def runMLKR(X_train, X_test, y_train, y_test):
transformer = MLKR(verbose=True)
transformer.fit(X_train, y_train)
X_train_proj = transformer.transform(X_train)
X_test_proj = transformer.transform(X_test)
np.save('X_train_MLKR', X_train_proj)
np.save('X_test_MLKR', X_test_proj)
return X_train_proj, X_test_proj
def test_mlkr(self):
mlkr = MLKR(n_components=2)
mlkr.fit(self.X, self.y)
res_1 = mlkr.transform(self.X)
mlkr = MLKR(n_components=2)
res_2 = mlkr.fit_transform(self.X, self.y)
assert_array_almost_equal(res_1, res_2)
def test_mlkr(self):
mlkr = MLKR(num_dims=2)
mlkr.fit(self.X, self.y)
res_1 = mlkr.transform(self.X)
mlkr = MLKR(num_dims=2)
res_2 = mlkr.fit_transform(self.X, self.y)
assert_array_almost_equal(res_1, res_2)
def get_embedding(args, rescaled_domain_lst, domain_name_lst, eval_lst):
if (args.embedding == "origin") or (args.embedding == "mds" and args.embedding_distance == "heuristic"):
return rescaled_domain_lst
elif (args.embedding == "bleu") or (args.embedding == "mds" and args.embedding_distance == "bleudif"):
return [[e] for e in eval_lst]
elif (args.embedding == "ml"):
mlkr = MLKR()
x = np.array(rescaled_domain_lst)
y = np.array(eval_lst)
mlkr.fit(x, y)
return mlkr.transform(x)
def test_iris(self):
mlkr = MLKR()
mlkr.fit(self.iris_points, self.iris_labels)
csep = class_separation(mlkr.transform(self.iris_points),
self.iris_labels)
self.assertLess(csep, 0.25)
def test_iris(self): mlkr = MLKR() mlkr.fit(self.iris_points, self.iris_labels) csep = class_separation(mlkr.transform(), self.iris_labels) self.assertLess(csep, 0.25)
def mlkr(data, label, dim):
mlkr = MLKR(num_dims=dim)
mlkr.fit(data, label)
result = mlkr.transform(data)
return result
