class_docstr: Leave-P-Out cross validation iterator:
  Provides train/test indexes to split data in train test sets
docstr: Leave-P-Out cross validation iterator:
  Provides train/test indexes to split data in train test sets
  
  Parameters
  ----------
  n: int
      Total number of elements
  p: int
      Size test sets
  
  Examples
  --------
  >>> from scikits.learn import cross_val
  >>> X = [[1, 2], [3, 4], [5, 6], [7, 8]]
  >>> y = [1, 2, 3, 4]
  >>> lpo = cross_val.LeavePOut(4, 2)
  >>> for train_index, test_index in lpo:
  ...    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: [False  True False  True] TEST: [ True False  True False]
  TRAIN: [False  True  True False] TEST: [ True False False  True]
  TRAIN: [ True False False  True] TEST: [False  True  True False]
  TRAIN: [ True False  True False] TEST: [False  True False  True]
  TRAIN: [ True  True False False] TEST: [False False  True  True]
docstr_tok: ["Leave-P-Out","cross","validation","iterator",":","Provides","train\/test","indexes","to","split","data","in","train","test","sets","Parameters","--","--","--","--","--","n",":","int","Total","number","of","elements","p",":","int","Size","test","sets","Examples","--","--","--","--",">",">",">","from","scikits.learn","import","cross_val",">",">",">","X","=","[","[","1",",","2","]",",","[","3",",","4","]",",","[","5",",","6","]",",","[","7",",","8","]","]",">",">",">","y","=","[","1",",","2",",","3",",","4","]",">",">",">","lpo","=","cross_val.LeavePOut","(","4",",","2",")",">",">",">","for","train_index",",","test_index","in","lpo",":","...","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",":","[","False","True","False","True","]","TEST",":","[","True","False","True","False","]","TRAIN",":","[","False","True","True","False","]","TEST",":","[","True","False","False","True","]","TRAIN",":","[","True","False","False","True","]","TEST",":","[","False","True","True","False","]","TRAIN",":","[","True","False","True","False","]","TEST",":","[","False","True","False","True","]","TRAIN",":","[","True","True","False","False","]","TEST",":","[","False","False","True","True","]"]
code: def __init__(self, n, p):
      """
      Leave-P-Out cross validation iterator:
      Provides train/test indexes to split data in train test sets
  
      Parameters
      ----------
      n: int
          Total number of elements
      p: int
          Size test sets
  
      Examples
      --------
      >>> from scikits.learn import cross_val
      >>> X = [[1, 2], [3, 4], [5, 6], [7, 8]]
      >>> y = [1, 2, 3, 4]
      >>> lpo = cross_val.LeavePOut(4, 2)
      >>> for train_index, test_index in lpo:
      ...    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: [False  True False  True] TEST: [ True False  True False]
      TRAIN: [False  True  True False] TEST: [ True False False  True]
      TRAIN: [ True False False  True] TEST: [False  True  True False]
      TRAIN: [ True False  True False] TEST: [False  True False  True]
      TRAIN: [ True  True False False] TEST: [False False  True  True]
      """
      self.n = n
      self.p = p
  
imports: from statsmodels.compat.python import lrange
  import numpy
  from itertools import combinations
label_desc: Use image node_id 1 to create a new LeavePOut object  with example: obj = LeavePOut(n, p)