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# `supervised` – Dataset for Supervised Regression Training

class `pybrain.datasets.supervised.``SupervisedDataSet`(inp, target)

  Bases: `pybrain.datasets.dataset.DataSet`

  SupervisedDataSets have two fields, one for input and one for the target.

  `__init__`(inp, target)

    Initialize an empty supervised dataset.

    Pass inp and target to specify the dimensions of the input and target vectors.

  `__len__`()
    Return the length of the linked data fields. If no linked fields exist, return the length of the longest field.
  `addSample`(inp, target)
    Add a new sample consisting of input and target.
  `batches`(label, n, permutation=None)

    Yield batches of the size of n from the dataset.

    A single batch is an array of with dim columns and n rows. The last batch is possibly smaller.

    If permutation is given, batches are yielded in the corresponding order.

  `clear`(unlinked=False)

    Clear the dataset.

    If linked fields exist, only the linked fields will be deleted unless unlinked is set to True. If no fields are linked, all data will be deleted.

  `copy`()
    Return a deep copy.
  classmethod `loadFromFile`(filename, format=None)
    Return an instance of the class that is saved in the file with the given filename in the specified format.
  `randomBatches`(label, n)
    Like .batches(), but the order is random.
  classmethod `reconstruct`(filename)
    Read an incomplete data set (option arraysonly) into the given one.
  `saveToFile`(filename, format=None, **kwargs)
    Save the object to file given by filename.
  `splitWithProportion`(proportion=0.5)
    Produce two new datasets, the first one containing the fraction given by proportion of the samples.

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