numpy的random模組詳細解析(轉帖)
原連結：https://www.cnblogs.com/zuoshoushizi/p/8727773.html
隨機抽樣(numpy.random)
簡單的隨機資料
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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]]) #random
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randn(d0,d1,...,dn) |
返回一個樣本，具有標準正態分佈。 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 |
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randint(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]]) |
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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)
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() |
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random_sample([size]) |
返回隨機的浮點數，在半開區間 [0.0, 1.0)。 To samplemultiply the output ofrandom_sampleby(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]])
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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） |
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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） |
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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） |
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choice(a[,size,replace,p]) |
生成一個隨機樣本，從一個給定的一維陣列 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‘)
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bytes(length) |
返回隨機位元組。 >>> np.random.bytes(10) ‘ eh\x85\x022SZ\xbf\xa4‘ #random |
排列
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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]])
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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]])
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分佈
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beta(a,b[,size]) |
貝塔分佈樣本，在[0,1]內。 |
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binomial(n,p[,size]) |
二項分佈的樣本。 |
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chisquare(df[,size]) |
卡方分佈樣本。 |
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dirichlet(alpha[,size]) |
狄利克雷分佈樣本。 |
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exponential([scale,size]) |
指數分佈 |
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f(dfnum,dfden[,size]) |
F分佈樣本。 |
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gamma(shape[,scale,size]) |
伽馬分佈 |
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geometric(p[,size]) |
幾何分佈 |
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gumbel([loc,scale,size]) |
耿貝爾分佈。 |
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hypergeometric(ngood,nbad,nsample[,size]) |
超幾何分佈樣本。 |
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laplace([loc,scale,size]) |
拉普拉斯或雙指數分佈樣本 |
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logistic([loc,scale,size]) |
Logistic分佈樣本 |
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lognormal([mean,sigma,size]) |
對數正態分佈 |
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logseries(p[,size]) |
對數級數分佈。 |
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multinomial(n,pvals[,size]) |
多項分佈 |
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multivariate_normal(mean,cov[,size]) |
多元正態分佈。 >>> 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() |
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negative_binomial(n,p[,size]) |
負二項分佈 |
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noncentral_chisquare(df,nonc[,size]) |
非中心卡方分佈 |
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noncentral_f(dfnum,dfden,nonc[,size]) |
非中心F分佈 |
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normal([loc,scale,size]) |
正態(高斯)分佈 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() |
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pareto(a[,size]) |
帕累託（Lomax）分佈 |
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poisson([lam,size]) |
泊松分佈 |
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power(a[,size]) |
Draws samples in [0, 1] from a power distribution with positive exponent a - 1. |
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rayleigh([scale,size]) |
Rayleigh分佈 |
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standard_cauchy([size]) |
標準柯西分佈 |
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standard_exponential([size]) |
標準的指數分佈 |
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standard_gamma(shape[,size]) |
標準伽馬分佈 |
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standard_normal([size]) |
標準正態分佈(mean=0, stdev=1). |
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standard_t(df[,size]) |
Standard Student’s t distribution with df degrees of freedom. |
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triangular(left,mode,right[,size]) |
三角形分佈 |
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uniform([low,high,size]) |
均勻分佈 |
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vonmises(mu,kappa[,size]) |
von Mises分佈 |
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wald(mean,scale[,size]) |
瓦爾德（逆高斯）分佈 |
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weibull(a[,size]) |
Weibull分佈 |
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zipf(a[,size]) |
齊普夫分佈 |
隨機數生成器
| Container for the Mersenne Twister pseudo-random number generator. | |
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seed([seed]) |
Seed the generator. |
| Return a tuple representing the internal state of the generator. | |
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set_state(state) |
Set the internal state of the generator from a tuple. |