model.py 文件源码

def __init__(self, past, future, features = None):
        """Create a training pattern.

        Parameters:
        past -- past feature vectors as a tensor of shape [P, V]
            where P is past days and V is the vectors/day
        future -- future feature vectors as a tensor of [F, V]
            where F is future days and V is the vectors/day
        features -- a sequence of feature names to use
            where None means use all features
        """

        # calculate training input from past features
        past_subfeatures = [[self._subfeatures(vector, features)
            for vector in vectors]
                for vectors in past]
        self._input = numpy.array(
            [list(util.flatten(vectors)) for vectors in past_subfeatures])

        # calculate training output from future volatility
        future_returns = numpy.log1p(
            [[vector.ret for vector in vectors] for vectors in future])
        self._output = numpy.std(future_returns, axis = 0, ddof = 1)\
            * numpy.sqrt(252)

        # calculate past returns for forecasts
        self._past_returns = numpy.log1p(
            [[vector.ret for vector in vectors] for vectors in past])