机器学习入门（python实现逻辑回归二分类）

数据准备

在前面一篇文章中已经为大家介绍了逻辑回归的原理，以及损失函数的推导，今天我们先来练练手，不借助任何机器学习库，用python实现逻辑回归的二分类。
这里数据是一份标准化后的男生女生身高体重的部分数据,最后一列是标签
data.txt

-0.017612	-14.053064	0.0
-1.395634	-4.662541	1.0
-0.752157	-6.53862	0.0
-1.322371	-7.152853	0.0
0.423363	-11.054677	0.0
0.406704	-7.067335	1.0
0.667394	-12.741452	0.0
-2.46015	-6.866805	1.0
0.569411	-9.548755	0.0
-0.026632	-10.427743	0.0
0.850433	-6.920334	1.0
1.347183	-13.1755	0.0
1.176813	-3.16702	1.0
-1.781871	-9.097953	0.0
-0.566606	-5.749003	1.0
0.931635	-1.589505	1.0
-0.024205	-6.151823	1.0
-0.036453	-2.690988	1.0
-0.196949	-0.444165	1.0
1.014459	-5.754399	1.0
1.985298	-3.230619	1.0
-1.693453	0.55754	1.0
-0.576525	-11.778922	0.0
-0.346811	1.67873	1.0
-2.124484	-2.672471	1.0
1.217916	-9.597015	0.0
-0.733928	-9.098687	0.0
-3.642001	1.618087	1.0
0.315985	-3.523953	1.0
1.416614	-9.619232	0.0
-0.386323	-3.989286	1.0
0.556921	-8.294984	1.0
1.224863	-11.58736	0.0
-1.347803	2.406051	1.0
1.196604	-4.951851	1.0
0.275221	-9.543647	0.0
0.470575	-9.332488	0.0
-1.889567	-9.542662	0.0
-1.527893	-12.150579	0.0
-1.185247	-11.309318	0.0
-0.445678	-3.297303	1.0
1.042222	-6.105155	1.0
-0.618787	-10.320986	0.0
1.152083	-0.548467	1.0
0.828534	-2.676045	1.0
-1.237728	-10.549033	0.0
-0.683565	2.166125	1.0
0.229456	-5.921938	1.0
-0.959885	-11.555336	0.0
0.492911	-10.993324	0.0
0.184992	-8.721488	0.0
-0.355715	-10.325976	0.0
-0.397822	-8.058397	0.0
0.824839	-13.730343	0.0
1.507278	-5.027866	1.0
0.099671	-6.835839	1.0
-0.344008	-10.717485	0.0
1.785928	-7.718645	1.0
-0.918801	-11.560217	0.0
-0.364009	-4.7473	1.0
-0.841722	-4.119083	1.0
0.490426	-1.960539	1.0
-0.007194	-9.075792	0.0
0.356107	-12.447863	0.0
0.342578	-12.281162	0.0
-0.810823	1.466018	1.0
2.530777	-6.476801	1.0
1.296683	-11.607559	0.0
0.475487	-12.040035	0.0
-0.783277	-11.009725	0.0
0.074798	-11.02365	0.0
-1.337472	-0.468339	1.0
-0.102781	-13.763651	0.0
-0.147324	-2.874846	1.0
0.518389	-9.887035	0.0
1.015399	-7.571882	0.0
-1.658086	0.027255	1.0
1.319944	-2.171228	1.0
2.056216	-5.019981	1.0
-0.851633	-4.375691	1.0
-1.510047	-6.061992	0.0
-1.076637	3.181888	1.0
1.821096	-10.28399	0.0
3.01015	-8.401766	1.0
-1.099458	-1.688274	1.0
-0.834872	1.733869	1.0
-0.846637	-3.849075	1.0
1.400102	-12.628781	0.0
1.752842	-5.468166	1.0
0.078557	-0.059736	1.0
0.089392	0.7153	1.0
1.825662	-12.693808