Satunnaisluku on NumPy-kirjastossa oleva moduuli. Tämä moduuli sisältää funktiot, joita käytetään satunnaislukujen generointiin. Tämä moduuli sisältää joitain yksinkertaisia satunnaisdatan generointimenetelmiä, joitain permutaatio- ja jakelufunktioita sekä satunnaisgeneraattorifunktioita.
Kaikki satunnaisen moduulin toiminnot ovat seuraavat:
Yksinkertainen satunnainen data
Yksinkertaisilla satunnaisilla tiedoilla on seuraavat toiminnot:
pitkä int javaan
1) p.random.rand(d0, d1, ..., dn)
Tätä satunnaismoduulin toimintoa käytetään generoimaan satunnaislukuja tai arvoja tietyssä muodossa.
Esimerkki:
import numpy as np a=np.random.rand(5,2) a
Lähtö:
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)
Tämä satunnaismoduulin funktio palauttaa näytteen 'normaalista' jakaumasta.
Esimerkki:
import numpy as np a=np.random.randn(2,2) a
Lähtö:
array([[ 1.43327469, -0.02019121], [ 1.54626422, 1.05831067]]) b=np.random.randn() b -0.3080190768904835
3) np.random.randint(pieni[, korkea, koko, dtyyppi])
Tätä satunnaismoduulin toimintoa käytetään satunnaisten kokonaislukujen luomiseen inklusiivisesta (pienestä) poissulkevaan (korkeaan).
Esimerkki:
import numpy as np a=np.random.randint(3, size=10) a
Lähtö:
array([1, 1, 1, 2, 0, 0, 0, 0, 0, 0])
4) np.random.random_integers(pieni[, korkea, koko])
Tätä satunnaismoduulin funktiota käytetään generoimaan satunnaisia kokonaislukuja, joiden tyyppi on np.int, välillä matala ja korkea.
Esimerkki:
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
Lähtö:
2 array([[1, 1], [2, 5], [1, 3]])
5) np.random.random_sample([koko])
Tätä satunnaismoduulin funktiota käytetään generoimaan satunnainen float-luku puoliavoimella aikavälillä [0.0, 1.0).
Esimerkki:
import numpy as np a=np.random.random_sample() a b=type(np.random.random_sample()) b c=np.random.random_sample((5,)) c
Lähtö:
0.09250360565571492 array([0.34665418, 0.47027209, 0.75944969, 0.37991244, 0.14159746])
6) np.random.random([koko])
Tätä satunnaismoduulin funktiota käytetään generoimaan satunnainen float-luku puoliavoimella aikavälillä [0.0, 1.0).
Esimerkki:
import numpy as np a=np.random.random() a b=type(np.random.random()) b c=np.random.random((5,)) c
Lähtö:
0.008786953974334155 array([0.05530122, 0.59133394, 0.17258794, 0.6912388 , 0.33412534])
7) np.random.ranf([koko])
Tätä satunnaismoduulin funktiota käytetään generoimaan satunnainen float-luku puoliavoimella aikavälillä [0.0, 1.0).
Esimerkki:
import numpy as np a=np.random.ranf() a b=type(np.random.ranf()) b c=np.random.ranf((5,)) c
Lähtö:
0.2907792098474542 array([0.34084881, 0.07268237, 0.38161256, 0.46494681, 0.88071377])
8) np.random.sample([koko])
Tätä satunnaismoduulin funktiota käytetään generoimaan satunnainen float-luku puoliavoimella aikavälillä [0.0, 1.0).
Esimerkki:
import numpy as np a=np.random.sample() a b=type(np.random.sample()) b c=np.random.sample((5,)) c
Lähtö:
0.012298209913766511 array([0.71878544, 0.11486169, 0.38189074, 0.14303308, 0.07217287])
9) np.random.choice(a[, koko, korvaa, p])
Tätä satunnaismoduulin toimintoa käytetään satunnaisnäytteen luomiseen tietystä 1-D-taulukosta.
Esimerkki:
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
Lähtö:
array([0, 3, 4]) array([2, 2, 2], dtype=int64)
10) np.random.bytes(length)
Tätä satunnaismoduulin toimintoa käytetään satunnaisten tavujen luomiseen.
Esimerkki:
import numpy as np a=np.random.bytes(7) a
Lähtö:
'nQx08x83xf9xdex8a'
Permutaatiot
Permutaatioilla on seuraavat toiminnot:
1) np.random.shuffle()
Tätä toimintoa käytetään sekvenssin muokkaamiseen paikan päällä sekoittamalla sen sisältöä.
Esimerkki:
import numpy as np a=np.arange(12) a np.random.shuffle(a) a
Lähtö:
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()
Tämä toiminto muuttaa sekvenssin satunnaisesti tai palauttaa permutoidun alueen.
Esimerkki:
import numpy as np a=np.random.permutation(12) a
Lähtö:
array([ 8, 7, 3, 11, 6, 0, 9, 10, 2, 5, 4, 1])
Jakelut
Permutaatioilla on seuraavat toiminnot:
1) beeta(a, b[, koko])
Tätä funktiota käytetään ottamaan näytteitä beta-jakaumasta.
Esimerkki:
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) binomi (n, p[, koko])
Tätä funktiota käytetään ottamaan näyte binomijakaumasta.
Esimerkki:
import numpy as np n, p = 10, .6 s1= np.random.binomial(n, p, 10) s1
Lähtö:
array([6, 7, 7, 9, 3, 7, 8, 6, 6, 4])
3) neliö (df[, koko])
Tätä funktiota käytetään ottamaan näyte binomijakaumasta.
Esimerkki:
import numpy as np np.random.chisquare(2,4) sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.
Lähtö:
array([6, 7, 7, 9, 3, 7, 8, 6, 6, 4])
4) dirichlet(alfa[, koko])
Tätä funktiota käytetään ottamaan näyte Dirichlet-jakaumasta.
Esimerkki:
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()
Lähtö:
5) eksponentiaalinen ([mittakaava, koko])
Tätä funktiota käytetään otoksen ottamiseksi eksponentiaalisesta jakaumasta.
Esimerkki:
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:>
Lähtö:
array([0.00264041, 0.04725478, 0.07140803, 0.19526217, 0.23979 , 0.24023478, 0.63141254, 0.95316446, 1.40281789, 1.68327507])
7) gamma(muoto[, mittakaava, koko])
Tätä funktiota käytetään näytteen ottamiseen gamma-jakaumasta
Esimerkki:
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) geometrinen (p[, koko])
Tätä funktiota käytetään otoksen ottamiseksi geometrisesta jakaumasta.
Esimerkki:
import numpy as np a = np.random.geometric(p=0.35, size=10000) (a == 1).sum() / 1000
Lähtö:
3.
9) gumbel([paikka, mittakaava, koko])
Tätä funktiota käytetään otoksen ottamiseksi Gumble-jakaumasta.
Esimerkki:
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()
Lähtö:
10) hypergeometrinen (ngood, nbad, nsample[, size])
Tätä funktiota käytetään otoksen ottamiseksi hypergeometrisesta jakaumasta.
Esimerkki:
import numpy as np good, bad, samp = 100, 2, 10 s1 = np.random.hypergeometric(good, bad, samp, 1000) plt.hist(s1) plt.show()
Lähtö:
(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) lapla ([paikka, mittakaava, koko])
Tätä funktiota käytetään otoksen ottamiseksi Laplacesta tai kaksoiseksponentiaalisesta jakaumasta tietyllä sijainnilla ja mittakaavalla.
Esimerkki:
import numpy as np location, scale = 0., 2. s = np.random.laplace(location, scale, 10) s
Lähtö:
array([-2.77127948, -1.46401453, -0.03723516, -1.61223942, 2.29590691, 1.74297722, 1.49438411, 0.30325513, -0.15948891, -4.99669747])
12) logistiikka ([paikka, mittakaava, koko])
Tätä toimintoa käytetään ottamaan näyte logistisesta jakelusta.
Esimerkki:
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()
Lähtö:
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) lognormaali ([keskiarvo, sigma, koko])
Tätä funktiota käytetään ottamaan näyte log-normaalijakaumasta.
Esimerkki:
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()
Lähtö:
14) lokisarjat (p[, koko])
Tätä funktiota käytetään otoksen ottamiseksi logaritmisesta jakaumasta.
java tapauslausunto
Esimerkki:
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()
Lähtö:
15) moninomi(n, pvals[, koko])
Tätä funktiota käytetään otoksen ottamiseen multinomijakaumasta.
Esimerkki:
import numpy as np np.random.multinomial(20, [1/6.]*6, size=1)
Lähtö:
array([[4, 2, 5, 5, 3, 1]])
16) monimuuttuja_normaali(keskiarvo, cov[, koko, ...)
Tätä funktiota käytetään otoksen ottamiseksi monimuuttujaisesta normaalijakaumasta.
Esimerkki:
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()
Lähtö:
17) negatiivinen_binomial(n, p[, koko])
Tätä funktiota käytetään ottamaan näyte negatiivisesta binomijakaumasta.
Esimerkki:
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)>
Lähtö:
21) teho (a[, koko])
Tätä funktiota käytetään ottamaan näytteitä arvoon [0, 1] tehojakaumasta, jossa on positiivinen eksponentti a-1.
Esimerkki:
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()
Lähtö:
22) rayeigh([mittakaava, koko])
Tätä funktiota käytetään näytteen ottamiseksi Rayleigh-jakaumasta.
Esimerkki:
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.
Lähtö:
0.087300000000000003
23) standard_cauchy([koko])
Tätä funktiota käytetään ottamaan näyte tavallisesta Cauchyn jakaumasta, jossa mode = 0.
Esimerkki:
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)]>
Lähtö:
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) standardi_gamma([koko])
Tätä toimintoa käytetään näytteen ottamiseksi tavallisesta gamma-jakaumasta.
Esimerkki:
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()
Lähtö:
26) standardi_normaali([koko])
Tätä funktiota käytetään otoksen ottamiseksi tavallisesta normaalijakaumasta.
Esimerkki:
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
Lähtö:
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) vakio_t(df[, koko])
Tätä funktiota käytetään ottamaan näyte tavallisesta Studentin jakaumasta df-vapausasteella.
matriisiohjelma c-kielellä
Esimerkki:
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) kolmiomainen (vasen, tila, oikea[, koko])
Tätä funktiota käytetään näytteen ottamiseksi kolmiojakaumasta intervallin yli.
Esimerkki:
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()
Lähtö:
29) yhtenäinen ([matala, korkea, koko])
Tätä funktiota käytetään ottamaan näyte tasaisesta jakaumasta.
Esimerkki:
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)>
Lähtö:
31) wald(keskiarvo, mittakaava[, koko])
Tätä funktiota käytetään otoksen ottamiseksi Wald- tai käänteisestä Gaussin jakaumasta.
Esimerkki:
import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True) plt.show()
Lähtö:
32) weibull(a[, koko])
Tätä funktiota käytetään ottamaan näyte Weibull-jakaumasta.
Esimerkki:
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()
Lähtö:
33) zipf(a[, koko])
Tätä funktiota käytetään näytteen ottamiseen Zipf-jakaumasta.
Esimerkki:
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],>