libfann.py

python 神经网络 数据挖掘 python实现的神经网络算法

    def set_activation_steepness_layer(*args): return _libfann.neural_net_parent_set_activation_steepness_layer(*args)
    def set_activation_steepness_hidden(*args): return _libfann.neural_net_parent_set_activation_steepness_hidden(*args)
    def set_activation_steepness_output(*args): return _libfann.neural_net_parent_set_activation_steepness_output(*args)
    def get_train_error_function(*args): return _libfann.neural_net_parent_get_train_error_function(*args)
    def set_train_error_function(*args): return _libfann.neural_net_parent_set_train_error_function(*args)
    def get_quickprop_decay(*args): return _libfann.neural_net_parent_get_quickprop_decay(*args)
    def set_quickprop_decay(*args): return _libfann.neural_net_parent_set_quickprop_decay(*args)
    def get_quickprop_mu(*args): return _libfann.neural_net_parent_get_quickprop_mu(*args)
    def set_quickprop_mu(*args): return _libfann.neural_net_parent_set_quickprop_mu(*args)
    def get_rprop_increase_factor(*args): return _libfann.neural_net_parent_get_rprop_increase_factor(*args)
    def set_rprop_increase_factor(*args): return _libfann.neural_net_parent_set_rprop_increase_factor(*args)
    def get_rprop_decrease_factor(*args): return _libfann.neural_net_parent_get_rprop_decrease_factor(*args)
    def set_rprop_decrease_factor(*args): return _libfann.neural_net_parent_set_rprop_decrease_factor(*args)
    def get_rprop_delta_min(*args): return _libfann.neural_net_parent_get_rprop_delta_min(*args)
    def set_rprop_delta_min(*args): return _libfann.neural_net_parent_set_rprop_delta_min(*args)
    def get_rprop_delta_max(*args): return _libfann.neural_net_parent_get_rprop_delta_max(*args)
    def set_rprop_delta_max(*args): return _libfann.neural_net_parent_set_rprop_delta_max(*args)
    def get_num_input(*args): return _libfann.neural_net_parent_get_num_input(*args)
    def get_num_output(*args): return _libfann.neural_net_parent_get_num_output(*args)
    def get_total_neurons(*args): return _libfann.neural_net_parent_get_total_neurons(*args)
    def get_total_connections(*args): return _libfann.neural_net_parent_get_total_connections(*args)
    def get_network_type(*args): return _libfann.neural_net_parent_get_network_type(*args)
    def get_connection_rate(*args): return _libfann.neural_net_parent_get_connection_rate(*args)
    def get_num_layers(*args): return _libfann.neural_net_parent_get_num_layers(*args)
    def get_layer_array(*args): return _libfann.neural_net_parent_get_layer_array(*args)
    def get_bias_array(*args): return _libfann.neural_net_parent_get_bias_array(*args)
    def get_connection_array(*args): return _libfann.neural_net_parent_get_connection_array(*args)
    def set_weight_array(*args): return _libfann.neural_net_parent_set_weight_array(*args)
    def set_weight(*args): return _libfann.neural_net_parent_set_weight(*args)
    def get_learning_momentum(*args): return _libfann.neural_net_parent_get_learning_momentum(*args)
    def set_learning_momentum(*args): return _libfann.neural_net_parent_set_learning_momentum(*args)
    def get_train_stop_function(*args): return _libfann.neural_net_parent_get_train_stop_function(*args)
    def set_train_stop_function(*args): return _libfann.neural_net_parent_set_train_stop_function(*args)
    def get_bit_fail_limit(*args): return _libfann.neural_net_parent_get_bit_fail_limit(*args)
    def set_bit_fail_limit(*args): return _libfann.neural_net_parent_set_bit_fail_limit(*args)
    def get_bit_fail(*args): return _libfann.neural_net_parent_get_bit_fail(*args)
    def cascadetrain_on_data(*args): return _libfann.neural_net_parent_cascadetrain_on_data(*args)
    def cascadetrain_on_file(*args): return _libfann.neural_net_parent_cascadetrain_on_file(*args)
    def get_cascade_output_change_fraction(*args): return _libfann.neural_net_parent_get_cascade_output_change_fraction(*args)
    def set_cascade_output_change_fraction(*args): return _libfann.neural_net_parent_set_cascade_output_change_fraction(*args)
    def get_cascade_output_stagnation_epochs(*args): return _libfann.neural_net_parent_get_cascade_output_stagnation_epochs(*args)
    def set_cascade_output_stagnation_epochs(*args): return _libfann.neural_net_parent_set_cascade_output_stagnation_epochs(*args)
    def get_cascade_candidate_change_fraction(*args): return _libfann.neural_net_parent_get_cascade_candidate_change_fraction(*args)
    def set_cascade_candidate_change_fraction(*args): return _libfann.neural_net_parent_set_cascade_candidate_change_fraction(*args)
    def get_cascade_candidate_stagnation_epochs(*args): return _libfann.neural_net_parent_get_cascade_candidate_stagnation_epochs(*args)
    def set_cascade_candidate_stagnation_epochs(*args): return _libfann.neural_net_parent_set_cascade_candidate_stagnation_epochs(*args)
    def get_cascade_weight_multiplier(*args): return _libfann.neural_net_parent_get_cascade_weight_multiplier(*args)
    def set_cascade_weight_multiplier(*args): return _libfann.neural_net_parent_set_cascade_weight_multiplier(*args)
    def get_cascade_candidate_limit(*args): return _libfann.neural_net_parent_get_cascade_candidate_limit(*args)
    def set_cascade_candidate_limit(*args): return _libfann.neural_net_parent_set_cascade_candidate_limit(*args)
    def get_cascade_max_out_epochs(*args): return _libfann.neural_net_parent_get_cascade_max_out_epochs(*args)
    def set_cascade_max_out_epochs(*args): return _libfann.neural_net_parent_set_cascade_max_out_epochs(*args)
    def get_cascade_max_cand_epochs(*args): return _libfann.neural_net_parent_get_cascade_max_cand_epochs(*args)
    def set_cascade_max_cand_epochs(*args): return _libfann.neural_net_parent_set_cascade_max_cand_epochs(*args)
    def get_cascade_num_candidates(*args): return _libfann.neural_net_parent_get_cascade_num_candidates(*args)
    def get_cascade_activation_functions_count(*args): return _libfann.neural_net_parent_get_cascade_activation_functions_count(*args)
    def get_cascade_activation_functions(*args): return _libfann.neural_net_parent_get_cascade_activation_functions(*args)
    def set_cascade_activation_functions(*args): return _libfann.neural_net_parent_set_cascade_activation_functions(*args)
    def get_cascade_activation_steepnesses_count(*args): return _libfann.neural_net_parent_get_cascade_activation_steepnesses_count(*args)
    def get_cascade_activation_steepnesses(*args): return _libfann.neural_net_parent_get_cascade_activation_steepnesses(*args)
    def set_cascade_activation_steepnesses(*args): return _libfann.neural_net_parent_set_cascade_activation_steepnesses(*args)
    def get_cascade_num_candidate_groups(*args): return _libfann.neural_net_parent_get_cascade_num_candidate_groups(*args)
    def set_cascade_num_candidate_groups(*args): return _libfann.neural_net_parent_set_cascade_num_candidate_groups(*args)
    def scale_train(*args): return _libfann.neural_net_parent_scale_train(*args)
    def descale_train(*args): return _libfann.neural_net_parent_descale_train(*args)
    def set_input_scaling_params(*args): return _libfann.neural_net_parent_set_input_scaling_params(*args)
    def set_output_scaling_params(*args): return _libfann.neural_net_parent_set_output_scaling_params(*args)
    def set_scaling_params(*args): return _libfann.neural_net_parent_set_scaling_params(*args)
    def clear_scaling_params(*args): return _libfann.neural_net_parent_clear_scaling_params(*args)
    def scale_input(*args): return _libfann.neural_net_parent_scale_input(*args)
    def scale_output(*args): return _libfann.neural_net_parent_scale_output(*args)
    def descale_input(*args): return _libfann.neural_net_parent_descale_input(*args)
    def descale_output(*args): return _libfann.neural_net_parent_descale_output(*args)
    def set_error_log(*args): return _libfann.neural_net_parent_set_error_log(*args)
    def get_errno(*args): return _libfann.neural_net_parent_get_errno(*args)
    def reset_errno(*args): return _libfann.neural_net_parent_reset_errno(*args)
    def reset_errstr(*args): return _libfann.neural_net_parent_reset_errstr(*args)
    def get_errstr(*args): return _libfann.neural_net_parent_get_errstr(*args)
    def print_error(*args): return _libfann.neural_net_parent_print_error(*args)

class neural_net_parentPtr(neural_net_parent):
    def __init__(self, this):
        _swig_setattr(self, neural_net_parent, 'this', this)
        if not hasattr(self,"thisown"): _swig_setattr(self, neural_net_parent, 'thisown', 0)
        _swig_setattr(self, neural_net_parent,self.__class__,neural_net_parent)
_libfann.neural_net_parent_swigregister(neural_net_parentPtr)

class training_data(training_data_parent):
    __swig_setmethods__ = {}
    for _s in [training_data_parent]: __swig_setmethods__.update(_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(_s.__swig_getmethods__)
    __getattr__ = lambda self, name: _swig_getattr(self, training_data, name)
    def __repr__(self):
        return "<%s.%s; proxy of C++ FANN::Training_data instance at %s>" % (self.__class__.__module__, self.__class__.__name__, self.this,)
    def __init__(self, *args):
        _swig_setattr(self, training_data, 'this', _libfann.new_training_data(*args))
        _swig_setattr(self, training_data, 'thisown', 1)
    def __del__(self, destroy=_libfann.delete_training_data):
        try:
            if self.thisown: destroy(self)
        except: pass

    def get_input(*args): return _libfann.training_data_get_input(*args)
    def get_output(*args): return _libfann.training_data_get_output(*args)
    def set_train_data(*args): return _libfann.training_data_set_train_data(*args)

class training_dataPtr(training_data):
    def __init__(self, this):
        _swig_setattr(self, training_data, 'this', this)
        if not hasattr(self,"thisown"): _swig_setattr(self, training_data, 'thisown', 0)
        _swig_setattr(self, training_data,self.__class__,training_data)
_libfann.training_data_swigregister(training_dataPtr)

class neural_net(neural_net_parent):
    __swig_setmethods__ = {}
    for _s in [neural_net_parent]: __swig_setmethods__.update(_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(_s.__swig_getmethods__)
    __getattr__ = lambda self, name: _swig_getattr(self, neural_net, name)
    def __repr__(self):
        return "<%s.%s; proxy of C++ FANN::Neural_net instance at %s>" % (self.__class__.__module__, self.__class__.__name__, self.this,)
    def __init__(self, *args):
        _swig_setattr(self, neural_net, 'this', _libfann.new_neural_net(*args))
        _swig_setattr(self, neural_net, 'thisown', 1)
    def __del__(self, destroy=_libfann.delete_neural_net):
        try:
            if self.thisown: destroy(self)
        except: pass

    def create_standard_array(*args): return _libfann.neural_net_create_standard_array(*args)
    def create_sparse_array(*args): return _libfann.neural_net_create_sparse_array(*args)
    def create_shortcut_array(*args): return _libfann.neural_net_create_shortcut_array(*args)
    def run(*args): return _libfann.neural_net_run(*args)
    def train(*args): return _libfann.neural_net_train(*args)
    def test(*args): return _libfann.neural_net_test(*args)
    def get_layer_array(*args): return _libfann.neural_net_get_layer_array(*args)
    def get_bias_array(*args): return _libfann.neural_net_get_bias_array(*args)
    def get_connection_array(*args): return _libfann.neural_net_get_connection_array(*args)
    def set_weight_array(*args): return _libfann.neural_net_set_weight_array(*args)
    def get_cascade_activation_steepnesses(*args): return _libfann.neural_net_get_cascade_activation_steepnesses(*args)
    def set_cascade_activation_steepnesses(*args): return _libfann.neural_net_set_cascade_activation_steepnesses(*args)

class neural_netPtr(neural_net):
    def __init__(self, this):
        _swig_setattr(self, neural_net, 'this', this)
        if not hasattr(self,"thisown"): _swig_setattr(self, neural_net, 'thisown', 0)
        _swig_setattr(self, neural_net,self.__class__,neural_net)
_libfann.neural_net_swigregister(neural_netPtr)