Python random.vonmisesvariate() Examples
The following are code examples for showing how to use random.vonmisesvariate(). They are extracted from open source Python projects. You can vote up the examples you like or vote down the ones you don't like. You can also save this page to your account.
Example 1
Project: ouroboros Author: pybee File: test_random.py (license) View Source Project | 7 votes |
def test_zeroinputs(self): # Verify that distributions can handle a series of zero inputs' g = random.Random() x = [g.random() for i in range(50)] + [0.0]*5 g.random = x[:].pop; g.uniform(1,10) g.random = x[:].pop; g.paretovariate(1.0) g.random = x[:].pop; g.expovariate(1.0) g.random = x[:].pop; g.weibullvariate(1.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0) g.random = x[:].pop; g.normalvariate(0.0, 1.0) g.random = x[:].pop; g.gauss(0.0, 1.0) g.random = x[:].pop; g.lognormvariate(0.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0) g.random = x[:].pop; g.gammavariate(0.01, 1.0) g.random = x[:].pop; g.gammavariate(1.0, 1.0) g.random = x[:].pop; g.gammavariate(200.0, 1.0) g.random = x[:].pop; g.betavariate(3.0, 3.0) g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
Example 2
Project: kbe_server Author: xiaohaoppy File: test_random.py (license) View Source Project | 7 votes |
def test_zeroinputs(self): # Verify that distributions can handle a series of zero inputs' g = random.Random() x = [g.random() for i in range(50)] + [0.0]*5 g.random = x[:].pop; g.uniform(1,10) g.random = x[:].pop; g.paretovariate(1.0) g.random = x[:].pop; g.expovariate(1.0) g.random = x[:].pop; g.weibullvariate(1.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0) g.random = x[:].pop; g.normalvariate(0.0, 1.0) g.random = x[:].pop; g.gauss(0.0, 1.0) g.random = x[:].pop; g.lognormvariate(0.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0) g.random = x[:].pop; g.gammavariate(0.01, 1.0) g.random = x[:].pop; g.gammavariate(1.0, 1.0) g.random = x[:].pop; g.gammavariate(200.0, 1.0) g.random = x[:].pop; g.betavariate(3.0, 3.0) g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
Example 3
Project: oil Author: oilshell File: test_random.py (license) View Source Project | 6 votes |
def test_zeroinputs(self): # Verify that distributions can handle a series of zero inputs' g = random.Random() x = [g.random() for i in xrange(50)] + [0.0]*5 g.random = x[:].pop; g.uniform(1,10) g.random = x[:].pop; g.paretovariate(1.0) g.random = x[:].pop; g.expovariate(1.0) g.random = x[:].pop; g.weibullvariate(1.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0) g.random = x[:].pop; g.normalvariate(0.0, 1.0) g.random = x[:].pop; g.gauss(0.0, 1.0) g.random = x[:].pop; g.lognormvariate(0.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0) g.random = x[:].pop; g.gammavariate(0.01, 1.0) g.random = x[:].pop; g.gammavariate(1.0, 1.0) g.random = x[:].pop; g.gammavariate(200.0, 1.0) g.random = x[:].pop; g.betavariate(3.0, 3.0) g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
Example 4
Project: oil Author: oilshell File: test_random.py (license) View Source Project | 6 votes |
def test_constant(self): g = random.Random() N = 100 for variate, args, expected in [ (g.uniform, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0, 10.0), 10.0), (g.expovariate, (float('inf'),), 0.0), (g.vonmisesvariate, (3.0, float('inf')), 3.0), (g.gauss, (10.0, 0.0), 10.0), (g.lognormvariate, (0.0, 0.0), 1.0), (g.lognormvariate, (-float('inf'), 0.0), 0.0), (g.normalvariate, (10.0, 0.0), 10.0), (g.paretovariate, (float('inf'),), 1.0), (g.weibullvariate, (10.0, float('inf')), 10.0), (g.weibullvariate, (0.0, 10.0), 0.0), ]: for i in range(N): self.assertEqual(variate(*args), expected)
Example 5
Project: python2-tracer Author: extremecoders-re File: test_random.py (license) View Source Project | 6 votes |
def test_zeroinputs(self): # Verify that distributions can handle a series of zero inputs' g = random.Random() x = [g.random() for i in xrange(50)] + [0.0]*5 g.random = x[:].pop; g.uniform(1,10) g.random = x[:].pop; g.paretovariate(1.0) g.random = x[:].pop; g.expovariate(1.0) g.random = x[:].pop; g.weibullvariate(1.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0) g.random = x[:].pop; g.normalvariate(0.0, 1.0) g.random = x[:].pop; g.gauss(0.0, 1.0) g.random = x[:].pop; g.lognormvariate(0.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0) g.random = x[:].pop; g.gammavariate(0.01, 1.0) g.random = x[:].pop; g.gammavariate(1.0, 1.0) g.random = x[:].pop; g.gammavariate(200.0, 1.0) g.random = x[:].pop; g.betavariate(3.0, 3.0) g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
Example 6
Project: python2-tracer Author: extremecoders-re File: test_random.py (license) View Source Project | 6 votes |
def test_constant(self): g = random.Random() N = 100 for variate, args, expected in [ (g.uniform, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0, 10.0), 10.0), (g.expovariate, (float('inf'),), 0.0), (g.vonmisesvariate, (3.0, float('inf')), 3.0), (g.gauss, (10.0, 0.0), 10.0), (g.lognormvariate, (0.0, 0.0), 1.0), (g.lognormvariate, (-float('inf'), 0.0), 0.0), (g.normalvariate, (10.0, 0.0), 10.0), (g.paretovariate, (float('inf'),), 1.0), (g.weibullvariate, (10.0, float('inf')), 10.0), (g.weibullvariate, (0.0, 10.0), 0.0), ]: for i in range(N): self.assertEqual(variate(*args), expected)
Example 7
Project: web_ctp Author: molebot File: test_random.py (license) View Source Project | 6 votes |
def test_zeroinputs(self): # Verify that distributions can handle a series of zero inputs' g = random.Random() x = [g.random() for i in range(50)] + [0.0]*5 g.random = x[:].pop; g.uniform(1,10) g.random = x[:].pop; g.paretovariate(1.0) g.random = x[:].pop; g.expovariate(1.0) g.random = x[:].pop; g.weibullvariate(1.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0) g.random = x[:].pop; g.normalvariate(0.0, 1.0) g.random = x[:].pop; g.gauss(0.0, 1.0) g.random = x[:].pop; g.lognormvariate(0.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0) g.random = x[:].pop; g.gammavariate(0.01, 1.0) g.random = x[:].pop; g.gammavariate(1.0, 1.0) g.random = x[:].pop; g.gammavariate(200.0, 1.0) g.random = x[:].pop; g.betavariate(3.0, 3.0) g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
Example 8
Project: web_ctp Author: molebot File: test_random.py (license) View Source Project | 6 votes |
def test_constant(self): g = random.Random() N = 100 for variate, args, expected in [ (g.uniform, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0), 10.0), #(g.triangular, (10.0, 10.0, 10.0), 10.0), (g.expovariate, (float('inf'),), 0.0), (g.vonmisesvariate, (3.0, float('inf')), 3.0), (g.gauss, (10.0, 0.0), 10.0), (g.lognormvariate, (0.0, 0.0), 1.0), (g.lognormvariate, (-float('inf'), 0.0), 0.0), (g.normalvariate, (10.0, 0.0), 10.0), (g.paretovariate, (float('inf'),), 1.0), (g.weibullvariate, (10.0, float('inf')), 10.0), (g.weibullvariate, (0.0, 10.0), 0.0), ]: for i in range(N): self.assertEqual(variate(*args), expected)
Example 9
Project: pefile.pypy Author: cloudtracer File: test_random.py (license) View Source Project | 6 votes |
def test_zeroinputs(self): # Verify that distributions can handle a series of zero inputs' g = random.Random() x = [g.random() for i in xrange(50)] + [0.0]*5 g.random = x[:].pop; g.uniform(1,10) g.random = x[:].pop; g.paretovariate(1.0) g.random = x[:].pop; g.expovariate(1.0) g.random = x[:].pop; g.weibullvariate(1.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0) g.random = x[:].pop; g.normalvariate(0.0, 1.0) g.random = x[:].pop; g.gauss(0.0, 1.0) g.random = x[:].pop; g.lognormvariate(0.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0) g.random = x[:].pop; g.gammavariate(0.01, 1.0) g.random = x[:].pop; g.gammavariate(1.0, 1.0) g.random = x[:].pop; g.gammavariate(200.0, 1.0) g.random = x[:].pop; g.betavariate(3.0, 3.0) g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
Example 10
Project: pefile.pypy Author: cloudtracer File: test_random.py (license) View Source Project | 6 votes |
def test_constant(self): g = random.Random() N = 100 for variate, args, expected in [ (g.uniform, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0, 10.0), 10.0), (g.expovariate, (float('inf'),), 0.0), (g.vonmisesvariate, (3.0, float('inf')), 3.0), (g.gauss, (10.0, 0.0), 10.0), (g.lognormvariate, (0.0, 0.0), 1.0), (g.lognormvariate, (-float('inf'), 0.0), 0.0), (g.normalvariate, (10.0, 0.0), 10.0), (g.paretovariate, (float('inf'),), 1.0), (g.weibullvariate, (10.0, float('inf')), 10.0), (g.weibullvariate, (0.0, 10.0), 0.0), ]: for i in range(N): self.assertEqual(variate(*args), expected)
Example 11
Project: ouroboros Author: pybee File: test_random.py (license) View Source Project | 6 votes |
def test_constant(self): g = random.Random() N = 100 for variate, args, expected in [ (g.uniform, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0, 10.0), 10.0), (g.expovariate, (float('inf'),), 0.0), (g.vonmisesvariate, (3.0, float('inf')), 3.0), (g.gauss, (10.0, 0.0), 10.0), (g.lognormvariate, (0.0, 0.0), 1.0), (g.lognormvariate, (-float('inf'), 0.0), 0.0), (g.normalvariate, (10.0, 0.0), 10.0), (g.paretovariate, (float('inf'),), 1.0), (g.weibullvariate, (10.0, float('inf')), 10.0), (g.weibullvariate, (0.0, 10.0), 0.0), ]: for i in range(N): self.assertEqual(variate(*args), expected)
Example 12
Project: ndk-python Author: gittor File: test_random.py (license) View Source Project | 6 votes |
def test_zeroinputs(self): # Verify that distributions can handle a series of zero inputs' g = random.Random() x = [g.random() for i in xrange(50)] + [0.0]*5 g.random = x[:].pop; g.uniform(1,10) g.random = x[:].pop; g.paretovariate(1.0) g.random = x[:].pop; g.expovariate(1.0) g.random = x[:].pop; g.weibullvariate(1.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(1.0, 1.0) g.random = x[:].pop; g.normalvariate(0.0, 1.0) g.random = x[:].pop; g.gauss(0.0, 1.0) g.random = x[:].pop; g.lognormvariate(0.0, 1.0) g.random = x[:].pop; g.vonmisesvariate(0.0, 1.0) g.random = x[:].pop; g.gammavariate(0.01, 1.0) g.random = x[:].pop; g.gammavariate(1.0, 1.0) g.random = x[:].pop; g.gammavariate(200.0, 1.0) g.random = x[:].pop; g.betavariate(3.0, 3.0) g.random = x[:].pop; g.triangular(0.0, 1.0, 1.0/3.0)
Example 13
Project: ndk-python Author: gittor File: test_random.py (license) View Source Project | 6 votes |
def test_constant(self): g = random.Random() N = 100 for variate, args, expected in [ (g.uniform, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0), 10.0), #(g.triangular, (10.0, 10.0, 10.0), 10.0), (g.expovariate, (float('inf'),), 0.0), (g.vonmisesvariate, (3.0, float('inf')), 3.0), (g.gauss, (10.0, 0.0), 10.0), (g.lognormvariate, (0.0, 0.0), 1.0), (g.lognormvariate, (-float('inf'), 0.0), 0.0), (g.normalvariate, (10.0, 0.0), 10.0), (g.paretovariate, (float('inf'),), 1.0), (g.weibullvariate, (10.0, float('inf')), 10.0), (g.weibullvariate, (0.0, 10.0), 0.0), ]: for i in range(N): self.assertEqual(variate(*args), expected)
Example 14
Project: kbe_server Author: xiaohaoppy File: test_random.py (license) View Source Project | 6 votes |
def test_constant(self): g = random.Random() N = 100 for variate, args, expected in [ (g.uniform, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0), 10.0), (g.triangular, (10.0, 10.0, 10.0), 10.0), (g.expovariate, (float('inf'),), 0.0), (g.vonmisesvariate, (3.0, float('inf')), 3.0), (g.gauss, (10.0, 0.0), 10.0), (g.lognormvariate, (0.0, 0.0), 1.0), (g.lognormvariate, (-float('inf'), 0.0), 0.0), (g.normalvariate, (10.0, 0.0), 10.0), (g.paretovariate, (float('inf'),), 1.0), (g.weibullvariate, (10.0, float('inf')), 10.0), (g.weibullvariate, (0.0, 10.0), 0.0), ]: for i in range(N): self.assertEqual(variate(*args), expected)
Example 15
Project: oil Author: oilshell File: test_random.py (license) View Source Project | 5 votes |
def test_avg_std(self): # Use integration to test distribution average and standard deviation. # Only works for distributions which do not consume variates in pairs g = random.Random() N = 5000 x = [i/float(N) for i in xrange(1,N)] for variate, args, mu, sigmasqrd in [ (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12), (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0), (g.expovariate, (1.5,), 1/1.5, 1/1.5**2), (g.vonmisesvariate, (1.23, 0), pi, pi**2/3), (g.paretovariate, (5.0,), 5.0/(5.0-1), 5.0/((5.0-1)**2*(5.0-2))), (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0), gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]: g.random = x[:].pop y = [] for i in xrange(len(x)): try: y.append(variate(*args)) except IndexError: pass s1 = s2 = 0 for e in y: s1 += e s2 += (e - mu) ** 2 N = len(y) self.assertAlmostEqual(s1/N, mu, places=2, msg='%s%r' % (variate.__name__, args)) self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2, msg='%s%r' % (variate.__name__, args))
Example 16
Project: oil Author: oilshell File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_range(self): # Issue 17149: von mises variates were not consistently in the # range [0, 2*PI]. g = random.Random() N = 100 for mu in 0.0, 0.1, 3.1, 6.2: for kappa in 0.0, 2.3, 500.0: for _ in range(N): sample = g.vonmisesvariate(mu, kappa) self.assertTrue( 0 <= sample <= random.TWOPI, msg=("vonmisesvariate({}, {}) produced a result {} out" " of range [0, 2*pi]").format(mu, kappa, sample))
Example 17
Project: oil Author: oilshell File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_large_kappa(self): # Issue #17141: vonmisesvariate() was hang for large kappas random.vonmisesvariate(0, 1e15) random.vonmisesvariate(0, 1e100)
Example 18
Project: python2-tracer Author: extremecoders-re File: test_random.py (license) View Source Project | 5 votes |
def test_avg_std(self): # Use integration to test distribution average and standard deviation. # Only works for distributions which do not consume variates in pairs g = random.Random() N = 5000 x = [i/float(N) for i in xrange(1,N)] for variate, args, mu, sigmasqrd in [ (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12), (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0), (g.expovariate, (1.5,), 1/1.5, 1/1.5**2), (g.vonmisesvariate, (1.23, 0), pi, pi**2/3), (g.paretovariate, (5.0,), 5.0/(5.0-1), 5.0/((5.0-1)**2*(5.0-2))), (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0), gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]: g.random = x[:].pop y = [] for i in xrange(len(x)): try: y.append(variate(*args)) except IndexError: pass s1 = s2 = 0 for e in y: s1 += e s2 += (e - mu) ** 2 N = len(y) self.assertAlmostEqual(s1/N, mu, places=2, msg='%s%r' % (variate.__name__, args)) self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2, msg='%s%r' % (variate.__name__, args))
Example 19
Project: python2-tracer Author: extremecoders-re File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_range(self): # Issue 17149: von mises variates were not consistently in the # range [0, 2*PI]. g = random.Random() N = 100 for mu in 0.0, 0.1, 3.1, 6.2: for kappa in 0.0, 2.3, 500.0: for _ in range(N): sample = g.vonmisesvariate(mu, kappa) self.assertTrue( 0 <= sample <= random.TWOPI, msg=("vonmisesvariate({}, {}) produced a result {} out" " of range [0, 2*pi]").format(mu, kappa, sample))
Example 20
Project: python2-tracer Author: extremecoders-re File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_large_kappa(self): # Issue #17141: vonmisesvariate() was hang for large kappas random.vonmisesvariate(0, 1e15) random.vonmisesvariate(0, 1e100)
Example 21
Project: web_ctp Author: molebot File: test_random.py (license) View Source Project | 5 votes |
def test_avg_std(self): # Use integration to test distribution average and standard deviation. # Only works for distributions which do not consume variates in pairs g = random.Random() N = 5000 x = [i/float(N) for i in range(1,N)] for variate, args, mu, sigmasqrd in [ (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12), (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0), (g.expovariate, (1.5,), 1/1.5, 1/1.5**2), (g.vonmisesvariate, (1.23, 0), pi, pi**2/3), (g.paretovariate, (5.0,), 5.0/(5.0-1), 5.0/((5.0-1)**2*(5.0-2))), (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0), gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]: g.random = x[:].pop y = [] for i in range(len(x)): try: y.append(variate(*args)) except IndexError: pass s1 = s2 = 0 for e in y: s1 += e s2 += (e - mu) ** 2 N = len(y) self.assertAlmostEqual(s1/N, mu, places=2, msg='%s%r' % (variate.__name__, args)) self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2, msg='%s%r' % (variate.__name__, args))
Example 22
Project: web_ctp Author: molebot File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_range(self): # Issue 17149: von mises variates were not consistently in the # range [0, 2*PI]. g = random.Random() N = 100 for mu in 0.0, 0.1, 3.1, 6.2: for kappa in 0.0, 2.3, 500.0: for _ in range(N): sample = g.vonmisesvariate(mu, kappa) self.assertTrue( 0 <= sample <= random.TWOPI, msg=("vonmisesvariate({}, {}) produced a result {} out" " of range [0, 2*pi]").format(mu, kappa, sample))
Example 23
Project: web_ctp Author: molebot File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_large_kappa(self): # Issue #17141: vonmisesvariate() was hang for large kappas random.vonmisesvariate(0, 1e15) random.vonmisesvariate(0, 1e100)
Example 24
Project: pefile.pypy Author: cloudtracer File: test_random.py (license) View Source Project | 5 votes |
def test_avg_std(self): # Use integration to test distribution average and standard deviation. # Only works for distributions which do not consume variates in pairs g = random.Random() N = 5000 x = [i/float(N) for i in xrange(1,N)] for variate, args, mu, sigmasqrd in [ (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12), (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0), (g.expovariate, (1.5,), 1/1.5, 1/1.5**2), (g.vonmisesvariate, (1.23, 0), pi, pi**2/3), (g.paretovariate, (5.0,), 5.0/(5.0-1), 5.0/((5.0-1)**2*(5.0-2))), (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0), gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]: g.random = x[:].pop y = [] for i in xrange(len(x)): try: y.append(variate(*args)) except IndexError: pass s1 = s2 = 0 for e in y: s1 += e s2 += (e - mu) ** 2 N = len(y) self.assertAlmostEqual(s1/N, mu, places=2, msg='%s%r' % (variate.__name__, args)) self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2, msg='%s%r' % (variate.__name__, args))
Example 25
Project: pefile.pypy Author: cloudtracer File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_range(self): # Issue 17149: von mises variates were not consistently in the # range [0, 2*PI]. g = random.Random() N = 100 for mu in 0.0, 0.1, 3.1, 6.2: for kappa in 0.0, 2.3, 500.0: for _ in range(N): sample = g.vonmisesvariate(mu, kappa) self.assertTrue( 0 <= sample <= random.TWOPI, msg=("vonmisesvariate({}, {}) produced a result {} out" " of range [0, 2*pi]").format(mu, kappa, sample))
Example 26
Project: pefile.pypy Author: cloudtracer File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_large_kappa(self): # Issue #17141: vonmisesvariate() was hang for large kappas random.vonmisesvariate(0, 1e15) random.vonmisesvariate(0, 1e100)
Example 27
Project: ouroboros Author: pybee File: test_random.py (license) View Source Project | 5 votes |
def test_avg_std(self): # Use integration to test distribution average and standard deviation. # Only works for distributions which do not consume variates in pairs g = random.Random() N = 5000 x = [i/float(N) for i in range(1,N)] for variate, args, mu, sigmasqrd in [ (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12), (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0), (g.expovariate, (1.5,), 1/1.5, 1/1.5**2), (g.vonmisesvariate, (1.23, 0), pi, pi**2/3), (g.paretovariate, (5.0,), 5.0/(5.0-1), 5.0/((5.0-1)**2*(5.0-2))), (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0), gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]: g.random = x[:].pop y = [] for i in range(len(x)): try: y.append(variate(*args)) except IndexError: pass s1 = s2 = 0 for e in y: s1 += e s2 += (e - mu) ** 2 N = len(y) self.assertAlmostEqual(s1/N, mu, places=2, msg='%s%r' % (variate.__name__, args)) self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2, msg='%s%r' % (variate.__name__, args))
Example 28
Project: ouroboros Author: pybee File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_range(self): # Issue 17149: von mises variates were not consistently in the # range [0, 2*PI]. g = random.Random() N = 100 for mu in 0.0, 0.1, 3.1, 6.2: for kappa in 0.0, 2.3, 500.0: for _ in range(N): sample = g.vonmisesvariate(mu, kappa) self.assertTrue( 0 <= sample <= random.TWOPI, msg=("vonmisesvariate({}, {}) produced a result {} out" " of range [0, 2*pi]").format(mu, kappa, sample))
Example 29
Project: ouroboros Author: pybee File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_large_kappa(self): # Issue #17141: vonmisesvariate() was hang for large kappas random.vonmisesvariate(0, 1e15) random.vonmisesvariate(0, 1e100)
Example 30
Project: ndk-python Author: gittor File: test_random.py (license) View Source Project | 5 votes |
def test_avg_std(self): # Use integration to test distribution average and standard deviation. # Only works for distributions which do not consume variates in pairs g = random.Random() N = 5000 x = [i/float(N) for i in xrange(1,N)] for variate, args, mu, sigmasqrd in [ (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12), (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0), (g.expovariate, (1.5,), 1/1.5, 1/1.5**2), (g.vonmisesvariate, (1.23, 0), pi, pi**2/3), (g.paretovariate, (5.0,), 5.0/(5.0-1), 5.0/((5.0-1)**2*(5.0-2))), (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0), gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]: g.random = x[:].pop y = [] for i in xrange(len(x)): try: y.append(variate(*args)) except IndexError: pass s1 = s2 = 0 for e in y: s1 += e s2 += (e - mu) ** 2 N = len(y) self.assertAlmostEqual(s1/N, mu, places=2, msg='%s%r' % (variate.__name__, args)) self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2, msg='%s%r' % (variate.__name__, args))
Example 31
Project: ndk-python Author: gittor File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_range(self): # Issue 17149: von mises variates were not consistently in the # range [0, 2*PI]. g = random.Random() N = 100 for mu in 0.0, 0.1, 3.1, 6.2: for kappa in 0.0, 2.3, 500.0: for _ in range(N): sample = g.vonmisesvariate(mu, kappa) self.assertTrue( 0 <= sample <= random.TWOPI, msg=("vonmisesvariate({}, {}) produced a result {} out" " of range [0, 2*pi]").format(mu, kappa, sample))
Example 32
Project: ndk-python Author: gittor File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_large_kappa(self): # Issue #17141: vonmisesvariate() was hang for large kappas random.vonmisesvariate(0, 1e15) random.vonmisesvariate(0, 1e100)
Example 33
Project: tcnc Author: utlco File: lines.py (license) View Source Project | 5 votes |
def angle_jittered_line(self, line): """ """ if geom.is_zero(self.angle_jitter): return line # This produces a random angle between -pi and pi kappa = self.angle_jitter_kappa norm_angle = random.vonmisesvariate(math.pi, kappa) - math.pi jitter_angle = norm_angle * self.angle_jitter / math.pi if not geom.is_zero(jitter_angle): mat = transform2d.matrix_rotate(jitter_angle, origin=line.midpoint()) line = line.transform(mat) return line
Example 34
Project: kbe_server Author: xiaohaoppy File: test_random.py (license) View Source Project | 5 votes |
def test_avg_std(self): # Use integration to test distribution average and standard deviation. # Only works for distributions which do not consume variates in pairs g = random.Random() N = 5000 x = [i/float(N) for i in range(1,N)] for variate, args, mu, sigmasqrd in [ (g.uniform, (1.0,10.0), (10.0+1.0)/2, (10.0-1.0)**2/12), (g.triangular, (0.0, 1.0, 1.0/3.0), 4.0/9.0, 7.0/9.0/18.0), (g.expovariate, (1.5,), 1/1.5, 1/1.5**2), (g.vonmisesvariate, (1.23, 0), pi, pi**2/3), (g.paretovariate, (5.0,), 5.0/(5.0-1), 5.0/((5.0-1)**2*(5.0-2))), (g.weibullvariate, (1.0, 3.0), gamma(1+1/3.0), gamma(1+2/3.0)-gamma(1+1/3.0)**2) ]: g.random = x[:].pop y = [] for i in range(len(x)): try: y.append(variate(*args)) except IndexError: pass s1 = s2 = 0 for e in y: s1 += e s2 += (e - mu) ** 2 N = len(y) self.assertAlmostEqual(s1/N, mu, places=2, msg='%s%r' % (variate.__name__, args)) self.assertAlmostEqual(s2/(N-1), sigmasqrd, places=2, msg='%s%r' % (variate.__name__, args))
Example 35
Project: kbe_server Author: xiaohaoppy File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_range(self): # Issue 17149: von mises variates were not consistently in the # range [0, 2*PI]. g = random.Random() N = 100 for mu in 0.0, 0.1, 3.1, 6.2: for kappa in 0.0, 2.3, 500.0: for _ in range(N): sample = g.vonmisesvariate(mu, kappa) self.assertTrue( 0 <= sample <= random.TWOPI, msg=("vonmisesvariate({}, {}) produced a result {} out" " of range [0, 2*pi]").format(mu, kappa, sample))
Example 36
Project: kbe_server Author: xiaohaoppy File: test_random.py (license) View Source Project | 5 votes |
def test_von_mises_large_kappa(self): # Issue #17141: vonmisesvariate() was hang for large kappas random.vonmisesvariate(0, 1e15) random.vonmisesvariate(0, 1e100)
Example 37
Project: HyperStream Author: IRC-SPHERE File: 2017-06-20_v1.0.0.py (license) View Source Project | 5 votes |
def _execute(self, sources, alignment_stream, interval): if alignment_stream is None: raise ToolExecutionError("Alignment stream expected") for ti, _ in alignment_stream.window(interval, force_calculation=True): yield StreamInstance(ti, random.vonmisesvariate(mu=self.mu, kappa=self.kappa))