Le random est un module présent dans la bibliothèque NumPy. Ce module contient les fonctions utilisées pour générer des nombres aléatoires. Ce module contient quelques méthodes simples de génération de données aléatoires, des fonctions de permutation et de distribution et des fonctions de générateur aléatoire.
Toutes les fonctions d'un module aléatoire sont les suivantes :
Données aléatoires simples
Il existe les fonctions suivantes des données aléatoires simples :
1) p.random.rand(d0, d1, ..., dn)
Cette fonction de module aléatoire est utilisée pour générer des nombres ou des valeurs aléatoires dans une forme donnée.
Exemple:
import numpy as np a=np.random.rand(5,2) a
Sortir:
array([[0.74710182, 0.13306399], [0.01463718, 0.47618842], [0.98980426, 0.48390004], [0.58661785, 0.62895758], [0.38432729, 0.90384119]])
2) np.random.randn(d0, d1, ..., dn)
Cette fonction de module aléatoire renvoie un échantillon de la distribution « normale standard ».
Exemple:
import numpy as np a=np.random.randn(2,2) a
Sortir:
array([[ 1.43327469, -0.02019121], [ 1.54626422, 1.05831067]]) b=np.random.randn() b -0.3080190768904835
3) np.random.randint(low[, high, size, dtype])
Cette fonction de module aléatoire est utilisée pour générer des entiers aléatoires de inclusif (faible) à exclusif (élevé).
Exemple:
import numpy as np a=np.random.randint(3, size=10) a
Sortir:
array([1, 1, 1, 2, 0, 0, 0, 0, 0, 0])
4) np.random.random_integers(low[, high, size])
Cette fonction de module aléatoire est utilisée pour générer un nombre entier aléatoire de type np.int entre bas et haut.
Exemple:
import numpy as np a=np.random.random_integers(3) a b=type(np.random.random_integers(3)) b c=np.random.random_integers(5, size=(3,2)) c
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2 array([[1, 1], [2, 5], [1, 3]])
5) np.random.random_sample([taille])
arrière-plan CSS
Cette fonction de module aléatoire est utilisée pour générer un nombre de flottants aléatoires dans l'intervalle semi-ouvert [0.0, 1.0).
Exemple:
import numpy as np a=np.random.random_sample() a b=type(np.random.random_sample()) b c=np.random.random_sample((5,)) c
Sortir:
0.09250360565571492 array([0.34665418, 0.47027209, 0.75944969, 0.37991244, 0.14159746])
6) np.random.random([taille])
Cette fonction de module aléatoire est utilisée pour générer un nombre de flottants aléatoires dans l'intervalle semi-ouvert [0.0, 1.0).
Exemple:
import numpy as np a=np.random.random() a b=type(np.random.random()) b c=np.random.random((5,)) c
Sortir:
0.008786953974334155 array([0.05530122, 0.59133394, 0.17258794, 0.6912388 , 0.33412534])
7) np.random.ranf([taille])
Cette fonction de module aléatoire est utilisée pour générer un nombre de flottants aléatoires dans l'intervalle semi-ouvert [0.0, 1.0).
Exemple:
import numpy as np a=np.random.ranf() a b=type(np.random.ranf()) b c=np.random.ranf((5,)) c
Sortir:
0.2907792098474542 array([0.34084881, 0.07268237, 0.38161256, 0.46494681, 0.88071377])
8) np.random.sample([taille])
Cette fonction de module aléatoire est utilisée pour générer un nombre de flottants aléatoires dans l'intervalle semi-ouvert [0.0, 1.0).
Exemple:
import numpy as np a=np.random.sample() a b=type(np.random.sample()) b c=np.random.sample((5,)) c
Sortir:
0.012298209913766511 array([0.71878544, 0.11486169, 0.38189074, 0.14303308, 0.07217287])
9) np.random.choice(a[, taille, remplacer, p])
Cette fonction du module aléatoire est utilisée pour générer un échantillon aléatoire à partir d'un tableau 1D donné.
Exemple:
import numpy as np a=np.random.choice(5,3) a b=np.random.choice(5,3, p=[0.2, 0.1, 0.4, 0.2, 0.1]) b
Sortir:
array([0, 3, 4]) array([2, 2, 2], dtype=int64)
10) np.random.bytes(longueur)
Cette fonction de module aléatoire est utilisée pour générer des octets aléatoires.
Exemple:
import numpy as np a=np.random.bytes(7) a
Sortir:
'nQx08x83xf9xdex8a'
Permutations
Il existe les fonctions de permutations suivantes :
1) np.random.shuffle()
Cette fonction est utilisée pour modifier une séquence sur place en mélangeant son contenu.
Exemple:
import numpy as np a=np.arange(12) a np.random.shuffle(a) a
Sortir:
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]) array([10, 3, 2, 4, 5, 8, 0, 9, 1, 11, 7, 6])
2) np.random.permutation()
Cette fonction permute une séquence de manière aléatoire ou renvoie une plage permutée.
Exemple:
import numpy as np a=np.random.permutation(12) a
Sortir:
array([ 8, 7, 3, 11, 6, 0, 9, 10, 2, 5, 4, 1])
Distribution
Il existe les fonctions de permutations suivantes :
10 pour cent de 60
1) bêta(a, b[, taille])
Cette fonction est utilisée pour extraire des échantillons d'une distribution bêta.
Exemple:
def setup(self): self.dist = dist.beta self.cargs = [] self.ckwd = dict(alpha=2, beta=3) self.np_rand_fxn = numpy.random.beta self.np_args = [2, 3] self.np_kwds = dict()
2) binôme(n, p[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution binomiale.
Exemple:
import numpy as np n, p = 10, .6 s1= np.random.binomial(n, p, 10) s1
Sortir:
array([6, 7, 7, 9, 3, 7, 8, 6, 6, 4])
3) chi carré (df[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution binomiale.
Exemple:
import numpy as np np.random.chisquare(2,4) sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.
Sortir:
array([6, 7, 7, 9, 3, 7, 8, 6, 6, 4])
4) dirichlet(alpha[, taille])
Cette fonction est utilisée pour tirer un échantillon de la distribution de Dirichlet.
Exemple:
Import numpy as np import matplotlib.pyplot as plt s1 = np.random.dirichlet((10, 5, 3), 20).transpose() plt.barh(range(20), s1[0]) plt.barh(range(20), s1[1], left=s1[0], color='g') plt.barh(range(20), s1[2], left=s1[0]+s1[1], color='r') plt.title('Lengths of Strings') plt.show()
Sortir:
5) exponentiel([échelle, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution exponentielle.
Exemple:
def __init__(self, sourceid, targetid): self.__type = 'Transaction' self.id = uuid4() self.source = sourceid self.target = targetid self.date = self._datetime.date(start=2015, end=2019) self.time = self._datetime.time() if random() <0.05: self.amount="self._numbers.between(100000," 1000000) if random() < 0.15: self.currency="self._business.currency_iso_code()" else: pre> <p> <strong>6) f(dfnum, dfden[, size])</strong> </p> <p>This function is used to draw sample from an F distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np dfno= 1. dfden = 48. s1 = np.random.f(dfno, dfden, 10) np.sort(s1) </pre> <p> <strong>Output:</strong> </p> <pre> array([0.00264041, 0.04725478, 0.07140803, 0.19526217, 0.23979 , 0.24023478, 0.63141254, 0.95316446, 1.40281789, 1.68327507]) </pre> <p> <strong>7) gamma(shape[, scale, size])</strong> </p> <p>This function is used to draw sample from a Gamma distribution </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np shape, scale = 2., 2. s1 = np.random.gamma(shape, scale, 1000) import matplotlib.pyplot as plt import scipy.special as spss count, bins, ignored = plt.hist(s1, 50, density=True) a = bins**(shape-1)*(np.exp(-bins/scale) / (spss.gamma(shape)*scale**shape)) plt.plot(bins, a, linewidth=2, color='r') plt.show() </pre> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-2.webp" alt="numpy.random in Python"> <p> <strong>8) geometric(p[, size])</strong> </p> <p>This function is used to draw sample from a geometric distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np a = np.random.geometric(p=0.35, size=10000) (a == 1).sum() / 1000 </pre> <p> <strong>Output:</strong> </p> <pre> 3. </pre> <p> <strong>9) gumbel([loc, scale, size])</strong> </p> <p>This function is used to draw sample from a Gumble distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np lov, scale = 0, 0.2 s1 = np.random.gumbel(loc, scale, 1000) import matplotlib.pyplot as plt count, bins, ignored = plt.hist(s1, 30, density=True) plt.plot(bins, (1/beta)*np.exp(-(bins - loc)/beta)* np.exp( -np.exp( -(bins - loc) /beta) ),linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-3.webp" alt="numpy.random in Python"> <p> <strong>10) hypergeometric(ngood, nbad, nsample[, size])</strong> </p> <p>This function is used to draw sample from a Hypergeometric distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np good, bad, samp = 100, 2, 10 s1 = np.random.hypergeometric(good, bad, samp, 1000) plt.hist(s1) plt.show() </pre> <p> <strong>Output:</strong> </p> <pre> (array([ 13., 0., 0., 0., 0., 163., 0., 0., 0., 824.]), array([ 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6, 9.8, 10. ]), <a 10 list of patch objects>) </a></pre> <p><img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-4.webp" alt="numpy.random in Python"></p> <p> <strong>11) laplace([loc, scale, size])</strong> </p> <p>This function is used to draw sample from the Laplace or double exponential distribution with specified location and scale.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np location, scale = 0., 2. s = np.random.laplace(location, scale, 10) s </pre> <p> <strong>Output:</strong> </p> <pre> array([-2.77127948, -1.46401453, -0.03723516, -1.61223942, 2.29590691, 1.74297722, 1.49438411, 0.30325513, -0.15948891, -4.99669747]) </pre> <p> <strong>12) logistic([loc, scale, size])</strong> </p> <p>This function is used to draw sample from logistic distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt location, scale = 10, 1 s1 = np.random.logistic(location, scale, 10000) count, bins, ignored = plt.hist(s1, bins=50) count bins ignored plt.show() </pre> <p> <strong>Output:</strong> </p> <pre> array([1.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 1.000e+00, 1.000e+00, 5.000e+00, 7.000e+00, 1.100e+01, 1.800e+01, 3.500e+01, 5.300e+01, 6.700e+01, 1.150e+02, 1.780e+02, 2.300e+02, 3.680e+02, 4.910e+02, 6.400e+02, 8.250e+02, 9.100e+02, 9.750e+02, 1.039e+03, 9.280e+02, 8.040e+02, 6.530e+02, 5.240e+02, 3.380e+02, 2.470e+02, 1.650e+02, 1.150e+02, 8.500e+01, 6.400e+01, 3.300e+01, 1.600e+01, 2.400e+01, 1.400e+01, 4.000e+00, 5.000e+00, 2.000e+00, 2.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00]) array([ 0.50643911, 0.91891814, 1.33139717, 1.7438762 , 2.15635523, 2.56883427, 2.9813133 , 3.39379233, 3.80627136, 4.2187504 , 4.63122943, 5.04370846, 5.45618749, 5.86866652, 6.28114556, 6.69362459, 7.10610362, 7.51858265, 7.93106169, 8.34354072, 8.75601975, 9.16849878, 9.58097781, 9.99345685, 10.40593588, 10.81841491, 11.23089394, 11.64337298, 12.05585201, 12.46833104, 12.88081007, 13.2932891 , 13.70576814, 14.11824717, 14.5307262 , 14.94320523, 15.35568427, 15.7681633 , 16.18064233, 16.59312136, 17.00560039, 17.41807943, 17.83055846, 18.24303749, 18.65551652, 19.06799556, 19.48047459, 19.89295362, 20.30543265, 20.71791168, 21.13039072]) <a 50 list of patch objects> </a></pre> <p><img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-5.webp" alt="numpy.random in Python"></p> <p> <strong>13) lognormal([mean, sigma, size])</strong> </p> <p>This function is used to draw sample from a log-normal distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np mu, sigma = 2., 1. s1 = np.random.lognormal(mu, sigma, 1000) import matplotlib.pyplot as plt count, bins, ignored = plt.hist(s1, 100, density=True, ) a = np.linspace(min(bins), max(bins), 10000) pdf = (np.exp(-(np.log(a) - mu)**2 / (2 * sigma**2))/ (a * sigma * np.sqrt(2 * np.pi))) plt.plot(a, pdf, linewidth=2, color='r') plt.axis('tight') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-6.webp" alt="numpy.random in Python"> <p> <strong>14) logseries(p[, size])</strong> </p> <p>This function is used to draw sample from a logarithmic distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np x = .6 s1 = np.random.logseries(x, 10000) count, bins, ignored = plt.hist(s1) def logseries(k, p): return -p**k/(k*log(1-p)) plt.plot(bins, logseries(bins, x)*count.max()/logseries(bins, a).max(), 'r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-7.webp" alt="numpy.random in Python"> <p> <strong>15) multinomial(n, pvals[, size])</strong> </p> <p>This function is used to draw sample from a multinomial distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np np.random.multinomial(20, [1/6.]*6, size=1) </pre> <p> <strong>Output:</strong> </p> <pre> array([[4, 2, 5, 5, 3, 1]]) </pre> <p> <strong>16) multivariate_normal(mean, cov[, size, ...)</strong> </p> <p>This function is used to draw sample from a multivariate normal distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np mean = (1, 2) coveriance = [[1, 0], [0, 100]] import matplotlib.pyplot as plt a, b = np.random.multivariate_normal(mean, coveriance, 5000).T plt.plot(a, b, 'x') plt.axis('equal'023 030 ) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-8.webp" alt="numpy.random in Python"> <p> <strong>17) negative_binomial(n, p[, size])</strong> </p> <p>This function is used to draw sample from a negative binomial distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np s1 = np.random.negative_binomial(1, 0.1, 100000) for i in range(1, 11): probability = sum(s1 <i) 36 100000. print i, 'wells drilled, probability of one success=", probability </pre> <p> <strong>Output:</strong> </p> <pre> 1 wells drilled, probability of one success = 0 2 wells drilled, probability of one success = 0 3 wells drilled, probability of one success = 0 4 wells drilled, probability of one success = 0 5 wells drilled, probability of one success = 0 6 wells drilled, probability of one success = 0 7 wells drilled, probability of one success = 0 8 wells drilled, probability of one success = 0 9 wells drilled, probability of one success = 0 10 wells drilled, probability of one success = 0 </pre> <p > <strong>18) noncentral_chisquare(df, nonc[, size])</strong> </p> <p>This function is used to draw sample from a noncentral chi-square distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt val = plt.hist(np.random.noncentral_chisquare(3, 25, 100000), bins=200, normed=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src=" techcodeview.com img numpy-tutorial numpy-random-python-9.webp' alt="numpy.random in Python"> <p> <strong>19) normal([loc, scale, size])</strong> </p> <p>This function is used to draw sample from a normal distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt mu, sigma = 0, 0.2 # mean and standard deviation s1 = np.random.normal(mu, sigma, 1000) abs(mu - np.mean(s1)) <0.01 1 abs(sigma - np.std(s1, ddof="1))" < 0.01 count, bins, ignored="plt.hist(s1," 30, density="True)" plt.plot(bins, (sigma * np.sqrt(2 np.pi)) *np.exp( (bins mu)**2 (2 sigma**2) ), linewidth="2," color="r" ) plt.show() pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-10.webp" alt="numpy.random in Python"> <p> <strong>20) pareto(a[, size])</strong> </p> <p>This function is used to draw samples from a Lomax or Pareto II with specified shape.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt b, m1 = 3., 2. # shape and mode s1 = (np.random.pareto(b, 1000) + 1) * m1 count, bins, _ = plt.hist(s1, 100, density=True) fit = b*m**b / bins**(b+1) plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-11.webp" alt="numpy.random in Python"> <p> <strong>21) power(a[, size])</strong> </p> <p>This function is used to draw samples in [0, 1] from a power distribution with positive exponent a-1.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np x = 5. # shape samples = 1000 s1 = np.random.power(x, samples) import matplotlib.pyplot as plt count, bins, ignored = plt.hist(s1, bins=30) a = np.linspace(0, 1, 100) b = x*a**(x-1.) density_b = samples*np.diff(bins)[0]*b plt.plot(a, density_b) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-12.webp" alt="numpy.random in Python"> <p> <strong>22) rayleigh([scale, size])</strong> </p> <p>This function is used to draw sample from a Rayleigh distribution.</p> <p> <strong>Example:</strong> </p> <pre> val = hist(np.random.rayleigh(3, 100000), bins=200, density=True) meanval = 1 modeval = np.sqrt(2 / np.pi) * meanval s1 = np.random.rayleigh(modeval, 1000000) 100.*sum(s1>3)/1000000. </pre> <p> <strong>Output:</strong> </p> <pre> 0.087300000000000003 </pre> <p><img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-13.webp" alt="numpy.random in Python"></p> <p> <strong>23) standard_cauchy([size])</strong> </p> <p>This function is used to draw sample from a standard Cauchy distribution with mode=0.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1 = np.random.standard_cauchy(1000000) s1 = s1[(s1>-25) & (s1<25)] # truncate distribution so it plots well plt.hist(s1, bins="100)" plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-14.webp" alt="numpy.random in Python"> <p> <strong>24) standard_exponential([size])</strong> </p> <p>This function is used to draw sample from a standard exponential distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np n = np.random.standard_exponential((2, 7000)) </pre> <p> <strong>Output:</strong> </p> <pre> array([[0.53857931, 0.181262 , 0.20478701, ..., 3.66232881, 1.83882709, 1.77963295], [0.65163973, 1.40001955, 0.7525986 , ..., 0.76516523, 0.8400617 , 0.88551011]]) </pre> <p> <strong>25) standard_gamma([size])</strong> </p> <p>This function is used to draw sample from a standard Gamma distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np shape, scale = 2., 1. s1 = np.random.standard_gamma(shape, 1000000) import matplotlib.pyplot as plt import scipy.special as sps count1, bins1, ignored1 = plt.hist(s, 50, density=True) y = bins1**(shape-1) * ((np.exp(-bins1/scale))/ (sps.gamma(shape) * scale**shape)) plt.plot(bins1, y, linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-15.webp" alt="numpy.random in Python"> <p> <strong>26) standard_normal([size])</strong> </p> <p>This function is used to draw sample from a standard Normal distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1= np.random.standard_normal(8000) s1 q = np.random.standard_normal(size=(3, 4, 2)) q </pre> <p> <strong>Output:</strong> </p> <pre> array([-3.14907597, 0.95366265, -1.20100026, ..., 3.47180222, 0.9608679 , 0.0774319 ]) array([[[ 1.55635461, -1.29541713], [-1.50534663, -0.02829194], [ 1.03949348, -0.26128132], [ 1.51921798, 0.82136178]], [[-0.4011052 , -0.52458858], [-1.31803814, 0.37415379], [-0.67077365, 0.97447018], [-0.20212115, 0.67840888]], [[ 1.86183474, 0.19946562], [-0.07376021, 0.84599701], [-0.84341386, 0.32081667], [-3.32016062, -1.19029818]]]) </pre> <p> <strong>27) standard_t(df[, size])</strong> </p> <p>This function is used to draw sample from a standard Student's distribution with df degree of freedom.</p> <p> <strong>Example:</strong> </p> <pre> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515,8230,8770]) s1 = np.random.standard_t(10, size=100000) np.mean(intake) intake.std(ddof=1) t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake))) h = plt.hist(s1, bins=100, density=True) np.sum(s1<t) float(len(s1)) plt.show() < pre> <p> <strong>Output:</strong> </p> <pre> 6677.5 1174.1101831694598 0.00864 </pre> <p><img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-16.webp" alt="numpy.random in Python"></p> <p> <strong>28) triangular(left, mode, right[, size])</strong> </p> <p>This function is used to draw sample from a triangular distribution over the interval.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.triangular(-4, 0, 8, 1000000), bins=300,density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-17.webp" alt="numpy.random in Python"> <p> <strong>29) uniform([low, high, size])</strong> </p> <p>This function is used to draw sample from a uniform distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1 = np.random.uniform(-1,0,1000) np.all(s1 >= -1) np.all(s1 <0) count, bins, ignored="plt.hist(s1," 15, density="True)" plt.plot(bins, np.ones_like(bins), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-18.webp" alt="numpy.random in Python"> <p> <strong>30) vonmises(m1, m2[, size])</strong> </p> <p>This function is used to draw sample from a von Mises distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt m1, m2 = 0.0, 4.0 s1 = np.random.vonmises(m1, m2, 1000) from scipy.special import i0 plt.hist(s1, 50, density=True) x = np.linspace(-np.pi, np.pi, num=51) y = np.exp(m2*np.cos(x-m1))/(2*np.pi*i0(m2)) plt.plot(x, y, linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-19.webp" alt="numpy.random in Python"> <p> <strong>31) wald(mean, scale[, size])</strong> </p> <p>This function is used to draw sample from a Wald, or inverse Gaussian distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-20.webp" alt="numpy.random in Python"> <p> <strong>32) weibull(a[, size])</strong> </p> <p>This function is used to draw sample from a Weibull distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.weibull(x, 1000) a = np.arange(1, 100.)/50. def weib(x, n, a): return (a/n)*(x/n)**np.exp(-(x/n)**a) count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) a= np.arange(1,100.)/50. scale = count.max()/weib(x, 1., 5.).max() scale = count.max()/weib(a, 1., 5.).max() plt.plot(x, weib(x, 1., 5.)*scale) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-21.webp" alt="numpy.random in Python"> <p> <strong>33) zipf(a[, size])</strong> </p> <p>This function is used to draw sample from a Zipf distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.zipf(x, 1000) count, bins, ignored = plt.hist(s[s<50], 50, density="True)" a="np.arange(1.," 50.) b="a**(-x)" special.zetac(x) plt.plot(a, max(b), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-22.webp" alt="numpy.random in Python"> <hr></50],></pre></0)></pre></t)></pre></25)]></pre></0.01></pre></i)></pre></0.05:>
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array([0.00264041, 0.04725478, 0.07140803, 0.19526217, 0.23979 , 0.24023478, 0.63141254, 0.95316446, 1.40281789, 1.68327507])
7) gamma(forme[, échelle, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution Gamma
Exemple:
import numpy as np shape, scale = 2., 2. s1 = np.random.gamma(shape, scale, 1000) import matplotlib.pyplot as plt import scipy.special as spss count, bins, ignored = plt.hist(s1, 50, density=True) a = bins**(shape-1)*(np.exp(-bins/scale) / (spss.gamma(shape)*scale**shape)) plt.plot(bins, a, linewidth=2, color='r') plt.show()
8) géométrique(p[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution géométrique.
Exemple:
import numpy as np a = np.random.geometric(p=0.35, size=10000) (a == 1).sum() / 1000
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3.
9) gumbel([localisation, échelle, taille])
supprimer le cache npm
Cette fonction est utilisée pour extraire un échantillon d'une distribution Gumble.
Exemple:
import numpy as np lov, scale = 0, 0.2 s1 = np.random.gumbel(loc, scale, 1000) import matplotlib.pyplot as plt count, bins, ignored = plt.hist(s1, 30, density=True) plt.plot(bins, (1/beta)*np.exp(-(bins - loc)/beta)* np.exp( -np.exp( -(bins - loc) /beta) ),linewidth=2, color='r') plt.show()
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10) hypergéométrique (nbon, nmauvais, nsample[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution hypergéométrique.
Exemple:
import numpy as np good, bad, samp = 100, 2, 10 s1 = np.random.hypergeometric(good, bad, samp, 1000) plt.hist(s1) plt.show()
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(array([ 13., 0., 0., 0., 0., 163., 0., 0., 0., 824.]), array([ 8. , 8.2, 8.4, 8.6, 8.8, 9. , 9.2, 9.4, 9.6, 9.8, 10. ]), <a 10 list of patch objects>) </a>
11) laplace([loc, échelle, taille])
Cette fonction est utilisée pour extraire un échantillon de la distribution Laplace ou double exponentielle avec un emplacement et une échelle spécifiés.
Exemple:
import numpy as np location, scale = 0., 2. s = np.random.laplace(location, scale, 10) s
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array([-2.77127948, -1.46401453, -0.03723516, -1.61223942, 2.29590691, 1.74297722, 1.49438411, 0.30325513, -0.15948891, -4.99669747])
12) logistique ([localisation, échelle, taille])
Cette fonction est utilisée pour prélever un échantillon de la distribution logistique.
Exemple:
import numpy as np import matplotlib.pyplot as plt location, scale = 10, 1 s1 = np.random.logistic(location, scale, 10000) count, bins, ignored = plt.hist(s1, bins=50) count bins ignored plt.show()
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array([1.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 1.000e+00, 1.000e+00, 5.000e+00, 7.000e+00, 1.100e+01, 1.800e+01, 3.500e+01, 5.300e+01, 6.700e+01, 1.150e+02, 1.780e+02, 2.300e+02, 3.680e+02, 4.910e+02, 6.400e+02, 8.250e+02, 9.100e+02, 9.750e+02, 1.039e+03, 9.280e+02, 8.040e+02, 6.530e+02, 5.240e+02, 3.380e+02, 2.470e+02, 1.650e+02, 1.150e+02, 8.500e+01, 6.400e+01, 3.300e+01, 1.600e+01, 2.400e+01, 1.400e+01, 4.000e+00, 5.000e+00, 2.000e+00, 2.000e+00, 1.000e+00, 1.000e+00, 0.000e+00, 1.000e+00, 0.000e+00, 0.000e+00, 0.000e+00, 1.000e+00]) array([ 0.50643911, 0.91891814, 1.33139717, 1.7438762 , 2.15635523, 2.56883427, 2.9813133 , 3.39379233, 3.80627136, 4.2187504 , 4.63122943, 5.04370846, 5.45618749, 5.86866652, 6.28114556, 6.69362459, 7.10610362, 7.51858265, 7.93106169, 8.34354072, 8.75601975, 9.16849878, 9.58097781, 9.99345685, 10.40593588, 10.81841491, 11.23089394, 11.64337298, 12.05585201, 12.46833104, 12.88081007, 13.2932891 , 13.70576814, 14.11824717, 14.5307262 , 14.94320523, 15.35568427, 15.7681633 , 16.18064233, 16.59312136, 17.00560039, 17.41807943, 17.83055846, 18.24303749, 18.65551652, 19.06799556, 19.48047459, 19.89295362, 20.30543265, 20.71791168, 21.13039072]) <a 50 list of patch objects> </a>
13) lognormal([moyenne, sigma, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution log-normale.
Exemple:
import numpy as np mu, sigma = 2., 1. s1 = np.random.lognormal(mu, sigma, 1000) import matplotlib.pyplot as plt count, bins, ignored = plt.hist(s1, 100, density=True, ) a = np.linspace(min(bins), max(bins), 10000) pdf = (np.exp(-(np.log(a) - mu)**2 / (2 * sigma**2))/ (a * sigma * np.sqrt(2 * np.pi))) plt.plot(a, pdf, linewidth=2, color='r') plt.axis('tight') plt.show()
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14) série de journaux (p[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution logarithmique.
Exemple:
import numpy as np x = .6 s1 = np.random.logseries(x, 10000) count, bins, ignored = plt.hist(s1) def logseries(k, p): return -p**k/(k*log(1-p)) plt.plot(bins, logseries(bins, x)*count.max()/logseries(bins, a).max(), 'r') plt.show()
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15) multinomial(n, pvals[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution multinomiale.
Exemple:
import numpy as np np.random.multinomial(20, [1/6.]*6, size=1)
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array([[4, 2, 5, 5, 3, 1]])
16) multivariate_normal(moyenne, cov[, taille, ...)
Cette fonction est utilisée pour extraire un échantillon d'une distribution normale multivariée.
Exemple:
import numpy as np mean = (1, 2) coveriance = [[1, 0], [0, 100]] import matplotlib.pyplot as plt a, b = np.random.multivariate_normal(mean, coveriance, 5000).T plt.plot(a, b, 'x') plt.axis('equal'023 030 ) plt.show()
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17) négatif_binomial(n, p[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution binomiale négative.
Exemple:
import numpy as np s1 = np.random.negative_binomial(1, 0.1, 100000) for i in range(1, 11): probability = sum(s1 <i) 36 100000. print i, \'wells drilled, probability of one success=", probability </pre> <p> <strong>Output:</strong> </p> <pre> 1 wells drilled, probability of one success = 0 2 wells drilled, probability of one success = 0 3 wells drilled, probability of one success = 0 4 wells drilled, probability of one success = 0 5 wells drilled, probability of one success = 0 6 wells drilled, probability of one success = 0 7 wells drilled, probability of one success = 0 8 wells drilled, probability of one success = 0 9 wells drilled, probability of one success = 0 10 wells drilled, probability of one success = 0 </pre> <p > <strong>18) noncentral_chisquare(df, nonc[, size])</strong> </p> <p>This function is used to draw sample from a noncentral chi-square distribution. </p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt val = plt.hist(np.random.noncentral_chisquare(3, 25, 100000), bins=200, normed=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src=" techcodeview.com img numpy-tutorial numpy-random-python-9.webp\' alt="numpy.random in Python"> <p> <strong>19) normal([loc, scale, size])</strong> </p> <p>This function is used to draw sample from a normal distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt mu, sigma = 0, 0.2 # mean and standard deviation s1 = np.random.normal(mu, sigma, 1000) abs(mu - np.mean(s1)) <0.01 1 abs(sigma - np.std(s1, ddof="1))" < 0.01 count, bins, ignored="plt.hist(s1," 30, density="True)" plt.plot(bins, (sigma * np.sqrt(2 np.pi)) *np.exp( (bins mu)**2 (2 sigma**2) ), linewidth="2," color="r" ) plt.show() pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-10.webp" alt="numpy.random in Python"> <p> <strong>20) pareto(a[, size])</strong> </p> <p>This function is used to draw samples from a Lomax or Pareto II with specified shape.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt b, m1 = 3., 2. # shape and mode s1 = (np.random.pareto(b, 1000) + 1) * m1 count, bins, _ = plt.hist(s1, 100, density=True) fit = b*m**b / bins**(b+1) plt.plot(bins, max(count)*fit/max(fit), linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-11.webp" alt="numpy.random in Python"> <p> <strong>21) power(a[, size])</strong> </p> <p>This function is used to draw samples in [0, 1] from a power distribution with positive exponent a-1.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np x = 5. # shape samples = 1000 s1 = np.random.power(x, samples) import matplotlib.pyplot as plt count, bins, ignored = plt.hist(s1, bins=30) a = np.linspace(0, 1, 100) b = x*a**(x-1.) density_b = samples*np.diff(bins)[0]*b plt.plot(a, density_b) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-12.webp" alt="numpy.random in Python"> <p> <strong>22) rayleigh([scale, size])</strong> </p> <p>This function is used to draw sample from a Rayleigh distribution.</p> <p> <strong>Example:</strong> </p> <pre> val = hist(np.random.rayleigh(3, 100000), bins=200, density=True) meanval = 1 modeval = np.sqrt(2 / np.pi) * meanval s1 = np.random.rayleigh(modeval, 1000000) 100.*sum(s1>3)/1000000. </pre> <p> <strong>Output:</strong> </p> <pre> 0.087300000000000003 </pre> <p><img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-13.webp" alt="numpy.random in Python"></p> <p> <strong>23) standard_cauchy([size])</strong> </p> <p>This function is used to draw sample from a standard Cauchy distribution with mode=0.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1 = np.random.standard_cauchy(1000000) s1 = s1[(s1>-25) & (s1<25)] # truncate distribution so it plots well plt.hist(s1, bins="100)" plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-14.webp" alt="numpy.random in Python"> <p> <strong>24) standard_exponential([size])</strong> </p> <p>This function is used to draw sample from a standard exponential distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np n = np.random.standard_exponential((2, 7000)) </pre> <p> <strong>Output:</strong> </p> <pre> array([[0.53857931, 0.181262 , 0.20478701, ..., 3.66232881, 1.83882709, 1.77963295], [0.65163973, 1.40001955, 0.7525986 , ..., 0.76516523, 0.8400617 , 0.88551011]]) </pre> <p> <strong>25) standard_gamma([size])</strong> </p> <p>This function is used to draw sample from a standard Gamma distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np shape, scale = 2., 1. s1 = np.random.standard_gamma(shape, 1000000) import matplotlib.pyplot as plt import scipy.special as sps count1, bins1, ignored1 = plt.hist(s, 50, density=True) y = bins1**(shape-1) * ((np.exp(-bins1/scale))/ (sps.gamma(shape) * scale**shape)) plt.plot(bins1, y, linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-15.webp" alt="numpy.random in Python"> <p> <strong>26) standard_normal([size])</strong> </p> <p>This function is used to draw sample from a standard Normal distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1= np.random.standard_normal(8000) s1 q = np.random.standard_normal(size=(3, 4, 2)) q </pre> <p> <strong>Output:</strong> </p> <pre> array([-3.14907597, 0.95366265, -1.20100026, ..., 3.47180222, 0.9608679 , 0.0774319 ]) array([[[ 1.55635461, -1.29541713], [-1.50534663, -0.02829194], [ 1.03949348, -0.26128132], [ 1.51921798, 0.82136178]], [[-0.4011052 , -0.52458858], [-1.31803814, 0.37415379], [-0.67077365, 0.97447018], [-0.20212115, 0.67840888]], [[ 1.86183474, 0.19946562], [-0.07376021, 0.84599701], [-0.84341386, 0.32081667], [-3.32016062, -1.19029818]]]) </pre> <p> <strong>27) standard_t(df[, size])</strong> </p> <p>This function is used to draw sample from a standard Student's distribution with df degree of freedom.</p> <p> <strong>Example:</strong> </p> <pre> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515,8230,8770]) s1 = np.random.standard_t(10, size=100000) np.mean(intake) intake.std(ddof=1) t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake))) h = plt.hist(s1, bins=100, density=True) np.sum(s1<t) float(len(s1)) plt.show() < pre> <p> <strong>Output:</strong> </p> <pre> 6677.5 1174.1101831694598 0.00864 </pre> <p><img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-16.webp" alt="numpy.random in Python"></p> <p> <strong>28) triangular(left, mode, right[, size])</strong> </p> <p>This function is used to draw sample from a triangular distribution over the interval.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.triangular(-4, 0, 8, 1000000), bins=300,density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-17.webp" alt="numpy.random in Python"> <p> <strong>29) uniform([low, high, size])</strong> </p> <p>This function is used to draw sample from a uniform distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1 = np.random.uniform(-1,0,1000) np.all(s1 >= -1) np.all(s1 <0) count, bins, ignored="plt.hist(s1," 15, density="True)" plt.plot(bins, np.ones_like(bins), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-18.webp" alt="numpy.random in Python"> <p> <strong>30) vonmises(m1, m2[, size])</strong> </p> <p>This function is used to draw sample from a von Mises distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt m1, m2 = 0.0, 4.0 s1 = np.random.vonmises(m1, m2, 1000) from scipy.special import i0 plt.hist(s1, 50, density=True) x = np.linspace(-np.pi, np.pi, num=51) y = np.exp(m2*np.cos(x-m1))/(2*np.pi*i0(m2)) plt.plot(x, y, linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-19.webp" alt="numpy.random in Python"> <p> <strong>31) wald(mean, scale[, size])</strong> </p> <p>This function is used to draw sample from a Wald, or inverse Gaussian distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-20.webp" alt="numpy.random in Python"> <p> <strong>32) weibull(a[, size])</strong> </p> <p>This function is used to draw sample from a Weibull distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.weibull(x, 1000) a = np.arange(1, 100.)/50. def weib(x, n, a): return (a/n)*(x/n)**np.exp(-(x/n)**a) count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) a= np.arange(1,100.)/50. scale = count.max()/weib(x, 1., 5.).max() scale = count.max()/weib(a, 1., 5.).max() plt.plot(x, weib(x, 1., 5.)*scale) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-21.webp" alt="numpy.random in Python"> <p> <strong>33) zipf(a[, size])</strong> </p> <p>This function is used to draw sample from a Zipf distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.zipf(x, 1000) count, bins, ignored = plt.hist(s[s<50], 50, density="True)" a="np.arange(1.," 50.) b="a**(-x)" special.zetac(x) plt.plot(a, max(b), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-22.webp" alt="numpy.random in Python"> <hr></50],></pre></0)></pre></t)></pre></25)]></pre></0.01></pre></i)>
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21) puissance(a[, taille])
Cette fonction est utilisée pour prélever des échantillons dans [0, 1] à partir d'une distribution de puissance avec un exposant positif a-1.
Exemple:
import numpy as np x = 5. # shape samples = 1000 s1 = np.random.power(x, samples) import matplotlib.pyplot as plt count, bins, ignored = plt.hist(s1, bins=30) a = np.linspace(0, 1, 100) b = x*a**(x-1.) density_b = samples*np.diff(bins)[0]*b plt.plot(a, density_b) plt.show()
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22) rayleigh([échelle, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution de Rayleigh.
Exemple:
val = hist(np.random.rayleigh(3, 100000), bins=200, density=True) meanval = 1 modeval = np.sqrt(2 / np.pi) * meanval s1 = np.random.rayleigh(modeval, 1000000) 100.*sum(s1>3)/1000000.
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0.087300000000000003
23) standard_cauchy([taille])
xor en c++
Cette fonction est utilisée pour extraire un échantillon d'une distribution de Cauchy standard avec mode=0.
Exemple:
import numpy as np import matplotlib.pyplot as plt s1 = np.random.standard_cauchy(1000000) s1 = s1[(s1>-25) & (s1<25)] # truncate distribution so it plots well plt.hist(s1, bins="100)" plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-14.webp" alt="numpy.random in Python"> <p> <strong>24) standard_exponential([size])</strong> </p> <p>This function is used to draw sample from a standard exponential distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np n = np.random.standard_exponential((2, 7000)) </pre> <p> <strong>Output:</strong> </p> <pre> array([[0.53857931, 0.181262 , 0.20478701, ..., 3.66232881, 1.83882709, 1.77963295], [0.65163973, 1.40001955, 0.7525986 , ..., 0.76516523, 0.8400617 , 0.88551011]]) </pre> <p> <strong>25) standard_gamma([size])</strong> </p> <p>This function is used to draw sample from a standard Gamma distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np shape, scale = 2., 1. s1 = np.random.standard_gamma(shape, 1000000) import matplotlib.pyplot as plt import scipy.special as sps count1, bins1, ignored1 = plt.hist(s, 50, density=True) y = bins1**(shape-1) * ((np.exp(-bins1/scale))/ (sps.gamma(shape) * scale**shape)) plt.plot(bins1, y, linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-15.webp" alt="numpy.random in Python"> <p> <strong>26) standard_normal([size])</strong> </p> <p>This function is used to draw sample from a standard Normal distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1= np.random.standard_normal(8000) s1 q = np.random.standard_normal(size=(3, 4, 2)) q </pre> <p> <strong>Output:</strong> </p> <pre> array([-3.14907597, 0.95366265, -1.20100026, ..., 3.47180222, 0.9608679 , 0.0774319 ]) array([[[ 1.55635461, -1.29541713], [-1.50534663, -0.02829194], [ 1.03949348, -0.26128132], [ 1.51921798, 0.82136178]], [[-0.4011052 , -0.52458858], [-1.31803814, 0.37415379], [-0.67077365, 0.97447018], [-0.20212115, 0.67840888]], [[ 1.86183474, 0.19946562], [-0.07376021, 0.84599701], [-0.84341386, 0.32081667], [-3.32016062, -1.19029818]]]) </pre> <p> <strong>27) standard_t(df[, size])</strong> </p> <p>This function is used to draw sample from a standard Student's distribution with df degree of freedom.</p> <p> <strong>Example:</strong> </p> <pre> intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515,8230,8770]) s1 = np.random.standard_t(10, size=100000) np.mean(intake) intake.std(ddof=1) t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake))) h = plt.hist(s1, bins=100, density=True) np.sum(s1<t) float(len(s1)) plt.show() < pre> <p> <strong>Output:</strong> </p> <pre> 6677.5 1174.1101831694598 0.00864 </pre> <p><img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-16.webp" alt="numpy.random in Python"></p> <p> <strong>28) triangular(left, mode, right[, size])</strong> </p> <p>This function is used to draw sample from a triangular distribution over the interval.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.triangular(-4, 0, 8, 1000000), bins=300,density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-17.webp" alt="numpy.random in Python"> <p> <strong>29) uniform([low, high, size])</strong> </p> <p>This function is used to draw sample from a uniform distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1 = np.random.uniform(-1,0,1000) np.all(s1 >= -1) np.all(s1 <0) count, bins, ignored="plt.hist(s1," 15, density="True)" plt.plot(bins, np.ones_like(bins), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-18.webp" alt="numpy.random in Python"> <p> <strong>30) vonmises(m1, m2[, size])</strong> </p> <p>This function is used to draw sample from a von Mises distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt m1, m2 = 0.0, 4.0 s1 = np.random.vonmises(m1, m2, 1000) from scipy.special import i0 plt.hist(s1, 50, density=True) x = np.linspace(-np.pi, np.pi, num=51) y = np.exp(m2*np.cos(x-m1))/(2*np.pi*i0(m2)) plt.plot(x, y, linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-19.webp" alt="numpy.random in Python"> <p> <strong>31) wald(mean, scale[, size])</strong> </p> <p>This function is used to draw sample from a Wald, or inverse Gaussian distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-20.webp" alt="numpy.random in Python"> <p> <strong>32) weibull(a[, size])</strong> </p> <p>This function is used to draw sample from a Weibull distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.weibull(x, 1000) a = np.arange(1, 100.)/50. def weib(x, n, a): return (a/n)*(x/n)**np.exp(-(x/n)**a) count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) a= np.arange(1,100.)/50. scale = count.max()/weib(x, 1., 5.).max() scale = count.max()/weib(a, 1., 5.).max() plt.plot(x, weib(x, 1., 5.)*scale) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-21.webp" alt="numpy.random in Python"> <p> <strong>33) zipf(a[, size])</strong> </p> <p>This function is used to draw sample from a Zipf distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.zipf(x, 1000) count, bins, ignored = plt.hist(s[s<50], 50, density="True)" a="np.arange(1.," 50.) b="a**(-x)" special.zetac(x) plt.plot(a, max(b), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-22.webp" alt="numpy.random in Python"> <hr></50],></pre></0)></pre></t)></pre></25)]>
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array([[0.53857931, 0.181262 , 0.20478701, ..., 3.66232881, 1.83882709, 1.77963295], [0.65163973, 1.40001955, 0.7525986 , ..., 0.76516523, 0.8400617 , 0.88551011]])
25) standard_gamma([taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution Gamma standard.
Exemple:
import numpy as np shape, scale = 2., 1. s1 = np.random.standard_gamma(shape, 1000000) import matplotlib.pyplot as plt import scipy.special as sps count1, bins1, ignored1 = plt.hist(s, 50, density=True) y = bins1**(shape-1) * ((np.exp(-bins1/scale))/ (sps.gamma(shape) * scale**shape)) plt.plot(bins1, y, linewidth=2, color='r') plt.show()
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26) standard_normal([taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution normale standard.
Exemple:
import numpy as np import matplotlib.pyplot as plt s1= np.random.standard_normal(8000) s1 q = np.random.standard_normal(size=(3, 4, 2)) q
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array([-3.14907597, 0.95366265, -1.20100026, ..., 3.47180222, 0.9608679 , 0.0774319 ]) array([[[ 1.55635461, -1.29541713], [-1.50534663, -0.02829194], [ 1.03949348, -0.26128132], [ 1.51921798, 0.82136178]], [[-0.4011052 , -0.52458858], [-1.31803814, 0.37415379], [-0.67077365, 0.97447018], [-0.20212115, 0.67840888]], [[ 1.86183474, 0.19946562], [-0.07376021, 0.84599701], [-0.84341386, 0.32081667], [-3.32016062, -1.19029818]]])
27) standard_t(df[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution de Student standard avec un degré de liberté df.
Exemple:
intake = np.array([5260., 5470, 5640, 6180, 6390, 6515, 6805, 7515,8230,8770]) s1 = np.random.standard_t(10, size=100000) np.mean(intake) intake.std(ddof=1) t = (np.mean(intake)-7725)/(intake.std(ddof=1)/np.sqrt(len(intake))) h = plt.hist(s1, bins=100, density=True) np.sum(s1<t) float(len(s1)) plt.show() < pre> <p> <strong>Output:</strong> </p> <pre> 6677.5 1174.1101831694598 0.00864 </pre> <p><img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-16.webp" alt="numpy.random in Python"></p> <p> <strong>28) triangular(left, mode, right[, size])</strong> </p> <p>This function is used to draw sample from a triangular distribution over the interval.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.triangular(-4, 0, 8, 1000000), bins=300,density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-17.webp" alt="numpy.random in Python"> <p> <strong>29) uniform([low, high, size])</strong> </p> <p>This function is used to draw sample from a uniform distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt s1 = np.random.uniform(-1,0,1000) np.all(s1 >= -1) np.all(s1 <0) count, bins, ignored="plt.hist(s1," 15, density="True)" plt.plot(bins, np.ones_like(bins), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-18.webp" alt="numpy.random in Python"> <p> <strong>30) vonmises(m1, m2[, size])</strong> </p> <p>This function is used to draw sample from a von Mises distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt m1, m2 = 0.0, 4.0 s1 = np.random.vonmises(m1, m2, 1000) from scipy.special import i0 plt.hist(s1, 50, density=True) x = np.linspace(-np.pi, np.pi, num=51) y = np.exp(m2*np.cos(x-m1))/(2*np.pi*i0(m2)) plt.plot(x, y, linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-19.webp" alt="numpy.random in Python"> <p> <strong>31) wald(mean, scale[, size])</strong> </p> <p>This function is used to draw sample from a Wald, or inverse Gaussian distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-20.webp" alt="numpy.random in Python"> <p> <strong>32) weibull(a[, size])</strong> </p> <p>This function is used to draw sample from a Weibull distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.weibull(x, 1000) a = np.arange(1, 100.)/50. def weib(x, n, a): return (a/n)*(x/n)**np.exp(-(x/n)**a) count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) a= np.arange(1,100.)/50. scale = count.max()/weib(x, 1., 5.).max() scale = count.max()/weib(a, 1., 5.).max() plt.plot(x, weib(x, 1., 5.)*scale) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-21.webp" alt="numpy.random in Python"> <p> <strong>33) zipf(a[, size])</strong> </p> <p>This function is used to draw sample from a Zipf distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.zipf(x, 1000) count, bins, ignored = plt.hist(s[s<50], 50, density="True)" a="np.arange(1.," 50.) b="a**(-x)" special.zetac(x) plt.plot(a, max(b), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-22.webp" alt="numpy.random in Python"> <hr></50],></pre></0)></pre></t)>
28) triangulaire (gauche, mode, droite [, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution triangulaire sur l'intervalle.
Exemple:
import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.triangular(-4, 0, 8, 1000000), bins=300,density=True) plt.show()
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29) uniforme([bas, haut, taille])
comment télécharger une vidéo youtube vlc
Cette fonction est utilisée pour prélever un échantillon à partir d’une distribution uniforme.
Exemple:
import numpy as np import matplotlib.pyplot as plt s1 = np.random.uniform(-1,0,1000) np.all(s1 >= -1) np.all(s1 <0) count, bins, ignored="plt.hist(s1," 15, density="True)" plt.plot(bins, np.ones_like(bins), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-18.webp" alt="numpy.random in Python"> <p> <strong>30) vonmises(m1, m2[, size])</strong> </p> <p>This function is used to draw sample from a von Mises distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt m1, m2 = 0.0, 4.0 s1 = np.random.vonmises(m1, m2, 1000) from scipy.special import i0 plt.hist(s1, 50, density=True) x = np.linspace(-np.pi, np.pi, num=51) y = np.exp(m2*np.cos(x-m1))/(2*np.pi*i0(m2)) plt.plot(x, y, linewidth=2, color='r') plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-19.webp" alt="numpy.random in Python"> <p> <strong>31) wald(mean, scale[, size])</strong> </p> <p>This function is used to draw sample from a Wald, or inverse Gaussian distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-20.webp" alt="numpy.random in Python"> <p> <strong>32) weibull(a[, size])</strong> </p> <p>This function is used to draw sample from a Weibull distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.weibull(x, 1000) a = np.arange(1, 100.)/50. def weib(x, n, a): return (a/n)*(x/n)**np.exp(-(x/n)**a) count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) a= np.arange(1,100.)/50. scale = count.max()/weib(x, 1., 5.).max() scale = count.max()/weib(a, 1., 5.).max() plt.plot(x, weib(x, 1., 5.)*scale) plt.show() </pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-21.webp" alt="numpy.random in Python"> <p> <strong>33) zipf(a[, size])</strong> </p> <p>This function is used to draw sample from a Zipf distribution.</p> <p> <strong>Example:</strong> </p> <pre> import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.zipf(x, 1000) count, bins, ignored = plt.hist(s[s<50], 50, density="True)" a="np.arange(1.," 50.) b="a**(-x)" special.zetac(x) plt.plot(a, max(b), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-22.webp" alt="numpy.random in Python"> <hr></50],></pre></0)>
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31) wald(moyenne, échelle[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution de Wald ou gaussienne inverse.
Exemple:
import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True) plt.show()
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32) weibull(a[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution de Weibull.
Exemple:
import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.weibull(x, 1000) a = np.arange(1, 100.)/50. def weib(x, n, a): return (a/n)*(x/n)**np.exp(-(x/n)**a) count, bins, ignored = plt.hist(np.random.weibull(5.,1000)) a= np.arange(1,100.)/50. scale = count.max()/weib(x, 1., 5.).max() scale = count.max()/weib(a, 1., 5.).max() plt.plot(x, weib(x, 1., 5.)*scale) plt.show()
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33) zipf(a[, taille])
Cette fonction est utilisée pour extraire un échantillon d'une distribution Zipf.
Exemple:
import numpy as np import matplotlib.pyplot as plt from scipy import special x=2.0 s=np.random.zipf(x, 1000) count, bins, ignored = plt.hist(s[s<50], 50, density="True)" a="np.arange(1.," 50.) b="a**(-x)" special.zetac(x) plt.plot(a, max(b), linewidth="2," color="r" ) plt.show() < pre> <p> <strong>Output:</strong> </p> <img src="//techcodeview.com/img/numpy-tutorial/36/numpy-random-python-22.webp" alt="numpy.random in Python"> <hr></50],>