ó ‡ˆ\c@ s§ddlmZddlZddljZddlZddlm Z ddl m Z ddl m Z ddgZe d ƒdeddd „ƒZddd „ZdS( iÿÿÿÿ(tdivisionN(tcheck_random_statei(tsample_without_replacement(t deprecatedRtchoices•sklearn.utils.random.choice was deprecated in version 0.19 and will be removed in 0.21. Use np.random.choice or np.random.RandomState.choice instead.cC sK|dk r.t|ƒ}|j||||ƒStjj||||ƒSdS(sÅ choice(a, size=None, replace=True, p=None) Generates a random sample from a given 1-D array .. versionadded:: 1.7.0 Parameters ----------- a : 1-D array-like or int If an ndarray, a random sample is generated from its elements. If an int, the random sample is generated as if a was np.arange(n) size : int or tuple of ints, optional Output shape. Default is None, in which case a single value is returned. replace : boolean, optional Whether the sample is with or without replacement. p : 1-D array-like, optional The probabilities associated with each entry in a. If not given the sample assumes a uniform distribution over all entries in a. random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by `np.random`. Returns -------- samples : 1-D ndarray, shape (size,) The generated random samples Raises ------- ValueError If a is an int and less than zero, if a or p are not 1-dimensional, if a is an array-like of size 0, if p is not a vector of probabilities, if a and p have different lengths, or if replace=False and the sample size is greater than the population size See Also --------- randint, shuffle, permutation Examples --------- Generate a uniform random sample from np.arange(5) of size 3: >>> np.random.choice(5, 3) # doctest: +SKIP array([0, 3, 4]) >>> #This is equivalent to np.random.randint(0,5,3) Generate a non-uniform random sample from np.arange(5) of size 3: >>> np.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0]) # doctest: +SKIP array([3, 3, 0]) Generate a uniform random sample from np.arange(5) of size 3 without replacement: >>> np.random.choice(5, 3, replace=False) # doctest: +SKIP array([3,1,0]) >>> #This is equivalent to np.random.shuffle(np.arange(5))[:3] Generate a non-uniform random sample from np.arange(5) of size 3 without replacement: >>> np.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0]) ... # doctest: +SKIP array([2, 3, 0]) Any of the above can be repeated with an arbitrary array-like instead of just integers. For instance: >>> aa_milne_arr = ['pooh', 'rabbit', 'piglet', 'Christopher'] >>> np.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3]) ... # doctest: +SKIP array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], dtype='|S11') N(tNoneRRtnptrandom(tatsizetreplacetpt random_state((s3lib/python2.7/site-packages/sklearn/utils/random.pyRs[  cC sótjdƒ}tjdƒ}tjddgƒ}x’tt|ƒƒD]~}tj||ƒ||<||jjdkr“td||jƒ‚n||jtj dt ƒ||<|dkrûtj d||j dƒ}|jd||j dƒntj||ƒ}tjtj|ƒdƒsAtdj|ƒƒ‚n|j d||j dkrtd j|||j d|j dƒƒ‚nd||krÔtj||ddƒ||s    _