def main():
#加载配置
cfg=cfgLoader.get_cfg(CFG_FILE_PATH)
if cfg.read_me_mode:show_readme()
#excel原始数据导入
raw_data=xlLoader.get_data(cfg)
#数据转化
data=dataConverter.convert(raw_data,cfg)
#输出
dataParser.parse(data,cfg)
def reParse(self):
'''
To re-parse the data set is the 'reParse=True'
'''
if self._check_exist():
print("It is already stored. Loading dataset...")
return
self._dataFrame['book_title'] = self._catalog
self._dataFrame['book_id'] = self._bookId
# print(self._catalog)
for index, tree_list in enumerate(self._dirTree.items()):
first, second = tree_list
temp_book = ''
print("Parsing...", self._catalog[index])
for item in second:
singleFilePath = self.dirPath + first + '/' + item
# parse single file, is temp
temp_book = temp_book + ' ' + dataParser.parse(singleFilePath)
self._dataFrame.iloc[index][1] = temp_book
# this line of code, may need to be changed when it comes error. a[index][index]=value
self._storeFile()
update_momentum=0.9,
regression=True, # flag to indicate we're dealing with regression problem
max_epochs=1500, # we want to train this many epochs
verbose=1,
)
#if (loadFile != ""):
#net1.load_params_from(loadFile)
net1.max_epochs = 50
net1.update_learning_rate = ln;
return net1
generations, generationsToInputs, generationsToOutputs = dataParser.parse(fname = "whole_population_0.txt")
iters = 150
saveFile = "LasagneWeights400_2Layer"
trainingInputs, trainingOutputs, testInputs, testOutputs = dataParser.makeSets(generationsToInputs, generationsToOutputs, generations[0:200], 1, 0.25)
ln = 0.01
X = Normalizers.gaussNormalize(trainingInputs)
Xtest = Normalizers.gaussNormalize(testInputs)
#
Y = Normalizers.gaussNormalize(trainingOutputs)
Ytest = Normalizers.gaussNormalize(testOutputs)
X = np.asarray(X, np.float32)
Y = np.asarray(Y, np.float32)
Xtest= np.asarray(Xtest, np.float32)
Ytest = np.asarray(Ytest, np.float32)
net = createNet(X, Y, ln, saveFile)
def on_data(self, data):
return dataParser.parse(data)
