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selector.fit_transform(X)
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argparse.ArgumentParser(description='PyTorch MNIST Example')
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training (default: 64)
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testing (default: 1000)
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train (default: 10)
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rate (default: 0.01)
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momentum (default: 0.5)
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seed (default: 1)
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attention (default: 1)
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parser.parse_args()
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list(10**np.arange(-4,-1,0.5)
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list(10**np.arange(-1,3,0.5)
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list(10**np.arange(-2,2,0.5)
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np.zeros([param_num,len(hyperparameter_choices)
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time.strftime('%Y-%m-%d_%H-%M-%S',time.localtime()
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range(param_num)
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enumerate(hyperparameter_choices.items()
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print(param_index,k)
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np.random.choice(len(v)
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isinstance(v[value_index],str)
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isinstance(v[value_index],bool)
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setattr(args,k,v[value_index])
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main(args)
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np.savetxt(record_name, record, delimiter=',')
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log.get_logger("data-hdf5", log.DEBUG)
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init_hdf5(file_path, mode="w", cam_type="davis")
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h5py.File(file_path, mode=mode)
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dataset.create_group("dvs")
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dataset.create_group("extra")
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dataset.create_group("aps")
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dataset.create_group("imu")
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h5py.File(file_path, mode=mode)
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FloatingPoint(object)
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GetRootAsFloatingPoint(cls, buf, offset)
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flatbuffers.encode.Get(flatbuffers.packer.uoffset, buf, offset)
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FloatingPoint()
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x.Init(buf, n + offset)
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Init(self, buf, pos)
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flatbuffers.table.Table(buf, pos)
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Precision(self)
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flatbuffers.number_types.UOffsetTFlags.py_type(self._tab.Offset(4)
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self._tab.Get(flatbuffers.number_types.Int16Flags, o + self._tab.Pos)
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FloatingPointStart(builder)
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builder.StartObject(1)
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FloatingPointAddPrecision(builder, precision)
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builder.PrependInt16Slot(0, precision, 0)
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FloatingPointEnd(builder)
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builder.EndObject()
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sub(x, y)
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Light()
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Ligth()
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BaseCounter(object)
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__init__(self)
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event(self, value=1)
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value(self, value)
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avg(self)
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sum(self)
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empty(self)
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TotalCounter(BaseCounter)
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__init__(self)
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event(self, value=1)
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value(self, value)
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avg(self)
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sum(self)
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empty(self)
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AverageWindowCounter(BaseCounter)
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N(window)
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__init__(self, window_size=300)
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deque(maxlen=window_size)
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event(self, value=1)
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self.values.append(value)
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avg(self)
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len(self.values)
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sum(self)
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sum(self.values)
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empty(self)
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TimebaseAverageWindowCounter(BaseCounter)
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__init__(self, window_size=30, window_interval=10)
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deque(maxlen=window_size)
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deque(maxlen=window_size)
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event(self, value=1)
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time.time()
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self.values.append(self.cache_value)
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self.times.append(self.cache_start)
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self.on_append(self.cache_value, self.cache_start)
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value(self, value)
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_trim_window(self)
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time.time()
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self.values.append(self.cache_value)
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self.times.append(self.cache_start)
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self.on_append(self.cache_value, self.cache_start)
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min(self.max_window_size, time_passed / self.window_interval)
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self.times.popleft()
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self.values.popleft()
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avg(self)
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float(self.sum)
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sum(self)
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self._trim_window()
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sum(self.values)
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empty(self)
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