural_net_parent_get_num_output(self) def get_total_neurons(self): return _libfann.neural_net_parent_get_total_neurons(self) def get_total_connections(self): return _libfann.neural_net_parent_get_total_connections(self) def get_network_type(self): return _libfann.neural_net_parent_get_network_type(self) def get_connection_rate(self): return _libfann.neural_net_parent_get_connection_rate(self) def get_num_layers(self): return _libfann.neural_net_parent_get_num_layers(self) def get_layer_array(self, layers): return _libfann.neural_net_parent_get_layer_array(self, layers) def get_bias_array(self, bias): return _libfann.neural_net_parent_get_bias_array(self, bias) def get_connection_array(self, connections): return _libfann.neural_net_parent_get_connection_array(self, connections) def set_weight_array(self, connections, num_connections): return _libfann.neural_net_parent_set_weight_array(self, connections, num_connections) def set_weight(self, from_neuron, to_neuron, weight): return _libfann.neural_net_parent_set_weight(self, from_neuron, to_neuron, weight) def get_learning_momentum(self): return _libfann.neural_net_parent_get_learning_momentum(self) def set_learning_momentum(self, learning_momentum): return _libfann.neural_net_parent_set_learning_momentum(self, learning_momentum) def get_train_stop_function(self): return _libfann.neural_net_parent_get_train_stop_function(self) def set_train_stop_function(self, train_stop_function): return _libfann.neural_net_parent_set_train_stop_function(self, train_stop_function) def get_bit_fail_limit(self): return _libfann.neural_net_parent_get_bit_fail_limit(self) def set_bit_fail_limit(self, bit_fail_limit): return _libfann.neural_net_parent_set_bit_fail_limit(self, bit_fail_limit) def get_bit_fail(self): return _libfann.neural_net_parent_get_bit_fail(self) def cascadetrain_on_data(self, data, max_neurons, neurons_between_reports, desired_error): return _libfann.neural_net_parent_cascadetrain_on_data(self, data, max_neurons, neurons_between_reports, desired_error) def cascadetrain_on_file(self, filename, max_neurons, neurons_between_reports, desired_error): return _libfann.neural_net_parent_cascadetrain_on_file(self, filename, max_neurons, neurons_between_reports, desired_error) def get_cascade_output_change_fraction(self): return _libfann.neural_net_parent_get_cascade_output_change_fraction(self) def set_cascade_output_change_fraction(self, cascade_output_change_fraction): return _libfann.neural_net_parent_set_cascade_output_change_fraction(self, cascade_output_change_fraction) def get_cascade_output_stagnation_epochs(self): return _libfann.neural_net_parent_get_cascade_output_stagnation_epochs(self) def set_cascade_output_stagnation_epochs(self, cascade_output_stagnation_epochs): return _libfann.neural_net_parent_set_cascade_output_stagnation_epochs(self, cascade_output_stagnation_epochs) def get_cascade_candidate_change_fraction(self): return _libfann.neural_net_parent_get_cascade_candidate_change_fraction(self) def set_cascade_candidate_change_fraction(self, cascade_candidate_change_fraction): return _libfann.neural_net_parent_set_cascade_candidate_change_fraction(self, cascade_candidate_change_fraction) def get_cascade_candidate_stagnation_epochs(self): return _libfann.neural_net_parent_get_cascade_candidate_stagnation_epochs(self) def set_cascade_candidate_stagnation_epochs(self, cascade_candidate_stagnation_epochs): return _libfann.neural_net_parent_set_cascade_candidate_stagnation_epochs(self, cascade_candidate_stagnation_epochs) def get_cascade_weight_multiplier(self): return _libfann.neural_net_parent_get_cascade_weight_multiplier(self) def set_cascade_weight_multiplier(self, cascade_weight_multiplier): return _libfann.neural_net_parent_set_cascade_weight_multiplier(self, cascade_weight_multiplier) def get_cascade_candidate_limit(self): return _libfann.neural_net_parent_get_cascade_candidate_limit(self) def set_cascade_candidate_limit(self, cascade_candidate_limit): return _libfann.neural_net_parent_set_cascade_candidate_limit(self, cascade_candidate_limit) def get_cascade_max_out_epochs(self): return _libfann.neural_net_parent_get_cascade_max_out_epochs(self) def set_cascade_max_out_epochs(self, cascade_max_out_epochs): return _libfann.neural_net_parent_set_cascade_max_out_epochs(self, cascade_max_out_epochs) def get_cascade_max_cand_epochs(self): return _libfann.neural_net_parent_get_cascade_max_cand_epochs(self) def set_cascade_max_cand_epochs(self, cascade_max_cand_epochs): return _libfann.neural_net_parent_set_cascade_max_cand_epochs(self, cascade_max_cand_epochs) def get_cascade_num_candidates(self): return _libfann.neural_net_parent_get_cascade_num_candidates(self) def get_cascade_activation_functions_count(self): return _libfann.neural_net_parent_get_cascade_activation_functions_count(self) def get_cascade_activation_functions(self): return _libfann.neural_net_parent_get_cascade_activation_functions(self) def set_cascade_activation_functions(self, cascade_activation_functions, cascade_activation_functions_count): return _libfann.neural_net_parent_set_cascade_activation_functions(self, cascade_activation_functions, cascade_activation_functions_count) def get_cascade_activation_steepnesses_count(self): return _libfann.neural_net_parent_get_cascade_activation_steepnesses_count(self) def get_cascade_activation_steepnesses(self): return _libfann.neural_net_parent_get_cascade_activation_steepnesses(self) def set_cascade_activation_steepnesses(self, cascade_activation_steepnesses, cascade_activation_steepnesses_count): return _libfann.neural_net_parent_set_cascade_activation_steepnesses(self, cascade_activation_steepnesses, cascade_activation_steepnesses_count) def get_cascade_num_candidate_groups(self): return _libfann.neural_net_parent_get_cascade_num_candidate_groups(self) def set_cascade_num_candidate_groups(self, cascade_num_candidate_groups): return _libfann.neural_net_parent_set_cascade_num_candidate_groups(self, cascade_num_candidate_groups) def scale_train(self, data): return _libfann.neural_net_parent_scale_train(self, data) def descale_train(self, data): return _libfann.neural_net_parent_descale_train(self, data) def set_input_scaling_params(self, data, new_input_min, new_input_max): return _libfann.neural_net_parent_set_input_scaling_params(self, data, new_input_min, new_input_max) def set_output_scaling_params(self, data, new_output_min, new_output_max): return _libfann.neural_net_parent_set_output_scaling_params(self, data, new_output_min, new_output_max) def set_scaling_params(self, data, new_input_min, new_input_max, new_output_min, new_output_max): return _libfann.neural_net_parent_set_scaling_params(self, data, new_input_min, new_input_max, new_output_min, new_output_max) def clear_scaling_params(self): return _libfann.neural_net_parent_clear_scaling_params(self) def scale_input(self, input_vector): return _libfann.neural_net_parent_scale_input(self, input_vector) def scale_output(self, output_vector): return _libfann.neural_net_parent_scale_output(self, output_vector) def descale_input(self, input_vector): return _libfann.neural_net_parent_descale_input(self, input_vector) def descale_output(self, output_vector): return _libfann.neural_net_parent_descale_output(self, output_vector) def set_error_log(self, log_file): return _libfann.neural_net_parent_set_error_log(self, log_file) def get_errno(self): return _libfann.neural_net_parent_get_errno(self) def reset_errno(self): return _libfann.neural_net_parent_reset_errno(self) def reset_errstr(self): return _libfann.neural_net_parent_reset_errstr(self) def get_errstr(self): return _libfann.neural_net_parent_get_errstr(self) def print_error(self): return _libfann.neural_net_parent_print_error(self) # Register neural_net_parent in _libfann: _libfann.neural_net_parent_swigregister(neural_net_parent) class training_data(training_data_parent): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self, *args): _libfann.training_data_swiginit(self, _libfann.new_training_data(*args)) __swig_destroy__ = _libfann.delete_training_data def get_input(self): return _libfann.training_data_get_input(self) def get_output(self): return _libfann.training_data_get_output(self) def set_train_data(self, input, output): return _libfann.training_data_set_train_data(self, input, output) # Register training_data in _libfann: _libfann.training_data_swigregister(training_data) class neural_net(neural_net_parent): thisown = property(lambda x: x.this.own(), lambda x, v: x.this.own(v), doc="The membership flag") __repr__ = _swig_repr def __init__(self): _libfann.neural_net_swiginit(self, _libfann.new_neural_net()) __swig_destroy__ = _libfann.delete_neural_net def create_standard_array(self, layers): return _libfann.neural_net_create_standard_array(self, layers) def create_sparse_array(self, connection_rate, layers): return _libfann.neural_net_create_sparse_array(self, connection_rate, layers) def create_shortcut_array(self, layers): return _libfann.neural_net_create_shortcut_array(self, layers) def run(self, input): return _libfann.neural_net_run(self, input) def train(self, input, desired_output): return _libfann.neural_net_train(self, input, desired_output) def test(self, input, desired_output): return _libfann.neural_net_test(self, input, desired_output) def get_layer_array(self): return _libfann.neural_net_get_layer_array(self) def get_bias_array(self): return _libfann.neural_net_get_bias_array(self) def get_connection_array(self): return _libfann.neural_net_get_connection_array(self) def set_weight_array(self, connections): return _libfann.neural_net_set_weight_array(self, connections) def get_cascade_activation_steepnesses(self): return _libfann.neural_net_get_cascade_activation_steepnesses(self) def set_cascade_activation_steepnesses(self, cascade_activation_steepnesses): return _libfann.neural_net_set_cascade_activation_steepnesses(self, cascade_activation_steepnesses) # Register neural_net in _libfann: _libfann.neural_net_swigregister(neural_net)