Python random 模块，gammavariate() 实例源码
我们从Python开源项目中，提取了以下27个代码示例，用于说明如何使用random.gammavariate()。
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)
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)
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def sample(self):
return random.gammavariate(self.k, self.theta)
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def rvs(self, size=1):
res = np.zeros(shape=(size, self._K))
for i in range(size):
alpha = np.array([random.gammavariate(self._a, 1. / self._b) for local in range(self._K)])
try:
res[i,:] = np.random.dirichlet(alpha, 1)
except:
res[i, :] = res[i-1,:]
# res[i, :] = np.array([random.gammavariate(local, 1) for local in alpha])
return res
def wishrnd(sigma, v_0, C=None):
"""Return a sample from a Wishart distribution."""
if C == None:
C = np.linalg.cholesky(sigma)
D = sigma.shape[0]
a = np.zeros((D, D), dtype=np.float32)
for r in xrange(D):
if r != 0:
a[r, :r] = np.random.normal(size=(r,))
a[r, r] = math.sqrt(random.gammavariate(0.5*(v_0 - D + 1), 2.0))
return np.dot(np.dot(np.dot(C, a), a.T), C.T)
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def wishartrand(self, nu, sigma, C=None):
"""Return a sample from a Wishart distribution."""
if C == None:
C = np.linalg.cholesky(sigma)
D = sigma.shape[0]
a = np.zeros((D, D), dtype=np.float32)
for r in xrange(D):
if r != 0:
a[r, :r] = np.random.normal(size=(r,))
a[r, r] = np.sqrt(random.gammavariate(0.5*(nu - D + 1), 2.0))
return np.dot(np.dot(np.dot(C, a), a.T), C.T)
def test_gammavariate_errors(self):
# Both alpha and beta must be > 0.0
self.assertRaises(ValueError, random.gammavariate, -1, 3)
self.assertRaises(ValueError, random.gammavariate, 0, 2)
self.assertRaises(ValueError, random.gammavariate, 2, 0)
self.assertRaises(ValueError, random.gammavariate, 1, -3)
def bandwidth(self, alpha=1.0, beta=0.5, bandwidth_max=100000):
"""Completely make-believe bandwith. It's calculated as a random point
on the probability density function of a gamma distribution over
(0,100000] in KB/s.
"""
if not self._bandwidth:
self._bandwidth = \
int(floor(random.gammavariate(alpha, beta) * bandwidth_max))
return self._bandwidth
def pwntime(self, alpha=1.0, beta=0.5, median=60*60*24*3):
"""The amount of time it takes to pwn this node.
:ivar median: The median amount of time it should take to own this node.
By default, three days. (It takes the skids a while to figure out
which metasploit module to use.)
"""
if not self._pwntime:
self._pwntime = int(floor(random.gammavariate(alpha, beta) * median))
return self._pwntime
def sample(self):
return random.gammavariate(self.k, self.theta)
def test_gammavariate_errors(self):
# Both alpha and beta must be > 0.0
self.assertRaises(ValueError, random.gammavariate, -1, 3)
self.assertRaises(ValueError, random.gammavariate, 0, 2)
self.assertRaises(ValueError, random.gammavariate, 2, 0)
self.assertRaises(ValueError, random.gammavariate, 1, -3)
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def wishrnd(sigma, v_0, C=None):
"""Return a sample from a Wishart distribution."""
if C == None:
C = np.linalg.cholesky(sigma)
D = sigma.shape[0]
a = np.zeros((D, D), dtype=np.float32)
for r in xrange(D):
if r != 0:
a[r, :r] = np.random.normal(size=(r,))
a[r, r] = math.sqrt(random.gammavariate(0.5*(v_0 - D + 1), 2.0))
return np.dot(np.dot(np.dot(C, a), a.T), C.T)
def _fix(self):
return int(round(random.gammavariate(self.alpha, self.beta)))
def constantFactory(constants, pset):
for const_block in constants:
if(not const_block.has_key("type")):
raise ValueError("Constant block in configuration missing type key.\n %s" % str(const_block));
typename = const_block["type"].lower()
if typename == "randint":
if(not const_block.has_key("min") or not const_block.has_key("max")):
raise ValueError("Constant randint in configuration must have min and max values.\n %s" % str(const_block));
minn = int(const_block["min"])
maxx = int(const_block["max"])
pset.addEphemeralConstant("randint%d.%d" %(minn, maxx), lambda: random.randint(minn,maxx))
elif typename == "uniform":
if(not const_block.has_key("min") or not const_block.has_key("max")):
raise ValueError("Constant uniform in configuration must have min and max values.\n %s" % str(const_block));
minn = int(const_block["min"])
maxx = int(const_block["max"])
pset.addEphemeralConstant("uniform%d.%d" %(minn, maxx), lambda: random.uniform(minn,maxx))
elif typename == "normal":
if(not const_block.has_key("mu") or not const_block.has_key("sigma")):
raise ValueError("Constant normal in configuration must have mu and sigma values.\n %s" % str(const_block));
mu = int(const_block["mu"])
sigma = int(const_block["sigma"])
pset.addEphemeralConstant("normal%d.%d" %(mu, sigma), lambda: random.normalvariate(mu,sigma))
elif typename == "gamma":
if(not const_block.has_key("alpha") or not const_block.has_key("beta")):
raise ValueError("Constant gamma in configuration must have alpha and beta values.\n %s" % str(const_block));
alpha = int(const_block["alpha"])
beta = int(const_block["beta"])
pset.addEphemeralConstant("normal%d.%d" %(alpha, beta), lambda: random.gammavariate(alpha,beta))
elif typename == "constant":
if(not const_block.has_key("value")):
raise ValueError("Constant constant in configuration must have value.\n %s" % str(const_block));
value = int(const_block["value"])
pset.addEphemeralConstant("const%d" %(value), lambda: value)
else:
raise ValueError("Unknown constant type %s\n" % typename)
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.gammavariate(alpha=self.alpha, beta=self.beta))