numpy的random模塊
隨機抽樣 (numpy.random)
簡單的隨機數據
rand(d0, d1, ..., dn)
隨機值
>>> np.random.rand(3,2)
array([[ 0.14022471, 0.96360618], #random
[ 0.37601032, 0.25528411], #random
[ 0.49313049, 0.94909878]]) #randomrandn(d0, d1, ..., dn)
返回一個樣本，具有標準正態分布。
Notes
For random samples from N(\mu, \sigma^2), use:
sigma * np.random.randn(...) + mu
Examples
>>> np.random.randn()
2.1923875335537315 #randomTwo-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]]) #randomrandint(low[, high, size])
返回隨機的整數，位於半開區間 [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])
返回隨機的整數，位於閉區間 [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)
>>> 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 {0, 5/8, 10/8, 15/8, 20/8}):
>>> 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 + d2Display results as a histogram:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(dsums, 11, normed=True)
>>> plt.show()random_sample([size])
返回隨機的浮點數，在半開區間 [0.0, 1.0)。
To sample Unif[a, b), b > a multiply the output of random_sample by (b-a) and add a:
(b - a) * random_sample() + aExamples
>>> 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])
返回隨機的浮點數，在半開區間 [0.0, 1.0)。
（官網例子與random_sample完全一樣）
ranf([size])
返回隨機的浮點數，在半開區間 [0.0, 1.0)。
（官網例子與random_sample完全一樣）
sample([size])
返回隨機的浮點數，在半開區間 [0.0, 1.0)。
（官網例子與random_sample完全一樣）
choice(a[, size, replace, p])
生成一個隨機樣本，從一個給定的一維數組
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)
返回隨機字節。
>>> np.random.bytes(10)
‘ eh\x85\x022SZ\xbf\xa4‘ #random排列
shuffle(x)
現場修改序列，改變自身內容。（類似洗牌，打亂順序）
>>> arr = np.arange(10)
>>> np.random.shuffle(arr)
>>> arr
[1 7 5 2 9 4 3 6 0 8]This function only shuffles the array along the first index of a multi-dimensional array:
>>> arr = np.arange(9).reshape((3, 3))
>>> np.random.shuffle(arr)
>>> arr
array([[3, 4, 5],
[6, 7, 8],
[0, 1, 2]])permutation(x)
返回一個隨機排列
>>> np.random.permutation(10)
array([1, 7, 4, 3, 0, 9, 2, 5, 8, 6])
>>> np.random.permutation([1, 4, 9, 12, 15])
array([15, 1, 9, 4, 12])
>>> arr = np.arange(9).reshape((3, 3))
>>> np.random.permutation(arr)
array([[6, 7, 8],
[0, 1, 2],
[3, 4, 5]])分布
beta(a, b[, size])
貝塔分布樣本，在 [0, 1]內。
binomial(n, p[, size])
二項分布的樣本。
chisquare(df[, size])
卡方分布樣本。
dirichlet(alpha[, size])
狄利克雷分布樣本。
exponential([scale, size])
指數分布
f(dfnum, dfden[, size])
F分布樣本。
gamma(shape[, scale, size])
伽馬分布
geometric(p[, size])
幾何分布
gumbel([loc, scale, size])
耿貝爾分布。
hypergeometric(ngood, nbad, nsample[, size])
超幾何分布樣本。
laplace([loc, scale, size])
拉普拉斯或雙指數分布樣本
logistic([loc, scale, size])
Logistic分布樣本
lognormal([mean, sigma, size])
對數正態分布
logseries(p[, size])
對數級數分布。
multinomial(n, pvals[, size])
多項分布
multivariate_normal(mean, cov[, size])
多元正態分布。
>>> mean = [0,0]
>>> cov = [[1,0],[0,100]] # diagonal covariance, points lie on x or y-axis
>>> import matplotlib.pyplot as plt
>>> x, y = np.random.multivariate_normal(mean, cov, 5000).T
>>> plt.plot(x, y, ‘x‘); plt.axis(‘equal‘); plt.show()negative_binomial(n, p[, size])
負二項分布
noncentral_chisquare(df, nonc[, size])
非中心卡方分布
noncentral_f(dfnum, dfden, nonc[, size])
非中心F分布
normal([loc, scale, size])
正態(高斯)分布
Notes
The probability density for the Gaussian distribution is
p(x) = \frac{1}{\sqrt{ 2 \pi \sigma^2 }}
e^{ - \frac{ (x - \mu)^2 } {2 \sigma^2} },where \mu is the mean and \sigma the standard deviation. The square of the standard deviation, \sigma^2, is called the variance.
The function has its peak at the mean, and its “spread” increases with the standard deviation (the function reaches 0.607 times its maximum at x + \sigma and x - \sigma [R217]).
Examples
Draw samples from the distribution:
>>> mu, sigma = 0, 0.1 # mean and standard deviation
>>> s = np.random.normal(mu, sigma, 1000)Verify the mean and the variance:
>>> abs(mu - np.mean(s)) < 0.01
True
>>> abs(sigma - np.std(s, ddof=1)) < 0.01
TrueDisplay the histogram of the samples, along with the probability density function:
>>> import matplotlib.pyplot as plt
>>> count, bins, ignored = plt.hist(s, 30, normed=True)
>>> plt.plot(bins, 1/(sigma * np.sqrt(2 * np.pi)) *
... np.exp( - (bins - mu)**2 / (2 * sigma**2) ),
... linewidth=2, color=‘r‘)
>>> plt.show()pareto(a[, size])
帕累托（Lomax）分布
poisson([lam, size])
泊松分布
power(a[, size])
Draws samples in [0, 1] from a power distribution with positive exponent a - 1.
rayleigh([scale, size])
Rayleigh 分布
standard_cauchy([size])
標準柯西分布
standard_exponential([size])
標準的指數分布
standard_gamma(shape[, size])
標準伽馬分布
standard_normal([size])
標準正態分布 (mean=0, stdev=1).
standard_t(df[, size])
Standard Student’s t distribution with df degrees of freedom.
triangular(left, mode, right[, size])
三角形分布
uniform([low, high, size])
均勻分布
vonmises(mu, kappa[, size])
von Mises分布
wald(mean, scale[, size])
瓦爾德（逆高斯）分布
weibull(a[, size])
Weibull 分布
zipf(a[, size])
齊普夫分布
隨機數生成器
RandomState
Container for the Mersenne Twister pseudo-random number generator.
seed([seed])
Seed the generator.
get_state()
Return a tuple representing the internal state of the generator.
set_state(state)
Set the internal state of the generator from a tuple.
numpy的random模塊