def __init__(self, source: Seq2Seq, src_counter: collections.Counter,
trg_counter: collections.Counter, use_cuda):
self.source = source
src_counter = collections.Counter(
{source.src_voc.itos[i]: c
for i, c in src_counter.items()})
self.src_voc = Vocab(
src_counter,
specials=source.src_voc.itos[0:source.config.common_size])
trg_counter = collections.Counter(
{source.trg_voc.itos[i]: c
for i, c in trg_counter.items()})
self.trg_voc = Vocab(
trg_counter,
specials=source.trg_voc.itos[0:source.config.common_size])
src_s2b = [source.src_voc.stoi[w] for w in self.src_voc.itos]
self.src_b2s = {b: s for s, b in enumerate(src_s2b)}
trg_s2b = [source.trg_voc.stoi[w] for w in self.trg_voc.itos]
self.trg_b2s = {b: s for s, b in enumerate(trg_s2b)}
self.src_s2b = torch.LongTensor(src_s2b)
self.trg_s2b = torch.LongTensor(trg_s2b)
self.pad_index = self.trg_voc.stoi[PAD]
self.sos_index = self.trg_voc.stoi[SOS]
self.eos_index = self.trg_voc.stoi[EOS]
self.use_cuda = use_cuda
def report(stats: collections.Counter, filename: str) -> None:
# sort user name by counter value
users = sorted(stats.items(), key=lambda kv: kv[1], reverse=True)
with open(filename, 'w') as f:
for user in users:
f.write(f"{user[1]:<5} https://twitter.com/{user[0]}\n")
def write_ref_counts(aln_counts: collections.Counter,
ref_counts_path: Path) -> None:
with ref_counts_path.open('w') as ref_counts_file:
for seq_name, count in sorted(aln_counts.items(),
reverse=True,
key=lambda item: item[1]):
ref_counts_file.write(f'{seq_name}\t{count}\n')
def __str__(self):
elt = []
for val, num in Counter.items(self):
if num == 1:
elt.append(repr(val))
else:
elt.append("%r:%s" % (val, num))
return "{%s}" % ", ".join(elt)
def get_best_seq(aln_counts: collections.Counter,
seq_sizes: Dict[str, int],
min_size: int = None) -> Optional[str]:
for seq_name, count in sorted(aln_counts.items(),
reverse=True,
key=lambda item: item[1]):
if min_size is None or seq_sizes[seq_name] >= min_size:
return seq_name
return None
def __imul__(self, num):
"""In-place multiplication by a non-negative integer.
>>> m = mset('abc')
>>> m *= 3
>>> m == mset('abc' * 3)
True
>>> m *= 0
>>> m == mset()
True
"""
if num == 0:
self.clear()
else:
for key, val in Counter.items(self):
self[key] = val * num
return self
def create_profession_vector(sum_of_aspects: collections.Counter) -> dict:
"""Function takes a dict where keys are aspects for profession, values
are number of aspect in profession and returns dict with all possible
aspects and if aspects is present then show number of this aspect. So simply
saying it's a vector for profession in a space of all possible aspects.
Args:
sum_of_aspects: this is receiver threough collections.Counter(), i.e.
it's a dict which inform about how many and what aspects occurs in
all sample in profession.
Returns a dict will number of aspects for a profession. Number of all aspects.
It's some aspects not presented then aspect equal to 0.
"""
# init dummy dict with all aspects combinations as keys as 0 values
dict_vector = dict.fromkeys(list(KEY2IND.keys()), 0)
# fill dict with presented in profession aspects and they numebers
for aspect in sum_of_aspects.items():
dict_vector[aspect[0]] = aspect[1] # 0 means key, 1 means value
return dict_vector
def _frequency_table_to_heap(ft: collections.Counter,
branching_factor: int = 2) -> DWayHeap:
"""Takes a frequency table and creates a heap whose elements are nodes of the Huffman tree,
with one node per unique character in the FT; for each element the priority associated to it is
the frequency of the corresponding character.
Args:
ft: The frequency table, with char/number of occurrences (or document frequency) pairs.
branching_factor: The branching factor for the d-ary heap that will be created.
Returns:
A d-ary heap containing one entry per unique character in the original text;
Each entry is going to be an instance of `HuffmanNode`.
"""
characters, priorities = list(zip(*ft.items()))
# Create a node for each character; use the inverse of the frequency because DWayHeap is a max heap
priorities = list(map(lambda p: -p, priorities))
elements = list(map(lambda c: HuffmanNode(c, -ft[c]), characters))
return DWayHeap(elements=elements,
priorities=priorities,
branching_factor=branching_factor)
def create_bigramlist(cnt: collections.Counter) -> list:
return [
item for item, _ in sorted(
cnt.items(), key=operator.itemgetter(1), reverse=True)
]
def counter2hash(self, counter: collections.Counter):
return reduce(mul, [self._alpha2prime[c] ** v for c, v in counter.items()]) % self._mod
from sys import stdin as Si, maxsize as m
from math import floor as F
from collections import defaultdict as dt, Counter as Co
from operator import itemgetter as ig
from math import pi
if __name__ == "__main__":
L = tuple(map(int, Si.readline().split()))
H, Max = Co(L), 0
for k, v in H.items():
if v > 1:
Max = max(Max, k * min(v, 3))
print(sum(L) - Max)
"""
A. Bear and Five Cards
time limit per test
2 seconds
memory limit per test
256 megabytes
input
standard input
output
standard output
A little bear Limak plays a game. He has five cards. There is one number written on each card. Each number is a positive integer.
Limak can discard (throw out) some cards. His goal is to minimize the sum of numbers written on remaining (not discarded) cards.
He is allowed to at most once discard two or three cards with the same number. Of course, he won't discard cards if it's impossible to choose two or three cards with the same number.
def _stringify_counter_by_popularity(c: collections.Counter) -> str:
"""Given a counter, give a string summary in descending popularity."""
return ", ".join(f"{v} {k}" for k, v in sorted(
c.items(), reverse=True, key=operator.itemgetter(1)))
for tempKey in sorted_freq:
# for tempKey in sorted_Errors:
reponse = motsDifficilesEtFrequence['motsDifficiles'][sorted_key[tempKey][
0]]['Der-Die-Das'] + ' ' + motsDifficilesEtFrequence['motsDifficiles'][
sorted_key[tempKey][0]]['Mot en ALL']
motsDifficilesEtFrequence = {}
motsDifficilesEtFrequence['frequenceErreurs'] = {}
# motsDifficilesEtFrequence['frequenceErreurs']= {Counter({'1': 2, '3': 1}), 'motsDifficiles': {'1': {...}, '3': {...}}}
motsDifficilesEtFrequence['frequenceErreurs'] = {'1': 1, '3': 4}
sorted_Errors = sorted(motsDifficilesEtFrequence['frequenceErreurs'].items(),
key=operator.itemgetter(1),
reverse=True)
# Counter = {'6': 2, '10': 1}
Counter = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_Errors = sorted(Counter.items(),
key=operator.itemgetter(1),
reverse=True)
print('*** Tes pires ennemis ***')
# for tempKey in motsDifficilesEtFrequence['motsDifficiles']:
# sorted_dict = collections.OrderedDict(sorted_Errors)
for tempKey in sorted_Errors:
pass
z = ['blue', 'red', 'blue', 'yellow', 'blue', 'red']
stat = Counter(z)
val = 'asdf'
try:
def _get_percentage(counts: collections.Counter) -> List[Tuple[int, float]]:
"""Convert to percentage."""
total_count = float(sum(counts.values()))
return [(k, v / total_count) for k, v in sorted(counts.items())]
def __iter__(self):
for key, val in Counter.items(self):
for i in range(val):
yield key
def pairs(self):
return Counter.items(self)
def create_wordstats(cnt: collections.Counter) -> list:
return sorted(cnt.items(), key=operator.itemgetter(1), reverse=True)
def freeze(counter: collections.Counter) -> CharCount:
"""Turn a counter into a hashable representation."""
# Oh well, not efficient - but hey ho.
return tuple(sorted(counter.items()))
Restdic, RestCnt = RestDicCnt
n = 100 # number of restaurants
m = 100 # number of customers
numtt = n * m # total number of reviews
num = m # number of non-missing reviews
def Randmfn(ttCusts, m=100):
idxs = list(range(len(ttCusts)))
idxs = npr.permutation(idxs)
return np.array(ttCusts)[idxs[:m]]
# select the n most frequent restaurants
subRests = Cntr()
for key, nn in RestCnt.items():
if key in CityDic[ccity]:
subRests[key] = nn
Rests = subRests.most_common(n)
#Rests = RestCnt.most_common(n)
currentRest = Rests[0][0]
ttCusts = list(Restdic[currentRest])
Custs = ttCusts
#Custs = Randmfn(ttCusts,m=m)
Custs = set(Custs)
res = Cntr()
for Cust in Custs:
for rest, _ in Rests:
custofrest = Restdic[rest]
freq_tech.update({k: cnt})
#Finds Unique coins in freq_tech
for k, v in freq_tech.items():
for item in v:
# print(k,v)
if item not in unique_coins:
unique_coins.append(item)
unique_coins = sorted(unique_coins)
#prints coins and theirs symobols that are found in unique coins_list
for item in unique_coins:
for k, v in coin_list.items():
if item == k:
print(item, v)
continue
for k, v in freq_tech.items():
# print("---")
for item, amount in v.items():
# print(item,amount)
Counter[item] += amount
print(Counter)
for k, v in Counter.items():
for coin, names in coin_list.items():
if k == coin:
print(coin, names, v)
from sys import stdin as Si, maxsize as m
from math import floor as F
from collections import defaultdict as dt, Counter as Co
from operator import itemgetter as ig
from math import pi
if __name__ == '__main__':
L = tuple(map(int, Si.readline().split()))
H, Max = Co(L), 0
for k, v in H.items():
if v > 1: Max = max(Max, k * min(v, 3))
print(sum(L) - Max)
'''
A. Bear and Five Cards
time limit per test
2 seconds
memory limit per test
256 megabytes
input
standard input
output
standard output
A little bear Limak plays a game. He has five cards. There is one number written on each card. Each number is a positive integer.
Limak can discard (throw out) some cards. His goal is to minimize the sum of numbers written on remaining (not discarded) cards.
He is allowed to at most once discard two or three cards with the same number. Of course, he won't discard cards if it's impossible to choose two or three cards with the same number.
Given five numbers written on cards, cay you find the minimum sum of numbers on remaining cards?
Input