def plot_glove_upto():
for i in [500]:
glove_data = utils.load_glove_data('train')
glove_q = utils.load_glove_data('query')[:300]
utils.plot_dist_hist_upto(glove_data, glove_q, i, 'glove')
for i in [500]:
glove_c_data = utils.load_glove_c_data('train')
glove_c_q = utils.load_glove_c_data('query')[:300]
utils.plot_dist_hist_upto(glove_c_data, glove_c_q, i, 'glove_c')
def plot_glove():
#glove_data = utils.load_sift_data('query')
#pdb.set_trace()
for i in [1, 100]:
glove_data = utils.load_glove_data('train')
glove_q = utils.load_glove_data('query')[:600]
_, plt = utils.plot_dist_hist(glove_data, glove_q, i, 'glove')
plt.clf()
for i in [1, 100]:
glove_c_data = utils.load_glove_c_data('train')
glove_c_q = utils.load_glove_c_data('query')[:600]
utils.plot_dist_hist(glove_c_data, glove_c_q, i, 'glove_c2')
Demo for running training or linear models.
'''
import utils
from kahip.kmkahip import run_kmkahip
if __name__ == '__main__':
opt = utils.parse_args()
#adjust the number of parts and the height of the hierarchy
n_cluster_l = [2]
height_l = [1]
# load dataset
if opt.glove:
dataset = utils.load_glove_data('train').to(utils.device)
queryset = utils.load_glove_data('query').to(utils.device)
neighbors = utils.load_glove_data('answers').to(utils.device)
elif opt.sift:
dataset = utils.load_sift_data('train').to(utils.device)
queryset = utils.load_sift_data('query').to(utils.device)
neighbors = utils.load_sift_data('answers').to(utils.device)
else:
dataset = utils.load_data('train').to(utils.device)
queryset = utils.load_data('query').to(utils.device)
neighbors = utils.load_data('answers').to(utils.device)
#specify which action to take at each level, actions can be km, kahip, train, or svm. Lower keys indicate closer to leaf.
#Note that if 'kahip' is included, evaluation must be on training rather than test set, since partitioning was performed on training, but not test, set.
#e.g.: opt.level2action = {0:'km', 1:'train', 3:'train'}
opt.level2action = {0: 'train'}
def compute_alpha_beta():
#dataset = utils.load_sift_data('train').to(utils.device)
dataset = utils.load_glove_data('train').to(utils.device)
alpha, beta = utils.compute_alpha_beta(dataset, 10)
print(alpha, beta)
pdb.set_trace()