# load two-moons-dataset
train_file = open('dataset/moons/moons_train.pkl', 'rb')
test_file = open('dataset/moons/moons_test.pkl', 'rb')
moons_train = pkl.load(train_file) # tuple of training data
moons_test = pkl.load(test_file) # tuple of testing data
train_file.close()
test_file.close()
train_X = moons_train[0] # instances of training data
train_Y = moons_train[1] # labels of training data
test_X = moons_test[0] # instances of testing data
test_Y = moons_test[1] # labels of testing data
# training
try:
myDML.train(moons_train)
except Exception as e:
print(e)
sys.exit(-1)
# evaluating
for K in [1, 3, 5]:
# testing with Euclidean_distance
predict_label = np.zeros(test_X.shape[0])
for i in range(test_X.shape[0]):
distance_vector = np.zeros(train_X.shape[0])
for j in range(train_X.shape[0]):
distance_vector[j] = myDML.Euclidean_distance(
test_X[i], train_X[j])
labels_of_K_neighbor = train_Y[distance_vector.argsort()[0:K]]
predict_label[i] = collections.Counter(
'dataset/Letter_Recognition/lr_train_' + str(r) + '.pkl', 'rb')
test_file = open(
'dataset/Letter_Recognition/lr_test_' + str(r) + '.pkl', 'rb')
LR_train = pkl.load(train_file) # tuple of training data
LR_test = pkl.load(test_file) # tuple of testing data
train_file.close()
test_file.close()
train_X = LR_train[0] # instances of training data
train_Y = LR_train[1] # labels of training data
test_X = LR_test[0] # instances of testing data
test_Y = LR_test[1] # labels of testing data
# training
try:
myDML.train(LR_train)
except Exception as e:
print('异常', e)
sys.exit(-1)
# evaluating
for K in [1, 3, 5]:
# testing with Euclidean_distance
predict_label = np.zeros(test_X.shape[0])
for i in range(test_X.shape[0]):
distance_vector = np.zeros(train_X.shape[0])
for j in range(train_X.shape[0]):
distance_vector[j] = myDML.Euclidean_distance(
test_X[i], train_X[j])
labels_of_K_neighbor = train_Y[distance_vector.argsort()[0:K]]
predict_label[i] = collections.Counter(