周志華-機器學習西瓜書-第三章習題3.3 編程實現對率迴歸

本文爲周志華機器學習西瓜書第三章課後習題3.3答案，編程實現對率迴歸，數據集爲書本第89頁的數據

使用tensorflow實現過程

# coding=utf-8
import tensorflow as tf from numpy import *
import numpy as np import matplotlib.pyplot as plt data = mat([[0.697,0.460,1], [0.774,0.376,1], [0.634,0.264,1], [0.608,0.318,1], [0.556,0.215,1], [0.403,0.237,1], [0.481,0.149,1], [0.437,0.211,1], [0.666,0.091,0], [0.243,0.267,0], [0.245,0.057,0], [0.343,0.099,0], [0.639,0.161,0], [0.657,0.198,0], [0.360,0.370,0], [0.593,0.042,0], [0.719,0.103,0]]) x_train=data[:,0:2].astype(float32) y_train=data[:,2].astype(float32) y_train = mat(y_train) w=tf.Variable(tf.zeros([2,1])) b=tf.Variable(tf.zeros([1,1])) y=1/(1+tf.exp(-(tf.matmul(x_train,w)+b))) loss = tf.reduce_mean(- y_train.reshape(-1, 1) * tf.log(y) - (1 - y_train.reshape(-1, 1)) * tf.log(1 - y)) train=tf.train.GradientDescentOptimizer(0.5).minimize(loss) init=tf.global_variables_initializer() sess=tf.Session() sess.run(init) for i in range(1000): sess.run(train) print(i,sess.run(w).flatten(),sess.run(b).flatten())

 

畫圖過程

#畫圖
X0 = array(data[:8])#選擇訓練集中爲好瓜的樣本
X1 = array(data[8:])#選擇訓練集中爲壞瓜的樣本
ax = plt.subplot(111) ax.scatter(X0[:,0],X0[:,1],c='g',label='+')#選擇好瓜樣本的第一列和第二列 畫出散點圖
ax.scatter(X1[:,0],X1[:,1],c='b',label='-')#選擇壞瓜樣本的第一列和第二列 畫出散點圖
x1 = arange(-0.9,1.0,0.1) y1 = (-b-w[0]*x1)/w[1] plt.sca(ax) plt.plot(x1,sess.run(y1).flatten()) plt.legend()#設置圖例
plt.show()

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