Python pymc3.HalfNormal() Examples

The following are 2 code examples for showing how to use pymc3.HalfNormal(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: nispat   Author: amarquand   File: hbr.py    License: GNU General Public License v3.0 5 votes vote down vote up
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) 
Example 2
Project: cs-ranking   Author: kiudee   File: model_selector.py    License: Apache License 2.0 5 votes vote down vote up
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__)