# NumPy's Random module

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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 , &NBSP;D1,&NBSP;...,&NBSP;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].NotesTo 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)) > >> 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 ()) >>> 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 arrayExamplesGenerate 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) distributionNotesThe probability density for the Gaussian distribution isThe 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]).ExamplesDraw 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

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