def search(): # return "the f**k" likeCat = request.form.get('interest') learnCat = request.form.get('learn') likeUrl = baseUrl + likeCat learnUrl = baseUrl + learnCat resultingKeywords = wc.keyWords(wc.getParagraph(wc.getBestSection(wc.getBestSpan(\ wc.findBestSpan(wc.subCatDict(learnUrl), wc.allConnections(likeUrl))), learnUrl), learnUrl)).split(' ') queries = resultingKeywords[:10] paragraphs = [] common_words = [] images_links = [] paragraphs = sample(queries) full_dict = defaultdict(dict) for x in range(0, len(queries)): paragraph = paragraphs[x] removed_paragraph = wc.keyWords(paragraph) query = queries[x] counter = Counter(removed_paragraph.split(' ')) most_common = counter.most_common(10) most_common = {x[0]: x[1] for x in most_common} images = getImages(query) full_dict[x]['query'] = query full_dict[x]['paragraph'] = paragraph full_dict[x]['common_words'] = most_common full_dict[x]['images'] = images return jsonify(dict(full_dict))
def newSample(self, samID, dataFile, samplName, samplID="", sampleType="unknown"): self.sampleList.append( sample(samID, self, dataFile, samplName, samplID, sampleType))
def random_select(): """ 随机选择一组号码 """ red_balls = [x for x in range(1, 34)] selected_balls = [] selected_balls = sample(red_balls, 6) selected_balls.sort() selected_balls.append(randint(1, 16)) return selected_balls
def sample_model(args, logger=None): if args.text == '': strings = [ 'call me ishmael some years ago', 'A project by Sam Greydanus', 'mmm mmm mmm mmm mmm mmm mmm', 'What I cannot create I do not understand', 'You know nothing Jon Snow' ] # test strings else: strings = [args.text] logger = Logger( args) if logger is None else logger # instantiate logger, if None logger.write("\nSAMPLING MODE...") logger.write("loading data...") logger.write("building model...") model = Model(args, logger) logger.write("attempt to load saved model...") load_was_success, global_step = model.try_load_model(args.save_path) if load_was_success: for s in strings: strokes, phis, windows, kappas = sample(s, model, args) w_save_path = '{}figures/iter-{}-w-{}'.format( args.log_dir, global_step, s[:10].replace(' ', '_')) g_save_path = '{}figures/iter-{}-g-{}'.format( args.log_dir, global_step, s[:10].replace(' ', '_')) l_save_path = '{}figures/iter-{}-l-{}'.format( args.log_dir, global_step, s[:10].replace(' ', '_')) window_plots(phis, windows, save_path=w_save_path) gauss_plot(strokes, 'Heatmap for "{}"'.format(s), figsize=(2 * len(s), 4), save_path=g_save_path) line_plot(strokes, 'Line plot for "{}"'.format(s), figsize=(len(s), 2), save_path=l_save_path) # make sure that kappas are reasonable logger.write("kappas: \n{}".format( str(kappas[min(kappas.shape[0] - 1, args.tsteps_per_ascii), :]))) else: logger.write("load failed, sampling canceled") if True: tf.reset_default_graph() time.sleep(args.sleep_time) sample_model(args, logger=logger)
def dev_or_test_data_feature_extract(data_file,dependency_file): """ load dev or test data from dev or test data file and extract features return a sample list """ dev_f = codecs.open(data_file, 'r', 'utf-8') dev_dependency_f = codecs.open(dependency_file, 'r', 'utf-8') lines = dev_f.readlines() dependency_lines = dev_dependency_f.readlines() dev_f.close() dev_dependency_f.close() samples = [] for i,(line, dependency_line) in enumerate(zip(lines,dependency_lines)): print i new_sample = sample() line = line.split('\t') dependency_line = dependency_line.split('\t') new_sample.sent1_id = int(line[0]) new_sample.sent2_id = int(line[1]) assert line[0] == dependency_line[0] assert line[1] == dependency_line[1] #preprocessing of text:tokenization and stemming sent1 = [porter.stem(t) for t in nltk.word_tokenize(line[2])] new_sample.sent1 = sent1 sent2 = [porter.stem(t) for t in nltk.word_tokenize(line[3])] new_sample.sent2 = sent2 #filter stop words sent1_nonstop_word = [word for word in sent1 if not word in english_stopwords and not word in english_punctuations] sent2_nonstop_word = [word for word in sent2 if not word in english_stopwords and not word in english_punctuations] #get n-gram features new_sample.word_overlap = unigram_overlap(sent1_nonstop_word,sent2_nonstop_word) new_sample.bigram_overlap = bigram_overlap(sent1, sent2) new_sample.trigram_overlap = trigram_overlap(sent1, sent2) new_sample.fourgram_overlap = fourgram_overlap(sent1, sent2) new_sample.lcs = float(max_LCS(sent1, sent2)) / min(len(sent1),len(sent2)) new_sample.length_diff = float(abs(len(sent1)-len(sent2))) / min(len(sent1),len(sent2)) pos1 = nltk.pos_tag(sent1) pos2 = nltk.pos_tag(sent2) pos1_nonstop_word = [word for word in pos1 if not word[0] in english_stopwords and not word[0] in english_punctuations] pos2_nonstop_word = [word for word in pos2 if not word[0] in english_stopwords and not word[0] in english_punctuations] new_sample.nnp_overlap = nnp_overlap(pos1, pos2) new_sample.ne_overlap = ne_overlap(pos1, pos2) new_sample.num_overlap = num_overlap(pos1,pos2) #get knowledge based features new_sample.path_best_sim = path_best_sim(pos1_nonstop_word, pos2_nonstop_word, pos_map, pos_list) new_sample.wup_best_sim = wup_best_sim(pos1_nonstop_word, pos2_nonstop_word, pos_map, pos_list) new_sample.lch_best_sim = lch_best_sim(pos1_nonstop_word, pos2_nonstop_word, pos_map, pos_list) new_sample.lin_best_sim = lin_best_sim(pos1_nonstop_word, pos2_nonstop_word, brown_ic, pos_map, pos_list) new_sample.res_best_sim = res_best_sim(pos1_nonstop_word, pos2_nonstop_word, brown_ic, pos_map, pos_list) new_sample.jcn_best_sim = jcn_best_sim(pos1_nonstop_word, pos2_nonstop_word, brown_ic, pos_map, pos_list) new_sample.dependency_sim = dependency_sim(dependency_line[2], dependency_line[3]) samples.append(new_sample) return samples
def sample_model(args, logger=None): if args.text == '': strings = ['very good', 'a project', 'hello', \ 'this', 'how is'] # test strings else: strings = [args.text] logger = Logger( args) if logger is None else logger # instantiate logger, if None logger.write("\nSAMPLING MODE...") logger.write("loading data...") logger.write("building model...") model = Model(args, logger) logger.write("attempt to load saved model...") load_was_success, global_step = model.try_load_model(args.save_path) if load_was_success: for s in strings: strokes, phis, windows, kappas = sample(s, model, args) w_save_path = '{}figures/iter-{}-w-{}'.format( args.log_dir, global_step, s[:10].replace(' ', '_')) g_save_path = '{}figures/iter-{}-g-{}'.format( args.log_dir, global_step, s[:10].replace(' ', '_')) l_save_path = '{}figures/iter-{}-l-{}'.format( args.log_dir, global_step, s[:10].replace(' ', '_')) window_plots(phis, windows, save_path=w_save_path) # gauss_plot(strokes, 'Heatmap for "{}"'.format(s), figsize = (2*len(s),2*len(s)), save_path=g_save_path) line_plot(strokes, '3D plot for "{}"'.format(s), figsize=(len(s), len(s)), save_path=l_save_path) # make sure that kappas are reasonable logger.write("kappas: \n{}".format( str(kappas[min(kappas.shape[0] - 1, args.tsteps_per_ascii), :]))) else: logger.write("load failed, sampling canceled") if True: tf.reset_default_graph() time.sleep(args.sleep_time) sample_model(args, logger=logger)
def post(self): """Says hi to given name.""" request, err = flask_request_response.json_request( SAMPLE_API, POST_REQUEST ) if err is not None: return flask_request_response.json_response( {"error_message": str(err)}, SAMPLE_API, POST_REQUEST, 400 ) if "name" not in request: return flask_request_response.json_response( {"error_message": "Unable to find the 'name' in the request"}, SAMPLE_API, POST_REQUEST, 400 ) return flask_request_response.json_response( {"text": sample(request["name"])}, SAMPLE_API, POST_REQUEST, 200 )
def sample_model(args, logger=None): if args.text == '': strings = [ 'call me ishmael some years ago', 'A project by Sam Greydanus', 'mmm mmm mmm mmm mmm mmm mmm', 'What I cannot create I do not understand', 'You know nothing Jon Snow' ] # test strings else: strings = [args.text] model = Model(args) load_was_success, global_step = load_pretrained_model( model, args['save_path']) if load_was_success: for s in strings: strokes = sample(s, model, args) w_save_path = '{}figures/iter-{}-w-{}'.format( args['logs_dir'], global_step, s[:10].replace(' ', '_')) g_save_path = '{}figures/iter-{}-g-{}'.format( args['logs_dir'], global_step, s[:10].replace(' ', '_')) l_save_path = '{}figures/iter-{}-l-{}'.format( args['logs_dir'], global_step, s[:10].replace(' ', '_')) gauss_plot(strokes, 'Heatmap for "{}"'.format(s), figsize=(2 * len(s), 4), save_path=g_save_path) line_plot(strokes, 'Line plot for "{}"'.format(s), figsize=(len(s), 2), save_path=l_save_path) else: print("load failed, sampling canceled") if True: tf.reset_default_graph() time.sleep(args.sleep_time) sample_model(args, logger=logger)
def get(self): """Says hi to cookiecutter.""" return flask_request_response.json_response( {"text": sample()}, SAMPLE_API, GET_REQUEST, 200 )
board = [0] * length for i in range(1, length): board[i] = random.randrange(11) return board def sample(board): # get the correct result with the test case generated import user41_LrDbe6YZgW_0 as sample sample_game = sample.SolitaireMancala() sample_game.set_board(board) return sample_game.plan_moves() def error(board): # get the output from error sample with the test case generated import user41_cwKeKZrsxa_1 as error error_game = error.SolitaireMancala() error_game.set_board(board) return error_game.plan_moves() for i in range(50): # run 50 times and find the mismatched cases and #output their corresponding test cases config = generate() sample_move = sample(config) error_move = error(config) if sample_move != error_move: print config
#"Model_LSTM_Scene_common_subgrids_nonlinear" : Model_LSTM_Scene_common_subgrids_nonlinear } return model_dict[args.model_name] if __name__ == '__main__': args = get_args() # Get input argurments args.max_datasets = 5 # Maximum number of sequences could be used for storing scene data args.log_dir = os.path.join(args.save_root, args.dataset_size, args.model_dir, str(args.model_dataset), 'log') args.save_model_dir = os.path.join(args.save_root, args.dataset_size, args.model_dir, str(args.model_dataset), 'model') logger = Logger(args, train=False) # make logging utility logger.write("{}\n".format(args)) data_loader = DataLoader(args, logger, train=False) model = get_model(args) logger.write('evaluating on test data ......') save_model_file = '{}/best_epoch_model.pt'.format(args.save_model_dir) mse_eval, nde_eval, fde_eval = sample(model, data_loader, save_model_file, args, test=True) # Print out results logger.write('mse_eval: {:.3f}, nde_eval: {:.3f}, fde_eval: {:.3f}'.format( mse_eval, nde_eval, fde_eval))
def test_sample(): assert sample(True) assert not sample(False)
import sample sample()
if hasattr(obj, '__name__') and hasattr(obj, '__module__'): return obj.__module__ + '.' + obj.__name__ raise AttributeError('no attributes found the make a usable string ' \ 'out oj the object') def change_line_identitation(lines, spaces = 4): """change the minimum identation of the given lines to the given amount of whitespaces""" min_ident = min([len(line) - len(line.lstrip()) for line in lines if \ line.lstrip() and line.lstrip()[0] != "#"]) if min_ident > spaces: return [line[min_ident - spaces:] for line in lines] return [(spaces - min_ident) * ' ' + line for line in lines] def sample(obj, count = 5): """print samples where the object is used""" for i, sample in enumerate(find_samples(obj)): if i >= count: break if i != 0: print() print(sample.project) for i, lines in enumerate(sample.sample_lines(5)): print('\n'.join(change_line_identitation(lines))) __all__ = ['sample', 'Samples', 'find_samples', 'nullege_url', 'nullege_json'] if __name__ == '__main__': sample('bytearray')
def register(): reg1 = sample() reg1.register()
def train_data_feature_extract(train_file, train_dependency_file): """ load train data from train data file and extract features return a sample list """ train_f = codecs.open(train_file, 'r', 'utf-8') train_dependency_f = codecs.open(train_dependency_file, 'r', 'utf-8') lines = train_f.readlines() dependency_lines = train_dependency_f.readlines() train_f.close() train_dependency_f.close() train_samples = [] for i, (line, dependency_line) in enumerate(zip(lines, dependency_lines)): print i new_sample = sample() line = line.split('\t') dependency_line = dependency_line.split('\t') if line[0] == '0': new_sample.is_repeat = 0 else: new_sample.is_repeat = 1 new_sample.sent1_id = int(line[1]) new_sample.sent2_id = int(line[2]) assert line[1] == dependency_line[0] assert line[2] == dependency_line[1] #preprocessing of text:tokenization and stemming sent1 = [porter.stem(t) for t in nltk.word_tokenize(line[3])] new_sample.sent1 = sent1 sent2 = [porter.stem(t) for t in nltk.word_tokenize(line[4])] new_sample.sent2 = sent2 #filter stop words sent1_nonstop_word = [ word for word in sent1 if not word in english_stopwords and not word in english_punctuations ] sent2_nonstop_word = [ word for word in sent2 if not word in english_stopwords and not word in english_punctuations ] #get n-gram features new_sample.word_overlap = unigram_overlap(sent1_nonstop_word, sent2_nonstop_word) new_sample.bigram_overlap = bigram_overlap(sent1, sent2) new_sample.trigram_overlap = trigram_overlap(sent1, sent2) new_sample.fourgram_overlap = fourgram_overlap(sent1, sent2) #get lcs feature new_sample.lcs = float(max_LCS(sent1, sent2)) / min( len(sent1), len(sent2)) #get length different feature new_sample.length_diff = float(abs(len(sent1) - len(sent2))) / min( len(sent1), len(sent2)) #pos texts pos1 = nltk.pos_tag(sent1) pos2 = nltk.pos_tag(sent2) #filter stop words pos1_nonstop_word = [ word for word in pos1 if not word[0] in english_stopwords and not word[0] in english_punctuations ] pos2_nonstop_word = [ word for word in pos2 if not word[0] in english_stopwords and not word[0] in english_punctuations ] #get proper noun, name entity and number overlap feature new_sample.nnp_overlap = nnp_overlap(pos1, pos2) new_sample.ne_overlap = ne_overlap(pos1, pos2) new_sample.num_overlap = num_overlap(pos1, pos2) #get knowledge based features new_sample.path_best_sim = path_best_sim(pos1_nonstop_word, pos2_nonstop_word, pos_map, pos_list) new_sample.wup_best_sim = wup_best_sim(pos1_nonstop_word, pos2_nonstop_word, pos_map, pos_list) new_sample.lch_best_sim = lch_best_sim(pos1_nonstop_word, pos2_nonstop_word, pos_map, pos_list) new_sample.lin_best_sim = lin_best_sim(pos1_nonstop_word, pos2_nonstop_word, brown_ic, pos_map, pos_list) new_sample.res_best_sim = res_best_sim(pos1_nonstop_word, pos2_nonstop_word, brown_ic, pos_map, pos_list) new_sample.jcn_best_sim = jcn_best_sim(pos1_nonstop_word, pos2_nonstop_word, brown_ic, pos_map, pos_list) new_sample.dependency_sim = dependency_sim(dependency_line[2], dependency_line[3]) #add sample to train sample set train_samples.append(new_sample) return train_samples
from sample import * SVJ_mZ1000_mDM20_rinv03_alpha02 = sample() SVJ_mZ1000_mDM20_rinv03_alpha02.mZ = 1000 SVJ_mZ1000_mDM20_rinv03_alpha02.mDark = 20 SVJ_mZ1000_mDM20_rinv03_alpha02.rinv = 0.3 SVJ_mZ1000_mDM20_rinv03_alpha02.alpha = 0.2 SVJ_mZ1000_mDM20_rinv03_alpha02.sigma = 1 SVJ_mZ1000_mDM20_rinv03_alpha02.color = ROOT.kBlue - 4 SVJ_mZ1000_mDM20_rinv03_alpha02.style = 1 SVJ_mZ1000_mDM20_rinv03_alpha02.fill = 1001 SVJ_mZ1000_mDM20_rinv03_alpha02.leglabel = "SVJ m_{Z}=%d,m_{DM}=%d,r_{inv}=%.1f,#alpha=%.1f" % ( SVJ_mZ1000_mDM20_rinv03_alpha02.mZ, SVJ_mZ1000_mDM20_rinv03_alpha02.mDark, SVJ_mZ1000_mDM20_rinv03_alpha02.rinv, SVJ_mZ1000_mDM20_rinv03_alpha02.alpha) SVJ_mZ1000_mDM20_rinv03_alpha02.label = "SVJ2_mZprime-1000_mDark-20_rinv-0.3_alpha-0.2" SVJ_mZ2000_mDM20_rinv03_alpha02 = sample() SVJ_mZ2000_mDM20_rinv03_alpha02.mZ = 2000 SVJ_mZ2000_mDM20_rinv03_alpha02.mDark = 20 SVJ_mZ2000_mDM20_rinv03_alpha02.rinv = 0.3 SVJ_mZ2000_mDM20_rinv03_alpha02.alpha = 0.2 SVJ_mZ2000_mDM20_rinv03_alpha02.sigma = 1 SVJ_mZ2000_mDM20_rinv03_alpha02.color = ROOT.kBlue - 3 SVJ_mZ2000_mDM20_rinv03_alpha02.style = 1 SVJ_mZ2000_mDM20_rinv03_alpha02.fill = 1001 SVJ_mZ2000_mDM20_rinv03_alpha02.leglabel = "SVJ m_{Z}=%d,m_{DM}=%d,r_{inv}=%.1f,#alpha=%.1f" % ( SVJ_mZ2000_mDM20_rinv03_alpha02.mZ, SVJ_mZ2000_mDM20_rinv03_alpha02.mDark, SVJ_mZ2000_mDM20_rinv03_alpha02.rinv, SVJ_mZ2000_mDM20_rinv03_alpha02.alpha) SVJ_mZ2000_mDM20_rinv03_alpha02.label = "SVJ2_mZprime-2000_mDark-20_rinv-0.3_alpha-0.2"