sklearn如何获得model里的参数

from __future__ import division
import time
import pickle
import numpy as np

from sklearn.model_selection import GridSearchCV
from sklearn.kernel_ridge import KernelRidge
import matplotlib.pyplot as plt
from sklearn.externals import joblib

rng = np.random.RandomState(0)

def gener(sta, end, num):  # 生成y=x^2的测试集
    # 随机干扰因子
    sampleNo = num
    mu = 0.01
    sigma = 0.2
    np.random.seed(0)
    s = np.random.normal(mu, sigma, sampleNo)

    X = np.linspace(sta, end, num)  # 在返回（-1, 1）范围内的等差序列
    pix = np.pi * X
    Y = np.sin(pix) / pix + 0.1 * X + s
    x = X.reshape(-1, 1)
    y = Y
    print(y.shape)
    return x, y, num

X, y, num = gener(-3,3,50)
X_plot, y_plot, NUM = gener(-3,3,1000)
# #############################################################################
# Fit regression model
train_size = 40

kr = GridSearchCV(KernelRidge(kernel='rbf', gamma=0.1), cv=5,
                  param_grid={"alpha": [1e0, 0.1, 1e-2, 1e-3],
                              "gamma": np.logspace(-2, 2, 5)})

t0 = time.time()
kr.fit(X[:train_size], y[:train_size])
kr_fit = time.time() - t0
print("KRR complexity and bandwidth selected and model fitted in %.3f s"
      % kr_fit)

t0 = time.time()
y_kr = kr.predict(X_plot)
kr_predict = time.time() - t0
print("KRR prediction for %d inputs in %.3f s"
      % (X_plot.shape[0], kr_predict))


# #############################################################################
# Look at the results

plt.scatter(X[:100], y[:100], c='k', label='data', zorder=1,
            edgecolors=(0, 0, 0))
plt.plot(X_plot, y_kr, c='g',
         label='KRR (fit: %.3fs, predict: %.3fs)' % (kr_fit, kr_predict))
plt.xlabel('data')
plt.ylabel('target')
plt.legend()
plt.show()

print(dir(kr))

print(dir(kr.best_estimator_))
print(kr.best_estimator_.get_params())
coef = kr.best_estimator_.dual_coef_

def ridge(X, x, NUM, num):
    x2 = x * x  # 样本集的参数 逐个求平方
    X2 = X * X  # 测试集的参数 逐个求平方
    h = 3  # 高斯核的带宽
    hh = 2 * h * h

    temp = np.tile(X2, num) + np.tile(np.matrix(x2).T, (NUM, 1)) - 2 * X * x.T
    k = np.exp(-temp)
    return k

k = ridge(X_plot, X[:40], NUM, 40)
y = np.matrix(k)*np.matrix(coef).T
plt.plot(X_plot,y,label='coef')
plt.legend()
plt.show()

print(coef)

这里用的KRR，所以只有一组系数，其他复杂模型不太了解
kr.best_estimator_.dual_coef_