本文整理汇总了Python中numpy.random.standard_normal方法的典型用法代码示例。如果您正苦于以下问题：Python random.standard_normal方法的具体用法？Python random.standard_normal怎么用？Python random.standard_normal使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类numpy.random的用法示例。
在下文中一共展示了random.standard_normal方法的10个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: step
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def step(self, th, z):
s = self.stepsize
randstep = npr.standard_normal(th.shape)
th_new = th + 0.5*s**2*self.D_prob(th,z) + randstep*s
# bonus for probability difference. Using cache-friendly order
diff_probs = -self.prob(th, z)+self.prob(th_new, z)
# penalty for having asymmetric proposals:
randstep_back = (th - (th_new + 0.5*s**2*self.D_prob(th_new,z)))/s
diff_proposal = 0.5*np.sum(randstep_back**2) - 0.5*np.sum(randstep**2)
# M-H accept/reject:
if np.log(npr.rand()) < diff_probs - diff_proposal:
self.num_rejects = 0
return th_new
else:
self.num_rejects = 1
return th 示例2: initialise
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def initialise(self, context):
self.r0 = 0.05 # current UK funding rate
self.theta = 0.10 # 1 % long term interest rate
self.k = 0.3
self.beta = 0.03
## simulate short rate paths
self.n = 1000 # MC simulation trials
self.T = 24. # total time
self.m = 100 # subintervals
self.dt = self.T/self.m # difference in time each subinterval
self.r = np.zeros(shape=(self.n, self.m), dtype=float) # matrix to hold short rate paths
# Tell the engine where to associate the data to security.
context['My Simple Model'] = Simulation(self.n, self.m, standard_normal)
self.fig = plt.figure()
self.ax = self.fig.add_subplot(111)
self.ax.autoscale_view(True,True,True) 示例3: setup_class
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def setup_class(cls):
R.seed(54321)
cls.X = R.standard_normal((40,10)) 示例4: setup_class
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def setup_class(cls):
np.random.seed(54321)
cls.X = standard_normal((40,10)) 示例5: setup
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def setup(self):
self.X = R.standard_normal((40,10))
self.namespace = {}
self.terms = []
for i in range(10):
name = '%s' % string.ascii_uppercase[i]
self.namespace[name] = self.X[:,i]
self.terms.append(formula.Term(name))
self.formula = self.terms[0]
for i in range(1, 10):
self.formula += self.terms[i]
self.formula.namespace = self.namespace 示例6: test_contrast3
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def test_contrast3(self):
X = self.formula.design()
P = np.dot(X, L.pinv(X))
dummy = formula.Term('noise')
resid = np.identity(40) - P
self.namespace['noise'] = np.transpose(np.dot(resid, R.standard_normal((40,5))))
terms = dummy + self.terms[2]
terms.namespace = self.formula.namespace
c = contrast.Contrast(terms, self.formula)
assert_equal(c.matrix.shape, (10,)) 示例7: test_extendedpinv
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def test_extendedpinv(self):
X = standard_normal((40, 10))
np_inv = np.linalg.pinv(X)
np_sing_vals = np.linalg.svd(X, 0, 0)
sm_inv, sing_vals = pinv_extended(X)
assert_almost_equal(np_inv, sm_inv)
assert_almost_equal(np_sing_vals, sing_vals) 示例8: test_extendedpinv_singular
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def test_extendedpinv_singular(self):
X = standard_normal((40, 10))
X[:, 5] = X[:, 1] + X[:, 3]
np_inv = np.linalg.pinv(X)
np_sing_vals = np.linalg.svd(X, 0, 0)
sm_inv, sing_vals = pinv_extended(X)
assert_almost_equal(np_inv, sm_inv)
assert_almost_equal(np_sing_vals, sing_vals) 示例9: test_fullrank
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def test_fullrank(self):
import warnings
with warnings.catch_warnings():
warnings.simplefilter("ignore")
X = standard_normal((40,10))
X[:,0] = X[:,1] + X[:,2]
Y = tools.fullrank(X)
assert_equal(Y.shape, (40,9))
X[:,5] = X[:,3] + X[:,4]
Y = tools.fullrank(X)
assert_equal(Y.shape, (40,8))
warnings.simplefilter("ignore") 示例10: setUp
# 需要导入模块: from numpy import random [as 别名]
# 或者: from numpy.random import standard_normal [as 别名]
def setUp(self):
rnd.seed(1234567)
self.xd1 = rnd.standard_normal(128)
self.xf1 = self.xd1.astype(np.float32)
self.xz1 = rnd.standard_normal((128,2)).view(dtype=np.complex128).squeeze()
self.xc1 = self.xz1.astype(np.complex64)