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
# 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:
