class_docstr: This class defines methods for generating and iterating over ticker datasets.
docstr: 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.
docstr_tok: ["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","."]
code: 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))
  
imports: import numpy
  from neon import NervanaObject
label_desc: Use image node_id 2 to create a new Ticker object from inherited base classes: NervanaObject with example: obj = Ticker(task)