class_docstr: K-Folds cross validation iterator:
  Provides train/test indexes to split data in train test sets
docstr: K-Folds cross validation iterator:
  Provides train/test indexes to split data in train test sets
  
  Parameters
  ----------
  n: int
      Total number of elements
  k: int
      number of folds
  
  Examples
  --------
  >>> from scikits.learn import cross_val
  >>> X = [[1, 2], [3, 4], [1, 2], [3, 4]]
  >>> y = [1, 2, 3, 4]
  >>> kf = cross_val.KFold(4, k=2)
  >>> for train_index, test_index in kf:
  ...    print "TRAIN:", train_index, "TEST:", test_index
  ...    X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y)
  TRAIN: [False False  True  True] TEST: [ True  True False False]
  TRAIN: [ True  True False False] TEST: [False False  True  True]
  
  Notes
  -----
  All the folds have size trunc(n/k), the last one has the complementary
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code: def __init__(self, n, k):
      """
      K-Folds cross validation iterator:
      Provides train/test indexes to split data in train test sets
  
      Parameters
      ----------
      n: int
          Total number of elements
      k: int
          number of folds
  
      Examples
      --------
      >>> from scikits.learn import cross_val
      >>> X = [[1, 2], [3, 4], [1, 2], [3, 4]]
      >>> y = [1, 2, 3, 4]
      >>> kf = cross_val.KFold(4, k=2)
      >>> for train_index, test_index in kf:
      ...    print "TRAIN:", train_index, "TEST:", test_index
      ...    X_train, X_test, y_train, y_test = cross_val.split(train_index, test_index, X, y)
      TRAIN: [False False  True  True] TEST: [ True  True False False]
      TRAIN: [ True  True False False] TEST: [False False  True  True]
  
      Notes
      -----
      All the folds have size trunc(n/k), the last one has the complementary
      """
      assert k > 0, ValueError("cannot have k below 1")
      assert k < n, ValueError(
          "cannot have k=%d greater than %d" % (k, n)
      )
      self.n = n
      self.k = k
  
imports: from statsmodels.compat.python import lrange
  import numpy
  from itertools import combinations
label_desc: Use image node_id 1 to create a new KFold object  with example: obj = KFold(n, k)