Numpy之random模块

简单的随机数据

rand(d0, d1, ..., dn)

随机值

>>> np.random.rand(3,2) array([[ 0.14022471, 0.96360618], #random [ 0.37601032, 0.25528411], #random [ 0.49313049, 0.94909878]]) #random

randn(d0, d1, ..., dn)

返回一个样本，具有标准正态分布。

Notes

For random samples from , use:

sigma * np.random.randn(...) + mu

Examples

>>> np.random.randn() 2.1923875335537315 #random

Two-by-four array of samples from N(3, 6.25):

>>> 2.5 * np.random.randn(2, 4) + 3 array([[-4.49401501, 4.00950034, -1.81814867, 7.29718677], #random [ 0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random

randint(low[, high, size])

返回随机的整数，位于半开区间 [low, high)。

>>> np.random.randint(2, size=10) array([1, 0, 0, 0, 1, 1, 0, 0, 1, 0]) >>> np.random.randint(1, size=10) array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

Generate a 2 x 4 array of ints between 0 and 4, inclusive:

>>> np.random.randint(5, size=(2, 4)) array([[4, 0, 2, 1], [3, 2, 2, 0]])

random_integers(low[, high, size])

返回随机的整数，位于闭区间 [low, high]。

Notes

To sample from N evenly spaced floating-point numbers between a and b, use:

a + (b - a) * (np.random.random_integers(N) - 1) / (N - 1.)

Examples

>>> np.random.random_integers(5) 4 >>> type(np.random.random_integers(5)) <type ‘int‘> >>> np.random.random_integers(5, size=(3.,2.)) array([[5, 4], [3, 3], [4, 5]])

Choose five random numbers from the set of five evenly-spaced numbers between 0 and 2.5, inclusive (i.e., from the set ):

>>> 2.5 * (np.random.random_integers(5, size=(5,)) - 1) / 4. array([ 0.625, 1.25 , 0.625, 0.625, 2.5 ])

Roll two six sided dice 1000 times and sum the results:

>>> d1 = np.random.random_integers(1, 6, 1000) >>> d2 = np.random.random_integers(1, 6, 1000) >>> dsums = d1 + d2

Display results as a histogram:

>>> import matplotlib.pyplot as plt >>> count, bins, ignored = plt.hist(dsums, 11, normed=True) >>> plt.show()

 

random_sample([size])

返回随机的浮点数，在半开区间 [0.0, 1.0)。

To sample  multiply the output of random_sample by (b-a) and add a:

(b - a) * random_sample() + a

Examples

>>> np.random.random_sample() 0.47108547995356098 >>> type(np.random.random_sample()) <type ‘float‘> >>> np.random.random_sample((5,)) array([ 0.30220482, 0.86820401, 0.1654503 , 0.11659149, 0.54323428])

Three-by-two array of random numbers from [-5, 0):

>>> 5 * np.random.random_sample((3, 2)) - 5 array([[-3.99149989, -0.52338984], [-2.99091858, -0.79479508], [-1.23204345, -1.75224494]])

 

random([size])

返回随机的浮点数，在半开区间 [0.0, 1.0)。

（官网例子与random_sample完全一样）

ranf([size])

返回随机的浮点数，在半开区间 [0.0, 1.0)。

（官网例子与random_sample完全一样）

sample([size])

返回随机的浮点数，在半开区间 [0.0, 1.0)。

（官网例子与random_sample完全一样）

choice(a[, size, replace, p])

生成一个随机样本，从一个给定的一维数组

Examples

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

>>> np.random.choice(5, 3) 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]) 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) array([3,1,0]) >>> #This is equivalent to np.random.permutation(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]) 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]) array([‘pooh‘, ‘pooh‘, ‘pooh‘, ‘Christopher‘, ‘piglet‘], dtype=‘|S11‘)

 

bytes(length)

返回随机字节。

>>> np.random.bytes(10) ‘ eh\x85\x022SZ\xbf\xa4‘ #random