"""Probability models (Chapter 13-15)"""

from collections import defaultdict
from functools import reduce

from .agents import Agent
from .utils import *


def DTAgentProgram(belief_state):
    """
    [Figure 13.1]
    A decision-theoretic agent.
    """

    def program(percept):
        belief_state.observe(program.action, percept)
        program.action = max(belief_state.actions(), key=belief_state.expected_outcome_utility)
        return program.action

    program.action = None
    return program


# ______________________________________________________________________________


class ProbDist:
    """A discrete probability distribution. You name the random variable
    in the constructor, then assign and query probability of values.
    >>> P = ProbDist('Flip'); P['H'], P['T'] = 0.25, 0.75; P['H']
    0.25
    >>> P = ProbDist('X', {'lo': 125, 'med': 375, 'hi': 500})
    >>> P['lo'], P['med'], P['hi']
    (0.125, 0.375, 0.5)
    """

    def __init__(self, var_name='?', freq=None):
        """If freq is given, it is a dictionary of values - frequency pairs,
        then ProbDist is normalized."""
        self.prob = {}
        self.var_name = var_name
        self.values = []
        if freq:
            for (v, p) in freq.items():
                self[v] = p
            self.normalize()

    def __getitem__(self, val):
        """Given a value, return P(value)."""
        try:
            return self.prob[val]
        except KeyError:
            return 0

    def __setitem__(self, val, p):
        """Set P(val) = p."""
        if val not in self.values:
            self.values.append(val)
        self.prob[val] = p

    def normalize(self):
        """Make sure the probabilities of all values sum to 1.
        Returns the normalized distribution.
        Raises a ZeroDivisionError if the sum of the values is 0."""
        total = sum(self.prob.values())
        if not np.isclose(total, 1.0):
            for val in self.prob:
                self.prob[val] /= total
        return self

    def show_approx(self, numfmt='{:.3g}'):
        """Show the probabilities rounded and sorted by key, for the
        sake of portable doctests."""
        return ', '.join([('{}: ' + numfmt).format(v, p) for (v, p) in sorted(self.prob.items())])

    def __repr__(self):
        return "P({})".format(self.var_name)


class JointProbDist(ProbDist):
    """A discrete probability distribute over a set of variables.
    >>> P = JointProbDist(['X', 'Y']); P[1, 1] = 0.25
    >>> P[1, 1]
    0.25
    >>> P[dict(X=0, Y=1)] = 0.5
    >>> P[dict(X=0, Y=1)]
    0.5"""

    def __init__(self, variables):
        self.prob = {}
        self.variables = variables
        self.vals = defaultdict(list)

    def __getitem__(self, values):
        """Given a tuple or dict of values, return P(values)."""
        values = event_values(values, self.variables)
        return ProbDist.__getitem__(self, values)

    def __setitem__(self, values, p):
        """Set P(values) = p. Values can be a tuple or a dict; it must
        have a value for each of the variables in the joint. Also keep track
        of the values we have seen so far for each variable."""
        values = event_values(values, self.variables)
        self.prob[values] = p
        for var, val in zip(self.variables, values):
            if val not in self.vals[var]:
                self.vals[var].append(val)

    def values(self, var):
        """Return the set of possible values for a variable."""
        return self.vals[var]

    def __repr__(self):
        return "P({})".format(self.variables)


def event_values(event, variables):
    """Return a tuple of the values of variables in event.
    >>> event_values ({'A': 10, 'B': 9, 'C': 8}, ['C', 'A'])
    (8, 10)
    >>> event_values ((1, 2), ['C', 'A'])
    (1, 2)
    """
    if isinstance(event, tuple) and len(event) == len(variables):
        return event
    else:
        return tuple([event[var] for var in variables])


# ______________________________________________________________________________


def enumerate_joint_ask(X, e, P):
    """
    [Section 13.3]
    Return a probability distribution over the values of the variable X,
    given the {var:val} observations e, in the JointProbDist P.
    >>> P = JointProbDist(['X', 'Y'])
    >>> P[0,0] = 0.25; P[0,1] = 0.5; P[1,1] = P[2,1] = 0.125
    >>> enumerate_joint_ask('X', dict(Y=1), P).show_approx()
    '0: 0.667, 1: 0.167, 2: 0.167'
    """
    assert X not in e, "Query variable must be distinct from evidence"
    Q = ProbDist(X)  # probability distribution for X, initially empty
    Y = [v for v in P.variables if v != X and v not in e]  # hidden variables.
    for xi in P.values(X):
        Q[xi] = enumerate_joint(Y, extend(e, X, xi), P)
    return Q.normalize()


def enumerate_joint(variables, e, P):
    """Return the sum of those entries in P consistent with e,
    provided variables is P's remaining variables (the ones not in e)."""
    if not variables:
        return P[e]
    Y, rest = variables[0], variables[1:]
    return sum([enumerate_joint(rest, extend(e, Y, y), P) for y in P.values(Y)])


# ______________________________________________________________________________


class BayesNet:
    """Bayesian network containing only boolean-variable nodes."""

    def __init__(self, node_specs=None):
        """Nodes must be ordered with parents before children."""
        self.nodes = []
        self.variables = []
        node_specs = node_specs or []
        for node_spec in node_specs:
            self.add(node_spec)

    def add(self, node_spec):
        """Add a node to the net. Its parents must already be in the
        net, and its variable must not."""
        node = BayesNode(*node_spec)
        assert node.variable not in self.variables
        assert all((parent in self.variables) for parent in node.parents)
        self.nodes.append(node)
        self.variables.append(node.variable)
        for parent in node.parents:
            self.variable_node(parent).children.append(node)

    def variable_node(self, var):
        """Return the node for the variable named var.
        >>> burglary.variable_node('Burglary').variable
        'Burglary'"""
        for n in self.nodes:
            if n.variable == var:
                return n
        raise Exception("No such variable: {}".format(var))

    def variable_values(self, var):
        """Return the domain of var."""
        return [True, False]

    def __repr__(self):
        return 'BayesNet({0!r})'.format(self.nodes)


class DecisionNetwork(BayesNet):
    """An abstract class for a decision network as a wrapper for a BayesNet.
    Represents an agent's current state, its possible actions, reachable states
    and utilities of those states."""

    def __init__(self, action, infer):
        """action: a single action node
        infer: the preferred method to carry out inference on the given BayesNet"""
        super(DecisionNetwork, self).__init__()
        self.action = action
        self.infer = infer

    def best_action(self):
        """Return the best action in the network"""
        return self.action

    def get_utility(self, action, state):
        """Return the utility for a particular action and state in the network"""
        raise NotImplementedError

    def get_expected_utility(self, action, evidence):
        """Compute the expected utility given an action and evidence"""
        u = 0.0
        prob_dist = self.infer(action, evidence, self).prob
        for item, _ in prob_dist.items():
            u += prob_dist[item] * self.get_utility(action, item)

        return u


class InformationGatheringAgent(Agent):
    """
    [Figure 16.9]
    A simple information gathering agent. The agent works by repeatedly selecting
    the observation with the highest information value, until the cost of the next
    observation is greater than its expected benefit."""

    def __init__(self, decnet, infer, initial_evidence=None):
        """decnet: a decision network
        infer: the preferred method to carry out inference on the given decision network
        initial_evidence: initial evidence"""
        self.decnet = decnet
        self.infer = infer
        self.observation = initial_evidence or []
        self.variables = self.decnet.nodes

    def integrate_percept(self, percept):
        """Integrate the given percept into the decision network"""
        raise NotImplementedError

    def execute(self, percept):
        """Execute the information gathering algorithm"""
        self.observation = self.integrate_percept(percept)
        vpis = self.vpi_cost_ratio(self.variables)
        j = max(vpis)
        variable = self.variables[j]

        if self.vpi(variable) > self.cost(variable):
            return self.request(variable)

        return self.decnet.best_action()

    def request(self, variable):
        """Return the value of the given random variable as the next percept"""
        raise NotImplementedError

    def cost(self, var):
        """Return the cost of obtaining evidence through tests, consultants or questions"""
        raise NotImplementedError

    def vpi_cost_ratio(self, variables):
        """Return the VPI to cost ratio for the given variables"""
        v_by_c = []
        for var in variables:
            v_by_c.append(self.vpi(var) / self.cost(var))
        return v_by_c

    def vpi(self, variable):
        """Return VPI for a given variable"""
        vpi = 0.0
        prob_dist = self.infer(variable, self.observation, self.decnet).prob
        for item, _ in prob_dist.items():
            post_prob = prob_dist[item]
            new_observation = list(self.observation)
            new_observation.append(item)
            expected_utility = self.decnet.get_expected_utility(variable, new_observation)
            vpi += post_prob * expected_utility

        vpi -= self.decnet.get_expected_utility(variable, self.observation)
        return vpi


class BayesNode:
    """A conditional probability distribution for a boolean variable,
    P(X | parents). Part of a BayesNet."""

    def __init__(self, X, parents, cpt):
        """X is a variable name, and parents a sequence of variable
        names or a space-separated string. cpt, the conditional
        probability table, takes one of these forms:

        * A number, the unconditional probability P(X=true). You can
          use this form when there are no parents.

        * A dict {v: p, ...}, the conditional probability distribution
          P(X=true | parent=v) = p. When there's just one parent.

        * A dict {(v1, v2, ...): p, ...}, the distribution P(X=true |
          parent1=v1, parent2=v2, ...) = p. Each key must have as many
          values as there are parents. You can use this form always;
          the first two are just conveniences.

        In all cases the probability of X being false is left implicit,
        since it follows from P(X=true).

        >>> X = BayesNode('X', '', 0.2)
        >>> Y = BayesNode('Y', 'P', {T: 0.2, F: 0.7})
        >>> Z = BayesNode('Z', 'P Q',
        ...    {(T, T): 0.2, (T, F): 0.3, (F, T): 0.5, (F, F): 0.7})
        """
        if isinstance(parents, str):
            parents = parents.split()

        # We store the table always in the third form above.
        if isinstance(cpt, (float, int)):  # no parents, 0-tuple
            cpt = {(): cpt}
        elif isinstance(cpt, dict):
            # one parent, 1-tuple
            if cpt and isinstance(list(cpt.keys())[0], bool):
                cpt = {(v,): p for v, p in cpt.items()}

        assert isinstance(cpt, dict)
        for vs, p in cpt.items():
            assert isinstance(vs, tuple) and len(vs) == len(parents)
            assert all(isinstance(v, bool) for v in vs)
            assert 0 <= p <= 1

        self.variable = X
        self.parents = parents
        self.cpt = cpt
        self.children = []

    def p(self, value, event):
        """Return the conditional probability
        P(X=value | parents=parent_values), where parent_values
        are the values of parents in event. (event must assign each
        parent a value.)
        >>> bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625})
        >>> bn.p(False, {'Burglary': False, 'Earthquake': True})
        0.375"""
        assert isinstance(value, bool)
        ptrue = self.cpt[event_values(event, self.parents)]
        return ptrue if value else 1 - ptrue

    def sample(self, event):
        """Sample from the distribution for this variable conditioned
        on event's values for parent_variables. That is, return True/False
        at random according with the conditional probability given the
        parents."""
        return probability(self.p(True, event))

    def __repr__(self):
        return repr((self.variable, ' '.join(self.parents)))


# Burglary example [Figure 14.2]

T, F = True, False

burglary = BayesNet([('Burglary', '', 0.001),
                     ('Earthquake', '', 0.002),
                     ('Alarm', 'Burglary Earthquake',
                      {(T, T): 0.95, (T, F): 0.94, (F, T): 0.29, (F, F): 0.001}),
                     ('JohnCalls', 'Alarm', {T: 0.90, F: 0.05}),
                     ('MaryCalls', 'Alarm', {T: 0.70, F: 0.01})])


# ______________________________________________________________________________


def enumeration_ask(X, e, bn):
    """
    [Figure 14.9]
    Return the conditional probability distribution of variable X
    given evidence e, from BayesNet bn.
    >>> enumeration_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary
    ...  ).show_approx()
    'False: 0.716, True: 0.284'"""
    assert X not in e, "Query variable must be distinct from evidence"
    Q = ProbDist(X)
    for xi in bn.variable_values(X):
        Q[xi] = enumerate_all(bn.variables, extend(e, X, xi), bn)
    return Q.normalize()


def enumerate_all(variables, e, bn):
    """Return the sum of those entries in P(variables | e{others})
    consistent with e, where P is the joint distribution represented
    by bn, and e{others} means e restricted to bn's other variables
    (the ones other than variables). Parents must precede children in variables."""
    if not variables:
        return 1.0
    Y, rest = variables[0], variables[1:]
    Ynode = bn.variable_node(Y)
    if Y in e:
        return Ynode.p(e[Y], e) * enumerate_all(rest, e, bn)
    else:
        return sum(Ynode.p(y, e) * enumerate_all(rest, extend(e, Y, y), bn)
                   for y in bn.variable_values(Y))


# ______________________________________________________________________________


def elimination_ask(X, e, bn):
    """
    [Figure 14.11]
    Compute bn's P(X|e) by variable elimination.
    >>> elimination_ask('Burglary', dict(JohnCalls=T, MaryCalls=T), burglary
    ...  ).show_approx()
    'False: 0.716, True: 0.284'"""
    assert X not in e, "Query variable must be distinct from evidence"
    factors = []
    for var in reversed(bn.variables):
        factors.append(make_factor(var, e, bn))
        if is_hidden(var, X, e):
            factors = sum_out(var, factors, bn)
    return pointwise_product(factors, bn).normalize()


def is_hidden(var, X, e):
    """Is var a hidden variable when querying P(X|e)?"""
    return var != X and var not in e


def make_factor(var, e, bn):
    """Return the factor for var in bn's joint distribution given e.
    That is, bn's full joint distribution, projected to accord with e,
    is the pointwise product of these factors for bn's variables."""
    node = bn.variable_node(var)
    variables = [X for X in [var] + node.parents if X not in e]
    cpt = {event_values(e1, variables): node.p(e1[var], e1)
           for e1 in all_events(variables, bn, e)}
    return Factor(variables, cpt)


def pointwise_product(factors, bn):
    return reduce(lambda f, g: f.pointwise_product(g, bn), factors)


def sum_out(var, factors, bn):
    """Eliminate var from all factors by summing over its values."""
    result, var_factors = [], []
    for f in factors:
        (var_factors if var in f.variables else result).append(f)
    result.append(pointwise_product(var_factors, bn).sum_out(var, bn))
    return result


class Factor:
    """A factor in a joint distribution."""

    def __init__(self, variables, cpt):
        self.variables = variables
        self.cpt = cpt

    def pointwise_product(self, other, bn):
        """Multiply two factors, combining their variables."""
        variables = list(set(self.variables) | set(other.variables))
        cpt = {event_values(e, variables): self.p(e) * other.p(e) for e in all_events(variables, bn, {})}
        return Factor(variables, cpt)

    def sum_out(self, var, bn):
        """Make a factor eliminating var by summing over its values."""
        variables = [X for X in self.variables if X != var]
        cpt = {event_values(e, variables): sum(self.p(extend(e, var, val)) for val in bn.variable_values(var))
               for e in all_events(variables, bn, {})}
        return Factor(variables, cpt)

    def normalize(self):
        """Return my probabilities; must be down to one variable."""
        assert len(self.variables) == 1
        return ProbDist(self.variables[0], {k: v for ((k,), v) in self.cpt.items()})

    def p(self, e):
        """Look up my value tabulated for e."""
        return self.cpt[event_values(e, self.variables)]


def all_events(variables, bn, e):
    """Yield every way of extending e with values for all variables."""
    if not variables:
        yield e
    else:
        X, rest = variables[0], variables[1:]
        for e1 in all_events(rest, bn, e):
            for x in bn.variable_values(X):
                yield extend(e1, X, x)


# ______________________________________________________________________________

# [Figure 14.12a]: sprinkler network


sprinkler = BayesNet([('Cloudy', '', 0.5),
                      ('Sprinkler', 'Cloudy', {T: 0.10, F: 0.50}),
                      ('Rain', 'Cloudy', {T: 0.80, F: 0.20}),
                      ('WetGrass', 'Sprinkler Rain',
                       {(T, T): 0.99, (T, F): 0.90, (F, T): 0.90, (F, F): 0.00})])


# ______________________________________________________________________________


def prior_sample(bn):
    """
    [Figure 14.13]
    Randomly sample from bn's full joint distribution.
    The result is a {variable: value} dict.
    """
    event = {}
    for node in bn.nodes:
        event[node.variable] = node.sample(event)
    return event


# _________________________________________________________________________


def rejection_sampling(X, e, bn, N=10000):
    """
    [Figure 14.14]
    Estimate the probability distribution of variable X given
    evidence e in BayesNet bn, using N samples.
    Raises a ZeroDivisionError if all the N samples are rejected,
    i.e., inconsistent with e.
    >>> random.seed(47)
    >>> rejection_sampling('Burglary', dict(JohnCalls=T, MaryCalls=T),
    ...   burglary, 10000).show_approx()
    'False: 0.7, True: 0.3'
    """
    counts = {x: 0 for x in bn.variable_values(X)}  # bold N in [Figure 14.14]
    for j in range(N):
        sample = prior_sample(bn)  # boldface x in [Figure 14.14]
        if consistent_with(sample, e):
            counts[sample[X]] += 1
    return ProbDist(X, counts)


def consistent_with(event, evidence):
    """Is event consistent with the given evidence?"""
    return all(evidence.get(k, v) == v for k, v in event.items())


# _________________________________________________________________________


def likelihood_weighting(X, e, bn, N=10000):
    """
    [Figure 14.15]
    Estimate the probability distribution of variable X given
    evidence e in BayesNet bn.
    >>> random.seed(1017)
    >>> likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T),
    ...   burglary, 10000).show_approx()
    'False: 0.702, True: 0.298'
    """
    W = {x: 0 for x in bn.variable_values(X)}
    for j in range(N):
        sample, weight = weighted_sample(bn, e)  # boldface x, w in [Figure 14.15]
        W[sample[X]] += weight
    return ProbDist(X, W)


def weighted_sample(bn, e):
    """
    Sample an event from bn that's consistent with the evidence e;
    return the event and its weight, the likelihood that the event
    accords to the evidence.
    """
    w = 1
    event = dict(e)  # boldface x in [Figure 14.15]
    for node in bn.nodes:
        Xi = node.variable
        if Xi in e:
            w *= node.p(e[Xi], event)
        else:
            event[Xi] = node.sample(event)
    return event, w


# _________________________________________________________________________


def gibbs_ask(X, e, bn, N=1000):
    """[Figure 14.16]"""
    assert X not in e, "Query variable must be distinct from evidence"
    counts = {x: 0 for x in bn.variable_values(X)}  # bold N in [Figure 14.16]
    Z = [var for var in bn.variables if var not in e]
    state = dict(e)  # boldface x in [Figure 14.16]
    for Zi in Z:
        state[Zi] = random.choice(bn.variable_values(Zi))
    for j in range(N):
        for Zi in Z:
            state[Zi] = markov_blanket_sample(Zi, state, bn)
            counts[state[X]] += 1
    return ProbDist(X, counts)


def markov_blanket_sample(X, e, bn):
    """Return a sample from P(X | mb) where mb denotes that the
    variables in the Markov blanket of X take their values from event
    e (which must assign a value to each). The Markov blanket of X is
    X's parents, children, and children's parents."""
    Xnode = bn.variable_node(X)
    Q = ProbDist(X)
    for xi in bn.variable_values(X):
        ei = extend(e, X, xi)
        # [Equation 14.12]
        Q[xi] = Xnode.p(xi, e) * product(Yj.p(ei[Yj.variable], ei) for Yj in Xnode.children)
    # (assuming a Boolean variable here)
    return probability(Q.normalize()[True])


# _________________________________________________________________________


class HiddenMarkovModel:
    """A Hidden markov model which takes Transition model and Sensor model as inputs"""

    def __init__(self, transition_model, sensor_model, prior=None):
        self.transition_model = transition_model
        self.sensor_model = sensor_model
        self.prior = prior or [0.5, 0.5]

    def sensor_dist(self, ev):
        if ev is True:
            return self.sensor_model[0]
        else:
            return self.sensor_model[1]


def forward(HMM, fv, ev):
    prediction = vector_add(scalar_vector_product(fv[0], HMM.transition_model[0]),
                            scalar_vector_product(fv[1], HMM.transition_model[1]))
    sensor_dist = HMM.sensor_dist(ev)

    return normalize(element_wise_product(sensor_dist, prediction))


def backward(HMM, b, ev):
    sensor_dist = HMM.sensor_dist(ev)
    prediction = element_wise_product(sensor_dist, b)

    return normalize(vector_add(scalar_vector_product(prediction[0], HMM.transition_model[0]),
                                scalar_vector_product(prediction[1], HMM.transition_model[1])))


def forward_backward(HMM, ev):
    """
    [Figure 15.4]
    Forward-Backward algorithm for smoothing. Computes posterior probabilities
    of a sequence of states given a sequence of observations.
    """
    t = len(ev)
    ev.insert(0, None)  # to make the code look similar to pseudo code

    fv = [[0.0, 0.0] for _ in range(len(ev))]
    b = [1.0, 1.0]
    sv = [[0, 0] for _ in range(len(ev))]

    fv[0] = HMM.prior

    for i in range(1, t + 1):
        fv[i] = forward(HMM, fv[i - 1], ev[i])
    for i in range(t, -1, -1):
        sv[i - 1] = normalize(element_wise_product(fv[i], b))
        b = backward(HMM, b, ev[i])

    sv = sv[::-1]

    return sv


def viterbi(HMM, ev):
    """
    [Equation 15.11]
    Viterbi algorithm to find the most likely sequence. Computes the best path and the
    corresponding probabilities, given an HMM model and a sequence of observations.
    """
    t = len(ev)
    ev = ev.copy()
    ev.insert(0, None)

    m = [[0.0, 0.0] for _ in range(len(ev) - 1)]

    # the recursion is initialized with m1 = forward(P(X0), e1)
    m[0] = forward(HMM, HMM.prior, ev[1])
    # keep track of maximizing predecessors
    backtracking_graph = []

    for i in range(1, t):
        m[i] = element_wise_product(HMM.sensor_dist(ev[i + 1]),
                                    [max(element_wise_product(HMM.transition_model[0], m[i - 1])),
                                     max(element_wise_product(HMM.transition_model[1], m[i - 1]))])
        backtracking_graph.append([np.argmax(element_wise_product(HMM.transition_model[0], m[i - 1])),
                                   np.argmax(element_wise_product(HMM.transition_model[1], m[i - 1]))])

    # computed probabilities
    ml_probabilities = [0.0] * (len(ev) - 1)
    # most likely sequence
    ml_path = [True] * (len(ev) - 1)

    # the construction of the most likely sequence starts in the final state with the largest probability, and
    # runs backwards; the algorithm needs to store for each xt its predecessor xt-1 maximizing its probability
    i_max = np.argmax(m[-1])

    for i in range(t - 1, -1, -1):
        ml_probabilities[i] = m[i][i_max]
        ml_path[i] = True if i_max == 0 else False
        if i > 0:
            i_max = backtracking_graph[i - 1][i_max]

    return ml_path, ml_probabilities


# _________________________________________________________________________


def fixed_lag_smoothing(e_t, HMM, d, ev, t):
    """
    [Figure 15.6]
    Smoothing algorithm with a fixed time lag of 'd' steps.
    Online algorithm that outputs the new smoothed estimate if observation
    for new time step is given.
    """
    ev.insert(0, None)

    T_model = HMM.transition_model
    f = HMM.prior
    B = [[1, 0], [0, 1]]

    O_t = np.diag(HMM.sensor_dist(e_t))
    if t > d:
        f = forward(HMM, f, e_t)
        O_tmd = np.diag(HMM.sensor_dist(ev[t - d]))
        B = matrix_multiplication(np.linalg.inv(O_tmd), np.linalg.inv(T_model), B, T_model, O_t)
    else:
        B = matrix_multiplication(B, T_model, O_t)
    t += 1

    if t > d:
        # always returns a 1x2 matrix
        return [normalize(i) for i in matrix_multiplication([f], B)][0]
    else:
        return None


# _________________________________________________________________________


def particle_filtering(e, N, HMM):
    """Particle filtering considering two states variables."""
    dist = [0.5, 0.5]
    # Weight Initialization
    w = [0 for _ in range(N)]
    # STEP 1
    # Propagate one step using transition model given prior state
    dist = vector_add(scalar_vector_product(dist[0], HMM.transition_model[0]),
                      scalar_vector_product(dist[1], HMM.transition_model[1]))
    # Assign state according to probability
    s = ['A' if probability(dist[0]) else 'B' for _ in range(N)]
    w_tot = 0
    # Calculate importance weight given evidence e
    for i in range(N):
        if s[i] == 'A':
            # P(U|A)*P(A)
            w_i = HMM.sensor_dist(e)[0] * dist[0]
        if s[i] == 'B':
            # P(U|B)*P(B)
            w_i = HMM.sensor_dist(e)[1] * dist[1]
        w[i] = w_i
        w_tot += w_i

    # Normalize all the weights
    for i in range(N):
        w[i] = w[i] / w_tot

    # Limit weights to 4 digits
    for i in range(N):
        w[i] = float("{0:.4f}".format(w[i]))

    # STEP 2
    s = weighted_sample_with_replacement(N, s, w)

    return s


# _________________________________________________________________________
# TODO: Implement continuous map for MonteCarlo similar to Fig25.10 from the book


class MCLmap:
    """Map which provides probability distributions and sensor readings.
    Consists of discrete cells which are either an obstacle or empty"""

    def __init__(self, m):
        self.m = m
        self.nrows = len(m)
        self.ncols = len(m[0])
        # list of empty spaces in the map
        self.empty = [(i, j) for i in range(self.nrows) for j in range(self.ncols) if not m[i][j]]

    def sample(self):
        """Returns a random kinematic state possible in the map"""
        pos = random.choice(self.empty)
        # 0N 1E 2S 3W
        orient = random.choice(range(4))
        kin_state = pos + (orient,)
        return kin_state

    def ray_cast(self, sensor_num, kin_state):
        """Returns distance to nearest obstacle or map boundary in the direction of sensor"""
        pos = kin_state[:2]
        orient = kin_state[2]
        # sensor layout when orientation is 0 (towards North)
        #  0
        # 3R1
        #  2
        delta = ((sensor_num % 2 == 0) * (sensor_num - 1), (sensor_num % 2 == 1) * (2 - sensor_num))
        # sensor direction changes based on orientation
        for _ in range(orient):
            delta = (delta[1], -delta[0])
        range_count = 0
        while 0 <= pos[0] < self.nrows and 0 <= pos[1] < self.nrows and not self.m[pos[0]][pos[1]]:
            pos = vector_add(pos, delta)
            range_count += 1
        return range_count


def monte_carlo_localization(a, z, N, P_motion_sample, P_sensor, m, S=None):
    """
    [Figure 25.9]
    Monte Carlo localization algorithm
    """

    def ray_cast(sensor_num, kin_state, m):
        return m.ray_cast(sensor_num, kin_state)

    M = len(z)
    S_ = [0] * N
    W_ = [0] * N
    v = a['v']
    w = a['w']

    if S is None:
        S = [m.sample() for _ in range(N)]

    for i in range(N):
        S_[i] = P_motion_sample(S[i], v, w)
        W_[i] = 1
        for j in range(M):
            z_ = ray_cast(j, S_[i], m)
            W_[i] = W_[i] * P_sensor(z[j], z_)

    S = weighted_sample_with_replacement(N, S_, W_)
    return S
