本文整理汇总了Python中pybrain.datasets.SequentialDataSet.getSample方法的典型用法代码示例。如果您正苦于以下问题：Python SequentialDataSet.getSample方法的具体用法？Python SequentialDataSet.getSample怎么用？Python SequentialDataSet.getSample使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类pybrain.datasets.SequentialDataSet的用法示例。
在下文中一共展示了SequentialDataSet.getSample方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train
# 需要导入模块: from pybrain.datasets import SequentialDataSet [as 别名]
# 或者: from pybrain.datasets.SequentialDataSet import getSample [as 别名]
def train(self, params):
self.net.reset()
ds = SequentialDataSet(self.nDimInput, self.nDimOutput)
trainer = RPropMinusTrainer(self.net, dataset=ds, verbose=False)
history = self.window(self.history, params)
resets = self.window(self.resets, params)
for i in xrange(params['prediction_nstep'], len(history)):
if not resets[i-1]:
ds.addSample(self.inputEncoder.encode(history[i-params['prediction_nstep']]),
self.outputEncoder.encode(history[i][0]))
if resets[i]:
ds.newSequence()
# print ds.getSample(0)
# print ds.getSample(1)
# print ds.getSample(1000)
# print " training data size", ds.getLength(), " len(history) ", len(history), " self.history ", len(self.history)
# print ds
if len(history) > 1:
trainer.trainEpochs(params['num_epochs'])
self.net.reset()
for i in xrange(len(history) - params['prediction_nstep']):
symbol = history[i]
output = self.net.activate(ds.getSample(i)[0])
if resets[i]:
self.net.reset()示例2: train
# 需要导入模块: from pybrain.datasets import SequentialDataSet [as 别名]
# 或者: from pybrain.datasets.SequentialDataSet import getSample [as 别名]
def train(self, params, verbose=False):
if params['reset_every_training']:
if verbose:
print 'create lstm network'
random.seed(6)
if params['output_encoding'] == None:
self.net = buildNetwork(self.nDimInput, params['num_cells'], self.nDimOutput,
hiddenclass=LSTMLayer, bias=True, outputbias=True, recurrent=True)
elif params['output_encoding'] == 'likelihood':
self.net = buildNetwork(self.nDimInput, params['num_cells'], self.nDimOutput,
hiddenclass=LSTMLayer, bias=True, outclass=SigmoidLayer, recurrent=True)
self.net.reset()
ds = SequentialDataSet(self.nDimInput, self.nDimOutput)
networkInput = self.window(self.networkInput, params)
targetPrediction = self.window(self.targetPrediction, params)
# prepare a training data-set using the history
for i in xrange(len(networkInput)):
ds.addSample(self.inputEncoder.encode(networkInput[i]),
self.outputEncoder.encode(targetPrediction[i]))
if params['num_epochs'] > 1:
trainer = RPropMinusTrainer(self.net, dataset=ds, verbose=verbose)
if verbose:
print " train LSTM on ", len(ds), " records for ", params['num_epochs'], " epochs "
if len(networkInput) > 1:
trainer.trainEpochs(params['num_epochs'])
else:
self.trainer.setData(ds)
self.trainer.train()
# run through the training dataset to get the lstm network state right
self.net.reset()
for i in xrange(len(networkInput)):
self.net.activate(ds.getSample(i)[0])