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随机抽样 (numpy.random)

發(fā)布時(shí)間：2024/1/3 综合教程 33 生活家

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隨機(jī)抽樣(`numpy.random`)

簡(jiǎn)單的隨機(jī)數(shù)據(jù)

`rand`(d0,d1,...,dn)	隨機(jī)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)	返回一個(gè)樣本，具有標(biāo)準(zhǔn)正態(tài)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])	返回隨機(jī)的整數(shù)，位于半開(kāi)區(qū)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(jī)的整數(shù)，位于閉區(qū)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) 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])	返回隨機(jī)的浮點(diǎn)數(shù)，在半開(kāi)區(qū)間 [0.0, 1.0)。 To samplemultiply the output of`random_sample`by(b-a)and adda: (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])	返回隨機(jī)的浮點(diǎn)數(shù)，在半開(kāi)區(qū)間 [0.0, 1.0)。（官網(wǎng)例子與random_sample完全一樣）
`ranf`([size])	返回隨機(jī)的浮點(diǎn)數(shù)，在半開(kāi)區(qū)間 [0.0, 1.0)。（官網(wǎng)例子與random_sample完全一樣）
`sample`([size])	返回隨機(jī)的浮點(diǎn)數(shù)，在半開(kāi)區(qū)間 [0.0, 1.0)。（官網(wǎng)例子與random_sample完全一樣）
`choice`(a[,size,replace,p])	生成一個(gè)隨機(jī)樣本，從一個(gè)給定的一維數(shù)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(jī)字節(jié)。 >>> np.random.bytes(10) ‘ ehx85x022SZxbfxa4‘ #random

排列

shuffle(x)

現(xiàn)場(chǎng)修改序列，改變自身內(nèi)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(gè)隨機(jī)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(nèi)。
`binomial`(n,p[,size])	二項(xiàng)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(shù)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(shù)分布樣本
`logistic`([loc,scale,size])	Logistic分布樣本
`lognormal`([mean,sigma,size])	對(duì)數(shù)正態(tài)分布
`logseries`(p[,size])	對(duì)數(shù)級(jí)數(shù)分布。
`multinomial`(n,pvals[,size])	多項(xiàng)分布
`multivariate_normal`(mean,cov[,size])	多元正態(tài)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(fù)二項(xiàng)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(tài)(高斯)分布 Notes The probability density for the Gaussian distribution is whereis the mean andthe standard deviation. The square of the standard deviation,, 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 atand[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 True Display 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(biāo)準(zhǔn)柯西分布
`standard_exponential`([size])	標(biāo)準(zhǔn)的指數(shù)分布
`standard_gamma`(shape[,size])	標(biāo)準(zhǔn)伽馬分布
`standard_normal`([size])	標(biāo)準(zhǔn)正態(tài)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(jī)數(shù)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.

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