.. AUTO-GENERATED FILE -- DO NOT EDIT!

.. _example_start_easy:


Tiny Example of a Full Cross-Validation
=======================================

Very, very simple example showing a complete cross-validation procedure
with no fancy additions whatsoever.

  >>> # get PyMVPA running
  >>> from mvpa.suite import *
  >>> 
  >>> # load PyMVPA example dataset
  >>> attr = SampleAttributes(os.path.join(pymvpa_dataroot, 'attributes.txt'))
  >>> dataset = NiftiDataset(samples=os.path.join(pymvpa_dataroot, 'bold.nii.gz'),
  >>>                        labels=attr.labels,
  >>>                        chunks=attr.chunks,
  >>>                        mask=os.path.join(pymvpa_dataroot, 'mask.nii.gz'))
  >>> 
  >>> # do chunkswise linear detrending on dataset
  >>> detrend(dataset, perchunk=True, model='linear')
  >>> 
  >>> # zscore dataset relative to baseline ('rest') mean
  >>> zscore(dataset, perchunk=True, baselinelabels=[0],
  >>>        targetdtype='float32')
  >>> 
  >>> # select class 1 and 2 for this demo analysis
  >>> # would work with full datasets (just a little slower)
  >>> dataset = dataset.selectSamples(
  >>>                 N.array([l in [1, 2] for l in dataset.labels],
  >>>                         dtype='bool'))
  >>> 
  >>> # setup cross validation procedure, using SMLR classifier
  >>> cv = CrossValidatedTransferError(
  >>>             TransferError(SMLR()),
  >>>             OddEvenSplitter())
  >>> # and run it
  >>> error = cv(dataset)
  >>> 
  >>> print "Error for %i-fold cross-validation on %i-class problem: %f" \
  >>>       % (len(dataset.uniquechunks), len(dataset.uniquelabels), error)

.. seealso::
  The full source code of this example is included in the PyMVPA source distribution (`doc/examples/start_easy.py`).
