def solve(self):
'''
insert your code
'''
with open('./neko.txt.mecab', 'r') as f:
morphologies = []
sentence = []
for line in f.readlines():
if line == 'EOS\n':
if len(sentence) > 0:
morphologies.append(sentence)
sentence = []
else:
surface, result = line.split()
results = result.split(',')
sentence.append({
'surface': surface,
'base': results[6],
'pos': results[0],
'pos1': results[1]
})
pc = PickleCache()
pc.set('./neko.txt.mecab.pickle', morphologies)
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
for sentence in morphologies:
for m in sentence:
if m['pos'] == '動詞':
print m['surface']
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
for sentence in morphologies:
for i in range(len(sentence)):
if sentence[i]['surface'] == 'の' and 0 < i < len(sentence) - 1 and sentence[i-1]['pos'] == '名詞' and sentence[i+1]['pos'] == '名詞':
print sentence[i-1]['surface'] + sentence[i]['surface'] + sentence[i+1]['surface']
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
for sentence in morphologies:
for m in sentence:
if m['pos'] == '名詞' and m['pos1'] == 'サ変接続':
print m['surface']
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
words = [m['base'] for sentence in morphologies for m in sentence]
count = Counter(words)
for word, freq in count.most_common():
print word, freq
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
words = [m['base'] for sentence in morphologies for m in sentence]
count = Counter(words)
plt.hist([f for w, f in count.items()], bins=100, range=(0, 10000))
plt.title('Histogram of Term Frequency in Bocchan')
plt.xlabel('Term Frequency')
plt.ylabel('Freqency')
plt.show()
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
for sentence in morphologies:
noun_phrase = []
for m in sentence:
if m['pos'] == '名詞':
noun_phrase.append(m['surface'])
else:
if len(noun_phrase) >= 2:
print ''.join(noun_phrase)
noun_phrase = []
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
for sentence in morphologies:
noun_phrase = []
for m in sentence:
if m['pos'] == '名詞':
noun_phrase.append(m['surface'])
else:
if len(noun_phrase) >= 2:
print ''.join(noun_phrase)
noun_phrase = []
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
words = [m['base'] for sentence in morphologies for m in sentence]
count = Counter(words)
top10 = count.most_common(10)
ind = np.arange(10)
width = 0.35
plt.bar(ind, [f for w, f in top10], width)
plt.title('Top 10 of Term Frequency in Bocchan')
plt.xlabel('Terms')
plt.ylabel('Frequency')
plt.xticks(ind + width / 2, [w.decode('utf-8') for w, f in top10])
plt.show()
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
words = [m['base'] for sentence in morphologies for m in sentence]
count = Counter(words)
top = count.most_common()
rank = np.arange(len(top)) + 1
freq = [f for w, f in top]
plt.scatter(rank, freq, s=10)
plt.title('Relationship between Rank and Value of Term Frequency')
plt.xlabel('Rank (Logarithmic)')
plt.ylabel('Term Frequency (Logarithmic)')
plt.xscale('log')
plt.yscale('log')
plt.show()
return None
def solve(self):
'''
insert your code
'''
pc = PickleCache()
morphologies = pc.get('./neko.txt.mecab.pickle')
words = [m['base'] for sentence in morphologies for m in sentence]
count = Counter(words)
top = count.most_common()
rank = np.arange(len(top)) + 1
freq = [f for w, f in top]
plt.scatter(rank, freq, s=10)
plt.title('Relationship between Rank and Value of Term Frequency')
plt.xlabel('Rank (Logarithmic)')
plt.ylabel('Term Frequency (Logarithmic)')
plt.xscale('log')
plt.yscale('log')
plt.show()
return None
def solve(self):
'''
insert your code
'''
with open('./neko.txt.mecab', 'r') as f:
morphologies = []
sentence = []
for line in f.readlines():
if line == 'EOS\n':
if len(sentence) > 0:
morphologies.append(sentence)
sentence = []
else:
surface, result = line.split()
results = result.split(',')
sentence.append({'surface': surface, 'base': results[6], 'pos': results[0], 'pos1': results[1]})
pc = PickleCache()
pc.set('./neko.txt.mecab.pickle', morphologies)
return None
import string
from random import randint, sample
from dataclasses import dataclass
from typing import Any
from random import choice
from graphviz import Digraph
import seaborn as sns
from pickle_cache import PickleCache
import kanren as kr
import os
import libcst as cst
import json
from pymongo import MongoClient
sns.set()
pcache = PickleCache()
all_names = list(set(string.ascii_lowercase) - set(["l", "i"]))
all_operators = ["+", "-"]
def shuffle(l):
return sample(l, k=len(l))
def shuffle_unique(l):
while True:
l2 = shuffle(l)
if l2 != l:
return l2
self._run = run
if run:
log.debug('-- START: {} --'.format(s))
def __enter__(self):
self.start = now()
def __exit__(self, a, b, c):
t = int(now() - self.start)
if self._run:
log.debug('-- END: {} -- {:02d}:{:02d}:{:02d}'.format(
self._s, int(t / 3600), int((t / 60) % 60),
int(t) % 60))
pcache = PickleCache(cache_dir='/app/.cache')
def crop(img, bbox):
[h, w] = img.shape[:2]
return img[int(bbox.bbox_y1 * h):int(bbox.bbox_y2 * h),
int(bbox.bbox_x1 * w):int(bbox.bbox_x2 * w)]
def resize(img, w, h):
th = int(img.shape[0] * (w / float(img.shape[1]))) if h is None else int(h)
tw = int(img.shape[1] * (h / float(img.shape[0]))) if w is None else int(w)
return cv2.resize(img, (tw, th))
def load_frame(video, frame, bboxes):
import javalang
from javalang import tree
import textwrap
import os
from pickle_cache import PickleCache
from dataclasses import dataclass, replace
from iterextras import par_for
import copy
from enum import Enum
from typing import Dict, Union, Optional, List, Any
from unionfind import UnionFind
from torch.distributions import Categorical
from torch import tensor as t
pcache = PickleCache('.pcache')
DATA_DIR = '../../data/cs106a/CheckerboardKarel/CheckerboardKarel_anonymized'
GRADES_DIR = '../../data/cs106a/CheckerboardKarel/CheckerboardKarel_results'
STUDENTS = os.listdir(DATA_DIR)
@dataclass
class Program:
source: str
ast: tree.CompilationUnit
def load_student_programs(student):
files = sorted(os.listdir(f'{DATA_DIR}/{student}'))
programs = []
for f in files:
def pc():
# with tempfile.TemporaryDirectory() as dirname:
dirname = tempfile.TemporaryDirectory().__enter__()
print(dirname)
pc = PickleCache(cache_dir=dirname)
yield pc