Source code for graphbrain.agents.agent

import progressbar


class Agent(object):
    """Base class for Graphbrain cognitive agents.

    These agents perform some change to an hypergraph, either by processing
    some external source of information of by inferring new knowledge from
    what is already contained in the hypregraph.
    """

    def __init__(self):
        self.search_pattern = '*'
        self.system = None
        self.running = False
        self.silent = False

    def name(self):
        """Returns the agent's name."""
        raise NotImplementedError()

    def languages(self):
        """Returns set of languages supported by the agent, or an empty set
        if the agent is language-agnostic.
        """
        return set()

    def input_edge(self, edge):
        """Feeds the agent an edge to process."""
        raise NotImplementedError()

    def on_start(self):
        """Called before a cycle of activity is started."""

    def on_end(self):
        """Called at the end of a cycle of activity."""
        return []

    def input(self):
        """Input to the agent all the edges corresponding to its current
        search pattern. A typical use is in knowledge inference agents, which
        infer new knowledge from edges already present in the hypergraph.
        """
        edge_count = self.system.hg.count(self.search_pattern)
        i = 0

        if self.silent:
            pbar = None
        else:
            pbar = progressbar.ProgressBar(max_value=edge_count).start()

        for edge in self.system.hg.search(self.search_pattern):
            ops = self.input_edge(edge)
            if ops:
                for op in ops:
                    yield op
            if i < edge_count:
                i += 1
                if not self.silent:
                    pbar.update(i)

        if not self.silent:
            pbar.finish()

    def report(self):
        """Produce a report of the agent's activities."""
        return ''

    def run(self):
        """High-level method to run an agent."""
        for op in self.input():
            yield op