from __future__ import division from __future__ import print_function # util.py # ------- # Licensing Information: You are free to use or extend these projects for # educational purposes provided that (1) you do not distribute or publish # solutions, (2) you retain this notice, and (3) you provide clear # attribution to UC Berkeley, including a link to http://ai.berkeley.edu. # # Attribution Information: The Pacman AI projects were developed at UC Berkeley. # The core projects and autograders were primarily created 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 (pabbeel@cs.berkeley.edu). # util.py # ------- # Licensing Information: You are free to use or extend these projects for # educational purposes provided that (1) you do not distribute or publish # solutions, (2) you retain this notice, and (3) you provide clear # attribution to UC Berkeley, including a link to http://ai.berkeley.edu. # # Attribution Information: The Pacman AI projects were developed at UC Berkeley. # The core projects and autograders were primarily created 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 (pabbeel@cs.berkeley.edu). from future import standard_library standard_library.install_aliases() from builtins import input from builtins import zip from builtins import str from builtins import range from future.utils import raise_ from past.utils import old_div from builtins import object import sys import inspect import heapq, random import io class FixedRandom(object): def __init__(self): fixed_state = ( 3, ( 2147483648, 507801126, 683453281, 310439348, 2597246090, 2209084787, 2267831527, 979920060, 3098657677, 37650879, 807947081, 3974896263, 881243242, 3100634921, 1334775171, 3965168385, 746264660, 4074750168, 500078808, 776561771, 702988163, 1636311725, 2559226045, 157578202, 2498342920, 2794591496, 4130598723, 496985844, 2944563015, 3731321600, 3514814613, 3362575829, 3038768745, 2206497038, 1108748846, 1317460727, 3134077628, 988312410, 1674063516, 746456451, 3958482413, 1857117812, 708750586, 1583423339, 3466495450, 1536929345, 1137240525, 3875025632, 2466137587, 1235845595, 4214575620, 3792516855, 657994358, 1241843248, 1695651859, 3678946666, 1929922113, 2351044952, 2317810202, 2039319015, 460787996, 3654096216, 4068721415, 1814163703, 2904112444, 1386111013, 574629867, 2654529343, 3833135042, 2725328455, 552431551, 4006991378, 1331562057, 3710134542, 303171486, 1203231078, 2670768975, 54570816, 2679609001, 578983064, 1271454725, 3230871056, 2496832891, 2944938195, 1608828728, 367886575, 2544708204, 103775539, 1912402393, 1098482180, 2738577070, 3091646463, 1505274463, 2079416566, 659100352, 839995305, 1696257633, 274389836, 3973303017, 671127655, 1061109122, 517486945, 1379749962, 3421383928, 3116950429, 2165882425, 2346928266, 2892678711, 2936066049, 1316407868, 2873411858, 4279682888, 2744351923, 3290373816, 1014377279, 955200944, 4220990860, 2386098930, 1772997650, 3757346974, 1621616438, 2877097197, 442116595, 2010480266, 2867861469, 2955352695, 605335967, 2222936009, 2067554933, 4129906358, 1519608541, 1195006590, 1942991038, 2736562236, 279162408, 1415982909, 4099901426, 1732201505, 2934657937, 860563237, 2479235483, 3081651097, 2244720867, 3112631622, 1636991639, 3860393305, 2312061927, 48780114, 1149090394, 2643246550, 1764050647, 3836789087, 3474859076, 4237194338, 1735191073, 2150369208, 92164394, 756974036, 2314453957, 323969533, 4267621035, 283649842, 810004843, 727855536, 1757827251, 3334960421, 3261035106, 38417393, 2660980472, 1256633965, 2184045390, 811213141, 2857482069, 2237770878, 3891003138, 2787806886, 2435192790, 2249324662, 3507764896, 995388363, 856944153, 619213904, 3233967826, 3703465555, 3286531781, 3863193356, 2992340714, 413696855, 3865185632, 1704163171, 3043634452, 2225424707, 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796817754, 1260148619, 3898237757, 176670141, 1870249326, 3317738680, 448918002, 4059166594, 2003827551, 987091377, 224855998, 3520570137, 789522610, 2604445123, 454472869, 475688926, 2990723466, 523362238, 3897608102, 806637149, 2642229586, 2928614432, 1564415411, 1691381054, 3816907227, 4082581003, 1895544448, 3728217394, 3214813157, 4054301607, 1882632454, 2873728645, 3694943071, 1297991732, 2101682438, 3952579552, 678650400, 1391722293, 478833748, 2976468591, 158586606, 2576499787, 662690848, 3799889765, 3328894692, 2474578497, 2383901391, 1718193504, 3003184595, 3630561213, 1929441113, 3848238627, 1594310094, 3040359840, 3051803867, 2462788790, 954409915, 802581771, 681703307, 545982392, 2738993819, 8025358, 2827719383, 770471093, 3484895980, 3111306320, 3900000891, 2116916652, 397746721, 2087689510, 721433935, 1396088885, 2751612384, 1998988613, 2135074843, 2521131298, 707009172, 2398321482, 688041159, 2264560137, 482388305, 207864885, 3735036991, 3490348331, 1963642811, 3260224305, 3493564223, 1939428454, 1128799656, 1366012432, 2858822447, 1428147157, 2261125391, 1611208390, 1134826333, 2374102525, 3833625209, 2266397263, 3189115077, 770080230, 2674657172, 4280146640, 3604531615, 4235071805, 3436987249, 509704467, 2582695198, 4256268040, 3391197562, 1460642842, 1617931012, 457825497, 1031452907, 1330422862, 4125947620, 2280712485, 431892090, 2387410588, 2061126784, 896457479, 3480499461, 2488196663, 4021103792, 1877063114, 2744470201, 1046140599, 2129952955, 3583049218, 4217723693, 2720341743, 820661843, 1079873609, 3360954200, 3652304997, 3335838575, 2178810636, 1908053374, 4026721976, 1793145418, 476541615, 973420250, 515553040, 919292001, 2601786155, 1685119450, 3030170809, 1590676150, 1665099167, 651151584, 2077190587, 957892642, 646336572, 2743719258, 866169074, 851118829, 4225766285, 963748226, 799549420, 1955032629, 799460000, 2425744063, 2441291571, 1928963772, 528930629, 2591962884, 3495142819, 1896021824, 901320159, 3181820243, 843061941, 3338628510, 3782438992, 9515330, 1705797226, 953535929, 764833876, 3202464965, 2970244591, 519154982, 3390617541, 566616744, 3438031503, 1853838297, 170608755, 1393728434, 676900116, 3184965776, 1843100290, 78995357, 2227939888, 3460264600, 1745705055, 1474086965, 572796246, 4081303004, 882828851, 1295445825, 137639900, 3304579600, 2722437017, 4093422709, 273203373, 2666507854, 3998836510, 493829981, 1623949669, 3482036755, 3390023939, 833233937, 1639668730, 1499455075, 249728260, 1210694006, 3836497489, 1551488720, 3253074267, 3388238003, 2372035079, 3945715164, 2029501215, 3362012634, 2007375355, 4074709820, 631485888, 3135015769, 4273087084, 3648076204, 2739943601, 1374020358, 1760722448, 3773939706, 1313027823, 1895251226, 4224465911, 421382535, 1141067370, 3660034846, 3393185650, 1850995280, 1451917312, 3841455409, 3926840308, 1397397252, 2572864479, 2500171350, 3119920613, 531400869, 1626487579, 1099320497, 407414753, 2438623324, 99073255, 3175491512, 656431560, 1153671785, 236307875, 2824738046, 2320621382, 892174056, 230984053, 719791226, 2718891946, 624, ), None, ) self.random = random.Random() self.random.setstate(fixed_state) """ Data structures useful for implementing SearchAgents """ class Stack(object): "A container with a last-in-first-out (LIFO) queuing policy." def __init__(self): self.list = [] def push(self, item): "Push 'item' onto the stack" self.list.append(item) def pop(self): "Pop the most recently pushed item from the stack" return self.list.pop() def is_empty(self): "Returns true if the stack is empty" return len(self.list) == 0 class Queue(object): "A container with a first-in-first-out (FIFO) queuing policy." def __init__(self): self.list = [] def push(self, item): "Enqueue the 'item' into the queue" self.list.insert(0, item) def pop(self): """ Dequeue the earliest enqueued item still in the queue. This operation removes the item from the queue. """ return self.list.pop() def is_empty(self): "Returns true if the queue is empty" return len(self.list) == 0 class PriorityQueue(object): """ Implements a priority queue data structure. Each inserted item has a priority associated with it and the client is usually interested in quick retrieval of the lowest-priority item in the queue. This data structure allows O(1) access to the lowest-priority item. """ def __init__(self): self.heap = [] self.count = 0 def push(self, item, priority): entry = (priority, self.count, item) heapq.heappush(self.heap, entry) self.count += 1 def pop(self): (_, _, item) = heapq.heappop(self.heap) return item def is_empty(self): return len(self.heap) == 0 def update(self, item, priority): # If item already in priority queue with higher priority, update its priority and rebuild the heap. # If item already in priority queue with equal or lower priority, do nothing. # If item not in priority queue, do the same thing as self.push. for index, (p, c, i) in enumerate(self.heap): if i == item: if p <= priority: break del self.heap[index] self.heap.append((priority, c, item)) heapq.heapify(self.heap) break else: self.push(item, priority) class PriorityQueueWithFunction(PriorityQueue): """ Implements a priority queue with the same push/pop signature of the Queue and the Stack classes. This is designed for drop-in replacement for those two classes. The caller has to provide a priority function, which extracts each item's priority. """ def __init__(self, priority_function): "priority_function (item) -> priority" self.priority_function = priority_function # store the priority function PriorityQueue.__init__(self) # super-class initializer def push(self, item): "Adds an item to the queue with priority from the priority function" PriorityQueue.push(self, item, self.priority_function(item)) def manhattan_distance(xy1, xy2): "Returns the Manhattan distance between points xy1 and xy2" return abs(xy1[0] - xy2[0]) + abs(xy1[1] - xy2[1]) """ Data structures and functions useful for various course projects The search project should not need anything below this line. """ class Counter(dict): """ A counter keeps track of counts for a set of keys. The counter class is an extension of the standard python dictionary type. It is specialized to have number values (integers or floats), and includes a handful of additional functions to ease the task of counting data. In particular, all keys are defaulted to have value 0. Using a dictionary: a = {} print a['test'] would give an error, while the Counter class analogue: >>> a = Counter() >>> print a['test'] 0 returns the default 0 value. Note that to reference a key that you know is contained in the counter, you can still use the dictionary syntax: >>> a = Counter() >>> a['test'] = 2 >>> print a['test'] 2 This is very useful for counting things without initializing their counts, see for example: >>> a['blah'] += 1 >>> print a['blah'] 1 The counter also includes additional functionality useful in implementing the classifiers for this assignment. Two counters can be added, subtracted or multiplied together. See below for details. They can also be normalized and their total count and arg max can be extracted. """ def __getitem__(self, idx): self.setdefault(idx, 0) return dict.__getitem__(self, idx) def increment_all(self, keys, count): """ Increments all elements of keys by the same count. >>> a = Counter() >>> a.increment_all(['one','two', 'three'], 1) >>> a['one'] 1 >>> a['two'] 1 """ for key in keys: self[key] += count def arg_max(self): """ Returns the key with the highest value. """ if len(list(self.keys())) == 0: return None all = list(self.items()) values = [x[1] for x in all] max_index = values.index(max(values)) return all[max_index][0] def sorted_keys(self): """ Returns a list of keys sorted by their values. Keys with the highest values will appear first. >>> a = Counter() >>> a['first'] = -2 >>> a['second'] = 4 >>> a['third'] = 1 >>> a.sorted_keys() ['second', 'third', 'first'] """ sorted_items = list(self.items()) compare = lambda x, y: sign(y[1] - x[1]) sorted_items.sort(cmp=compare) return [x[0] for x in sorted_items] def total_count(self): """ Returns the sum of counts for all keys. """ return sum(self.values()) def normalize(self): """ Edits the counter such that the total count of all keys sums to 1. The ratio of counts for all keys will remain the same. Note that normalizing an empty Counter will result in an error. """ total = float(self.total_count()) if total == 0: return for key in list(self.keys()): self[key] = old_div(self[key], total) def divide_all(self, divisor): """ Divides all counts by divisor """ divisor = float(divisor) for key in self: self[key] /= divisor def copy(self): """ Returns a copy of the counter """ return Counter(dict.copy(self)) def __mul__(self, y): """ Multiplying two counters gives the dot product of their vectors where each unique label is a vector element. >>> a = Counter() >>> b = Counter() >>> a['first'] = -2 >>> a['second'] = 4 >>> b['first'] = 3 >>> b['second'] = 5 >>> a['third'] = 1.5 >>> a['fourth'] = 2.5 >>> a * b 14 """ sum = 0 x = self if len(x) > len(y): x, y = y, x for key in x: if key not in y: continue sum += x[key] * y[key] return sum def __radd__(self, y): """ Adding another counter to a counter increments the current counter by the values stored in the second counter. >>> a = Counter() >>> b = Counter() >>> a['first'] = -2 >>> a['second'] = 4 >>> b['first'] = 3 >>> b['third'] = 1 >>> a += b >>> a['first'] 1 """ for key, value in list(y.items()): self[key] += value def __add__(self, y): """ Adding two counters gives a counter with the union of all keys and counts of the second added to counts of the first. >>> a = Counter() >>> b = Counter() >>> a['first'] = -2 >>> a['second'] = 4 >>> b['first'] = 3 >>> b['third'] = 1 >>> (a + b)['first'] 1 """ addend = Counter() for key in self: if key in y: addend[key] = self[key] + y[key] else: addend[key] = self[key] for key in y: if key in self: continue addend[key] = y[key] return addend def __sub__(self, y): """ Subtracting a counter from another gives a counter with the union of all keys and counts of the second subtracted from counts of the first. >>> a = Counter() >>> b = Counter() >>> a['first'] = -2 >>> a['second'] = 4 >>> b['first'] = 3 >>> b['third'] = 1 >>> (a - b)['first'] -5 """ addend = Counter() for key in self: if key in y: addend[key] = self[key] - y[key] else: addend[key] = self[key] for key in y: if key in self: continue addend[key] = -1 * y[key] return addend class NotDefined(Exception): pass def raise_not_defined(): file_name = inspect.stack()[1][1] line = inspect.stack()[1][2] method = inspect.stack()[1][3] raise NotDefined(f'''*** Method not implemented: {method} at line {line} of {file_name}''') def normalize(vector_or_counter): """ normalize a vector or counter by dividing each value by the sum of all values """ normalized_counter = Counter() if type(vector_or_counter) == type(normalized_counter): counter = vector_or_counter total = float(counter.total_count()) if total == 0: return counter for key in list(counter.keys()): value = counter[key] normalized_counter[key] = old_div(value, total) return normalized_counter else: vector = vector_or_counter s = float(sum(vector)) if s == 0: return vector return [old_div(el, s) for el in vector] def n_sample(distribution, values, n): if sum(distribution) != 1: distribution = normalize(distribution) rand = [random.random() for i in range(n)] rand.sort() samples = [] sample_pos, dist_pos, cdf = 0, 0, distribution[0] while sample_pos < n: if rand[sample_pos] < cdf: sample_pos += 1 samples.append(values[dist_pos]) else: dist_pos += 1 cdf += distribution[dist_pos] return samples def sample(distribution, values=None): if type(distribution) == Counter: items = sorted(distribution.items()) distribution = [i[1] for i in items] values = [i[0] for i in items] if sum(distribution) != 1: distribution = normalize(distribution) choice = random.random() i, total = 0, distribution[0] while choice > total: i += 1 total += distribution[i] return values[i] def sample_from_counter(ctr): items = sorted(ctr.items()) return sample([v for k, v in items], [k for k, v in items]) def get_probability(value, distribution, values): """ Gives the probability of a value under a discrete distribution defined by (distributions, values). """ total = 0.0 for prob, val in zip(distribution, values): if val == value: total += prob return total def flip_coin(p): r = random.random() return r < p def choose_from_distribution(distribution): "Takes either a counter or a list of (prob, key) pairs and samples" if type(distribution) == dict or type(distribution) == Counter: return sample(distribution) r = random.random() base = 0.0 for prob, element in distribution: base += prob if r <= base: return element def nearest_point(pos): """ Finds the nearest grid point to a position (discretizes). """ (current_row, current_col) = pos grid_row = int(current_row + 0.5) grid_col = int(current_col + 0.5) return (grid_row, grid_col) def sign(x): """ Returns 1 or -1 depending on the sign of x """ if x >= 0: return 1 else: return -1 def array_invert(array): """ Inverts a matrix stored as a list of lists. """ result = [[] for i in array] for outer in array: for inner in range(len(outer)): result[inner].append(outer[inner]) return result def matrix_as_list(matrix, value=True): """ Turns a matrix into a list of coordinates matching the specified value """ rows, cols = len(matrix), len(matrix[0]) cells = [] for row in range(rows): for col in range(cols): if matrix[row][col] == value: cells.append((row, col)) return cells def lookup(name, namespace): """ Get a method or class from any imported module from its name. Usage: lookup(function_name, globals()) """ dots = name.count(".") if dots > 0: module_name, obj_name = ".".join(name.split(".")[:-1]), name.split(".")[-1] module = __import__(module_name) return getattr(module, obj_name) else: modules = [ obj for obj in list(namespace.values()) if str(type(obj)) == "" ] options = [getattr(module, name) for module in modules if name in dir(module)] options += [obj[1] for obj in list(namespace.items()) if obj[0] == name] if len(options) == 1: return options[0] if len(options) > 1: raise Exception("Name conflict for %s") raise_(Exception, "%s not found as a method or class" % name) def pause(): """ Pauses the output stream awaiting user feedback. """ print("") input() # code to handle timeouts # # FIXME # NOTE: TimeoutFuncton is NOT reentrant. Later timeouts will silently # disable earlier timeouts. Could be solved by maintaining a global list # of active time outs. Currently, questions which have test cases calling # this have all student code so wrapped. # import signal import time class TimeoutFunctionException(Exception): """Exception to raise on a timeout""" pass class TimeoutFunction(object): def __init__(self, function, timeout): self.timeout = timeout self.function = function def handle_timeout(self, signum, frame): raise TimeoutFunctionException() def __call__(self, *args, **key_args): # If we have SIGALRM signal, use it to cause an exception if and # when this function runs too long. Otherwise check the time taken # after the method has returned, and throw an exception then. if hasattr(signal, "SIGALRM"): old = signal.signal(signal.SIGALRM, self.handle_timeout) signal.alarm(self.timeout) try: result = self.function(*args, **key_args) finally: signal.signal(signal.SIGALRM, old) signal.alarm(0) else: start_time = time.time() result = self.function(*args, **key_args) time_elapsed = time.time() - start_time if time_elapsed >= self.timeout: self.handle_timeout(None, None) return result _ORIGINAL_STDOUT = None _ORIGINAL_STDERR = None _MUTED = False class WritableNull(object): def write(self, string): pass def mute_print(): global _ORIGINAL_STDOUT, _ORIGINAL_STDERR, _MUTED if _MUTED: return _MUTED = True _ORIGINAL_STDOUT = sys.stdout # _ORIGINAL_STDERR = sys.stderr sys.stdout = WritableNull() # sys.stderr = WritableNull() def unmute_print(): global _ORIGINAL_STDOUT, _ORIGINAL_STDERR, _MUTED if not _MUTED: return _MUTED = False sys.stdout = _ORIGINAL_STDOUT # sys.stderr = _ORIGINAL_STDERR