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sk-神经网络.py
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sk-神经网络.py
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# # =============神经网络用于分类=============
# import numpy as np
# import matplotlib.pyplot as plt
# from sklearn.neural_network import MLPClassifier
# from sklearn.preprocessing import StandardScaler
# data = [
# [-0.017612, 14.053064, 0],[-1.395634, 4.662541, 1],[-0.752157, 6.53862, 0],[-1.322371, 7.152853, 0],[0.423363, 11.054677, 0],
# [0.406704, 7.067335, 1],[0.667394, 12.741452, 0],[-2.46015, 6.866805, 1],[0.569411, 9.548755, 0],[-0.026632, 10.427743, 0],
# [0.850433, 6.920334, 1],[1.347183, 13.1755, 0],[1.176813, 3.16702, 1],[-1.781871, 9.097953, 0],[-0.566606, 5.749003, 1],
# [0.931635, 1.589505, 1],[-0.024205, 6.151823, 1],[-0.036453, 2.690988, 1],[-0.196949, 0.444165, 1],[1.014459, 5.754399, 1],
# [1.985298, 3.230619, 1],[-1.693453, -0.55754, 1],[-0.576525, 11.778922, 0],[-0.346811, -1.67873, 1],[-2.124484, 2.672471, 1],
# [1.217916, 9.597015, 0],[-0.733928, 9.098687, 0],[1.416614, 9.619232, 0],[1.38861, 9.341997, 0],[0.317029, 14.739025, 0]
# ]
#
# dataMat = np.array(data)
# X=dataMat[:,0:2]
# y = dataMat[:,2]
# # 神经网络对数据尺度敏感，所以最好在训练前标准化，或者归一化，或者缩放到[-1,1]
# scaler = StandardScaler() # 标准化转换
# scaler.fit(X) # 训练标准化对象
# X = scaler.transform(X) # 转换数据集
# # solver='lbfgs', MLP的求解方法：L-BFGS 在小数据上表现较好，Adam 较为鲁棒，SGD在参数调整较优时会有最佳表现（分类效果与迭代次数）；SGD标识随机梯度下降。
# # alpha:L2的参数：MLP是可以支持正则化的，默认为L2，具体参数需要调整
# # hidden_layer_sizes=(5, 2) hidden层2层,第一层5个神经元，第二层2个神经元)，2层隐藏层，也就有3层神经网络
#
# clf = MLPClassifier(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5,2), random_state=1) # 神经网络输入为2，第一隐藏层神经元个数为5，第二隐藏层神经元个数为2，输出结果为2分类。
# clf.fit(X, y)
# print('每层网络层系数矩阵维度：\n',[coef.shape for coef in clf.coefs_])
# y_pred = clf.predict([[0.317029, 14.739025]])
# print('预测结果：',y_pred)
# y_pred_pro =clf.predict_proba([[0.317029, 14.739025]])
# print('预测结果概率：\n',y_pred_pro)
#
# cengindex = 0
# for wi in clf.coefs_:
# cengindex += 1 # 表示底第几层神经网络。
# print('第%d层网络层:' % cengindex)
# print('权重矩阵维度:',wi.shape)
# print('系数矩阵:\n',wi)
#
#
# # 绘制分割区域
# x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1 # 寻找每个维度的范围
# y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1 # 寻找每个维度的范围
# xx1, xx2 = np.meshgrid(np.arange(x_min, x_max, 0.01),np.arange(y_min, y_max,0.01)) # 在特征范围以0.01位步长预测每一个点的输出结果
# Z = clf.predict(np.c_[xx1.ravel(), xx2.ravel()]) # 先形成待测样本的形式，在通过模型进行预测。
# Z = Z.reshape(xx1.shape) # 将输出结果转换为和网格的矩阵形式，以便绘图
# # 绘制区域网格图
# plt.pcolormesh(xx1, xx2, Z, cmap=plt.cm.Paired)
# # 绘制样本点
# plt.scatter(X[:,0],X[:,1],c=y)
# plt.show()
# # =============神经网络用于回归=============
import numpy as np
from sklearn.neural_network import MLPRegressor # 多层线性回归
from sklearn.preprocessing import StandardScaler
data = [
[ -0.017612,14.053064,14.035452],[ -1.395634, 4.662541, 3.266907],[ -0.752157, 6.53862,5.786463],[ -1.322371, 7.152853, 5.830482],
[0.423363,11.054677,11.47804 ],[0.406704, 7.067335, 7.474039],[0.667394,12.741452,13.408846],[ -2.46015,6.866805, 4.406655],
[0.569411, 9.548755,10.118166],[ -0.026632,10.427743,10.401111],[0.850433, 6.920334, 7.770767],[1.347183,13.1755,14.522683],
[1.176813, 3.16702,4.343833],[ -1.781871, 9.097953, 7.316082],[ -0.566606, 5.749003, 5.182397],[0.931635, 1.589505, 2.52114 ],
[ -0.024205, 6.151823, 6.127618],[ -0.036453, 2.690988, 2.654535],[ -0.196949, 0.444165, 0.247216],[1.014459, 5.754399, 6.768858],
[1.985298, 3.230619, 5.215917],[ -1.693453,-0.55754, -2.250993],[ -0.576525,11.778922,11.202397],[ -0.346811,-1.67873, -2.025541],
[ -2.124484, 2.672471, 0.547987],[1.217916, 9.597015,10.814931],[ -0.733928, 9.098687, 8.364759],[1.416614, 9.619232,11.035846],
[1.38861,9.341997,10.730607],[0.317029,14.739025,15.056054]
]
dataMat = np.array(data)
X=dataMat[:,0:2]
y = dataMat[:,2]
scaler = StandardScaler() # 标准化转换
scaler.fit(X) # 训练标准化对象
X = scaler.transform(X) # 转换数据集
# solver='lbfgs', MLP的求解方法：L-BFGS 在小数据上表现较好，Adam 较为鲁棒，SGD在参数调整较优时会有最佳表现（分类效果与迭代次数）；SGD标识随机梯度下降。
# alpha:L2的参数：MLP是可以支持正则化的，默认为L2，具体参数需要调整
# hidden_layer_sizes=(5, 2) hidden层2层,第一层5个神经元，第二层2个神经元)，2层隐藏层，也就有3层神经网络
clf = MLPRegressor(solver='lbfgs', alpha=1e-5,hidden_layer_sizes=(5, 2), random_state=1)
clf.fit(X, y)
print('预测结果：', clf.predict([[0.317029, 14.739025]])) # 预测某个输入对象
cengindex = 0
for wi in clf.coefs_:
cengindex += 1 # 表示底第几层神经网络。
print('第%d层网络层:' % cengindex)
print('权重矩阵维度:',wi.shape)
print('系数矩阵：\n',wi)