def kmeans_anneal(X,Y,idx_condensor,name): import time import pickle import numpy as np from subset_selection.rec_annealing import anneal_optimize cond_anneal = [] for index in idx_condensor: cond_anneal.append(anneal_optimize(index, X, Y)) pickle.dump(cond_anneal, open('data_sub/'+name+'_kmeans_anneal.p', 'wb')) print('saved_kmeans_anneal', len(index))
def random_anneal(X,Y,name,size=100,frac=1.1,tl=60,trial=1): import time import pickle import numpy as np from subset_selection.rec_annealing import anneal_optimize #X, Y = pickle.load(open('data_embed/mnist_dim32.p', 'rb')) cond_anneal = [] while size < len(Y): for i in range(trial): index = np.random.choice(list(range(len(Y))), size, replace=False) cond_anneal.append(anneal_optimize(index, X, Y, tl)) pickle.dump(cond_anneal, open('data_sub/'+name+'_random_anneal.p', 'wb')) size = int(size * frac) print('saved_random_anneal', size)
def flat_anneal(X_tr_emb,Y_tr,idx_condensor,name,size = 100): import pickle from subset_selection.rec_annealing import anneal_optimize #data_path = 'data_sub/mnist_tiers.p' #idx_condensor = index #print(idx_condensor.shape) X, Y = X_tr_emb, Y_tr #size = 100 frac = 1.1 cond_anneal = [] while size < idx_condensor.shape[0]: index = idx_condensor[:size] tl = 60 #if size == 100: # tl = 300 cond_anneal.append(anneal_optimize(index, X, Y, tl)) pickle.dump(cond_anneal, open('data_sub/'+name+'_tiers_anneal.p', 'wb')) size = int(size * frac) print('saved_anneal', size)
import time import pickle import numpy as np from subset_selection.rec_annealing import anneal_optimize data_path = 'data_sub/mnist_tiers.p' idx_condensor = pickle.load(open(data_path, "rb")) print(idx_condensor.shape) X, Y = pickle.load(open('data_embed/mnist_dim32.p', 'rb')) size = 100 frac = 1.1 cond_anneal = [] while size < idx_condensor.shape[0]: index = idx_condensor[:size] tl = 60 if size == 100: tl = 300 cond_anneal.append(anneal_optimize(index, X, Y, tl)) pickle.dump(cond_anneal, open('data_sub/condensor_anneal.p', 'wb')) size = int(size * frac) print('saved', size)