本文整理汇总了Python中pymc3.HalfNormal方法的典型用法代码示例。如果您正苦于以下问题：Python pymc3.HalfNormal方法的具体用法？Python pymc3.HalfNormal怎么用？Python pymc3.HalfNormal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.HalfNormal方法的2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: from_posterior
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfNormal [as 别名]
def from_posterior(param, samples, distribution = None, half = False, freedom=10):
if len(samples.shape)>1:
shape = samples.shape[1:]
else:
shape = None
if (distribution is None):
smin, smax = np.min(samples), np.max(samples)
width = smax - smin
x = np.linspace(smin, smax, 1000)
y = stats.gaussian_kde(samples)(x)
if half:
x = np.concatenate([x, [x[-1] + 0.1 * width]])
y = np.concatenate([y, [0]])
else:
x = np.concatenate([[x[0] - 0.1 * width], x, [x[-1] + 0.1 * width]])
y = np.concatenate([[0], y, [0]])
return pm.distributions.Interpolated(param, x, y)
elif (distribution=='normal'):
temp = stats.norm.fit(samples)
if shape is None:
return pm.Normal(param, mu=temp[0], sigma=freedom*temp[1])
else:
return pm.Normal(param, mu=temp[0], sigma=freedom*temp[1], shape=shape)
elif (distribution=='hnormal'):
temp = stats.halfnorm.fit(samples)
if shape is None:
return pm.HalfNormal(param, sigma=freedom*temp[1])
else:
return pm.HalfNormal(param, sigma=freedom*temp[1], shape=shape)
elif (distribution=='hcauchy'):
temp = stats.halfcauchy.fit(samples)
if shape is None:
return pm.HalfCauchy(param, freedom*temp[1])
else:
return pm.HalfCauchy(param, freedom*temp[1], shape=shape) 示例2: __init__
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfNormal [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__)