def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/ScjPKhZeCk8", "https://youtu.be/IzR96zU8C5Q", "https://youtu.be/Gr0DaLg1K4k", "https://youtu.be/Mzs9peurc-8" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/vUos4Qaxu08", "https://youtu.be/FogX5i9JO4w", "https://youtu.be/fgD_o3nwsGI", "https://youtu.be/EY2JikssROI" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/xGVNmYgpK7U", "https://youtu.be/VqJGeBzTqtE", "https://youtu.be/cz9YCcCjXNI", "https://youtu.be/yenrsHuUmeo", "https://youtu.be/FQUfHP-tQ8s", "https://youtu.be/3Dl9vuWHzto" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/jUm2Eu6G3Og", "https://youtu.be/T7w33eSs-SY", "https://youtu.be/IdXG3qT7F5s", "https://youtu.be/XzyA124MKdM", "https://youtu.be/LlOF4dcdfRs", "https://youtu.be/IEsUSLFlUtM" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/FT4PBJcyAsY", "https://youtu.be/6Y6luXfLOHU", "https://youtu.be/mlWZT-OJfe4", "https://youtu.be/TCtIVuVhE5I", "https://youtu.be/tqH6wf8G_V8", "https://youtu.be/GxUnUcSdRhk" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/JP508l9jd-w", "https://youtu.be/XGZwjxN3Bns", "https://youtu.be/3yf0LHYJwMI", "https://youtu.be/jMw5Wt7qQB0", "https://youtu.be/CqFmjv0zp6g", "https://youtu.be/W7Y3zlHah28" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/oBM37bYX4dU", "https://youtu.be/5HeKFck9SWY", "https://youtu.be/wH5xX2hH6xA", "https://youtu.be/IkByqFNzQAU", "https://youtu.be/6Ie9llP16og", "https://youtu.be/gzkS2oX1RF8" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/iR4c4T5XlVU", "https://youtu.be/Vn2411mYUFE", "https://youtu.be/X72HgBrMccc", "https://youtu.be/BrASylNQj8c", "https://youtu.be/vnXwAJDxtKo", "https://youtu.be/AOcX6lcLYvk" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/59eLfJYZR7M", "https://youtu.be/F9vUqdZMVwI", "https://youtu.be/1EGupU4_Osg", "https://youtu.be/bIZJFFo-iBM", "https://youtu.be/CPTX9x759Ds", "https://youtu.be/kNKktE8W91k" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/WmGT7HoUqi0", "https://youtu.be/H5dd2x8MV2o", "https://youtu.be/BloMUUgaQXo", "https://youtu.be/oFuY5M1lwiY", "https://youtu.be/1dxnzJEusc4", "https://youtu.be/CGuShacPoJ8" ] webbrowser.open(link[num]) read(final)
def Link(self, num, final): #버튼 마다 링크 연결 link = [ "https://youtu.be/ulPxvDHN8v0", "https://youtu.be/tOSSzsNCSzA", "https://youtu.be/yR70ANNk-Vo", "https://youtu.be/8oFFdhowiUs", "https://youtu.be/rjfJshn20Vs", "https://youtu.be/KfcMCOjGU9Q" ] webbrowser.open(link[num]) read(final)
def check_and_load(): global theme, path, idx, sng_name, ismini dir_path = create() if not os.path.exists(dir_path + '/settings.toml'): write() theme, path, idx, sng_name, ismini = read()
def main_rendu(accuracy_on_train_set=False): ls_kernel = [ kernels.MismatchKernel(12, 2, 4, False), kernels.MismatchKernel(12, 2, 4, True), kernels.MismatchKernel(9, 2, 4, False) ] ls_reg_val = [100 * 0.03162277660168379, .1, 1000 * 0.03162277660168379] ls_methods = [ methods.KernelRidgeRegression(ls_kernel[i], reg_val=ls_reg_val[i]) for i in range(3) ] for i in range(3): print("##################", f"i={i}") # X = read_write.read_X100(f"data/Xtr{i}_mat100.csv") X = read_write.read(f"data/Xtr{i}.csv") # print(X.shape) # X_cat = np.concatenate((X, X), axis=-1) # X_test = read_write.read_X100(f"data/Xte{i}_mat100.csv") X_test = read_write.read(f"data/Xte{i}.csv") # X_test_cat = np.concatenate((X_test, X_test), axis=-1) y = read_write.read_labels(f"data/Ytr{i}.csv") # X_cat = np.concatenate((X, X), axis=-1) # X_test_cat = np.concatenate((X_test, X_test), axis=-1) ls_methods[i].learn(X, y) # FOR ACCURACY ON TRAINING SET if accuracy_on_train_set: y_pred = ls_methods[i].predict(X) print(methods.accuracy(y, y_pred)) y_test = ls_methods[i].predict(X_test) read_write.write(y_test, "predictions/Yte.csv", offset=i * 1000, append=(i != 0))
def main_poly(): ls_kernel = [ kernels.SpectrumKernel(15), kernels.SpectrumKernel(6), kernels.SpectrumKernel(6) ] for i in range(3): print("##################", f"i={i}") X = read_write.read(f"data/Xtr{i}.csv") y = read_write.read_labels(f"data/Ytr{i}.csv") kernel_class = kernels.PolyKernel method_class = methods.KernelLogisticRegression print(kernel_class, method_class) params_kernel = [(ls_kernel[i], deg, True) for deg in range(2, 6)] # [(k, max(floor(k / 10), 1), 4) for k in range(6, 18, 3)] # 10. ** reg_vals = 10.**np.arange(-3, 4, 1) validation(X, y, kernel_class, method_class, params_kernel, reg_vals)
def main_val(): for i in range(3): print("##################", f"i={i}") # X = read_write.read_X100(f"data/Xtr{i}_mat100.csv") X = read_write.read(f"data/Xtr{i}.csv") # X_test = read_write.read_X100(f"data/Xte{i}_mat100.csv") # X_test = read_write.read(f"data/Xte{i}.csv") y = read_write.read_labels(f"data/Ytr{i}.csv") # k,m,A kernel_class = kernels.MismatchKernel method_class = methods.KernelRidgeRegression # params_kernel = [(i, True) for i in range(4, 16, 2)] # Spectrum params_kernel = [(i, 2, 4, False) for i in [7, 9, 12] ] # Mismatch #max(floor(i / 5)#range(4, 16, 1) # params_kernel = [(10. ** i, True) for i in range(-3, 3, 1)] # gaussian reg_vals = 10.**np.arange(-2, 3, 1 / 2) validation(X, y, kernel_class, method_class, params_kernel, reg_vals)
def main_sum(): ls_kernel = [ kernels.MismatchKernel(12, 1, 4, False), kernels.MismatchKernel(12, 1, 4, False), kernels.MismatchKernel(9, 1, 4, False) ] ls_kernel_prime = [ kernels.Gaussian(.1, False), kernels.Gaussian(.1, False), kernels.Gaussian(.1, False) ] # ls_methods = [ # methods.SVM(kernel_0, reg_val=.1), # methods.SVM(kernel_1, reg_val=.1), # methods.SVM(kernel_2, reg_val=.01), # ] for i in range(3): print("##################", f"i={i}") # X = read_write.read_X100(f"data/Xtr{i}_mat100.csv") X = read_write.read(f"data/Xtr{i}.csv") length = X.shape[1] X_cat = np.concatenate((X, X), axis=-1) # X_test = read_write.read_X100(f"data/Xte{i}_mat100.csv") # X_test = read_write.read(f"data/Xte{i}.csv") y = read_write.read_labels(f"data/Ytr{i}.csv") kernel_class = kernels.SumKernel method_class = methods.KernelRidgeRegression params_kernel = [(ls_kernel[i], ls_kernel_prime[i], length, length, False)] # [(k, max(floor(k / 10), 1), 4) for k in range(6, 18, 3)] # 10. ** reg_vals = 10.**np.arange(-2, 3, 1 / 4) validation(X_cat, y, kernel_class, method_class, params_kernel, reg_vals)
# put the urls in the list for gallery_link in gallery_links: gallery_url = gallery_link['href'] if gallery_link['href'] not in exclusions: gallery_urls.append(gallery_url) print('We have a list of URLs!') print(gallery_urls) ################################### # open the json file for reading and load to dict json_dict = read_write.read('images') if json_dict: images = json_dict else: images = {} ################################### # here we start looking at each page for url in gallery_urls: # get the page gallery = requests.get(url) # check if we get an error code
state = settings.state random_image = settings.random_image exclusions = settings.post_exclusions more_text = settings.more_text more_url = settings.more_url # Authenticate via OAuth client = pytumblr.TumblrRestClient( settings.consumer_key, settings.consumer_secret, settings.oauth_token, settings.oauth_secret, ) # open the json file for reading and load to dict images = read_write.read('images') # set the count count = 0 # ask user how many posts (up to 300) user_count = input( '>> How many images to you want to send to Tumblr? (300 max if sending to queue): ' ) # Should we parse the dictionary randomly? if random_image == True: sample = random.sample(images.keys(), len(images)) else: sample = images
def Link(self,num,final): #버튼 마다 링크 연결 link = ["https://youtu.be/7Zk5apxCYmI","https://youtu.be/_x6_pR--5Vs","https://youtu.be/yzPBBNowM4w","https://youtu.be/HKZ8p-NqWqE","https://youtu.be/P9tw9smiRBQ","https://youtu.be/Q2dAkDBMwV0"] webbrowser.open(link[num]) read(final)
state = settings.state random_image = settings.random_image exclusions = settings.post_exclusions more_text = settings.more_text more_url = settings.more_url # Authenticate via OAuth client = pytumblr.TumblrRestClient( settings.consumer_key, settings.consumer_secret, settings.oauth_token, settings.oauth_secret, ) # open the json file for reading and load to dict images = read_write.read('images') # set the count count = 0 # ask user how many posts (up to 300) user_count = input('>> How many images to you want to send to Tumblr? (300 max if sending to queue): ') # Should we parse the dictionary randomly? if random_image == True: sample = random.sample(images.keys(), len(images)) else: sample = images for image in sample: