Python pymc3.HalfNormal() Examples

The following are 2 code examples of pymc3.HalfNormal(). 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. You may also want to check out all available functions/classes of the module pymc3 , or try the search function .
Example #1
Source File: hbr.py    From nispat with 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
Source File: model_selector.py    From cs-ranking with 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__)