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import os | |
import torch | |
import lineflow as lf | |
class Dictionary(object): | |
def __init__(self): | |
self.word2idx = {} | |
self.idx2word = [] | |
def add_word(self, word): |
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this is the first line . | |
this is the second line . | |
this is the third line . |
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from collections import deque | |
INF = float('inf') | |
def bfs(graph, s): | |
d = {v: INF for v in graph} | |
d[s] = 0 | |
pi = {s: None} | |
queue = deque([s]) | |
while queue: |
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INF = float('inf') | |
def bellman_ford(graph, weights, s): | |
d = {v: INF for v in graph} | |
d[s] = 0 | |
pi = {s: None} | |
edges = [(u, v) for u, vs in graph.items() for v in vs] | |
for _ in range(len(graph) - 1): | |
for (u, v) in edges: | |
d_temp = d[u] + weights[u, v] |
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INF = float('inf') | |
def dag_shortest_path(graph, weights, torder, s): | |
d = {v: INF for v in graph} | |
d[s] = 0 | |
pi = {s: None} | |
for u in torder: | |
for v in graph[u]: | |
d_temp = d[u] + weights[u, v] | |
if d_temp < d[v]: |
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import heapq | |
INF = float('inf') | |
def dijkstra(graph, weights, s): | |
d = {v: INF for v in graph} | |
d[s] = 0 | |
pi = {s: None} | |
queue = [] | |
heapq.heappush(queue, (0, s)) |
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$latex = 'platex'; | |
$bibtex = 'pbibtex'; | |
$dvipdf = 'dvipdfmx %O -o %D %S'; | |
$makeindex = 'mendex %O -o %D %S'; | |
$pdf_mode = 3; | |
$ENV{TZ} = 'Asia/Tokyo'; | |
$ENV{OPENTYPEFONTS} = '/usr/share/fonts//:'; | |
$ENV{TTFONTS} = '/usr/share/fonts//:'; |
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ls -t * | xargs -I{} file {} | grep "PNG image data" | cut -d ":" -f 1 | parallel -j 8 convert "{}" -colorspace sRGB -type truecolor "target/{.}.jpg" |
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import numpy as np | |
def batch_normalization(x, gamma, beta, eps=2e-5): | |
mean = x.mean(axis=0) | |
var = x.var(axis=0) | |
x_hat = (x - mean) / np.sqrt(var + eps) | |
return gamma * x_hat + beta | |
def layer_normalization(x, gamma, beta, eps=2e-5): | |
mean = x.mean(axis=1)[:, np.newaxis] |
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def replace_chars(text): | |
chars = ",.?!:;" | |
for c in chars: | |
if c in text: | |
text = text.replace(c, '') | |
return text |
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