本文整理汇总了Python中pymc3.HalfCauchy方法的典型用法代码示例。如果您正苦于以下问题:Python pymc3.HalfCauchy方法的具体用法?Python pymc3.HalfCauchy怎么用?Python pymc3.HalfCauchy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.HalfCauchy方法的13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 HalfCauchy [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: fit
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def fit(self, X, y):
"""Fit the Imputer to the dataset by fitting bayesian model.
Args:
X (pd.Dataframe): dataset to fit the imputer.
y (pd.Series): response, which is eventually imputed.
Returns:
self. Instance of the class.
"""
_not_num_series(self.strategy, y)
nc = len(X.columns)
# initialize model for bayesian linear reg. Default vals for priors
# assume data is scaled and centered. Convergence can struggle or fail
# if not the case and proper values for the priors are not specified
# separately, also assumes each beta is normal and "independent"
# while betas likely not independent, this is technically a rule of OLS
with pm.Model() as fit_model:
alpha = pm.Normal("alpha", self.am, sd=self.asd)
beta = pm.Normal("beta", self.bm, sd=self.bsd, shape=nc)
sigma = pm.HalfCauchy("σ", self.sig)
mu = alpha+beta.dot(X.T)
score = pm.Normal("score", mu, sd=sigma, observed=y)
self.statistics_ = {"param": fit_model, "strategy": self.strategy}
return self
示例3: fit
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def fit(self, X, y):
"""Fit the Imputer to the dataset by fitting bayesian and LS model.
Args:
X (pd.Dataframe): dataset to fit the imputer.
y (pd.Series): response, which is eventually imputed.
Returns:
self. Instance of the class.
"""
_not_num_series(self.strategy, y)
nc = len(X.columns)
# get predictions for the data, which will be used for "closest" vals
y_pred = self.lm.fit(X, y).predict(X)
y_df = DataFrame({"y": y, "y_pred": y_pred})
# calculate bayes and use appropriate means for alpha and beta priors
# here we specify the point estimates from the linear regression as the
# means for the priors. This will greatly speed up posterior sampling
# and help ensure that convergence occurs
if self.am is None:
self.am = self.lm.intercept_
if self.bm is None:
self.bm = self.lm.coef_
# initialize model for bayesian linear reg. Default vals for priors
# assume data is scaled and centered. Convergence can struggle or fail
# if not the case and proper values for the priors are not specified
# separately, also assumes each beta is normal and "independent"
# while betas likely not independent, this is technically a rule of OLS
with pm.Model() as fit_model:
alpha = pm.Normal("alpha", self.am, sd=self.asd)
beta = pm.Normal("beta", self.bm, sd=self.bsd, shape=nc)
sigma = pm.HalfCauchy("σ", self.sig)
mu = alpha+beta.dot(X.T)
score = pm.Normal("score", mu, sd=sigma, observed=y)
params = {"model": fit_model, "y_obs": y_df}
self.statistics_ = {"param": params, "strategy": self.strategy}
return self
示例4: _pyro_noncentered_model
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def _pyro_noncentered_model(J, sigma, y=None):
import pyro
import pyro.distributions as dist
mu = pyro.sample("mu", dist.Normal(0, 5))
tau = pyro.sample("tau", dist.HalfCauchy(5))
with pyro.plate("J", J):
eta = pyro.sample("eta", dist.Normal(0, 1))
theta = mu + tau * eta
return pyro.sample("obs", dist.Normal(theta, sigma), obs=y)
示例5: _numpyro_noncentered_model
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def _numpyro_noncentered_model(J, sigma, y=None):
import numpyro
import numpyro.distributions as dist
mu = numpyro.sample("mu", dist.Normal(0, 5))
tau = numpyro.sample("tau", dist.HalfCauchy(5))
with numpyro.plate("J", J):
eta = numpyro.sample("eta", dist.Normal(0, 1))
theta = mu + tau * eta
return numpyro.sample("obs", dist.Normal(theta, sigma), obs=y)
示例6: pymc3_noncentered_schools
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def pymc3_noncentered_schools(data, draws, chains):
"""Non-centered eight schools implementation for pymc3."""
import pymc3 as pm
with pm.Model() as model:
mu = pm.Normal("mu", mu=0, sd=5)
tau = pm.HalfCauchy("tau", beta=5)
eta = pm.Normal("eta", mu=0, sd=1, shape=data["J"])
theta = pm.Deterministic("theta", mu + tau * eta)
pm.Normal("obs", mu=theta, sd=data["sigma"], observed=data["y"])
trace = pm.sample(draws, chains=chains)
return model, trace
示例7: __init__
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [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__)
示例8: model_configuration
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def model_configuration(self):
"""
Constructs the dictionary containing the priors for the weight vectors for the model according to the
regularization function. The parameters are:
* **weights** : Weights to evaluates the utility of the objects
For ``l1`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)
For ``l2`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w)
"""
if self.regularization == "l2":
weight = pm.Normal
prior = "sd"
elif self.regularization == "l1":
weight = pm.Laplace
prior = "b"
configuration = {
"weights": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 10}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
]
}
self.logger.info(
"Creating default config {}".format(print_dictionary(configuration))
)
return configuration
示例9: fit_cross_cov
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def fit_cross_cov(self, n_exp=2, n_gauss=2, range_mu=None):
"""
Fit an analytical covariance to the experimental data.
Args:
n_exp (int): number of exponential basic functions
n_gauss (int): number of gaussian basic functions
range_mu: prior mean of the range. Default mean of the lags
Returns:
pymc.Model: PyMC3 model to be sampled using MCMC
"""
self.n_exp = n_exp
self.n_gauss = n_gauss
n_var = self.n_properties
df = self.exp_var
lags = self.lags
# Prior standard deviation for the error of the regression
prior_std_reg = df.std(0).max() * 10
# Prior value for the mean of the ranges
if not range_mu:
range_mu = lags.mean()
# pymc3 Model
with pm.Model() as model: # model specifications in PyMC3 are wrapped in a with-statement
# Define priors
sigma = pm.HalfCauchy('sigma', beta=prior_std_reg, testval=1., shape=n_var)
psill = pm.Normal('sill', prior_std_reg, sd=.5 * prior_std_reg, shape=(n_exp + n_gauss))
range_ = pm.Normal('range', range_mu, sd=range_mu * .3, shape=(n_exp + n_gauss))
lambda_ = pm.Uniform('weights', 0, 1, shape=(n_var * (n_exp + n_gauss)))
# Exponential covariance
exp = pm.Deterministic('exp',
# (lambda_[:n_exp*n_var]*
psill[:n_exp] *
(1. - T.exp(T.dot(-lags.values.reshape((len(lags), 1)),
(range_[:n_exp].reshape((1, n_exp)) / 3.) ** -1))))
gauss = pm.Deterministic('gaus',
psill[n_exp:] *
(1. - T.exp(T.dot(-lags.values.reshape((len(lags), 1)) ** 2,
(range_[n_exp:].reshape((1, n_gauss)) * 4 / 7.) ** -2))))
# We stack the basic functions in the same matrix and tile it to match the number of properties we have
func = pm.Deterministic('func', T.tile(T.horizontal_stack(exp, gauss), (n_var, 1, 1)))
# We weight each basic function and sum them
func_w = pm.Deterministic("func_w", T.sum(func * lambda_.reshape((n_var, 1, (n_exp + n_gauss))), axis=2))
for e, cross in enumerate(df.columns):
# Likelihoods
pm.Normal(cross + "_like", mu=func_w[e], sd=sigma[e], observed=df[cross].values)
return model
示例10: model_configuration
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def model_configuration(self):
"""
Constructs the dictionary containing the priors for the weight vectors for the model according to the
regularization function. The parameters are:
* **weights** : Weights to evaluates the utility of the objects
* **weights_k** : Weights to evaluates the fractional allocation of each object in :math:'Q' to each nest
For ``l1`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)
For ``l2`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w)
Returns
-------
configuration : dict
Dictionary containing the priors applies on the weights
"""
if self._config is None:
if self.regularization == "l2":
weight = pm.Normal
prior = "sd"
elif self.regularization == "l1":
weight = pm.Laplace
prior = "b"
self._config = {
"weights": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 5}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
],
"weights_ik": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 5}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
],
}
self.logger.info(
"Creating model with config {}".format(print_dictionary(self._config))
)
return self._config
示例11: model_configuration
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def model_configuration(self):
"""
Constructs the dictionary containing the priors for the weight vectors for the model according to the
regularization function. The parameters are:
* **weights** : Distribution of the weigh vectors to evaluates the utility of the objects
For ``l1`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)
For ``l2`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w)
"""
if self._config is None:
if self.regularization == "l2":
weight = pm.Normal
prior = "sd"
elif self.regularization == "l1":
weight = pm.Laplace
prior = "b"
self._config = {
"weights": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 5}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
]
}
self.logger.info(
"Creating model with config {}".format(print_dictionary(self._config))
)
return self._config
示例12: model_configuration
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def model_configuration(self):
"""
Constructs the dictionary containing the priors for the weight vectors for the model according to the
regularization function. The parameters are:
* **weights** : Weights to evaluates the utility of the objects
For ``l1`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)
For ``l2`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w)
Returns
-------
configuration : dict
Dictionary containing the priors applies on the weights
"""
if self._config is None:
if self.regularization == "l2":
weight = pm.Normal
prior = "sd"
elif self.regularization == "l1":
weight = pm.Laplace
prior = "b"
self._config = {
"weights": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 5}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
]
}
self.logger.info(
"Creating model with config {}".format(print_dictionary(self._config))
)
return self._config
#
示例13: model_configuration
# 需要导入模块: import pymc3 [as 别名]
# 或者: from pymc3 import HalfCauchy [as 别名]
def model_configuration(self):
"""
Constructs the dictionary containing the priors for the weight vectors for the model according to the
regularization function. The parameters are:
* **weights** : Weights to evaluates the utility of the objects
* **weights_k** : Weights to evaluates the utility of the nests
For ``l1`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{b}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Laplace}(\\text{mu}=\\text{mu}_w, \\text{b}=\\text{b}_w)
For ``l2`` regularization the priors are:
.. math::
\\text{mu}_w \\sim \\text{Normal}(\\text{mu}=0, \\text{sd}=5.0) \\\\
\\text{sd}_w \\sim \\text{HalfCauchy}(\\beta=1.0) \\\\
\\text{weights} \\sim \\text{Normal}(\\text{mu}=\\text{mu}_w, \\text{sd}=\\text{sd}_w)
Returns
-------
configuration : dict
Dictionary containing the priors applies on the weights
"""
if self._config is None:
if self.regularization == "l2":
weight = pm.Normal
prior = "sd"
elif self.regularization == "l1":
weight = pm.Laplace
prior = "b"
self._config = {
"weights": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 5}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
],
"weights_k": [
weight,
{
"mu": (pm.Normal, {"mu": 0, "sd": 5}),
prior: (pm.HalfCauchy, {"beta": 1}),
},
],
}
self.logger.info(
"Creating model with config {}".format(print_dictionary(self._config))
)
return self._config