NumPy's Random module

Documents translated from the official website. Transfer from http://www.mamicode.com/info-detail-507676.html

Random Sampling (Numpy.random) Simple Random data

Rand (D0, D1, ..., DN)	Random values >>> 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)	Returns a sample with a standard normal distribution. notes For random samples from , and use: sigma * NP.RANDOM.RANDN (...) + mu P class= "Rubric" >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) + 3array ([[-4.49401501, 4.00950034,-1 .81814867, 7.29718677], #random [0.39924804, 4.68456316, 4.99394529, 4.84057254]]) #random
Randint (low[, high, size])	Returns a random integer that is located in the half open interval [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) a Rray ([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])	Returns a random integer in the closed interval [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 th E set): >>> 2.5 * (Np.random.random_integers (5, size= (5,))-1)/4.array ([0.625, 1.25, 0.625, 0.625, 2.5 ]) Roll six sided dice and sum the results: >>> D1 = np.random.random_integers (1, 6, +) >>> D2 = Np.random.random_integers (1, 6, +) >>& Gt dsums = D1 + d2 Display results as a histogram: >>> Import Matplotlib.pyplot as plt>>> count, bins, ignored = plt.hist (Dsums, one, normed=true) >>& Gt Plt.show ()
random_sample ([size])	Returns a random floating-point number in the half-open interval [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))-5array ([[-3.99149989,-0.52338984], [ -2.99091858, -0.79479508], [ -1.23204345, -1.75224494]])
Random ([size])	Returns the random floating-point number in the half-open interval [0.0, 1.0]. (The official Website example is exactly the same as Random_sample)
ranf ([size])	Returns the random floating-point number in the half-open interval [0.0, 1.0]. (The official Website example is exactly the same as Random_sample)
Sample ([size])	Returns the random floating-point number in the half-open interval [0.0, 1.0]. (The official Website example is exactly the same as Random_sample)
Choice (a[, size, replace, p])	Generate a random sample from a given one-dimensional array 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 is 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)	Returns a random byte. >>> np.random.bytes ' eh\x85\x022sz\xbf\xa4 ' #random

Arranged

shuffle (x	Modifies the sequence in the field and changes its contents. (Similar shuffle, scrambled order) >>> arr = np.arange (+) >>> np.random.shuffle (arr) >>> arr[1 7 5 2 9 4 3 6 0 8]   This function is only shuffles the array along the first index of a multi-dimensional array: >>> arr = np.arange (9). Reshape ((3, 3)) >>> np.random.shuffle (arr) >>> Arrarray ([[3, 4, 5], [6, 7, 8], [0, 1, 2]])
permutation (x)	Returns a random arrangement >>> np.random.permutation (+) array ([1, 7, 4, 3, 0, 9, 2, 5, 8, 6]) >>> np.random.permutation ([1, 4, 9, A, a]) array ([, 1, 9, 4, 12]) >>> arr = np.arange (9). Reshape ((3, 3)) >>> np.random.permutation (arr) array ([[[6, 7, 8], [0, 1, 2],< C6/>[3, 4, 5]])

Distribution

Beta (A, b[, size])	Beta distribution sample, within [0, 1] .
binomial (n, p[, size])	Sample of two distributions.
Chisquare (df[, size])	Chi-square distribution samples.
Dirichlet (alpha[, size])	Dirichlet distribution samples.
exponential ([scale, size])	Exponential distribution
F (Dfnum, dfden[, size])	F Distribution Sample.
Gamma (shape[, scale, size])	Gamma distribution
Geometric (p[, size])	Geometric distribution
Gumbel ([Loc, scale, size])	Gumbel distribution.
hypergeometric (Ngood, Nbad, nsample[, size])	hypergeometric distribution Sample.
Laplace ([Loc, scale, size])	Laplace or double exponential distribution sample
Logistic ([Loc, scale, size])	Logistic Distribution Sample
lognormal ([Mean, Sigma, size])	Logarithmic normal distribution
logseries (p[, size])	The distribution of the number of levels.
multinomial (n, pvals[, size])	Multi-item Distribution
Multivariate_normal (mean, cov[, size])	Multivariate normal distribution. >>> 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])	Negative two-item distribution
Noncentral_chisquare (DF, nonc[, size])	Non-central Chi-square distribution
Noncentral_f (Dfnum, Dfden, nonc[, size])	Non-center F distribution
Normal ([Loc, scale, size])	Normal (Gaussian) distribution Notes The probability density for the Gaussian distribution is The where is the mean and the standard deviation. The square of the deviation, is called the variance. The function has a peak at the mean, and its ' spread ' increases with the standard deviation (the function reaches 0.607 Times its maximum at and [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)) < 0.01true>>> ABS (SIGMA-NP.STD (S, ddof=1)) < 0.01True Display the histogram of the samples, along with the probability density function: >>> Import Matplotlib.pyplot as plt>>> count, bins, ignored = plt.hist (s, A, normed=true) >>> p Lt.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])	Pareto (Lomax) distribution
Poisson ([Lam, size])	Poisson distribution
Power (a[, size])	Draws samples in [0, 1] from a power distribution with positive exponent A-1.
Rayleigh ([scale, size])	Rayleigh distribution
Standard_cauchy ([size])	Standard Cauchy distribution
standard_exponential ([size])	The standard exponential distribution
Standard_gamma (shape[, size])	Standard Gamma distribution
Standard_normal ([size])	Standard normal distribution (mean=0, stdev=1).
standard_t (df[, size])	Standard Student's t distribution with DF degrees of freedom.
Triangular (left, mode, right[, size])	Triangular distribution
Uniform ([Low, high, size])	Evenly distributed
vonmises (MU, kappa[, size])	von Mises Distribution
Wald (mean, scale[, size])	Wald (inverse Gaussian) distribution
Weibull (a[, size])	Weibull distribution
Zipf (a[, size])	Zipf distribution

Random number generator

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's Random module