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 restore_settings():
global idx
if ismini:
path_type = is_dir_file(path)
load_songs(path, path_type)
try:
idx = name_to_idx(sng_name)
except:
write()
return
song_list.selection_clear(0, 'end')
song_list.selection_set(idx)
song_list.activate(idx)
song_list.see(idx)
play(idx, init_player, True)
show_mini()
if theme == 'dark':
change_theme(init_player, file_menu)
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))
# get the link in the thumbnail div
image_link = a_thumb.find('a')
# get the url and the title info
image_url = image_link['href']
image_meta = image_link['title'].replace('12 - inch', '12\"').replace(
'12\u201d', '12\"').replace('33.3', '33')
image_id = image_link['data-image-id']
# write to the sub-dict
image['image_url'] = image_url
image['image_meta'] = image_meta
image['gallery'] = gallery_title
image['gallery_url'] = url
image['posted_to_tumblr'] = False
# write sub-dict to the main dict with id as key
# if the image is not already in our dictionary, let's add it
if image_id not in images:
images[image_id] = image
# if the image IS already in our dictionary, but hasn't been posted to social media yet, let's update the info in case something changed
if image_id in images and images[image_id]['posted_to_tumblr'] == False:
images[image_id] = image
print('We now have images from', gallery_title)
#write it to json
read_write.write('images', images)
print('We just dumped', len(images), 'images to images.json')
# get the link in the thumbnail div
image_link = a_thumb.find('a')
# get the url and the title info
image_url = image_link['href']
image_meta = image_link['title'].replace('12 - inch', '12\"').replace('12\u201d', '12\"').replace('33.3', '33')
image_id = image_link['data-image-id']
# write to the sub-dict
image['image_url'] = image_url
image['image_meta'] = image_meta
image['gallery'] = gallery_title
image['gallery_url'] = url
image['posted_to_tumblr'] = False
# write sub-dict to the main dict with id as key
# if the image is not already in our dictionary, let's add it
if image_id not in images:
images[image_id] = image
# if the image IS already in our dictionary, but hasn't been posted to social media yet, let's update the info in case something changed
if image_id in images and images[image_id]['posted_to_tumblr'] == False:
images[image_id] = image
print('We now have images from', gallery_title)
#write it to json
read_write.write('images', images)
print('We just dumped', len(images), 'images to images.json')