def runProgram(youtube_search_query, text_to_find): dl = Downloader() celeb_videos = Search.search(youtube_search_query) links = [Caption.YOUTUBE_LINK + x for x in celeb_videos] print links count = 0 for videoid in celeb_videos: cap = Caption(videoid) results = cap.find(text_to_find) for result in results: dl.download(result, text_to_find + str(count)) count += 1
def tune( dataFilename ): Nvalues = [8] Kvalues = [4, 8] print "Starting" for n in Nvalues: print n for k in Kvalues: print k Parameters.N = n Parameters.K = k t = int((Parameters.E - Parameters.N)/Parameters.K) Parameters.T = t print Parameters.T from Search import Search results = Search.search() f = open( dataFilename , 'a' ) f.write( str( Parameters.N ) + " " + str( Parameters.K ) + " " + str( Parameters.T ) + " " + str( results[ 0 ].get_fitness() ) + "\n" ) f.close()
def tune(dataFilename): Nvalues = [8] Kvalues = [4, 8] print "Starting" for n in Nvalues: print n for k in Kvalues: print k Parameters.N = n Parameters.K = k t = int((Parameters.E - Parameters.N) / Parameters.K) Parameters.T = t print Parameters.T from Search import Search results = Search.search() f = open(dataFilename, 'a') f.write( str(Parameters.N) + " " + str(Parameters.K) + " " + str(Parameters.T) + " " + str(results[0].get_fitness()) + "\n") f.close()
def search(): query = request.form['query'] s = Search() results = s.search(query) return render_template('search.html', query=query, results=results, clippyjs_agent=random_clippyjs_agent())
try: initial_puzzle = list(map(int, args.puzzle.split())) except ValueError: parser.error("Please input a string of numbers as puzzle") if len(initial_puzzle) % args.columns != 0: parser.error("Incompatible puzzle and columns value(" + str(args.columns) + ").") if len(set(initial_puzzle)) != len(initial_puzzle): parser.error("Please do not have any duplicate values in puzzle.") if args.b: start_breadth_first = perf_counter() initial_node = Node(initial_puzzle, args.columns, None, "0", 0) breadth_first = Search(initial_node) breadth_first.search("breadth_first") end_breadth_first = perf_counter() start_depth_first = perf_counter() initial_node = Node(initial_puzzle, args.columns, None, "0", 0) depth_first = Search(initial_node) depth_first.search("depth_first", args.max_depth, args.i) end_depth_first = perf_counter() start_best_first_linear = perf_counter() initial_node = Node(initial_puzzle, args.columns, None, "0", 0, "linear_distance") best_first_linear = Search(initial_node) best_first_linear.search("best_first") end_best_first_linear = perf_counter() start_best_first_wrong = perf_counter()
def main(fn): # data= pd.read_csv("../data/kddcup.data_10_percent_corrected", names=cols) data = pd.read_csv(fn, header=-1) # data= remove_missing(data) # data= impute_missing(data) data = impute_missing2(data) # Features to be used in classification features = [x for x in range(1, len(data.columns))] X = data[features] y = data[0] #h= TGaussianNB(X, y) #h.run() print("GaussianNB") h = Test(X, y, GaussianNB()) h.run() h.report(fn="../Report/results/cancer.gnb.cm.tex") s = Search(X, y, GaussianNB(), [{}]) s.search() s.report("../Report/results/cancer.gnb.tex") print("DTree neu") parameters = [{ 'criterion': ['gini', 'entropy'], 'max_features': ['auto', 'sqrt', 'log2'] }] s = Search(X, y, DTree(), parameters) s.search() s.report("../Report/results/cancer.dt.tex") h = Test(X, y, DTree(max_features='log2', criterion='gini', random_state=1234)) h.run() h.report(fn="../Report/results/cancer.dt.cm.tex") print("RF") parameters = [{ 'n_estimators': range(1, 15), 'criterion': ['gini', 'entropy'], 'max_features': ['auto', 'sqrt', 'log2'] }] s = Search(X, y, RandomForestClassifier(), parameters) s.search() s.report("../Report/results/cancer.rf.tex", ) h = Test( X, y, RandomForestClassifier(n_estimators=6, criterion='gini', max_features='sqrt', random_state=1234)) h.run() h.report(fn="../Report/results/cancer.rf.cm.tex") parameters = [{ 'kernel': ['linear', 'sigmoid', 'rbf', 'poly'], 'C': [0.1, 1, 10, 11, 20] }] print("SVM") from sklearn import preprocessing X_scaled = preprocessing.scale(X) s = Search(X_scaled, y, SVC(), parameters) s.search() s.report("../Report/results/cancer.svm.tex") h = Test(X, y, SVC(C=11, kernel='poly')) h.run() h.report(fn="../Report/results/cancer.svm.cm.tex") print("KNeighborsClassifier") parameters = [{ 'n_neighbors': range(4, 8), 'weights': ['uniform', 'distance'], 'p': [1, 2] }] s = Search(X, y, KNeighborsClassifier(), parameters) s.search() s.report("../Report/results/cancer.knn.tex") h = Test(X, y, KNeighborsClassifier(n_neighbors=5, weights='uniform', p=2)) h.run() h.report(fn="../Report/results/cancer.knn.cm.tex")