本文整理汇总了Python中sklearn.neural_network.MLPRegressor.get_params方法的典型用法代码示例。如果您正苦于以下问题：Python MLPRegressor.get_params方法的具体用法？Python MLPRegressor.get_params怎么用？Python MLPRegressor.get_params使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.neural_network.MLPRegressor的用法示例。
在下文中一共展示了MLPRegressor.get_params方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: make_pipeline
# 需要导入模块: from sklearn.neural_network import MLPRegressor [as 别名]
# 或者: from sklearn.neural_network.MLPRegressor import get_params [as 别名]
#poly = make_pipeline(PolynomialFeatures(3), Ridge())
mpl = MLPRegressor(beta_1=0.99)
'''
y_t = y[-1000:-2]
y = y[0:-1000]
X_t = X[-1000:-2]
X = X[0:-1000]
mpl.fit(X, y)
poly.fit(X, y)
mpl_pred = mpl.predict(X_t)
poly_pred = poly.predict(X_t)
'''
mpl_pred = cross_val_predict(mpl, X, y, cv=10)
#poly_pred = cross_val_predict(poly, X, y, cv=10)
#nn_pred = cross_val_predict(model, X, y, cv=10)
print mpl.get_params()
def plot_cross():
fig, ax = plt.subplots()
ax.scatter(y, mpl_pred, c='b', marker='x')
# ax.scatter(y, poly_pred, c='y', marker ='+')
#ax.scatter(y, nn_pred, c='r')
ax.plot([y.min(), y.max()], [y.min(), y.max()], 'k--', lw=4)
ax.set_xlabel('Measured')
ax.set_ylabel('Predicted')
plt.show()
#mpl_pred = shift(mpl_pred, 30, cval=0)
#poly_pred = shift(poly_pred, 30, cval=0)
def plot_time():