本文整理汇总了Python中pymc3.Exponential方法的典型用法代码示例。如果您正苦于以下问题：Python pymc3.Exponential方法的具体用法？Python pymc3.Exponential怎么用？Python pymc3.Exponential使用的例子？那么, 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pymc3的用法示例。
在下文中一共展示了pymc3.Exponential方法的3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: apply_parameters
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import Exponential [as 别名]
def apply_parameters(self, g, df, initialization_trace=None):
for node in nx.topological_sort(g):
parent_names = g.nodes()[node]["parent_names"]
if parent_names:
if not initialization_trace:
sd = np.array([df[node].std()] + (df[node].std() / df[parent_names].std()).tolist())
mu = np.array([df[node].std()] + (df[node].std() / df[parent_names].std()).tolist())
node_sd = df[node].std()
else:
node_sd = initialization_trace["{}_sd".format(node)].mean()
mu = initialization_trace["beta_{}".format(node)].mean(axis=0)
sd = initialization_trace["beta_{}".format(node)].std(axis=0)
g.nodes()[node]["parameters"] = pm.Normal("beta_{}".format(node), mu=mu, sd=sd,
shape=len(parent_names) + 1)
g.nodes()[node]["sd"] = pm.Exponential("{}_sd".format(node), lam=node_sd)
return g 示例2: main
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import Exponential [as 别名]
def main():
#load data
returns = data.get_data_google('SPY', start='2008-5-1', end='2009-12-1')['Close'].pct_change()
returns.plot()
plt.ylabel('daily returns in %');
with pm.Model() as sp500_model:
nu = pm.Exponential('nu', 1./10, testval=5.0)
sigma = pm.Exponential('sigma', 1./0.02, testval=0.1)
s = pm.GaussianRandomWalk('s', sigma**-2, shape=len(returns))
r = pm.StudentT('r', nu, lam=pm.math.exp(-2*s), observed=returns)
with sp500_model:
trace = pm.sample(2000)
pm.traceplot(trace, [nu, sigma]);
plt.show()
plt.figure()
returns.plot()
plt.plot(returns.index, np.exp(trace['s',::5].T), 'r', alpha=.03)
plt.legend(['S&P500', 'stochastic volatility process'])
plt.show() 示例3: __init__
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import Exponential [as 别名]
def __init__(
self,
learner_cls,
parameter_keys,
model_params,
fit_params,
model_path,
**kwargs,
):
self.priors = [
[pm.Normal, {"mu": 0, "sd": 10}],
[pm.Laplace, {"mu": 0, "b": 10}],
]
self.uniform_prior = [pm.Uniform, {"lower": -20, "upper": 20}]
self.prior_indices = np.arange(len(self.priors))
self.parameter_f = [
(pm.Normal, {"mu": 0, "sd": 5}),
(pm.Cauchy, {"alpha": 0, "beta": 1}),
0,
-5,
5,
]
self.parameter_s = [
(pm.HalfCauchy, {"beta": 1}),
(pm.HalfNormal, {"sd": 0.5}),
(pm.Exponential, {"lam": 0.5}),
(pm.Uniform, {"lower": 1, "upper": 10}),
10,
]
# ,(pm.HalfCauchy, {'beta': 2}), (pm.HalfNormal, {'sd': 1}),(pm.Exponential, {'lam': 1.0})]
self.learner_cls = learner_cls
self.model_params = model_params
self.fit_params = fit_params
self.parameter_keys = parameter_keys
self.parameters = list(product(self.parameter_f, self.parameter_s))
pf_arange = np.arange(len(self.parameter_f))
ps_arange = np.arange(len(self.parameter_s))
self.parameter_ind = list(product(pf_arange, ps_arange))
self.model_path = model_path
self.models = dict()
self.logger = logging.getLogger(ModelSelector.__name__)