def user_show_configs_all(s: collections.defaultdict,
fill_character='_',
text=' Current User Configuration '):
"""Print all current configs to user"""
# For `settings` number and value, print it's value to user
l = utils_str_length_bigger(s)
m = 0
final_text = ''
# This first loop is for get max length needed
for n, v in s.items():
t1 = f"· ({n}) {v[0]}:\t ".ljust(l)
# t2 = f"\t {v[-1]}"
# t3 = t1 + t2
if len(t1) > m:
m = len(t1)
print()
print(vals['default_char_sep'] * vals['terminal_size'])
print(text.center(vals['terminal_size']))
print(vals['default_char_sep'] * vals['terminal_size'])
print()
# This loop actualy print the correct message to user
for n, v in s.items():
t1 = f"· ({n}) {v[0]}: ".ljust(m, fill_character)
t2 = f" {v[-1]}"
t3 = t1 + t2
print(t3)
print()
def translate_evol_data(evolutions: defaultdict, pokedex : dict):
for evolver, evolvees in evolutions.items():
pokedex[evolver].to_list = evolvees
pokedex[evolver].to_list_as_ids = [pokedex[v].id for v in evolvees]
for v in evolvees:
pokedex[v].ev_from.append(evolver)
pokedex[v].ev_from_as_ids.append(pokedex[evolver].id)
def msq_error(self, q_star: defaultdict):
msqe = 0
total_num = 0
for key, value in q_star.items():
msqe += (self.q_value[key] - value)**2
total_num += 1
return msqe / total_num
def write_gdp(regions_gdp: defaultdict, other_countries: defaultdict):
region_key, gdp_key = 'region', 'GDP, millions of dollars'
csv_columns = [region_key, gdp_key]
dict_data = []
for region, gdp in regions_gdp.items():
dict_data.append({region_key: region, gdp_key: gdp})
csv_file = "../data/postprocessed/gdp_by_region_incomplete.csv"
print_csv_to_file(dict_data, csv_columns, csv_file)
country_key = "country"
csv_columns = [country_key, gdp_key]
dict_data = []
for country, gdp in other_countries.items():
dict_data.append({country_key: country, gdp_key: gdp})
csv_file = "../data/postprocessed/other_countries_gdp.csv"
print_csv_to_file(dict_data, csv_columns, csv_file)
def diameter(self, graph: dd):
c = dd()
def maxLength(start: int, counter: dd) -> int:
counter[start] = 0
if start not in graph:
return 0
for node in graph[start]:
temp = maxLength(node, counter) + 1
counter[start] = max(counter[start], temp)
return counter[start]
maxv = 0
node = 0
for key, value in graph.items():
if key not in c:
maxLength(key, c)
print("c: ", c)
if c[key] > maxv:
maxv = c[key]
node = key
print(c)
return node, maxv
def create_report(log_stat: defaultdict, report_size: int, count_all: int,
time_all: float) -> list:
"""
На основе агрегированных данных логов строит отчет, находит все метрики
"""
log_stat_list = list(log_stat.items())
log_stat_list.sort(key=lambda i: i[1]["time_sum"], reverse=True)
i = 0
report = []
while i < report_size:
log = {
"url":
log_stat_list[i][0],
"count":
log_stat_list[i][1]["count"],
"count_perc":
log_stat_list[i][1]["count"] / count_all * 100,
"time_sum":
log_stat_list[i][1]["time_sum"],
"time_perc":
log_stat_list[i][1]["time_sum"] / time_all * 100,
"time_avg":
log_stat_list[i][1]["time_sum"] / log_stat_list[i][1]["count"],
"time_max":
max(log_stat_list[i][1]["values"]),
"time_med":
mediana(log_stat_list[i][1]["values"]),
}
report.append(log)
i += 1
return report
def save_phrase_table(phrase_table:defaultdict, filename):
with open("phrase_table/"+filename, 'w') as file:
file.write("Default = " + str(phrase_table.default_factory().default_factory())+"\n")
for f,e_prob in phrase_table.items():
file.write("********** f = "+f+"\n")
for e,prob in e_prob.items():
file.write(e +": "+str(prob)+"\n")
def save_to_excel_pegas_data(data: defaultdict) -> None:
wb = Workbook()
for branche_code, supporting_dates in data.items():
ws = wb.create_sheet(title=str(branche_code))
ws.merge_cells("A1:C1")
ws["A1"].value = "Для файла Пегас"
ws.column_dimensions["A"].width = 15
ws.column_dimensions["B"].width = 15
ws.column_dimensions["C"].width = 15
ws["A2"] = "Начало периода"
ws["B2"] = "Конец периода"
ws["C2"] = "Количество дней"
cell_number = 3
day_range = 20
period = 60
for support_date in supporting_dates:
for dt in date_range(support_date, day_range):
ws[f"A{cell_number}"].value = (
dt - timedelta(days=period)).strftime("%Y-%m-%d")
ws[f"B{cell_number}"].value = dt.strftime("%Y-%m-%d")
ws[f"C{cell_number}"].value = period
cell_number += 1
cell_number += 1
wb.save("test.xlsx")
def printFifty(Frequencies: defaultdict):
file = open("top50words.txt", "w")
sorted_dict = sorted(Frequencies.items(), key=lambda x: x[1], reverse=True)
for t in sorted_dict: # O(n)
s = str(t[0]) + " -> " + str(t[1]) + '\n'
file.write(s) # O(1) + O(1) + O(1) + O(1) = O(1)
file.close()
def topFifty(Frequencies: defaultdict):
sorted_dict = sorted(Frequencies.items(), key=lambda x: x[1], reverse=True)
Fifty = sorted_dict[0:50]
newDic = defaultdict(int)
for i in Fifty:
newDic[i[0]] = i[1]
return newDic
def frequency_search(freq: int, hist: defaultdict):
word = []
for v, k in hist.items():
if k == freq:
word.append(v)
# print(word)
return word
def defaultdict_to_dict(def_a: defaultdict):
a = dict()
for k, v in def_a.items():
if isinstance(v, defaultdict):
v = defaultdict_to_dict(v)
a[k] = v
return a
def __init__(self, metadata_list: List[AudioClipMetadata],
label_map: defaultdict, **kwargs):
super().__init__(**kwargs)
self.metadata_list = metadata_list
self.label_map = label_map
self.vocab = {v: k for k, v in label_map.items()}
self.vocab[label_map.get(None)] = NEGATIVE_CLASS
def _make_sketch(kmer_counts_dict: defaultdict) -> CountMinSketch:
# Read the dictionary into a compressed data structure to allow deleting kmer_counts_dict
NUM_ROWS = 8
kmer_counts = CountMinSketch(NUM_ROWS)
for kmer, count in kmer_counts_dict.items():
kmer_counts.update(kmer, count)
return kmer_counts
def get_prerequisites(
steps_dict: collections.defaultdict) -> collections.defaultdict:
prerequisites = collections.defaultdict(list)
for index, next_steps in steps_dict.items():
for step in next_steps:
prerequisites[step].append(index)
return prerequisites
def _end(self, context: defaultdict, output_file: str) -> None:
out_path = os.path.join(self.out_file, output_file + '.phrases.tsv')
with open(out_path, 'w') as out:
print('writing phrases to %s...' % out_path)
for phrase_label, phrase in context.items():
for span, count in phrase.items():
out.write('%s\t%d\t%s\n' %
(arg_to_a(phrase_label), count, span))
def update_occurrences(pair_counts: defaultdict, rules: map) -> None:
new_pair_counts = defaultdict(int)
for pair, count in pair_counts.items():
new = rules[pair]
new_pair_counts[pair[0] + new] += count
new_pair_counts[new + pair[1]] += count
pair_counts.clear()
pair_counts.update(new_pair_counts)
def utils_str_length_bigger(st: collections.defaultdict):
"""Return the longest string length in a given list"""
m = 0
for i in st.items():
i = i[1][0]
if len(i) > m:
m = len(i)
return m
def _freeze_nested_defaultdict(d: defaultdict) -> dict:
d = dict(d)
for k, v in d.items():
if isinstance(v, defaultdict):
d[k] = _freeze_nested_defaultdict(v)
return d
def fill_board_manhattan_distance_less_than(
board: np.array, maximum: int,
manhattan_distance_dict: collections.defaultdict) -> np.array:
board_copy = board.copy()
for index, distances in manhattan_distance_dict.items():
sum_distances = sum(map(lambda x: x['distance'], distances))
board_copy[index] = (1 if sum_distances < maximum else 0)
return board_copy
def count_stats(stats_data: defaultdict):
counter = {}
for stat, inc in stats_data.items():
if stat is enum.Enum:
stat = stat.value
counter[stat] = len(inc)
return sorted(counter.items(), key=lambda i: i[1], reverse=True)
def guess_symbol(frequency_map: defaultdict, frequency: float) -> str:
s = ''
diff = 100.0
for symbol, freq in frequency_map.items():
if abs(frequency - freq) < diff:
diff = abs(frequency - freq)
s = symbol
return s
def comb0(coins: list, deno: int, cur: defaultdict, ans: set):
if deno < 0:
return
elif deno == 0:
ans.add(tuple((key, val) for key, val in cur.items()))
else:
for coin in coins:
cur[coin] += 1
comb0(coins, deno - coin, cur, ans)
cur[coin] -= 1
def filter_words(histogram: defaultdict):
filtered_words = []
for w, i in histogram.items():
if w.endswith('\'s') and w[:-2] in histogram:
histogram[w[:-2]] += i
filtered_words.append(w)
elif w.endswith('s') and w[:-1] in histogram:
histogram[w[:-1]] += i
filtered_words.append(w)
return filtered_words
def get_statistics_info(self, main_dict: defaultdict) -> defaultdict:
average_by_all = self.get_average_profit_by_all_enterprize(main_dict)
statistic_dict = defaultdict(list)
for key, item in main_dict.items():
if item <= average_by_all:
statistic_dict["less_than_average"].append(key)
else:
statistic_dict["more_than_average"].append(key)
return statistic_dict
def visualize(title: str, series: defaultdict, filename: str,
per_continent: bool) -> None:
if per_continent:
world = pygal.maps.world.SupranationalWorld()
else:
world = pygal.maps.world.World()
world.title = title
for s in series.items():
world.add(*s)
world.render_to_file(f'images/{filename}.svg')
def printFrequencies(Frequencies: defaultdict):
sorted_dict = sorted(Frequencies.items(), key=lambda x: x[1],
reverse=True) # O(n*log(n))
# for t in sorted_dict: # O(n)
# print(t[0],"-> ",t[1]) # O(1) + O(1) + O(1) + O(1) = O(1)
print(sorted_dict)
file = open("outputA.txt", "w")
for t in sorted_dict: # O(n)
s = t[0] + "-> " + str(t[1]) + '\n'
file.write(s) # O(1) + O(1) + O(1) + O(1) = O(1)
file.close()
def fill_board_manhattan_distance(
board: np.array,
manhattan_distance_dict: collections.defaultdict) -> np.array:
board_copy = board.copy()
for index, distances in manhattan_distance_dict.items():
min_distance = min(distances, key=lambda x: x['distance'])
board_copy[index] = (min_distance['id'] if len([
distance for distance in distances
if (distance['distance'] == min_distance['distance'])
]) == 1 else '')
return board_copy
def _fetch_broker_info(ctr_relation_buf: defaultdict):
"""Fetch broker information."""
ctr_list = list()
default_policy = {"broker_id": -1, "explorer_id": -1}
for _broker, _explorer in ctr_relation_buf.items():
default_policy.update({
"broker_id": _broker,
"explorer_id": list(_explorer)
})
ctr_list.append(default_policy.copy())
return ctr_list
def defaultdict_to_dict(dictionary: defaultdict) -> Dict:
"""Recursively convert nested :obj:`defaultdict` to :obj:`dict`.
Args:
dictionary: A defaultdict.
Returns:
The defaultdict as a :obj:`dict`.
"""
if isinstance(dictionary, defaultdict):
dictionary = {k: defaultdict_to_dict(v) for k, v in dictionary.items()}
return dictionary
def _gather_losses(self, loss_dict: defaultdict) -> dict:
"""Gather all values per loss in one tensor
:param loss_dict: dictionary containing lists of various losses
:type loss_dict: defaultdict
:return: dict containing a running list of various losses per batch
"""
for loss_name, loss_value in loss_dict.items():
loss_dict[loss_name] = torch.cat(
loss_dict[loss_name]).detach().cpu().numpy()
return loss_dict
def get_cities(cities: defaultdict, region_id: int, users, needed_cities):
for city_tup in sorted(cities.items(), key=lambda y : y[1]["uc"], reverse=True):
city = city_tup[1]
city_id = city["id"]
if city["uc"] == -1:
cities[city_id]["uc"] = get_city_users_count(city_id)
city["uc"] = cities[city_id]["uc"]
dumpData(cities, citiesFile)
if city["uc"] > 5000:
print(city["id"], ": ", city["title"], " - ", city["uc"])
needed_cities.add(city_id)
if users.get(city_id) == None:
users[city_id] = defaultdict()
users[city_id]["users"] = set()
users[city_id]["count"] = 0
dumpData(users,usersFile)
find_users(users, city["id"], cities, needed_cities)
dumpData(users,usersFile)
sleep(randint(1,3))
return cities
mu = c.means_
cov = c._get_covars()
c_inv = []
for co in cov:
c_inv.append(np.linalg.inv(co))
e_pr = np.sort(c.predict_proba(train_fd))
'''
dist = np.sort(c.transform(c_fn))
ex = DD(list) #example id, distance to centroid
ex_id = DD(list) #example id for each C
ex_N = [] #number of examples for each C
#for i,j,k in zip(c_labels, train, e_pr):
for i,j,k in zip(c.labels_, train, dist):
ex[i].append([j,k[0]])
ex_id[i].append(int(j))
for i,j in ex.items():
ex[i] = sorted(j, key=lambda x: x[-1])
ex_N.append([i,len(ex[i])])
ex_N = sorted(ex_N, key=lambda x: x[-1],reverse=True) #sort cluster by density
#confidence of training ex
label_pr = np.sort(md.predict_proba(data2[train]))
cf_d = DD()
for i,pr in zip(train, label_pr):
if len(pr)<2:
margin = 1
else:
margin = pr[-1]-pr[-2]
cf_d[i] = margin
#cf_d = sorted(cf_d, key=lambda x: x[-1])
def word_search(key: str, hist: defaultdict):
freq = 0
for v, k in hist.items():
if v == key:
return k
return freq
'''
c = AC(n_clusters=n_class, affinity='cosine', linkage='average')
c.fit(train_fd)
tmp = dd(list)
for i,j in zip(c.labels_, train):
tmp[i].append(j)
for k,v in tmp.items():
for vv in v:
print k, input3[vv]
'''
c = KMeans(init='k-means++', n_clusters=n_class, n_init=10)
c.fit(train_fn)
tmp = DD(list)
for i,j in zip(c.labels_,train):
tmp[i].append(j)
for k,v in tmp.items():
for vv in v:
pass
#print k, input1[vv]
ex = DD(list)
dist = np.sort(c.transform(train_fn))
for i,j,k in zip(c.labels_,train,dist):
ex[i].append([j,k[0]])
print i,k[0],input3[j]
for i,j in ex.items():
ex[i] = sorted(j, key=lambda x: x[-1])
ex_auto = []
ex_ora = []
ora_idx = []
corr, std = calculateCorrelation(cogDist, taxDist)
print("CORRELATION: %f STD: %f" % (corr, std))
print("\nStoring COG distance dictionary...")
UtilStore(cogDist, COG_DIST_DICT())
sys.exit(0)
if (len(sys.argv) == 2) and (sys.argv[1] == "distCounts"):
print("Building dict of taxonomy dist counts...")
_, _, taxaDict, taxDist = \
buildCogTaxaDict(noWeights = True)
genTaxDistCntDict = DefDict(lambda: [0] *
(TaxaType.maxDistance() + 1))
for dir, tdd in taxDist.items():
for d in tdd.values():
genTaxDistCntDict[dir][d] += 1
UtilStore(genTaxDistCntDict, GENOME_TAX_DIST_CNT_DICT())
ttTaxDistCntDict = {}
for dir, l in genTaxDistCntDict.items():
ttTaxDistCntDict[taxaDict[dir].type.key] = l
UtilStore(ttTaxDistCntDict, TAXTYPE_TAX_DIST_CNT_DICT())
sys.exit(0)
print("WRONG COMMAND LINE")
