# This file was automatically generated by SWIG (http://www.swig.org). # Version 2.0.4 # # 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 if version_info >= (2,6,0): def swig_import_helper(): from os.path import dirname import imp fp = None try: fp, pathname, description = imp.find_module('_libfann', [dirname(__file__)]) except ImportError: import _libfann return _libfann if fp is not None: try: _mod = imp.load_module('_libfann', fp, pathname, description) finally: fp.close() return _mod _libfann = swig_import_helper() del swig_import_helper else: import _libfann del version_info try: _swig_property = property except NameError: pass # Python < 2.2 doesn't have 'property'. def _swig_setattr_nondynamic(self,class_type,name,value,static=1): if (name == "thisown"): return self.this.own(value) if (name == "this"): if type(value).__name__ == 'SwigPyObject': self.__dict__[name] = value return method = class_type.__swig_setmethods__.get(name,None) if method: return method(self,value) if (not static): self.__dict__[name] = value else: raise AttributeError("You cannot add attributes to %s" % self) def _swig_setattr(self,class_type,name,value): return _swig_setattr_nondynamic(self,class_type,name,value,0) def _swig_getattr(self,class_type,name): if (name == "thisown"): return self.this.own() method = class_type.__swig_getmethods__.get(name,None) if method: return method(self) raise AttributeError(name) def _swig_repr(self): try: strthis = "proxy of " + self.this.__repr__() except: strthis = "" return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) try: _object = object _newclass = 1 except AttributeError: class _object : pass _newclass = 0 class SwigPyIterator(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, SwigPyIterator, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, SwigPyIterator, name) def __init__(self, *args, **kwargs): raise AttributeError("No constructor defined - class is abstract") __repr__ = _swig_repr __swig_destroy__ = _libfann.delete_SwigPyIterator __del__ = lambda self : None; 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, *args): return _libfann.SwigPyIterator_distance(self, *args) def equal(self, *args): return _libfann.SwigPyIterator_equal(self, *args) 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, *args): return _libfann.SwigPyIterator_advance(self, *args) def __eq__(self, *args): return _libfann.SwigPyIterator___eq__(self, *args) def __ne__(self, *args): return _libfann.SwigPyIterator___ne__(self, *args) def __iadd__(self, *args): return _libfann.SwigPyIterator___iadd__(self, *args) def __isub__(self, *args): return _libfann.SwigPyIterator___isub__(self, *args) def __add__(self, *args): return _libfann.SwigPyIterator___add__(self, *args) def __sub__(self, *args): return _libfann.SwigPyIterator___sub__(self, *args) def __iter__(self): return self SwigPyIterator_swigregister = _libfann.SwigPyIterator_swigregister 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 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): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, training_data_parent, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, training_data_parent, name) __repr__ = _swig_repr def __init__(self, *args): this = _libfann.new_training_data_parent(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _libfann.delete_training_data_parent __del__ = lambda self : None; def destroy_train(self): return _libfann.training_data_parent_destroy_train(self) def read_train_from_file(self, *args): return _libfann.training_data_parent_read_train_from_file(self, *args) def save_train(self, *args): return _libfann.training_data_parent_save_train(self, *args) def save_train_to_fixed(self, *args): return _libfann.training_data_parent_save_train_to_fixed(self, *args) def shuffle_train_data(self): return _libfann.training_data_parent_shuffle_train_data(self) def merge_train_data(self, *args): return _libfann.training_data_parent_merge_train_data(self, *args) 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, *args): return _libfann.training_data_parent_set_train_data(self, *args) def create_train_from_callback(self, *args): return _libfann.training_data_parent_create_train_from_callback(self, *args) def scale_input_train_data(self, *args): return _libfann.training_data_parent_scale_input_train_data(self, *args) def scale_output_train_data(self, *args): return _libfann.training_data_parent_scale_output_train_data(self, *args) def scale_train_data(self, *args): return _libfann.training_data_parent_scale_train_data(self, *args) def subset_train_data(self, *args): return _libfann.training_data_parent_subset_train_data(self, *args) training_data_parent_swigregister = _libfann.training_data_parent_swigregister training_data_parent_swigregister(training_data_parent) class neural_net_parent(_object): __swig_setmethods__ = {} __setattr__ = lambda self, name, value: _swig_setattr(self, neural_net_parent, name, value) __swig_getmethods__ = {} __getattr__ = lambda self, name: _swig_getattr(self, neural_net_parent, name) __repr__ = _swig_repr def __init__(self): this = _libfann.new_neural_net_parent() try: self.this.append(this) except: self.this = this __swig_destroy__ = _libfann.delete_neural_net_parent __del__ = lambda self : None; def destroy(self): return _libfann.neural_net_parent_destroy(self) def create_standard(self, *args): return _libfann.neural_net_parent_create_standard(self, *args) def create_standard_array(self, *args): return _libfann.neural_net_parent_create_standard_array(self, *args) def create_sparse(self, *args): return _libfann.neural_net_parent_create_sparse(self, *args) def create_sparse_array(self, *args): return _libfann.neural_net_parent_create_sparse_array(self, *args) def create_shortcut(self, *args): return _libfann.neural_net_parent_create_shortcut(self, *args) def create_shortcut_array(self, *args): return _libfann.neural_net_parent_create_shortcut_array(self, *args) def run(self, *args): return _libfann.neural_net_parent_run(self, *args) def randomize_weights(self, *args): return _libfann.neural_net_parent_randomize_weights(self, *args) def init_weights(self, *args): return _libfann.neural_net_parent_init_weights(self, *args) def print_connections(self): return _libfann.neural_net_parent_print_connections(self) def create_from_file(self, *args): return _libfann.neural_net_parent_create_from_file(self, *args) def save(self, *args): return _libfann.neural_net_parent_save(self, *args) def save_to_fixed(self, *args): return _libfann.neural_net_parent_save_to_fixed(self, *args) def train(self, *args): return _libfann.neural_net_parent_train(self, *args) def train_epoch(self, *args): return _libfann.neural_net_parent_train_epoch(self, *args) def train_on_data(self, *args): return _libfann.neural_net_parent_train_on_data(self, *args) def train_on_file(self, *args): return _libfann.neural_net_parent_train_on_file(self, *args) def test(self, *args): return _libfann.neural_net_parent_test(self, *args) def test_data(self, *args): return _libfann.neural_net_parent_test_data(self, *args) 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, *args): return _libfann.neural_net_parent_set_callback(self, *args) 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, *args): return _libfann.neural_net_parent_set_training_algorithm(self, *args) def get_learning_rate(self): return _libfann.neural_net_parent_get_learning_rate(self) def set_learning_rate(self, *args): return _libfann.neural_net_parent_set_learning_rate(self, *args) def get_activation_function(self, *args): return _libfann.neural_net_parent_get_activation_function(self, *args) def set_activation_function(self, *args): return _libfann.neural_net_parent_set_activation_function(self, *args) def set_activation_function_layer(self, *args): return _libfann.neural_net_parent_set_activation_function_layer(self, *args) def set_activation_function_hidden(self, *args): return _libfann.neural_net_parent_set_activation_function_hidden(self, *args) def set_activation_function_output(self, *args): return _libfann.neural_net_parent_set_activation_function_output(self, *args) def get_activation_steepness(self, *args): return _libfann.neural_net_parent_get_activation_steepness(self, *args) def set_activation_steepness(self, *args): return _libfann.neural_net_parent_set_activation_steepness(self, *args) def set_activation_steepness_layer(self, *args): return _libfann.neural_net_parent_set_activation_steepness_layer(self, *args) def set_activation_steepness_hidden(self, *args): return _libfann.neural_net_parent_set_activation_steepness_hidden(self, *args) def set_activation_steepness_output(self, *args): return _libfann.neural_net_parent_set_activation_steepness_output(self, *args) def get_train_error_function(self): return _libfann.neural_net_parent_get_train_error_function(self) def set_train_error_function(self, *args): return _libfann.neural_net_parent_set_train_error_function(self, *args) def get_quickprop_decay(self): return _libfann.neural_net_parent_get_quickprop_decay(self) def set_quickprop_decay(self, *args): return _libfann.neural_net_parent_set_quickprop_decay(self, *args) def get_quickprop_mu(self): return _libfann.neural_net_parent_get_quickprop_mu(self) def set_quickprop_mu(self, *args): return _libfann.neural_net_parent_set_quickprop_mu(self, *args) def get_rprop_increase_factor(self): return _libfann.neural_net_parent_get_rprop_increase_factor(self) def set_rprop_increase_factor(self, *args): return _libfann.neural_net_parent_set_rprop_increase_factor(self, *args) def get_rprop_decrease_factor(self): return _libfann.neural_net_parent_get_rprop_decrease_factor(self) def set_rprop_decrease_factor(self, *args): return _libfann.neural_net_parent_set_rprop_decrease_factor(self, *args) def get_rprop_delta_min(self): return _libfann.neural_net_parent_get_rprop_delta_min(self) def set_rprop_delta_min(self, *args): return _libfann.neural_net_parent_set_rprop_delta_min(self, *args) def get_rprop_delta_max(self): return _libfann.neural_net_parent_get_rprop_delta_max(self) def set_rprop_delta_max(self, *args): return _libfann.neural_net_parent_set_rprop_delta_max(self, *args) 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, *args): return _libfann.neural_net_parent_get_layer_array(self, *args) def get_bias_array(self, *args): return _libfann.neural_net_parent_get_bias_array(self, *args) def get_connection_array(self, *args): return _libfann.neural_net_parent_get_connection_array(self, *args) def set_weight_array(self, *args): return _libfann.neural_net_parent_set_weight_array(self, *args) def set_weight(self, *args): return _libfann.neural_net_parent_set_weight(self, *args) def get_learning_momentum(self): return _libfann.neural_net_parent_get_learning_momentum(self) def set_learning_momentum(self, *args): return _libfann.neural_net_parent_set_learning_momentum(self, *args) def get_train_stop_function(self): return _libfann.neural_net_parent_get_train_stop_function(self) def set_train_stop_function(self, *args): return _libfann.neural_net_parent_set_train_stop_function(self, *args) def get_bit_fail_limit(self): return _libfann.neural_net_parent_get_bit_fail_limit(self) def set_bit_fail_limit(self, *args): return _libfann.neural_net_parent_set_bit_fail_limit(self, *args) def get_bit_fail(self): return _libfann.neural_net_parent_get_bit_fail(self) def cascadetrain_on_data(self, *args): return _libfann.neural_net_parent_cascadetrain_on_data(self, *args) def cascadetrain_on_file(self, *args): return _libfann.neural_net_parent_cascadetrain_on_file(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_output_change_fraction(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_output_stagnation_epochs(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_candidate_change_fraction(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_candidate_stagnation_epochs(self, *args) def get_cascade_weight_multiplier(self): return _libfann.neural_net_parent_get_cascade_weight_multiplier(self) def set_cascade_weight_multiplier(self, *args): return _libfann.neural_net_parent_set_cascade_weight_multiplier(self, *args) def get_cascade_candidate_limit(self): return _libfann.neural_net_parent_get_cascade_candidate_limit(self) def set_cascade_candidate_limit(self, *args): return _libfann.neural_net_parent_set_cascade_candidate_limit(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_max_out_epochs(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_max_cand_epochs(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_activation_functions(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_activation_steepnesses(self, *args) 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, *args): return _libfann.neural_net_parent_set_cascade_num_candidate_groups(self, *args) def scale_train(self, *args): return _libfann.neural_net_parent_scale_train(self, *args) def descale_train(self, *args): return _libfann.neural_net_parent_descale_train(self, *args) def set_input_scaling_params(self, *args): return _libfann.neural_net_parent_set_input_scaling_params(self, *args) def set_output_scaling_params(self, *args): return _libfann.neural_net_parent_set_output_scaling_params(self, *args) def set_scaling_params(self, *args): return _libfann.neural_net_parent_set_scaling_params(self, *args) def clear_scaling_params(self): return _libfann.neural_net_parent_clear_scaling_params(self) def scale_input(self, *args): return _libfann.neural_net_parent_scale_input(self, *args) def scale_output(self, *args): return _libfann.neural_net_parent_scale_output(self, *args) def descale_input(self, *args): return _libfann.neural_net_parent_descale_input(self, *args) def descale_output(self, *args): return _libfann.neural_net_parent_descale_output(self, *args) def set_error_log(self, *args): return _libfann.neural_net_parent_set_error_log(self, *args) 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) neural_net_parent_swigregister = _libfann.neural_net_parent_swigregister neural_net_parent_swigregister(neural_net_parent) class training_data(training_data_parent): __swig_setmethods__ = {} for _s in [training_data_parent]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, training_data, name, value) __swig_getmethods__ = {} for _s in [training_data_parent]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, training_data, name) __repr__ = _swig_repr def __init__(self, *args): this = _libfann.new_training_data(*args) try: self.this.append(this) except: self.this = this __swig_destroy__ = _libfann.delete_training_data __del__ = lambda self : None; 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, *args): return _libfann.training_data_set_train_data(self, *args) training_data_swigregister = _libfann.training_data_swigregister training_data_swigregister(training_data) class neural_net(neural_net_parent): __swig_setmethods__ = {} for _s in [neural_net_parent]: __swig_setmethods__.update(getattr(_s,'__swig_setmethods__',{})) __setattr__ = lambda self, name, value: _swig_setattr(self, neural_net, name, value) __swig_getmethods__ = {} for _s in [neural_net_parent]: __swig_getmethods__.update(getattr(_s,'__swig_getmethods__',{})) __getattr__ = lambda self, name: _swig_getattr(self, neural_net, name) __repr__ = _swig_repr def __init__(self): this = _libfann.new_neural_net() try: self.this.append(this) except: self.this = this __swig_destroy__ = _libfann.delete_neural_net __del__ = lambda self : None; def create_standard_array(self, *args): return _libfann.neural_net_create_standard_array(self, *args) def create_sparse_array(self, *args): return _libfann.neural_net_create_sparse_array(self, *args) def create_shortcut_array(self, *args): return _libfann.neural_net_create_shortcut_array(self, *args) def run(self, *args): return _libfann.neural_net_run(self, *args) def train(self, *args): return _libfann.neural_net_train(self, *args) def test(self, *args): return _libfann.neural_net_test(self, *args) def get_layer_array(self, *args): return _libfann.neural_net_get_layer_array(self, *args) def get_bias_array(self, *args): return _libfann.neural_net_get_bias_array(self, *args) def get_connection_array(self, *args): return _libfann.neural_net_get_connection_array(self, *args) def set_weight_array(self, *args): return _libfann.neural_net_set_weight_array(self, *args) def get_cascade_activation_steepnesses(self): return _libfann.neural_net_get_cascade_activation_steepnesses(self) def set_cascade_activation_steepnesses(self, *args): return _libfann.neural_net_set_cascade_activation_steepnesses(self, *args) neural_net_swigregister = _libfann.neural_net_swigregister neural_net_swigregister(neural_net) # This file is compatible with both classic and new-style classes.