def model_training(n, i):
meth = method[i]
y, x = liblinearutil.svm_read_problem("other_method/kmeans_linear/%s/train_%s" % (
name, meth))
y_test, x_test = liblinearutil.svm_read_problem("other_method/kmeans_linear/%s/test_%s" % (
name, meth))
prob = liblinearutil.problem(y, x)
temp_result = np.empty((14))
for idx, val in enumerate(cost):
param = liblinearutil.parameter(' -q -c %f' % (val))
m = liblinearutil.train(prob, param)
pred_labels, (temp_result[idx], MSE, SCC), pred_values = liblinearutil.predict(y_test, x_test, m)
return (i, f_idx, n, temp_result)
def model_training():
meth = method[0]
y, x = liblinearutil.svm_read_problem("other_method/kmeans_linear/%s/train_%s" % (
name, meth))
y_test, x_test = liblinearutil.svm_read_problem("other_method/kmeans_linear/%s/test_%s" % (
name, meth))
prob = liblinearutil.problem(y, x)
temp_result = np.zeros((12))
# print(x.shape(1))
for idx, val in enumerate(cost):
start = time.time()
param = liblinearutil.parameter(' -q -c %f' % (val))
m = liblinearutil.train(prob, param)
pred_labels, (temp_result[idx], MSE, SCC), pred_values = liblinearutil.predict(y_test, x_test, m)
# print(temp_result)
t2 = time.time()-start
return np.max(temp_result),t2
def model_training(n, i):
meth = method[i]
y, x = liblinearutil.svm_read_problem(
"other_method/kmeans_linear/%s/original_%s" % (name, meth))
prob = liblinearutil.problem(y, x)
temp_result = np.empty((13))
for idx, val in enumerate(cost):
param = liblinearutil.parameter('-v 5 -q -c %f' % (val))
temp_result[idx] = liblinearutil.train(prob, param)
return (i, f_idx, n, temp_result)