(self, task)
Construct a ticker dataset object.

        Arguments:
            task: An object representing the task to be trained on
                  It contains information about input and output size,
                  sequence length, etc. It also implements a synthesize function,
                  which is used to generate the next minibatch of data.
Construct a ticker dataset object.
Construct a ticker dataset object .
def __init__(self, task):
        """
        Construct a ticker dataset object.

        Arguments:
            task: An object representing the task to be trained on
                  It contains information about input and output size,
                  sequence length, etc. It also implements a synthesize function,
                  which is used to generate the next minibatch of data.
        """

        self.task = task

        # These attributes don't make much sense in the context of tickers
        # but I suspect it will be hard to get rid of them
        self.batch_index = 0
        self.nbatches = 100
        self.ndata = self.nbatches * self.be.bsz

        # Alias these because other code relies on datasets having nin and nout
        self.nout = task.nout
        self.nin = task.nin

        # Configuration elsewhere relies on the existence of this
        self.shape = (self.nin, self.task.time_steps_max)

        # Initialize the inputs, the outputs, and the mask
        self.dev_X = self.be.iobuf((self.nin, self.task.time_steps_max))
        self.dev_y = self.be.iobuf((self.nout, self.task.time_steps_max))
        self.mask = self.be.iobuf((self.nout, self.task.time_steps_max))
def __init__ ( self , task ) : self . task = task # These attributes don't make much sense in the context of tickers # but I suspect it will be hard to get rid of them self . batch_index = 0 self . nbatches = 100 self . ndata = self . nbatches * self . be . bsz # Alias these because other code relies on datasets having nin and nout self . nout = task . nout self . nin = task . nin # Configuration elsewhere relies on the existence of this self . shape = ( self . nin , self . task . time_steps_max ) # Initialize the inputs, the outputs, and the mask self . dev_X = self . be . iobuf ( ( self . nin , self . task . time_steps_max ) ) self . dev_y = self . be . iobuf ( ( self . nout , self . task . time_steps_max ) ) self . mask = self . be . iobuf ( ( self . nout , self . task . time_steps_max ) )