# choice#

class brainpy.math.random.choice(a, size=None, replace=True, p=None, key=None)[source]#

Generates a random sample from a given 1-D array

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 it were `np.arange(a)`

• size (int or tuple of ints, optional) – Output shape. If the given shape is, e.g., `(m, n, k)`, then `m * n * k` samples are drawn. Default is None, in which case a single value is returned.

• replace (boolean, optional) – Whether the sample is with or without replacement. Default is True, meaning that a value of `a` can be selected multiple times.

• 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`.

Returns:

samples – The generated random samples

Return type:

single item or ndarray

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

`Generator.choice`

which should be used in new code

Notes

Setting user-specified probabilities through `p` uses a more general but less efficient sampler than the default. The general sampler produces a different sample than the optimized sampler even if each element of `p` is 1 / len(a).

Sampling random rows from a 2-D array is not possible with this function, but is possible with Generator.choice through its `axis` keyword.

Examples

Generate a uniform random sample from np.arange(5) of size 3:

```>>> import brainpy.math as bm
>>> bm.random.choice(5, 3)
array([0, 3, 4]) # random
>>> #This is equivalent to brainpy.math.random.randint(0,5,3)
```

Generate a non-uniform random sample from np.arange(5) of size 3:

```>>> bm.random.choice(5, 3, p=[0.1, 0, 0.3, 0.6, 0])
array([3, 3, 0]) # random
```

Generate a uniform random sample from np.arange(5) of size 3 without replacement:

```>>> bm.random.choice(5, 3, replace=False)
array([3,1,0]) # random
>>> #This is equivalent to brainpy.math.random.permutation(np.arange(5))[:3]
```

Generate a non-uniform random sample from np.arange(5) of size 3 without replacement:

```>>> bm.random.choice(5, 3, replace=False, p=[0.1, 0, 0.3, 0.6, 0])
array([2, 3, 0]) # random
```

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']
>>> bm.random.choice(aa_milne_arr, 5, p=[0.5, 0.1, 0.1, 0.3])
array(['pooh', 'pooh', 'pooh', 'Christopher', 'piglet'], # random
dtype='<U11')
```