<commit_before># -*- coding: utf-8 -*-

import pybayes as pb
import numpy as np
from qsrrep_pf.probability_density_functions import PredictionPdf, ObservationPdf, UniIntPdf
from qsrrep_pf.particle_filter_base import ParticleFilter
from collections import OrderedDict
from copy import deepcopy
import time


class ParticleFilterPredictor(object):

    def create(self, **kwargs):
        models = OrderedDict(kwargs["models"])
        states = kwargs["state_lookup_table"]
        x_t = pb.RVComp(2, 'x_t')
        p = PredictionPdf(models=models, states=states, rv=x_t, cond_rv=pb.RVComp(2, 'x_tp'))
        o = ObservationPdf(states=states, models=models, rv=pb.RVComp(2, 'y_t'), cond_rv=[x_t])

        # prepare initial particle density:
        init_pdf = UniIntPdf(
            np.array([0., 0.]),
            np.array([float(len(states)-1), float(len(models.keys())-1)]),
            cheat=kwargs["ensure_particle_per_state"]
        )

        return {
            "filter": ParticleFilter(kwargs["num_particles"], init_pdf, p, o, starvation_factor=1.-kwargs["starvation_factor"]),
            "models": models.keys(),
            "states": kwargs["state_lookup_table"]
        }

    def predict(self, **kwargs):
        ret = []
        if kwargs["debug"]: start = time.time()
        p = deepcopy(kwargs["filter"].emp.particles)
        for n in range(kwargs["num_steps"]):
            if kwargs["debug"]: start = time.time()
            pn = kwargs["filter"].p_xt_xtp.sample_multiple(p)
            if kwargs["debug"]:<commit_msg>A little bit of error handling for incorrect model and state numbers.<commit_after># -*- coding: utf-8 -*-

import pybayes as pb
import numpy as np
from qsrrep_pf.probability_density_functions import PredictionPdf, ObservationPdf, UniIntPdf
from qsrrep_pf.particle_filter_base import ParticleFilter
from collections import OrderedDict
from copy import deepcopy
import time


class ParticleFilterPredictor(object):

    def create(self, **kwargs):
        models = OrderedDict(kwargs["models"])
        states = kwargs["state_lookup_table"]
        x_t = pb.RVComp(2, 'x_t')
        p = PredictionPdf(models=models, states=states, rv=x_t, cond_rv=pb.RVComp(2, 'x_tp'))
        o = ObservationPdf(states=states, models=models, rv=pb.RVComp(2, 'y_t'), cond_rv=[x_t])

        # prepare initial particle density:
        try:
            init_pdf = UniIntPdf(
                np.array([0., 0.]),
                np.array([float(len(states)-1), float(len(models.keys())-1)]),
                cheat=kwargs["ensure_particle_per_state"]
            )
        except ValueError as e:
            print "### Encountered a problem while creating intial particle distribution:", e
            print "### This might happen if there is only one model or one state defined."
            print "### Currently defined number of states: %s and number of models: %s" % (str(len(states)),str(len(models.keys()))), models.keys()
            return None

        return {
            "filter": ParticleFilter(kwargs["num_particles"], init_pdf, p, o, starvation_factor=1.-kwargs["starvation_factor"]),
            "models": models.keys(),
            "states": kwargs["state_lookup_table"]
        }

    def predict(self, **kwargs):
        ret = []
        if kwargs["debug"]: start = time.time()
        p = deepcopy(kwargs["filter"].emp.particles)
        for n in range(kwargs["num_steps"]):
            if kwargs["debug"]: start = time.time()
            pn = kwargs["filter"].p_xt_xtp.sample_multiple(p)
            if kwargs["debug"]:
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