if __name__ == "__main__":
source = 'linear'
if source == 'temp':
data = pd.read_csv('../dataset/simple/TempLinkoping2016.txt', sep="\t")
time = np.atleast_2d(data["time"].values).T
temp = np.atleast_2d(data["temp"].values).T
X = standardize(time) # Time. Fraction of the year [0, 1]
y = temp[:, 0] # Temperature. Reduce to one-dim
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = LinearRegression(X_train, y_train).train()
y_pred = model.evaluation(X_test, y_test, title="test")
if source == 'linear':
dataset = RegressionDataset(n_samples=500, n_features=1, n_targets=1, noise=5)
X = dataset.datas
y = dataset.labels
# plt.scatter(X, y)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
model = LinearRegression(X_train, y_train, lr=0.001, n_iters=100).train()
y_pred = model.evaluation(X_test, y_test, title='test')
self.add(BatchNorm2d(256))
self.add(Activation('softmax'))
if __name__ == "__main__":
id = 'lenet5'
if id == 'lenet5':
dataset = DigitsDataset(norm=True, one_hot=True)
datas = dataset.datas
labels = dataset.labels
# 分隔数据集
train_x, test_x, train_y, test_y = train_test_split(dataset.datas,
dataset.labels,
test_size=0.3,
shuffle=True)
# test_x, val_x, test_y, val_y = train_test_split(test_x,
# test_y,
# test_size=0.3,
# shuffle=True)
train_x = train_x.reshape(-1, 1, 8, 8)
test_x = test_x.reshape(-1, 1, 8, 8)
# val_x = val_x.reshape(-1, 1, 8, 8)
optimizer = SGDM(lr=0.01, weight_decay=0.1, regularization_type='l2')
loss_func = CrossEntropy()
clf = LeNet5(train_x,
train_y,
loss=loss_func,
min_samples_split=3,
max_depth=5,
min_impurity_reduction=1e-7).train()
gb.evaluation(x, y)
gb.vis_boundary(plot_step=0.01)
if source == '5class':
dataset = MultiClassDataset(n_samples=500,
centers=4,
n_features=2,
center_box=(-8, +8),
cluster_std=0.8,
one_hot=True)
x = dataset.datas
y = dataset.labels
train_x, test_x, train_y, test_y = train_test_split(
x, y, test_size=0.3) # (n, 13) (n,)
gb = GBDT(x,
y,
n_clfs=2,
learning_rate=0.5,
min_samples_split=3,
max_depth=5,
min_impurity_reduction=1e-7).train()
acc1 = gb.evaluation(train_x, train_y)
print('train acc = %f' % (acc1))
acc2 = gb.evaluation(test_x, test_y)
print('test acc = %f' % (acc2))
gb.vis_boundary(plot_step=0.1)