# featureExtractors.py
# --------------------
# Licensing Information: Please do not distribute or publish solutions to this
# project. You are free to use and extend these projects for educational
# purposes. The Pacman AI projects were developed at UC Berkeley, primarily by
# John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
# Student side autograding was added by Brad Miller, Nick Hay, and Pieter
# Abbeel in Spring 2013.
# For more info, see http://inst.eecs.berkeley.edu/~cs188/pacman/pacman.html

"Feature extractors for Pacman game states"

from game import Directions, Actions
import util


class FeatureExtractor:

    def getFeatures(self, state, action):
        """
          Returns a dict from features to counts
          Usually, the count will just be 1.0 for
          indicator functions.
        """
        util.raiseNotDefined()


class IdentityExtractor(FeatureExtractor):

    def getFeatures(self, state, action):
        feats = util.Counter()
        feats[(state, action)] = 1.0
        return feats


class CoordinateExtractor(FeatureExtractor):

    def getFeatures(self, state, action):
        feats = util.Counter()
        feats[state] = 1.0
        feats['x=%d' % state[0]] = 1.0
        feats['y=%d' % state[0]] = 1.0
        feats['action=%s' % action] = 1.0
        return feats


def closestFood(pos, food, walls):
    """
    closestFood -- this is similar to the function that we have
    worked on in the search project; here its all in one place
    """
    fringe = [(pos[0], pos[1], 0)]
    expanded = set()
    while fringe:
        pos_x, pos_y, dist = fringe.pop(0)
        if (pos_x, pos_y) in expanded:
            continue
        expanded.add((pos_x, pos_y))
        # if we find a food at this location then exit
        if food[pos_x][pos_y]:
            return dist
        # otherwise spread out from the location to its neighbours
        nbrs = Actions.getLegalNeighbors((pos_x, pos_y), walls)
        for nbr_x, nbr_y in nbrs:
            fringe.append((nbr_x, nbr_y, dist + 1))
    # no food found
    return None


class SimpleExtractor(FeatureExtractor):

    """
    Returns simple features for a basic reflex Pacman:
    - whether food will be eaten
    - how far away the next food is
    - whether a ghost collision is imminent
    - whether a ghost is one step away
    """

    def getFeatures(self, state, action):
        # extract the grid of food and wall locations and get the ghost
        # locations
        food = state.getFood()
        walls = state.getWalls()
        ghosts = state.getGhostPositions()

        features = util.Counter()

        features["bias"] = 1.0

        # compute the location of pacman after he takes the action
        x, y = state.getPacmanPosition()
        dx, dy = Actions.directionToVector(action)
        next_x, next_y = int(x + dx), int(y + dy)

        # count the number of ghosts 1-step away
        features["#-of-ghosts-1-step-away"] = sum((next_x, next_y) in Actions.getLegalNeighbors(g, walls)
                                                  for g in ghosts)

        # if there is no danger of ghosts then add the food feature
        if not features["#-of-ghosts-1-step-away"] and food[next_x][next_y]:
            features["eats-food"] = 1.0

        dist = closestFood((next_x, next_y), food, walls)
        if dist is not None:
            # make the distance a number less than one otherwise the update
            # will diverge wildly
            features["closest-food"] = float(dist) / \
                (walls.width * walls.height)
        features.divideAll(10.0)
        return features
