On Sunday, December 13, 2015 at 8:10:25 PM UTC-5, Peter Otten wrote: > Robert wrote: > > > Hi, > > > > I follow code example at link: > > > > https://users.obs.carnegiescience.edu/cburns/ipynbs/PyMC.html > > > > > > There is the following code line: > > > > sampler = pymc.MCMC([alpha,betax,betay,eps,model,tau,z_obs,x_true,y_true]) > > > > > > I want to know the detail of pymc.MCMC, then I get help content of it > > with: > > > > ///////////// > > help(pymc.MCMC) > > Help on class MCMC in module pymc.MCMC: > > > > class MCMC(pymc.Model.Sampler) > > | This class fits probability models using Markov Chain Monte Carlo. > > | Each stochastic variable is assigned a StepMethod object, which makes > > | it take a single MCMC step conditional on the rest of the model. These > > | step methods are called in turn. > > | > > | >>> A = MCMC(input, db, verbose=0) > > | > > \\\\\\\\\\\\\\\\\\ > > > > > > help('pymc.Model.Sampler') > > no Python documentation found for 'pymc.Model.Sampler' > > > > > > help('pymc.Model') > > Help on class Model in pymc: > > > > pymc.Model = class Model(pymc.Container.ObjectContainer) > > | The base class for all objects that fit probability models. Model is > > | initialized with: > > | > > | >>> A = Model(input, verbose=0) > > | > > | :Parameters: > > | - input : module, list, tuple, dictionary, set, object or nothing. > > | Model definition, in terms of Stochastics, Deterministics, > > | Potentials and Containers. If nothing, all nodes are collected > > | from the base namespace. > > | > > | Attributes: > > | - deterministics > > | - stochastics (with observed=False) > > | - data (stochastic variables with observed=True) > > | - variables > > | - potentials > > | - containers > > | - nodes > > | - all_objects > > | - status: Not useful for the Model base class, but may be used by > > | subclasses. > > | > > | The following attributes only exist after the appropriate method is > > | called: > > | - moral_neighbors: The edges of the moralized graph. A dictionary, > > | keyed by stochastic variable, > > | whose values are sets of stochastic variables. Edges exist between > > | the key variable and all variables in the value. Created by method > > | _moralize. > > | - extended_children: The extended children of self's stochastic > > | variables. See the docstring of > > | extend_children. This is a dictionary keyed by stochastic > > | variable. > > | - generations: A list of sets of stochastic variables. The members > > | of each element only have parents in > > | previous elements. Created by method find_generations. > > | > > | Methods: > > | - sample_model_likelihood(iter): Generate and return iter samples > > | of p(data and potentials|model). > > | Can be used to generate Bayes' factors. > > | > > | :SeeAlso: Sampler, MAP, NormalApproximation, weight, Container, graph. > > | > > | Method resolution order: > > | Model > > | pymc.Container.ObjectContainer > > | pymc.six.NewBase > > | pymc.Node.ContainerBase > > | __builtin__.object > > | > > | Methods defined here: > > | > > | __init__(self, input=None, name=None, verbose=-1) > > | Initialize a Model instance. > > | > > | :Parameters: > > | - input : module, list, tuple, dictionary, set, object or > > | nothing. > > | Model definition, in terms of Stochastics, Deterministics, > > | Potentials and Containers. If nothing, all nodes are > > | collected from the base namespace. > > | > > | draw_from_prior(self) > > | Sets all variables to random values drawn from joint 'prior', > > | meaning contributions of data and potentials to the joint > > | distribution are not considered. > > | > > | get_node(self, node_name) > > | Retrieve node with passed name > > | > > | seed(self) > > | Seed new initial values for the stochastics. > > | > > | ---------------------------------------------------------------------- > > | Data descriptors defined here: > > | > > | generations > > | > > | ---------------------------------------------------------------------- > > | Data and other attributes defined here: > > | > > | __slotnames__ = [] > > | > > | register = False > > | > > | ---------------------------------------------------------------------- > > | Methods inherited from pymc.Container.ObjectContainer: > > | > > | replace(self, item, new_container, key) > > | > > | ---------------------------------------------------------------------- > > | Data descriptors inherited from pymc.Container.ObjectContainer: > > | > > | value > > | A copy of self, with all variables replaced by their values. > > | > > | ---------------------------------------------------------------------- > > | Methods inherited from pymc.Node.ContainerBase: > > | > > | assimilate(self, new_container) > > | > > | ---------------------------------------------------------------------- > > | Data descriptors inherited from pymc.Node.ContainerBase: > > | > > | __dict__ > > | dictionary for instance variables (if defined) > > | > > | __weakref__ > > | list of weak references to the object (if defined) > > | > > | logp > > | The summed log-probability of all stochastic variables (data > > | or otherwise) and factor potentials in self. > > | > > | ---------------------------------------------------------------------- > > | Data and other attributes inherited from pymc.Node.ContainerBase: > > | > > | change_methods = [] > > | > > | containing_classes = [] > > --------- > > > > > > Now, I have puzzles on the class constructor input parameter: > > [alpha,betax,betay,eps,model,tau,z_obs,x_true,y_true] > > > > 1. 'class MCMC(pymc.Model.Sampler)' says its inheritance is from > > 'pymc.Model.Sampler' > > > > 2. When I try to get help on 'pymc.Model.Sampler', it says: > > 'no Python documentation found for 'pymc.Model.Sampler' > > > > 3. When I continue to catch help on 'pymc.Model.Sampler', I don't see > > content mentions 'Sampler'. This complete help message is shown above. > > > > So, what is 'pymc.Model.Sampler'? > > Unfortunately there is a module pymc.Model and a class pymc.Model.Model, and > in pymc.__init__.py there is code that overwrites the module with the class. > Therefore when you write > > pymc.Model > > you get > > pymc.Model.Model > > as you can see when you type > > >>> import pymc > >>> pymc.Model > <class 'pymc.Model.Model'> > > To get around this bad naming use > > >>> from pymc.Model import Sampler > >>> help(Sampler)
Thanks. Your answer does solve the problem, but I cannot follow your words. When you run below code, what is 'pymc.Model'? >>> import pymc >>> pymc.Model <class 'pymc.Model.Model'> When I run: aaa=pymc.Model type(aaa) Out[160]: pymc.Node.ContainerMeta type(pymc.Model) Out[161]: pymc.Node.ContainerMeta I see that it is not '<class 'pymc.Model.Model'>'. Thanks again. -- https://mail.python.org/mailman/listinfo/python-list
