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103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
#!/usr/bin/env python
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# encoding: utf-8
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from numpy import *
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import matplotlib.pyplot as plt
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import time
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'''
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1、需要安装模块：pip install matplotlib-1.5.0-cp27-none-win_amd64.whl
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由于直接安装会出现问题，所以建议下载whl包进行安装，下载网址：
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https://pypi.python.org/pypi/matplotlib/1.5.0
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2、可以看见画出的图像
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'''
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"""
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@version:
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@author: yangjf
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@license: ApacheCN
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@contact: highfei2011@126.com
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@site: https://github.com/apachecn/MachineLearning
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@software: PyCharm
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@file: logRegression01.py
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@time: 2017/3/3 22:03
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@test result: ok
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"""
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# sigmoid函数
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def sigmoid(inX):
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return 1.0 / (1 + exp(-inX))
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def trainLogRegres(train_x, train_y, opts):
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# 计算训练时间
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startTime = time.time()
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numSamples, numFeatures = shape(train_x)
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alpha = opts['alpha']; maxIter = opts['maxIter']
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weights = ones((numFeatures, 1))
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# 通过梯度下降算法优化
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for k in range(maxIter):
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if opts['optimizeType'] == 'gradDescent': # 梯度下降算法
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output = sigmoid(train_x * weights)
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error = train_y - output
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weights = weights + alpha * train_x.transpose() * error
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elif opts['optimizeType'] == 'stocGradDescent': # 随机梯度下降
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for i in range(numSamples):
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output = sigmoid(train_x[i, :] * weights)
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error = train_y[i, 0] - output
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weights = weights + alpha * train_x[i, :].transpose() * error
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elif opts['optimizeType'] == 'smoothStocGradDescent': # 光滑随机梯度下降
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# 随机选择样本以优化以减少周期波动
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dataIndex = range(numSamples)
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for i in range(numSamples):
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alpha = 4.0 / (1.0 + k + i) + 0.01
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randIndex = int(random.uniform(0, len(dataIndex)))
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output = sigmoid(train_x[randIndex, :] * weights)
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error = train_y[randIndex, 0] - output
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weights = weights + alpha * train_x[randIndex, :].transpose() * error
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del(dataIndex[randIndex]) # 在一次交互期间，删除优化的样品
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else:
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raise NameError('Not support optimize method type!')
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print 'Congratulations, training complete! Took %fs!' % (time.time() - startTime)
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return weights
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#测试给定测试集的训练Logistic回归模型
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def testLogRegres(weights, test_x, test_y):
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numSamples, numFeatures = shape(test_x)
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matchCount = 0
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for i in xrange(numSamples):
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predict = sigmoid(test_x[i, :] * weights)[0, 0] > 0.5
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if predict == bool(test_y[i, 0]):
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matchCount += 1
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accuracy = float(matchCount) / numSamples
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return accuracy
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# 显示你的训练逻辑回归模型只有2-D数据可用
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def showLogRegres(weights, train_x, train_y):
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# 注意：train_x和train_y是垫数据类型
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numSamples, numFeatures = shape(train_x)
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if numFeatures != 3:
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print "抱歉! 我不能绘制，因为你的数据的维度不是2！"
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return 1
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# 画出所有抽样数据
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for i in xrange(numSamples):
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if int(train_y[i, 0]) == 0:
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plt.plot(train_x[i, 1], train_x[i, 2], 'or')
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elif int(train_y[i, 0]) == 1:
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plt.plot(train_x[i, 1], train_x[i, 2], 'ob')
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# 画图操作
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min_x = min(train_x[:, 1])[0, 0]
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max_x = max(train_x[:, 1])[0, 0]
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weights = weights.getA() # 将mat转换为数组
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y_min_x = float(-weights[0] - weights[1] * min_x) / weights[2]
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y_max_x = float(-weights[0] - weights[1] * max_x) / weights[2]
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plt.plot([min_x, max_x], [y_min_x, y_max_x], '-g')
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plt.xlabel('X1'); plt.ylabel('X2')
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#显示图像
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plt.show() |