العشوائية هي وحدة موجودة في مكتبة NumPy. تحتوي هذه الوحدة على الوظائف المستخدمة لتوليد أرقام عشوائية. تحتوي هذه الوحدة على بعض طرق توليد البيانات العشوائية البسيطة، وبعض وظائف التقليب والتوزيع، ووظائف المولد العشوائي.
جميع الوظائف في الوحدة العشوائية هي كما يلي:
بيانات عشوائية بسيطة
هناك الوظائف التالية للبيانات العشوائية البسيطة:
1) p.random.rand(d0, d1, ..., dn)
تُستخدم وظيفة الوحدة العشوائية هذه لإنشاء أرقام أو قيم عشوائية في شكل معين.
مثال:
import numpy as np a=np.random.rand(5,2) a
انتاج:
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)
تقوم هذه الوظيفة للوحدة العشوائية بإرجاع عينة من التوزيع 'العادي القياسي'.
مثال:
import numpy as np a=np.random.randn(2,2) a
انتاج:
array([[ 1.43327469, -0.02019121], [ 1.54626422, 1.05831067]]) b=np.random.randn() b -0.3080190768904835
3) np.random.randint (منخفض [، عالي، حجم، dtype])
تُستخدم وظيفة الوحدة العشوائية هذه لإنشاء أعداد صحيحة عشوائية من شامل (منخفض) إلى حصري (عالي).
مثال:
سلسلة إلى شار في جافا
import numpy as np a=np.random.randint(3, size=10) a
انتاج:
array([1, 1, 1, 2, 0, 0, 0, 0, 0, 0])
4) np.random.random_integers(منخفض [، عالي، الحجم])
يتم استخدام هذه الوظيفة للوحدة العشوائية لإنشاء عدد صحيح عشوائي من النوع np.int بين المنخفض والعالي.
مثال:
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
انتاج:
2 array([[1, 1], [2, 5], [1, 3]])
5) np.random.random_sample([الحجم])
تُستخدم وظيفة الوحدة العشوائية هذه لإنشاء أرقام عشوائية في الفاصل الزمني نصف المفتوح [0.0، 1.0).
مثال:
import numpy as np a=np.random.random_sample() a b=type(np.random.random_sample()) b c=np.random.random_sample((5,)) c
انتاج:
0.09250360565571492 array([0.34665418, 0.47027209, 0.75944969, 0.37991244, 0.14159746])
6) np.random.random([الحجم])
تُستخدم وظيفة الوحدة العشوائية هذه لإنشاء أرقام عشوائية في الفاصل الزمني نصف المفتوح [0.0، 1.0).
مثال:
import numpy as np a=np.random.random() a b=type(np.random.random()) b c=np.random.random((5,)) c
انتاج:
0.008786953974334155 array([0.05530122, 0.59133394, 0.17258794, 0.6912388 , 0.33412534])
7) np.random.ranf([الحجم])
تُستخدم وظيفة الوحدة العشوائية هذه لإنشاء أرقام عشوائية في الفاصل الزمني نصف المفتوح [0.0، 1.0).
مثال:
import numpy as np a=np.random.ranf() a b=type(np.random.ranf()) b c=np.random.ranf((5,)) c
انتاج:
0.2907792098474542 array([0.34084881, 0.07268237, 0.38161256, 0.46494681, 0.88071377])
8) np.random.sample([الحجم])
تُستخدم وظيفة الوحدة العشوائية هذه لإنشاء أرقام عشوائية في الفاصل الزمني نصف المفتوح [0.0، 1.0).
مثال:
import numpy as np a=np.random.sample() a b=type(np.random.sample()) b c=np.random.sample((5,)) c
انتاج:
0.012298209913766511 array([0.71878544, 0.11486169, 0.38189074, 0.14303308, 0.07217287])
9) np.random.choice(أ[، الحجم، استبدال، ع])
تُستخدم وظيفة الوحدة العشوائية هذه لإنشاء عينة عشوائية من مصفوفة أحادية الأبعاد معينة.
مثال:
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
انتاج:
array([0, 3, 4]) array([2, 2, 2], dtype=int64)
10) np.random.bytes(الطول)
تُستخدم وظيفة الوحدة العشوائية هذه لإنشاء بايتات عشوائية.
مثال:
import numpy as np a=np.random.bytes(7) a
انتاج:
'nQx08x83xf9xdex8a'
التباديل
هناك وظائف التباديل التالية:
1) np.random.shuffle()
تُستخدم هذه الوظيفة لتعديل التسلسل في مكانه عن طريق خلط محتوياته.
مثال:
import numpy as np a=np.arange(12) a np.random.shuffle(a) a
انتاج:
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()
تقوم هذه الوظيفة بتبديل التسلسل بشكل عشوائي أو إرجاع نطاق متبادل.
مثال:
import numpy as np a=np.random.permutation(12) a
انتاج:
array([ 8, 7, 3, 11, 6, 0, 9, 10, 2, 5, 4, 1])
توزيعات
هناك وظائف التباديل التالية:
1) بيتا (أ، ب[، الحجم])
تُستخدم هذه الوظيفة لسحب عينات من توزيعة بيتا.
مثال:
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) ذات الحدين (ن، ص[، الحجم])
تستخدم هذه الدالة لسحب عينة من التوزيع ذي الحدين.
مثال:
import numpy as np n, p = 10, .6 s1= np.random.binomial(n, p, 10) s1
انتاج:
array([6, 7, 7, 9, 3, 7, 8, 6, 6, 4])
3) تشيسكوير (مدافع [، الحجم])
تستخدم هذه الدالة لسحب عينة من التوزيع ذي الحدين.
مثال:
import numpy as np np.random.chisquare(2,4) sum(np.random.binomial(9, 0.1, 20000) == 0)/20000.
انتاج:
array([6, 7, 7, 9, 3, 7, 8, 6, 6, 4])
4) ديريشليت (ألفا [، الحجم])
تُستخدم هذه الدالة لسحب عينة من توزيع ديريشليت.
مثال:
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()
انتاج:
5) الأسي([المقياس، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من التوزيع الأسي.
مثال:
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:>
انتاج:
array([0.00264041, 0.04725478, 0.07140803, 0.19526217, 0.23979 , 0.24023478, 0.63141254, 0.95316446, 1.40281789, 1.68327507])
7) جاما (الشكل [، المقياس، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع جاما
مثال:
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) هندسية (ع [، الحجم])
تستخدم هذه الدالة لرسم عينة من التوزيع الهندسي.
مثال:
import numpy as np a = np.random.geometric(p=0.35, size=10000) (a == 1).sum() / 1000
انتاج:
3.
9) غامبل([الموضع، المقياس، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيعة Gumble.
مثال:
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()
انتاج:
10) الهندسة الفائقة (ngood، nbad، nsample[، size])
تُستخدم هذه الدالة لسحب عينة من التوزيع الهندسي الزائدي.
مثال:
import numpy as np good, bad, samp = 100, 2, 10 s1 = np.random.hypergeometric(good, bad, samp, 1000) plt.hist(s1) plt.show()
انتاج:
(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) لابلاس([الموضع، المقياس، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع لابلاس أو التوزيع الأسي المزدوج مع الموقع والمقياس المحددين.
مثال:
import numpy as np location, scale = 0., 2. s = np.random.laplace(location, scale, 10) s
انتاج:
array([-2.77127948, -1.46401453, -0.03723516, -1.61223942, 2.29590691, 1.74297722, 1.49438411, 0.30325513, -0.15948891, -4.99669747])
12) لوجستية([الموقع، النطاق، الحجم])
النطاق الأساسي مقابل النطاق العريض
تُستخدم هذه الوظيفة لسحب عينة من التوزيع اللوجستي.
مثال:
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()
انتاج:
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) اللوغاريتمي الطبيعي([المتوسط، سيجما، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع السجل الطبيعي.
مثال:
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()
انتاج:
14) سلسلة السجلات (ع [، الحجم])
تُستخدم هذه الدالة لسحب عينة من التوزيع اللوغاريتمي.
مثال:
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()
انتاج:
15) متعدد الحدود (ن، pvals[، الحجم])
تُستخدم هذه الدالة لسحب عينة من توزيع متعدد الحدود.
مثال:
import numpy as np np.random.multinomial(20, [1/6.]*6, size=1)
انتاج:
array([[4, 2, 5, 5, 3, 1]])
16) multivariate_normal(mean, cov[, size, ...)
تُستخدم هذه الوظيفة لسحب عينة من التوزيع الطبيعي متعدد المتغيرات.
مثال:
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()
انتاج:
17) سلبي ذو الحدين (ن، ص[، الحجم])
تستخدم هذه الدالة لسحب عينة من التوزيع السالب ذو الحدين.
مثال:
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)>
انتاج:
21) القوة (أ[، الحجم])
تُستخدم هذه الوظيفة لسحب عينات في [0, 1] من توزيع القدرة ذو الأس الموجب a-1.
مثال:
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()
انتاج:
22) رايلي([المقياس، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع رايلي.
مثال:
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.
انتاج:
0.087300000000000003
23) Standard_cauchy([الحجم])
تُستخدم هذه الدالة لسحب عينة من توزيع كوشي القياسي بالوضع = 0.
مثال:
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)]>
انتاج:
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) ستاندرد_جاما([الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع جاما القياسي.
مثال:
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()
انتاج:
26) معيار_عادي([الحجم])
تُستخدم هذه الوظيفة لسحب عينة من التوزيع الطبيعي القياسي.
مثال:
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
انتاج:
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[، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع الطالب القياسي بدرجة حرية df.
تعيين مقابل الخريطة
مثال:
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) الثلاثي (يسار، الوضع، اليمين [، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من التوزيع الثلاثي خلال الفترة.
مثال:
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()
انتاج:
29) زي موحد([منخفض، عالي، الحجم])
تستخدم هذه الدالة لسحب عينة من التوزيع الموحد.
مثال:
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)>
انتاج:
31) والد (يعني، مقياس [، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع والد أو التوزيع الغوسي العكسي.
مثال:
import numpy as np import matplotlib.pyplot as plt h = plt.hist(np.random.wald(3, 3, 100000), bins=250, density=True) plt.show()
انتاج:
32) ويبول(أ[، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع Weibull.
مثال:
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()
انتاج:
33) زيبف(أ[، الحجم])
تُستخدم هذه الوظيفة لسحب عينة من توزيع Zipf.
مثال:
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],>