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]]) #random
|
| randn(d0, d1, ..., dn) |
返回一個樣本，具有標准正態分布。 Notes For random samples from 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)
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]) |
返回隨機的浮點數，在半開區間 [0.0, 1.0)。 To sample (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
|
, use:
):
multiply the output of 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
where 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
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]) |
標准柯西分布 |
| standard_exponential([size]) |
標准的指數分布 |
| standard_gamma(shape[, size]) |
標准伽馬分布 |
| standard_normal([size]) |
標准正態分布 (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 |
is the mean and 
the standard deviation. The square of the standard deviation, 
, is called the variance.
and 
隨機數生成器
| Container for the Mersenne Twister pseudo-random number generator. | |
| seed([seed]) |
Seed the generator. |
| Return a tuple representing the internal state of the generator. | |
| set_state(state) |
Set the internal state of the generator from a tuple. |