sk-随机梯度下降.py

# #===============随机梯度下降法分类===============
#
# from sklearn.linear_model import SGDClassifier
# from sklearn.datasets.samples_generator import make_blobs
# import numpy as np
# import matplotlib.pyplot as plt
#
# X, y = make_blobs(n_samples=50, centers=2, random_state=0, cluster_std=0.60)
# # loss：损失项。hinge：（软-间隔）线性支持向量机，modified_huber：平滑的 hinge 损失，log：logistic 回归，其他所有的回归损失
# # penalty：惩罚项。l2：L2正则，l1：L1正则，elasticnet：(1 - l1_ratio) * L2 + l1_ratio * L1
# clf = SGDClassifier(loss="hinge", alpha=0.01, max_iter=200, fit_intercept=True)  #
# clf.fit(X, y)  # 训练模型
#
# print('回归系数：',clf.coef_)
# print('偏差：',clf.intercept_ )
#
# # 绘制线，点
# xx1 = np.linspace(-1, 5, 10)
# xx2 = np.linspace(-1, 5, 10)
#
# X1, X2 = np.meshgrid(xx1, xx2)  # X1、X2都是10*10的矩阵
# Z = np.empty(X1.shape)
# for (i, j), val in np.ndenumerate(X1):  # 迭代第i行第j列的坐标xx1取值为val
#     x1 = val
#     x2 = X2[i, j]  #
#     p = clf.decision_function([[x1, x2]])  # 计算输出值，也就是到超平面的符号距离。（支持向量到最佳超平面的符号距离为-1和+1）
#     Z[i, j] = p[0]
# levels = [-1.0, 0.0, 1.0]  # 将输出值分为-1,0,1几个区间
# linestyles = ['dashed', 'solid', 'dashed']
# plt.contour(X1, X2, Z, levels, colors='k', linestyles=linestyles)  # 绘制等高线图，高度为-1,0,1，也就是支持向量形成的线和最佳分割超平面
# plt.scatter(X[:, 0], X[:, 1], c=y, s=20)  # 绘制样本点
# plt.show()



# # ==============随机梯度下降法进行多分类=============
# from sklearn.linear_model import SGDClassifier
# from sklearn.metrics import accuracy_score
# from sklearn import datasets
# iris = datasets.load_iris()
# X,y=iris.data,iris.target
# clf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)
# y_pred = clf.predict(X)
# print('三分类花卉数据准确率：',accuracy_score(y,y_pred))
# print('包含的二分类器索引：',clf.classes_)    # one versus all 方法来组合多个二分类器
# print('回归系数：',clf.coef_)  # 每一个二分类器的回归系数
# print('偏差：',clf.intercept_ ) # 每一个二分类器的偏差



# #===============随机梯度下降法回归===============
from sklearn import linear_model
from sklearn.datasets import load_boston
X,y = load_boston().data,load_boston().target
clf = linear_model.SGDRegressor(loss='squared_loss',penalty='l2',alpha=0.01,max_iter=1000)
clf.fit(X, y)
print('得分：',clf.score(X,y))
print('回归系数：',clf.coef_)
print('偏差：',clf.intercept_ )