from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1)
# print x_train, y_train
linreg = LinearRegression()
model = linreg.fit(x_train, y_train)
# model
# linreg.coef_
# linreg.intercept_

y_hat = linreg.predict(np.array(x_test))
mse = np.average((y_hat - np.array(y_test)) ** 2)  # Mean Squared Error
rmse = np.sqrt(mse)  # Root Mean Squared Error

t = np.arange(len(x_test))
plt.plot(t, y_test, 'r-', linewidth=2, label='Test')
plt.plot(t, y_hat, 'g-', linewidth=2, label='Predict')

from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lasso, Ridge
from sklearn.model_selection import GridSearchCV
x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1)
# print x_train, y_train
model = Lasso()
# model = Ridge()
alpha_can = np.logspace(-3, 2, 10)
lasso_model = GridSearchCV(model, param_grid={'alpha': alpha_can}, cv=5)
lasso_model.fit(x, y)
# lasso_model.best_params_
# pd.DataFrame(lasso_model.cv_results_)

lr = Pipeline([('sc', StandardScaler()),('clf', LogisticRegression()) ])
lr.fit(x, y.ravel())N, M = 500, 500     # 横纵各采样多少个值
x1_min, x1_max = x[:, 0].min(), x[:, 0].max()   # 第0列的范围
x2_min, x2_max = x[:, 1].min(), x[:, 1].max()   # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2)                    # 生成网格采样点
x_test = np.stack((x1.flat, x2.flat), axis=1)   # 测试点

cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_hat = lr.predict(x_test)                  # 预测值
y_hat = y_hat.reshape(x1.shape)                 # 使之与输入的形状相同
plt.pcolormesh(x1, x2, y_hat, cmap=cm_light)     # 预测值的显示
plt.scatter(x[:, 0], x[:, 1], c=y, edgecolors='k', s=50, cmap=cm_dark)    # 样本的显示
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid()