def get_clustering(data_to_cluster, cluster_model_type, cluster_model_params):
sw = Stopwatch()
sw.start()
if cluster_model_type == 'KMEANS':
cluster_model = MiniBatchKMeans(**cluster_model_params)
elif cluster_model_type == 'DBSCAN':
cluster_model = DBSCAN_w_prediction(**cluster_model_params)
cluster_model.fit(data_to_cluster)
cluster_model.X = data_to_cluster
sw.stop()
logging.debug('Descriptors clustered into %d clusters.' %
cluster_model.n_clusters)
return cluster_model
#print "Sudoku dpll",dpll(X2SATsudoku(sud)) # Opomba: tega ne premelje skozi v normalnem casu.
print "::: END DPLL :::\n\n"
"""
######################################################
#################UTILS################################
######################################################
"""
#Razred stoparica, ki nam na enostaven nacin omogoca merjenje ter primerjanje casa med razlicnimi metodami.
from utils import Stopwatch
print "::: BEGIN UTILS :::"
stoparica = Stopwatch("Primer uporabe") # Kot parameter lahko podamo ime (tag).
# Stoparica se avtomatsko zazene, ko jo ustvarimo.
stoparica.stop() # Ustavimo jo s stop.
stoparica.restart() # Ko jo restartamo, pocisti vse prejsnje vrednosti.
hadamardova_matrika(8)
stoparica.intermediate("Vmesni cas 10") # Lahko dodamo vec vmesnih casov.
hadamardova_matrika(10)
stoparica.intermediate("Vmesni cas 12")
stoparica.stop("Skupaj")
print stoparica # Rezultat izpisemo tako, da stoparico enostavno izpisemo z print.
# Pri izpisu se vsak vmesni cas meri od prejsnjega vmesnega casa,
# TOTAL pa je razlika od (konec-start).
#Primer uporabe
st = Stopwatch("Optimizacija")
hadamardova_matrikaOLD(8)
st.intermediate("Old 10")
hadamardova_matrika(8)
# Evaluate on the validation set
# avg_loss_valid = evaluate(net, criterion, valid_loader, device)
# y_loss_valid.append(avg_loss_valid)
# logger.log("Average validation loss: {}".format(avg_loss_valid))
#x_error_median, q_error_median, med_valid_loss = evaluate_median(net, criterion, itertools.islice(valid_loader, 10), device)
x_error_median, q_error_median, med_valid_loss = evaluate_median(
net, criterion, valid_loader, device)
# y_loss_valid.append(loss_median)
# y_training.append(np.median(training_loss))
net.log("Median validation error: {:.2f} m, {:.2f} °".format(
x_error_median, q_error_median))
net.log("Median validation loss: {}".format(med_valid_loss))
# Print some stats
elapsed_time = stopwatch_epoch.stop()
net.log("Epoch time: {:0.2f} minutes".format(elapsed_time))
# print_torch_cuda_mem_usage()
total_epoch_time += elapsed_time
avg_epoch_time = total_epoch_time / (epoch + 1)
training_time_left = (EPOCHS - epoch - 1) * avg_epoch_time
net.log("Training time left: ~ {:.2f} minutes ({:.2f} hours)".format(
training_time_left, training_time_left / 60))
# # Plot the average loss over the epochs
# loss_fig = plt.figure()
# loss_ax = loss_fig.gca()
# loss_ax.plot(x, y_loss_valid, "r", label = "Validation")
# loss_ax.plot(x, y_training, "b", label = "Training")
# plt.xlabel("Epoch")
# plt.ylabel("Median loss")