def test_query5(self):
actual1 = [x for x in query("bp+oil")]
actual2 = [x for x in query("oil+bp")]
expected = ['http://tcp-connections.herokuapp.com/crises/7/',
'http://tcp-connections.herokuapp.com/organizations/10/']
self.assertEquals(actual1, expected)
self.assertEquals(actual2, expected)
def test_query3(self):
actual1 = [x for x in query("")]
actual2 = [x for x in query("+")]
actual3 = [x for x in query("!@#$%^&*()")]
actual4 = [x for x in query(" ")]
self.assertEquals(actual1, [])
self.assertEquals(actual2, [])
self.assertEquals(actual3, [])
self.assertEquals(actual4, [])
def test_query1(self):
actual1 = [x for x in query("bill+gates")]
actual2 = [x for x in query("BILL+GATES")]
actual3 = [x for x in query(" !#@$ BILL + GATES \n ")]
expected = ['http://tcp-connections.herokuapp.com/organizations/5/',
'http://tcp-connections.herokuapp.com/crises/8/',
'http://tcp-connections.herokuapp.com/crises/1/',
'http://tcp-connections.herokuapp.com/people/4/',
'http://tcp-connections.herokuapp.com/people/7/']
self.assertEquals(actual1, expected)
self.assertEquals(actual2, expected)
self.assertEquals(actual3, expected)
def search_nodes():
q = request.args.get('q').strip().lower()
if len(q) < 3:
abort(400)
nodes = unique_list( search.query(q), max=40 )
nodes = map(render_summary, nodes)
nodes = map(render_dates, nodes)
return json_response(nodes)
def query(self, search_string, rel, pop): html = "<html><body>" + form results = search.query(search_string, rel, pop) if results != ["No results found."]: html += "<ul>" for result in search.query(search_string, int(rel), int(pop)): url = result[0] score = str(result[1]) html += "<li>[" + score + "] <a href='" + url + "'>" + url + "</a></li>" html += "</ul></body></html>" else: html += "<p>No results found.</p></body></html>" return html
def optimized_search(query) : q = search.query(query) ratio = 50 q_ratio_threshold = uphold_ratio_threshold(q, ratio) while q_ratio_threshold is None and ratio > 0 : ratio -= 10 q_ratio_threshold = uphold_ratio_threshold(q, ratio) q_best_ordering = get_best_ordering(q_ratio_threshold) return q_best_ordering, q_ratio_threshold
def main():
selection = ""
while selection.lower() not in ['a', 'b', 'c', 'd']:
selection = input("Please select an action: \
\n [A] List all images \n [B] Search for an image \
\n [C] Add an image \n [D] Delete an image \n")
if selection.lower() == "a":
if len(repo.df) == 0:
print("No images here yet.")
else:
print(repo.df)
elif selection.lower() == "b":
category = input("Enter a category to search, or press Enter if you don't want a certain category: ")
keywords = input("Enter some keywords, like 'beach picture', or press Enter: ")
result = query(category, keywords)
if len(result) == 0:
print("No images were found.")
else:
print("Top Results:")
print(result)
see_images = input("Would you like to see the images? Input Y if yes. ")
if see_images.lower() == "y":
images = list(result['path'])
for img in images:
Image.open(img).show()
elif selection.lower() == "c":
print("Select an image: ")
pathname = filedialog.askopenfilename()
possible_categories = repo.df['category'].unique()
categories_string = "\n".join([f"\t[{i}] {name}" for i, name in enumerate(possible_categories)])
print("Select a category from the following, or input a new category name: ")
category = input(categories_string + "\n")
if category.isdigit() and int(category) < len(possible_categories):
category = possible_categories[int(category)]
description = input("Enter a short phrase describing your image: ")
if repo.add_image(pathname, category, description):
print("Image successfully added!")
else:
print("Image already exists. ")
else:
print(repo.df)
ids = input("Enter the ID number of each image you want to delete separated by spaces: ").split()
ids = [int(id) for id in ids]
if repo.delete_image(ids):
print("Image successfully deleted.")
else:
print("This image does not exist in the repository.")
return
def get(self):
query = self.request.get('q')[:100].strip()
if len(query) == 0:
self.response.write('')
return
cinemas, films = search.query(query)
self.response.headers['Content-Type'] = 'application/json'
self.render('search_ajax.json', {
'n_cinemas': cinemas.number_found,
'results_cinemas': cinemas.results,
'n_films': films.number_found,
'results_films': films.results,
})
def select(s, db, cursor): s = s.split() if s[0] == 'register': res = user.register(s[1], s[2], s[3], s[4], db, cursor) if s[0] == 'login': res = user.login(s[1], s[2], db, cursor) if s[0] == 'dingdan': res = order.check(s[1], db, cursor) all = "" for r in res: all += sendms(r) + "\n" print all return all if s[0] == 'zhanghu': res = user.inform(s[1], db, cursor) if s[0] == 'zuche': res = order.rent(s[1], int(s[2]), db, cursor) if s[0] == 'huanche': res = order.rent_back(s[1], int(s[2]), db, cursor) if s[0] == 'chongzhi': res = user.charge(s[1], float(s[2]), db, cursor) if s[0] == 'shanchu': res = user.delete(s[1], db, cursor) if s[0] == 'jiache': res = bike.insert(int(s[1]), s[2:], db, cursor) if s[0] == 'shanche': res = bike.delete(int(s[1]), db, cursor) if s[0] == 'chache': res = bike.query(int(s[1]), db, cursor) if s[0] == 'fujin': res = bike.nearby(s[1], s[2], db, cursor) if s[0] == 'sousuo': res = search.query(int(s[1]), db, cursor) if s[0] == 'xml': res = xml.ret_xml(s[1], [s[2], s[3]], db, cursor) print sendms(res) return sendms(res)
def create_widgets(self, master):
self.text_sp = tk.Text(master, width=55, height=5, font=('標楷', 12))
self.text_sp.place(
x=240, y=160, anchor='center'
) # writing in same line would contribute AttributeError
self.btn_record = tk.Button(
master,
text='Record',
width=15,
height=2,
font=('times', 11),
command=lambda: self.text_sp.insert('end', Speech_text()))
self.btn_record.place(x=160, y=240, anchor='center')
self.btn_confirm = tk.Button(
master,
text='Confirm',
width=15,
height=2,
font=('times', 11),
command=lambda: query(self.text_sp.get(0.0, 'end')))
self.btn_confirm.place(x=320, y=240, anchor='center')
def do_GET(self):
self.send_response(200)
self.send_header('Content-type', 'application/json')
self.end_headers()
if '?' in self.path:
# 如果带有参数
self.queryString = urllib.parse.unquote(self.path.split('?', 1)[1])
respone = {
"kw": '',
"question": "服务器错误",
"answer": []
}
try:
params = urllib.parse.parse_qs(self.queryString)
respone = search.query(workbook=workbook, keyWord=params["kw"][0])
print(respone)
except Exception as err:
print(err)
finally:
self.wfile.write(json.dumps(respone).encode('utf-8'))
else:
self.wfile.write(json.dumps(data).encode('utf-8'))
return
def search():
q = request.args.get('q')
if not q:
return 'not query str'
items = query(q)
return render_template('search.html', items=items)
def search (request):
search_terms = request.GET.get('query')
print "searched: %s" % search_terms
query_results = query(search_terms)
print "results: %s" % query_results
return render(request, 'search.html', {'search_terms': search_terms.replace('+', ' '), 'query_results': query_results.items()})
def search():
q = request.args.get('q')
if not q:
return 'not query str'
items = query(q)
return render_template('search.html', items=items)
def do_query():
while True:
sen = raw_input(u'Start Query Here->:')
result = search.query(sen)
for item in result:
print 'similarity : {0} \n {1}'.format(item[0], item[1])
def search(q): return query(q)
def probablisticSearch(map, targetx, targety, policy):
dim = map.shape[0]
initbelief = 1.0 / (dim * dim)
beliefmap = np.full((dim, dim), initbelief)
count = 0
search_count = 0
move_count = 0
current_i = 0
current_j = 0
destination_i = 0
destination_j = 0
while True:
count += 1
maxbelief = 0
maxcells = []
if policy == 1: # highest prob of containing the target
for i in range(dim):
for j in range(dim):
if beliefmap[i][j] > maxbelief:
maxbelief = beliefmap[i][j]
maxcells.clear()
maxcells.append((i, j))
elif beliefmap[i][j] == maxbelief:
maxcells.append((i, j))
else: # highest prob of finding the target
for i in range(dim):
for j in range(dim):
p = 0
if map[i][j] == flat:
p = 0.9
elif map[i][j] == hilly:
p = 0.7
elif map[i][j] == forested:
p = 0.3
else:
p = 0.1
if beliefmap[i][j] * p > maxbelief:
maxbelief = beliefmap[i][j] * p
maxcells.clear()
maxcells.append((i, j))
elif beliefmap[i][j] * p == maxbelief:
maxcells.append((i, j))
# break ties arbitrarily
seed = random.random()
#print(maxcells)
query_cell_index = int(seed * (len(maxcells) - 1))
query_i = maxcells[query_cell_index][0]
query_j = maxcells[query_cell_index][1]
for cell in maxcells:
if cell[0] == destination_i and cell[
1] == destination_j and count != 1:
query_i = destination_i
query_j = destination_j
#print("==========")
#print("Max belief is %s" % maxbelief)
#print("Max belief cell is [%s,%s]" % (query_i,query_j))
#print("Current cell is [%s,%s]" % (current_i,current_j))
if_search = 1
# the first search is starting point
if count == 1 or (current_i == query_i and current_j == query_j):
#print("No need to move")
current_i = query_i
current_j = query_j
else: # decide whether to move or search
distance = manhattanDistance(current_i, current_j, query_i,
query_j)
#print("distance:%s" % distance)
destination = maxbelief
for i in range(distance):
destination = destination * 0.95
if policy == 1:
if beliefmap[current_i][current_j] < destination:
current_i, current_j = move(current_i, current_j, query_i,
query_j)
#print("moving to [%s,%s]" % (current_i,current_j))
move_count += 1
if_search = 0
else:
p = 0
if map[current_i][current_j] == flat:
p = 0.9
elif map[current_i][current_j] == hilly:
p = 0.7
elif map[current_i][current_j] == forested:
p = 0.3
else:
p = 0.1
if beliefmap[current_i][current_j] * p < destination:
current_i, current_j = move(current_i, current_j, query_i,
query_j)
move_count += 1
if_search = 0
query_result = 0
if if_search == 1:
search_count += 1
query_result = query(current_i, current_j, targetx, targety,
map[targetx][targety])
if query_result == 1:
print("Target found at [%s,%s], %s." %
(current_i, current_j, map[current_i][current_j]))
print("Totoal Search Steps:%d" % search_count)
return search_count, move_count
else:
# print("Search [%d,%d]" % (query_i,query_j))
makeMove(map, beliefmap, current_i, current_j)
for i in range(dim):
for j in range(dim):
if beliefmap[i][j] < 0:
raise Exception("ERROR")
def POST(self, query, page):
res = search.query(query, page)
res = json.dumps(res)
cherrypy.response.headers['Access-Control-Allow-Origin'] = '*'
return res
#coding=utf-8 import corpus import lda import search from gensim import corpora, models """ corpus.build_corpus() c = corpus.load_corpus() print 'Hello' for i in c: print i lda.init_tfidf() lda.train_lda() post = lda.load_topic_of_post() for p in post: print p """ h = search.query(u'女歌手') for i in h: print i[0] print i[1]
#coding=utf-8
import corpus
import lda
import search
from gensim import corpora, models
"""
corpus.build_corpus()
c = corpus.load_corpus()
print 'Hello'
for i in c:
print i
lda.init_tfidf()
lda.train_lda()
post = lda.load_topic_of_post()
for p in post:
print p
"""
h = search.query(u'女歌手')
for i in h:
print i[0]
print i[1]
def test_query2(self):
actual = [x for x in query("al+gore")]
expected = ['http://tcp-connections.herokuapp.com/people/7/',
'http://tcp-connections.herokuapp.com/people/10/']
self.assertEquals(actual, expected)
def do_query():
while True:
sen = raw_input(u'Start Query Here->:')
result = search.query(sen)
for item in result:
print 'similarity : {0} \n {1}'.format(item[0], item[1])
def test_query6(self):
actual = [x for x in query("oil")]
expected = ['http://tcp-connections.herokuapp.com/crises/7/',
'http://tcp-connections.herokuapp.com/organizations/10/']
self.assertEquals(actual, expected)
