libfann.py

python 神经网络 数据挖掘 python实现的神经网络算法

# This file was created automatically by SWIG.
# Don't modify this file, modify the SWIG interface instead.
# This file is compatible with both classic and new-style classes.

import _libfann

def _swig_setattr_nondynamic(self,class_type,name,value,static=1):
    if (name == "this"):
        if isinstance(value, class_type):
            self.__dict__[name] = value.this
            if hasattr(value,"thisown"): self.__dict__["thisown"] = value.thisown
            del value.thisown
            return
    method = class_type.__swig_setmethods__.get(name,None)
    if method: return method(self,value)
    if (not static) or hasattr(self,name) or (name == "thisown"):
        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):
    method = class_type.__swig_getmethods__.get(name,None)
    if method: return method(self)
    raise AttributeError,name

import types
try:
    _object = types.ObjectType
    _newclass = 1
except AttributeError:
    class _object : pass
    _newclass = 0
del types


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)
    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_parent, 'this', _libfann.new_training_data_parent(*args))
        _swig_setattr(self, training_data_parent, 'thisown', 1)
    def __del__(self, destroy=_libfann.delete_training_data_parent):
        try:
            if self.thisown: destroy(self)
        except: pass

    def destroy_train(*args): return _libfann.training_data_parent_destroy_train(*args)
    def read_train_from_file(*args): return _libfann.training_data_parent_read_train_from_file(*args)
    def save_train(*args): return _libfann.training_data_parent_save_train(*args)
    def save_train_to_fixed(*args): return _libfann.training_data_parent_save_train_to_fixed(*args)
    def shuffle_train_data(*args): return _libfann.training_data_parent_shuffle_train_data(*args)
    def merge_train_data(*args): return _libfann.training_data_parent_merge_train_data(*args)
    def length_train_data(*args): return _libfann.training_data_parent_length_train_data(*args)
    def num_input_train_data(*args): return _libfann.training_data_parent_num_input_train_data(*args)
    def num_output_train_data(*args): return _libfann.training_data_parent_num_output_train_data(*args)
    def get_input(*args): return _libfann.training_data_parent_get_input(*args)
    def get_output(*args): return _libfann.training_data_parent_get_output(*args)
    def set_train_data(*args): return _libfann.training_data_parent_set_train_data(*args)
    def create_train_from_callback(*args): return _libfann.training_data_parent_create_train_from_callback(*args)
    def scale_input_train_data(*args): return _libfann.training_data_parent_scale_input_train_data(*args)
    def scale_output_train_data(*args): return _libfann.training_data_parent_scale_output_train_data(*args)
    def scale_train_data(*args): return _libfann.training_data_parent_scale_train_data(*args)
    def subset_train_data(*args): return _libfann.training_data_parent_subset_train_data(*args)

class training_data_parentPtr(training_data_parent):
    def __init__(self, this):
        _swig_setattr(self, training_data_parent, 'this', this)
        if not hasattr(self,"thisown"): _swig_setattr(self, training_data_parent, 'thisown', 0)
        _swig_setattr(self, training_data_parent,self.__class__,training_data_parent)
_libfann.training_data_parent_swigregister(training_data_parentPtr)

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)
    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_parent, 'this', _libfann.new_neural_net_parent(*args))
        _swig_setattr(self, neural_net_parent, 'thisown', 1)
    def __del__(self, destroy=_libfann.delete_neural_net_parent):
        try:
            if self.thisown: destroy(self)
        except: pass

    def destroy(*args): return _libfann.neural_net_parent_destroy(*args)
    def create_standard(*args): return _libfann.neural_net_parent_create_standard(*args)
    def create_standard_array(*args): return _libfann.neural_net_parent_create_standard_array(*args)
    def create_sparse(*args): return _libfann.neural_net_parent_create_sparse(*args)
    def create_sparse_array(*args): return _libfann.neural_net_parent_create_sparse_array(*args)
    def create_shortcut(*args): return _libfann.neural_net_parent_create_shortcut(*args)
    def create_shortcut_array(*args): return _libfann.neural_net_parent_create_shortcut_array(*args)
    def run(*args): return _libfann.neural_net_parent_run(*args)
    def randomize_weights(*args): return _libfann.neural_net_parent_randomize_weights(*args)
    def init_weights(*args): return _libfann.neural_net_parent_init_weights(*args)
    def print_connections(*args): return _libfann.neural_net_parent_print_connections(*args)
    def create_from_file(*args): return _libfann.neural_net_parent_create_from_file(*args)
    def save(*args): return _libfann.neural_net_parent_save(*args)
    def save_to_fixed(*args): return _libfann.neural_net_parent_save_to_fixed(*args)
    def train(*args): return _libfann.neural_net_parent_train(*args)
    def train_epoch(*args): return _libfann.neural_net_parent_train_epoch(*args)
    def train_on_data(*args): return _libfann.neural_net_parent_train_on_data(*args)
    def train_on_file(*args): return _libfann.neural_net_parent_train_on_file(*args)
    def test(*args): return _libfann.neural_net_parent_test(*args)
    def test_data(*args): return _libfann.neural_net_parent_test_data(*args)
    def get_MSE(*args): return _libfann.neural_net_parent_get_MSE(*args)
    def reset_MSE(*args): return _libfann.neural_net_parent_reset_MSE(*args)
    def set_callback(*args): return _libfann.neural_net_parent_set_callback(*args)
    def print_parameters(*args): return _libfann.neural_net_parent_print_parameters(*args)
    def get_training_algorithm(*args): return _libfann.neural_net_parent_get_training_algorithm(*args)
    def set_training_algorithm(*args): return _libfann.neural_net_parent_set_training_algorithm(*args)
    def get_learning_rate(*args): return _libfann.neural_net_parent_get_learning_rate(*args)
    def set_learning_rate(*args): return _libfann.neural_net_parent_set_learning_rate(*args)
    def get_activation_function(*args): return _libfann.neural_net_parent_get_activation_function(*args)
    def set_activation_function(*args): return _libfann.neural_net_parent_set_activation_function(*args)
    def set_activation_function_layer(*args): return _libfann.neural_net_parent_set_activation_function_layer(*args)
    def set_activation_function_hidden(*args): return _libfann.neural_net_parent_set_activation_function_hidden(*args)
    def set_activation_function_output(*args): return _libfann.neural_net_parent_set_activation_function_output(*args)
    def get_activation_steepness(*args): return _libfann.neural_net_parent_get_activation_steepness(*args)
    def set_activation_steepness(*args): return _libfann.neural_net_parent_set_activation_steepness(*args)