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import re | |
import os | |
from math import log | |
from collections import Counter | |
import pandas as pd | |
def logL(p, k, n): | |
return k * log(p) + (n - k) * log(1 - p) |
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from itertools import combinations, permutations | |
from collections import Counter | |
import gurobipy as gb | |
from gurobipy import GRB | |
def get_pairwise_ordering(all_deprels: set, training_set_constraints: Counter): | |
''' | |
Solves an integer program and returns a non-loopy ordering |
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# params: | |
# $1 input-file path, | |
# $2 page range (e.g., "1-1", "10-39", "5,9-12"), | |
# $3 output-file path | |
# ex.: pages_from_pdf input.pdf "1,3,8-9" test.pdf | |
# qpdf should be installed | |
function pages_from_pdf() { | |
qpdf $1 --pages $1 $2 -- $3 | |
} |
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import pandas as pd | |
import matplotlib.pyplot as plt | |
import numpy as np | |
# test.csv: | |
# ,b,c,d | |
# p,1,2,3 | |
# q,4,5,6 | |
# r,7,8,9 |
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import numpy as np | |
import matplotlib.pyplot as plt | |
N = 5 | |
menMeans = (20, 35, 30, 35, 27) | |
womenMeans = (25, 32, 34, 20, 25) | |
menStd = (2, 3, 4, 1, 2) | |
womenStd = (3, 5, 2, 3, 3) | |
ind = np.arange(N) # the x locations for the groups | |
width = 0.35 # the width of the bars: can also be len(x) sequence |
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confusion_dict_pos = {} | |
confusion_dict_paths = {} | |
# NEW STUFF # | |
addition_stats_pos = Counter() | |
addition_stats_rel = Counter() | |
# NEW STUFF # | |
strip_direction = lambda x: x.split('_')[0] |
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import plotly.express as px | |
import plotly.offline | |
template = """ | |
<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="utf-8"/> | |
<script>{plotly}</script> | |
</head> |
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// A JS version of Python's "get" method for dicts. | |
function get(dict: object, key: any, plug: any) { | |
if (dict.hasOwnProperty(key)) | |
return dict[key]; | |
else | |
return plug; | |
} | |
function convertToUnicode(input: string): string { |
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ssh-rsa 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 dniko@localhost.localdomain |
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import sqlite3 | |
import pandas as pd | |
import html | |
import re | |
# Убираем <br>, <br/>, </br>; заменяем любые последовательности | |
# whitespace-символов на один пробел. | |
def normalise_ws(s): | |
s = re.sub(r'</?br/?>', ' ', s) | |
s = re.sub(r'[\n\r]+ *', ' ', s) |