def potential_move(self):
"""move ahead"""
next_pos_x = self.current_position['x'] + cos(self.current_direction)
next_pos_y = self.current_position['y'] - sin(self.current_direction)
if not self.__can_move(next_pos_x, next_pos_y):
self.__visited_position[str(next_pos_x) + "_" + str(next_pos_y)] = -1
return False
return True
def _cal_acc(embedding, label_arr):
assert len(embedding) == len(label_arr) == 12000, "wrong feature num"
fea_l = embedding[:6000]
fea_r = embedding[6000:]
assert label_arr[:6000].all() == label_arr[6000:].all(
), "wrong label order"
labels = label_arr[:6000]
scores = utils.cos(fea_l, fea_r)
return utils.best_acc(scores, labels)
def search(query_pil, model, db='index.json'):
if type(db) == str:
with open(db) as json_file:
data = json.load(json_file)
db = pd.DataFrame(data)
query = transform(query_pil.convert('RGB'))
qFeatures = model(query.unsqueeze(0))
qGram = gram_matrix(qFeatures).flatten()
scores = db['Gram'].apply(lambda x: cos(torch.tensor(x), qGram).item())
name = db['Name'][scores.argmax()]
score = round(scores.max(), 3)
return name, score
def move(self):
"""move ahead"""
next_pos_x = self.current_position['x'] + cos(self.current_direction)
next_pos_y = self.current_position['y'] - sin(self.current_direction)
if not self.__can_move(next_pos_x, next_pos_y):
self.__visited_position[str(next_pos_x) + "_" +
str(next_pos_y)] = -1
return False
self.move_count += 1
self.current_position['x'] = next_pos_x
self.current_position['y'] = next_pos_y
self.__visited_position[str(next_pos_x) + "_" + str(next_pos_y)] = 1
if self.loggable:
self.log()
return True
def move(self):
"""move ahead"""
next_pos_x = self.current_position['x'] + cos(self.current_direction)
next_pos_y = self.current_position['y'] - sin(self.current_direction)
if not self.__can_move(next_pos_x, next_pos_y):
self.__visited_position[str(next_pos_x) + "_" + str(next_pos_y)] = -1
return False
self.move_count += 1
self.current_position['x'] = next_pos_x
self.current_position['y'] = next_pos_y
self.__visited_position[str(next_pos_x) + "_" + str(next_pos_y)] = 1
#print("[x,y]" + str(self.current_position['x']) + "," + str(self.current_position['y']))
self.path_history.append([self.current_position['x'], self.current_position['y']])
if self.loggable:
self.log()
return True
def axisAndAngle2RotMatrix(axis, angle):
"""
http://stackoverflow.com/questions/6802577/python-rotation-of-3d-vector
Return the rotation matrix associated with counterclockwise rotation about
the given axis by angle radians.
"""
axis = np.asarray(axis)
angle = np.asarray(angle)
axis = old_div(axis, utils.sqrt(np.dot(axis, axis)))
a = utils.cos(old_div(angle, 2))
b, c, d = -axis * utils.sin(old_div(angle, 2))
aa, bb, cc, dd = a * a, b * b, c * c, d * d
bc, ad, ac, ab, bd, cd = b * c, a * d, a * c, a * b, b * d, c * d
return np.array([[aa + bb - cc - dd, 2 * (bc + ad), 2 * (bd - ac)],
[2 * (bc - ad), aa + cc - bb - dd, 2 * (cd + ab)],
[2 * (bd + ac), 2 * (cd - ab), aa + dd - bb - cc]])
def move(self):
"""move ahead"""
next_pos_x = self.current_position['x'] + cos(self.current_direction)
next_pos_y = self.current_position['y'] - sin(self.current_direction)
if not self.__can_move(next_pos_x, next_pos_y):
self.__visited_position[str(next_pos_x) + "_" + str(next_pos_y)] = -1
return False
self.move_count += 1
self.current_position['x'] = next_pos_x
self.current_position['y'] = next_pos_y
self.__visited_position[str(next_pos_x) + "_" + str(next_pos_y)] = 1
#self.diffrobot.go_forward(distance=200, dc=100)
self.diffrobot.driveGyro(10)
self.shovel.moveShovel()
#sleep(3)
if self.loggable:
self.log()
return True
def move(self):
"""move ahead"""
next_pos_x = self.current_position['x'] + cos(self.current_direction)
next_pos_y = self.current_position['y'] - sin(self.current_direction)
if not self.__can_move(next_pos_x, next_pos_y):
self.__visited_position[str(next_pos_x) + "_" +
str(next_pos_y)] = -1
return False
self.move_count += 1
self.current_position['x'] = next_pos_x
self.current_position['y'] = next_pos_y
self.__visited_position[str(next_pos_x) + "_" + str(next_pos_y)] = 1
if self.loggable:
self.log()
#self.car.rotate(90*self.current_direction)
#print("Move")
#print('%d, %d' % (next_pos_x, next_pos_y))
self.car.move_step_grid(Vector(next_pos_x, next_pos_y))
time.sleep(0.1)
return True
texts = [[word for word in document.lower().split()] for document in documents]
dictionary = corpora.Dictionary(texts)
print len(dictionary.token2id)
corpus = [dictionary.doc2bow(text) for text in texts]
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
file = open(output_file3, "w")
c = 0
for doc in corpus_tfidf:
print c
if c < len(event_map.keys()):
event_map[c]["feature"] = doc
else:
maxsim, assign = 0, -1
for k, v in event_map.iteritems():
if weibo[c -
len(event_map.keys())]["cls"] == v["cls"] and 0 <= weibo[
c - len(event_map.keys())]["day"] - v["stime"] <= 28:
sim = cos(v["feature"], doc)
if sim > maxsim:
maxsim, assign = sim, k
if maxsim >= 0.15:
file.write(
str(assign) + "\t" +
str(weibo[c - len(event_map.keys())]["cls"]) + "\t" +
str(weibo[c - len(event_map.keys())]["day"]) + "\t" +
weibo[c - len(event_map.keys())]["cid"] + "\t" +
weibo[c - len(event_map.keys())]["content"] + "\n")
c += 1
file.close()
def calculate_next_pos(self):
next_pos_x = self.current_position['x'] + cos(self.current_direction)
next_pos_y = self.current_position['y'] - sin(self.current_direction)
return {'x': next_pos_x, 'y': next_pos_y}
texts = [[word for word in document.lower().split()] for document in documents]
dictionary = corpora.Dictionary(texts)
print len(dictionary.token2id)
corpus = [dictionary.doc2bow(text) for text in texts]
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
event = {1:[],2:[],3:[]}
c = 0
for doc in corpus_tfidf:
print c
feature = doc
maxsim, assign = 0, -1
for e in xrange(len(event[news[c][0]])):
if abs(event[news[c][0]][e]["stime"] - news[c][1]) <= 14:
sim = utils.cos(event[news[c][0]][e]["feature"],feature)
# print "---- ---- ----"
# print event[news[c][0]][e]["title"][0], news[c][2], sim
# print event[news[c][0]][e]["feature"], feature
# print "---- ---- ----"
if sim > maxsim:
maxsim, assign = sim, e
# print maxsim
if maxsim >= 0.15:
event[news[c][0]][assign]["title"].append(news[c][2])
fmap = {}
for (p,s) in event[news[c][0]][assign]["feature"]:
fmap[p] = s
for (p,s) in feature:
fmap[p] = s if not fmap.has_key(p) else fmap[p]+s
event[news[c][0]][assign]["feature"] = [(p,s) for p,s in fmap.iteritems()]
else: file2.write(line) fileinput.close() file1.close() file2.close() # Do classifying texts = [[word for word in document.lower().split()] for document in documents] dictionary = corpora.Dictionary(texts) print len(dictionary.token2id) corpus = [dictionary.doc2bow(text) for text in texts] tfidf = models.TfidfModel(corpus) corpus_tfidf = tfidf[corpus] file = open(output_file3,"w") c = 0 for doc in corpus_tfidf: print c if c < len(event_map.keys()): event_map[c]["feature"] = doc else: maxsim, assign = 0, -1 for k, v in event_map.iteritems(): if weibo[c-len(event_map.keys())]["cls"] == v["cls"] and 0 <= weibo[c-len(event_map.keys())]["day"] - v["stime"] <= 28: sim = cos(v["feature"],doc) if sim > maxsim: maxsim, assign = sim, k if maxsim >= 0.15: file.write(str(assign)+"\t"+str(weibo[c-len(event_map.keys())]["cls"])+"\t"+str(weibo[c-len(event_map.keys())]["day"])+"\t"+weibo[c-len(event_map.keys())]["cid"]+"\t"+weibo[c-len(event_map.keys())]["content"]+"\n") c += 1 file.close()
def is_moving_west(droid):
return droid.speed > 0 and round(cos(-droid.heading + 90), 3) < 0
def paintEvent(self, e):
qp = QPainter()
qp.begin(self)
self.resize((len(self.data) + 2) * self.interval,
config.channels * config.min_channel_size)
qp.setPen(
QPen(config.second_channel_point_color, config.
first_channel_line_spacing)) ######可以试下画刷 setBrush,10指定点的大小
qp.drawLine(0, 0, 0, config.channels * config.min_channel_size)
qp.drawLine(0, config.min_channel_size, self.width(),
config.min_channel_size)
qp.setPen(QPen(Qt.black, 5)) ######可以试下画刷 setBrush,10指定点的大小
for index in range(0, config.min_channel_size,
config.coordinate_interval):
qp.drawPoint(0, config.min_channel_size - index)
qp.setFont(QFont("Decorative", config.font_size))
qp.drawText(
QRect(config.y_axes_distance, config.min_channel_size - index,
config.coordinate_interval,
config.min_channel_size - index + config.font_size),
Qt.AlignLeft | Qt.AlignTop, str(index))
for index in range(0, config.min_channel_size,
config.coordinate_interval):
qp.drawPoint(0, 2 * config.min_channel_size - index)
qp.setFont(QFont("Decorative", config.font_size))
qp.drawText(
QRect(config.y_axes_distance,
2 * config.min_channel_size - index,
config.coordinate_interval,
2 * config.min_channel_size - index + config.font_size),
Qt.AlignLeft | Qt.AlignTop, str(index))
for index in range(0, self.width(), int(self.interval)):
qp.drawPoint(index, config.min_channel_size)
qp.setFont(QFont("Decorative", config.font_size))
qp.drawText(
QRect(index, config.min_channel_size - config.font_size,
index + config.font_size, config.min_channel_size),
Qt.AlignLeft | Qt.AlignTop, str(index))
if self.my_sender:
if isinstance(self.my_sender, QSlider):
print("滑动了")
print(self.my_sender.value())
val = self.my_sender.value()
self.interval = int(config.default_interval +
config.default_slider_interval *
(val - config.default_slider_value))
## 第二条线
if self.my_sender:
if isinstance(self.my_sender, QAction):
if self.my_sender.text() == "sin":
self.new_data = utils.sin(self.data)
else:
self.new_data = utils.cos(self.data)
# else:
# new_data = utils.sin(self.data)
if self.new_data:
qp.setPen(
QPen(config.second_channel_point_color,
config.point_size)) ######可以试下画刷 setBrush,10指定点的大小
for index, x in enumerate(self.new_data):
qp.drawPoint((index + 1) * self.interval,
2 * config.min_channel_size - x)
qp.setPen(
QPen(config.second_channel_line_color,
config.second_channel_line_spacing,
config.second_channel_line_type)
) ####前一个random是线条粗线，后一个random是线条类型
for index, x in enumerate(self.new_data):
if index == len(self.new_data) - 1:
break
qp.drawLine(
(index + 1) * self.interval,
2 * config.min_channel_size - x,
(index + 2) * self.interval,
2 * config.min_channel_size - self.new_data[index + 1])
self.drawLines(qp) ######画线
self.drawPoints(qp) ###画点
qp.end()
texts = [[word for word in document.lower().split()] for document in documents]
dictionary = corpora.Dictionary(texts)
print len(dictionary.token2id)
corpus = [dictionary.doc2bow(text) for text in texts]
tfidf = models.TfidfModel(corpus)
corpus_tfidf = tfidf[corpus]
event = {1: [], 2: [], 3: []}
c = 0
for doc in corpus_tfidf:
print c
feature = doc
maxsim, assign = 0, -1
for e in xrange(len(event[news[c][0]])):
if abs(event[news[c][0]][e]["stime"] - news[c][1]) <= 14:
sim = utils.cos(event[news[c][0]][e]["feature"], feature)
# print "---- ---- ----"
# print event[news[c][0]][e]["title"][0], news[c][2], sim
# print event[news[c][0]][e]["feature"], feature
# print "---- ---- ----"
if sim > maxsim:
maxsim, assign = sim, e
# print maxsim
if maxsim >= 0.15:
event[news[c][0]][assign]["title"].append(news[c][2])
fmap = {}
for (p, s) in event[news[c][0]][assign]["feature"]:
fmap[p] = s
for (p, s) in feature:
fmap[p] = s if not fmap.has_key(p) else fmap[p] + s
event[news[c][0]][assign]["feature"] = [(p, s)
