err_min = 1.0
best_err = [0.0, 0.0]
best_param = [0.0,0.0]
training_time_sum = 0.0
predicting_time_outliers = 0.0
predicting_time_targets = 0.0
# grid search
for index, i in enumerate(nu_list):
for jndex, j in enumerate(gamma_list):
print("nu=%r, gamma=%r"%(i,j))
# model fitting
start = timeit.default_timer()
clf = ocsvm.fit(new_data, i, j)
stop = timeit.default_timer()
training_time_sum += stop - start
# predicting
start = timeit.default_timer()
y_outliers = ocsvm.predict(clf, pseudo_outliers)
stop = timeit.default_timer()
predicting_time_outliers += stop - start
start = timeit.default_timer()
y_targets = ocsvm.predict(clf, pseudo_targets)
stop = timeit.default_timer()
predicting_time_targets += stop - start
# calculate the error
