"""Provides some utilities widely used by other modules"""
import bisect
import collections
import collections.abc
import functools
import heapq
import operator
import os.path
import random
from itertools import chain, combinations
from statistics import mean
import numpy as np
# ______________________________________________________________________________
# Functions on Sequences and Iterables
def sequence(iterable):
"""Converts iterable to sequence, if it is not already one."""
return iterable if isinstance(iterable, collections.abc.Sequence) else tuple([iterable])
def remove_all(item, seq):
"""Return a copy of seq (or string) with all occurrences of item removed."""
if isinstance(seq, str):
return seq.replace(item, '')
elif isinstance(seq, set):
rest = seq.copy()
rest.remove(item)
return rest
else:
return [x for x in seq if x != item]
def unique(seq):
"""Remove duplicate elements from seq. Assumes hashable elements."""
return list(set(seq))
def count(seq):
"""Count the number of items in sequence that are interpreted as true."""
return sum(map(bool, seq))
def multimap(items):
"""Given (key, val) pairs, return {key: [val, ....], ...}."""
result = collections.defaultdict(list)
for (key, val) in items:
result[key].append(val)
return dict(result)
def multimap_items(mmap):
"""Yield all (key, val) pairs stored in the multimap."""
for (key, vals) in mmap.items():
for val in vals:
yield key, val
def product(numbers):
"""Return the product of the numbers, e.g. product([2, 3, 10]) == 60"""
result = 1
for x in numbers:
result *= x
return result
def first(iterable, default=None):
"""Return the first element of an iterable; or default."""
return next(iter(iterable), default)
def is_in(elt, seq):
"""Similar to (elt in seq), but compares with 'is', not '=='."""
return any(x is elt for x in seq)
def mode(data):
"""Return the most common data item. If there are ties, return any one of them."""
[(item, count)] = collections.Counter(data).most_common(1)
return item
def power_set(iterable):
"""power_set([1,2,3]) --> (1,) (2,) (3,) (1,2) (1,3) (2,3) (1,2,3)"""
s = list(iterable)
return list(chain.from_iterable(combinations(s, r) for r in range(len(s) + 1)))[1:]
def extend(s, var, val):
"""Copy dict s and extend it by setting var to val; return copy."""
return {**s, var: val}
def flatten(seqs):
return sum(seqs, [])
# ______________________________________________________________________________
# argmin and argmax
identity = lambda x: x
def argmin_random_tie(seq, key=identity):
"""Return a minimum element of seq; break ties at random."""
return min(shuffled(seq), key=key)
def argmax_random_tie(seq, key=identity):
"""Return an element with highest fn(seq[i]) score; break ties at random."""
return max(shuffled(seq), key=key)
def shuffled(iterable):
"""Randomly shuffle a copy of iterable."""
items = list(iterable)
random.shuffle(items)
return items
# ______________________________________________________________________________
# Statistical and mathematical functions
def histogram(values, mode=0, bin_function=None):
"""Return a list of (value, count) pairs, summarizing the input values.
Sorted by increasing value, or if mode=1, by decreasing count.
If bin_function is given, map it over values first."""
if bin_function:
values = map(bin_function, values)
bins = {}
for val in values:
bins[val] = bins.get(val, 0) + 1
if mode:
return sorted(list(bins.items()), key=lambda x: (x[1], x[0]), reverse=True)
else:
return sorted(bins.items())
def dot_product(x, y):
"""Return the sum of the element-wise product of vectors x and y."""
return sum(_x * _y for _x, _y in zip(x, y))
def element_wise_product(x, y):
"""Return vector as an element-wise product of vectors x and y."""
assert len(x) == len(y)
return np.multiply(x, y)
def matrix_multiplication(x, *y):
"""Return a matrix as a matrix-multiplication of x and arbitrary number of matrices *y."""
result = x
for _y in y:
result = np.matmul(result, _y)
return result
def vector_add(a, b):
"""Component-wise addition of two vectors."""
return tuple(map(operator.add, a, b))
def scalar_vector_product(x, y):
"""Return vector as a product of a scalar and a vector"""
return np.multiply(x, y)
def probability(p):
"""Return true with probability p."""
return p > random.uniform(0.0, 1.0)
def weighted_sample_with_replacement(n, seq, weights):
"""Pick n samples from seq at random, with replacement, with the
probability of each element in proportion to its corresponding
weight."""
sample = weighted_sampler(seq, weights)
return [sample() for _ in range(n)]
def weighted_sampler(seq, weights):
"""Return a random-sample function that picks from seq weighted by weights."""
totals = []
for w in weights:
totals.append(w + totals[-1] if totals else w)
return lambda: seq[bisect.bisect(totals, random.uniform(0, totals[-1]))]
def weighted_choice(choices):
"""A weighted version of random.choice"""
# NOTE: should be replaced by random.choices if we port to Python 3.6
total = sum(w for _, w in choices)
r = random.uniform(0, total)
upto = 0
for c, w in choices:
if upto + w >= r:
return c, w
upto += w
def rounder(numbers, d=4):
"""Round a single number, or sequence of numbers, to d decimal places."""
if isinstance(numbers, (int, float)):
return round(numbers, d)
else:
constructor = type(numbers) # Can be list, set, tuple, etc.
return constructor(rounder(n, d) for n in numbers)
def num_or_str(x): # TODO: rename as `atom`
"""The argument is a string; convert to a number if possible, or strip it."""
try:
return int(x)
except ValueError:
try:
return float(x)
except ValueError:
return str(x).strip()
def euclidean_distance(x, y):
return np.sqrt(sum((_x - _y) ** 2 for _x, _y in zip(x, y)))
def manhattan_distance(x, y):
return sum(abs(_x - _y) for _x, _y in zip(x, y))
def hamming_distance(x, y):
return sum(_x != _y for _x, _y in zip(x, y))
def cross_entropy_loss(x, y):
return (-1.0 / len(x)) * sum(_x * np.log(_y) + (1 - _x) * np.log(1 - _y) for _x, _y in zip(x, y))
def mean_squared_error_loss(x, y):
return (1.0 / len(x)) * sum((_x - _y) ** 2 for _x, _y in zip(x, y))
def rms_error(x, y):
return np.sqrt(ms_error(x, y))
def ms_error(x, y):
return mean((_x - _y) ** 2 for _x, _y in zip(x, y))
def mean_error(x, y):
return mean(abs(_x - _y) for _x, _y in zip(x, y))
def mean_boolean_error(x, y):
return mean(_x != _y for _x, _y in zip(x, y))
def normalize(dist):
"""Multiply each number by a constant such that the sum is 1.0"""
if isinstance(dist, dict):
total = sum(dist.values())
for key in dist:
dist[key] = dist[key] / total
assert 0 0 else alpha * np.exp(value)
def tanh(x):
return np.tanh(x)
def tanh_derivative(value):
return 1 - (value ** 2)
def leaky_relu(x, alpha=0.01):
return x if x > 0 else alpha * x
def leaky_relu_derivative(value, alpha=0.01):
return 1 if value > 0 else alpha
def relu(x):
return max(0, x)
def relu_derivative(value):
return 1 if value > 0 else 0
def step(x):
"""Return activation value of x with sign function"""
return 1 if x >= 0 else 0
def gaussian(mean, st_dev, x):
"""Given the mean and standard deviation of a distribution, it returns the probability of x."""
return 1 / (np.sqrt(2 * np.pi) * st_dev) * np.e ** (-0.5 * (float(x - mean) / st_dev) ** 2)
def linear_kernel(x, y=None):
if y is None:
y = x
return np.dot(x, y.T)
def polynomial_kernel(x, y=None, degree=2.0):
if y is None:
y = x
return (1.0 + np.dot(x, y.T)) ** degree
def rbf_kernel(x, y=None, gamma=None):
"""Radial-basis function kernel (aka squared-exponential kernel)."""
if y is None:
y = x
if gamma is None:
gamma = 1.0 / x.shape[1] # 1.0 / n_features
return np.exp(-gamma * (-2.0 * np.dot(x, y.T) +
np.sum(x * x, axis=1).reshape((-1, 1)) + np.sum(y * y, axis=1).reshape((1, -1))))
# ______________________________________________________________________________
# Grid Functions
orientations = EAST, NORTH, WEST, SOUTH = [(1, 0), (0, 1), (-1, 0), (0, -1)]
turns = LEFT, RIGHT = (+1, -1)
def turn_heading(heading, inc, headings=orientations):
return headings[(headings.index(heading) + inc) % len(headings)]
def turn_right(heading):
return turn_heading(heading, RIGHT)
def turn_left(heading):
return turn_heading(heading, LEFT)
def distance(a, b):
"""The distance between two (x, y) points."""
xA, yA = a
xB, yB = b
return np.hypot((xA - xB), (yA - yB))
def distance_squared(a, b):
"""The square of the distance between two (x, y) points."""
xA, yA = a
xB, yB = b
return (xA - xB) ** 2 + (yA - yB) ** 2
# ______________________________________________________________________________
# Misc Functions
class injection:
"""Dependency injection of temporary values for global functions/classes/etc.
E.g., `with injection(DataBase=MockDataBase): ...`"""
def __init__(self, **kwds):
self.new = kwds
def __enter__(self):
self.old = {v: globals()[v] for v in self.new}
globals().update(self.new)
def __exit__(self, type, value, traceback):
globals().update(self.old)
def memoize(fn, slot=None, maxsize=32):
"""Memoize fn: make it remember the computed value for any argument list.
If slot is specified, store result in that slot of first argument.
If slot is false, use lru_cache for caching the values."""
if slot:
def memoized_fn(obj, *args):
if hasattr(obj, slot):
return getattr(obj, slot)
else:
val = fn(obj, *args)
setattr(obj, slot, val)
return val
else:
@functools.lru_cache(maxsize=maxsize)
def memoized_fn(*args):
return fn(*args)
return memoized_fn
def name(obj):
"""Try to find some reasonable name for the object."""
return (getattr(obj, 'name', 0) or getattr(obj, '__name__', 0) or
getattr(getattr(obj, '__class__', 0), '__name__', 0) or
str(obj))
def isnumber(x):
"""Is x a number?"""
return hasattr(x, '__int__')
def issequence(x):
"""Is x a sequence?"""
return isinstance(x, collections.abc.Sequence)
def print_table(table, header=None, sep=' ', numfmt='{}'):
"""Print a list of lists as a table, so that columns line up nicely.
header, if specified, will be printed as the first row.
numfmt is the format for all numbers; you might want e.g. '{:.2f}'.
(If you want different formats in different columns,
don't use print_table.) sep is the separator between columns."""
justs = ['rjust' if isnumber(x) else 'ljust' for x in table[0]]
if header:
table.insert(0, header)
table = [[numfmt.format(x) if isnumber(x) else x for x in row]
for row in table]
sizes = list(map(lambda seq: max(map(len, seq)), list(zip(*[map(str, row) for row in table]))))
for row in table:
print(sep.join(getattr(str(x), j)(size) for (j, size, x) in zip(justs, sizes, row)))
def open_data(name, mode='r'):
aima_root = os.path.dirname(__file__)
aima_file = os.path.join(aima_root, *['aima-data', name])
return open(aima_file, mode=mode)
def failure_test(algorithm, tests):
"""Grades the given algorithm based on how many tests it passes.
Most algorithms have arbitrary output on correct execution, which is difficult
to check for correctness. On the other hand, a lot of algorithms output something
particular on fail (for example, False, or None).
tests is a list with each element in the form: (values, failure_output)."""
return mean(int(algorithm(x) != y) for x, y in tests)
# ______________________________________________________________________________
# Expressions
# See https://docs.python.org/3/reference/expressions.html#operator-precedence
# See https://docs.python.org/3/reference/datamodel.html#special-method-names
class Expr:
"""A mathematical expression with an operator and 0 or more arguments.
op is a str like '+' or 'sin'; args are Expressions.
Expr('x') or Symbol('x') creates a symbol (a nullary Expr).
Expr('-', x) creates a unary; Expr('+', x, 1) creates a binary."""
def __init__(self, op, *args):
self.op = str(op)
self.args = args
# Operator overloads
def __neg__(self):
return Expr('-', self)
def __pos__(self):
return Expr('+', self)
def __invert__(self):
return Expr('~', self)
def __add__(self, rhs):
return Expr('+', self, rhs)
def __sub__(self, rhs):
return Expr('-', self, rhs)
def __mul__(self, rhs):
return Expr('*', self, rhs)
def __pow__(self, rhs):
return Expr('**', self, rhs)
def __mod__(self, rhs):
return Expr('%', self, rhs)
def __and__(self, rhs):
return Expr('&', self, rhs)
def __xor__(self, rhs):
return Expr('^', self, rhs)
def __rshift__(self, rhs):
return Expr('>>', self, rhs)
def __lshift__(self, rhs):
return Expr('>', lhs, self)
def __rlshift__(self, lhs):
return Expr('', P), then combine with Q."""
def __init__(self, op, lhs):
self.op, self.lhs = op, lhs
def __or__(self, rhs):
return Expr(self.op, self.lhs, rhs)
def __repr__(self):
return "PartialExpr('{}', {})".format(self.op, self.lhs)
def expr(x):
"""Shortcut to create an Expression. x is a str in which:
- identifiers are automatically defined as Symbols.
- ==> is treated as an infix |'==>'|, as are >> expr('P & Q ==> Q')
((P & Q) ==> Q)
"""
return eval(expr_handle_infix_ops(x), defaultkeydict(Symbol)) if isinstance(x, str) else x
infix_ops = '==> '|, etc.
>>> expr_handle_infix_ops('P ==> Q')
"P |'==>'| Q"
"""
for op in infix_ops:
x = x.replace(op, '|' + repr(op) + '|')
return x
class defaultkeydict(collections.defaultdict):
"""Like defaultdict, but the default_factory is a function of the key.
>>> d = defaultkeydict(len); d['four']
4
"""
def __missing__(self, key):
self[key] = result = self.default_factory(key)
return result
class hashabledict(dict):
"""Allows hashing by representing a dictionary as tuple of key:value pairs.
May cause problems as the hash value may change during runtime."""
def __hash__(self):
return 1
# ______________________________________________________________________________
# Queues: Stack, FIFOQueue, PriorityQueue
# Stack and FIFOQueue are implemented as list and collection.deque
# PriorityQueue is implemented here
class PriorityQueue:
"""A Queue in which the minimum (or maximum) element (as determined by f and
order) is returned first.
If order is 'min', the item with minimum f(x) is
returned first; if order is 'max', then it is the item with maximum f(x).
Also supports dict-like lookup."""
def __init__(self, order='min', f=lambda x: x):
self.heap = []
if order == 'min':
self.f = f
elif order == 'max': # now item with max f(x)
self.f = lambda x: -f(x) # will be popped first
else:
raise ValueError("Order must be either 'min' or 'max'.")
def append(self, item):
"""Insert item at its correct position."""
heapq.heappush(self.heap, (self.f(item), item))
def extend(self, items):
"""Insert each item in items at its correct position."""
for item in items:
self.append(item)
def pop(self):
"""Pop and return the item (with min or max f(x) value)
depending on the order."""
if self.heap:
return heapq.heappop(self.heap)[1]
else:
raise Exception('Trying to pop from empty PriorityQueue.')
def __len__(self):
"""Return current capacity of PriorityQueue."""
return len(self.heap)
def __contains__(self, key):
"""Return True if the key is in PriorityQueue."""
return any([item == key for _, item in self.heap])
def __getitem__(self, key):
"""Returns the first value associated with key in PriorityQueue.
Raises KeyError if key is not present."""
for value, item in self.heap:
if item == key:
return value
raise KeyError(str(key) + " is not in the priority queue")
def __delitem__(self, key):
"""Delete the first occurrence of key."""
try:
del self.heap[[item == key for _, item in self.heap].index(True)]
except ValueError:
raise KeyError(str(key) + " is not in the priority queue")
heapq.heapify(self.heap)
# ______________________________________________________________________________
# Useful Shorthands
class Bool(int):
"""Just like `bool`, except values display as 'T' and 'F' instead of 'True' and 'False'."""
__str__ = __repr__ = lambda self: 'T' if self else 'F'
T = Bool(True)
F = Bool(False)