Add files via upload

Author: wzy6642

LinearRegression_Project1/LinearRegression.py

@@ -0,0 +1,106 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Jul 29 16:03:37 2018
+
+@author: wzy
+"""
+import matplotlib.pyplot as plt
+import numpy as np
+
+"""
+函数说明：加载数据
+
+Parameters:
+    filename - 文件名
+    
+Returns:
+    xArr - x数据集
+    yArr - y数据集
+
+Modify:
+    2018-07-29
+"""
+def loadDataSet(filename):
+    # 计算特征个数，由于最后一列为y值所以减一
+    numFeat = len(open(filename).readline().split('\t')) - 1
+    xArr = []
+    yArr = []
+    fr = open(filename)
+    for line in fr.readlines():
+        lineArr = []
+        curLine = line.strip().split('\t')
+        for i in range(numFeat):
+            lineArr.append(float(curLine[i]))
+        xArr.append(lineArr)
+        yArr.append(float(curLine[-1]))
+    return xArr, yArr
+
+
+"""
+函数说明：计算回归系数w
+
+Parameters:
+    xArr - x数据集
+    yArr - y数据集
+    
+Returns:
+    ws - 回归系数
+
+Modify:
+    2018-07-29
+"""
+def standRegres(xArr, yArr):
+    xMat = np.mat(xArr)
+    yMat = np.mat(yArr).T
+    xTx = xMat.T * xMat
+    # 求矩阵的行列式
+    if np.linalg.det(xTx) == 0.0:
+        print("矩阵为奇异矩阵，不能求逆")
+        return
+    # .I求逆矩阵
+    ws = (xTx.I) * (xMat.T) * yMat
+    return ws
+
+
+"""
+函数说明：绘制数据集
+
+Parameters:
+    None
+    
+Returns:
+    None
+
+Modify:
+    2018-07-29
+"""
+def plotDataSet():
+    xArr, yArr = loadDataSet('ex0.txt')
+    ws = standRegres(xArr, yArr)
+    xMat = np.mat(xArr)
+    yMat = np.mat(yArr)
+    xCopy = xMat.copy()
+    # 排序
+    xCopy.sort(0)
+    yHat = xCopy * ws
+    # 以下两行是通过corrcoef函数比较预测值和真实值的相关性
+    # corrcoef函数得到相关系数矩阵
+    # 得到的结果中对角线上的数据是1.0，因为yMat和自己的匹配是完美的
+    # 而yHat1和yMat的相关系数为0.98
+    yHat1 = xMat * ws
+    print(np.corrcoef(yHat1.T, yMat))
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    ax.plot(xCopy[:, 1], yHat, c='red')
+    # 绘制样本点即
+    # flatten返回一个折叠成一维的数组。但是该函数只能适用于numpy对象，即array或者mat，普通的list列表是不行的
+    # 矩阵.A(等效于矩阵.getA())变成了数组
+    ax.scatter(xMat[:, 1].flatten().A[0], yMat.flatten().A[0], s=20, c='blue', alpha=.5)
+    plt.title('DataSet')
+    plt.xlabel('X')
+    plt.show()
+    
+    
+if __name__ == '__main__':
+    plotDataSet()
+    

LinearRegression_Project1/ex0.txt

@@ -0,0 +1,200 @@
+1.000000	0.067732	3.176513
+1.000000	0.427810	3.816464
+1.000000	0.995731	4.550095
+1.000000	0.738336	4.256571
+1.000000	0.981083	4.560815
+1.000000	0.526171	3.929515
+1.000000	0.378887	3.526170
+1.000000	0.033859	3.156393
+1.000000	0.132791	3.110301
+1.000000	0.138306	3.149813
+1.000000	0.247809	3.476346
+1.000000	0.648270	4.119688
+1.000000	0.731209	4.282233
+1.000000	0.236833	3.486582
+1.000000	0.969788	4.655492
+1.000000	0.607492	3.965162
+1.000000	0.358622	3.514900
+1.000000	0.147846	3.125947
+1.000000	0.637820	4.094115
+1.000000	0.230372	3.476039
+1.000000	0.070237	3.210610
+1.000000	0.067154	3.190612
+1.000000	0.925577	4.631504
+1.000000	0.717733	4.295890
+1.000000	0.015371	3.085028
+1.000000	0.335070	3.448080
+1.000000	0.040486	3.167440
+1.000000	0.212575	3.364266
+1.000000	0.617218	3.993482
+1.000000	0.541196	3.891471
+1.000000	0.045353	3.143259
+1.000000	0.126762	3.114204
+1.000000	0.556486	3.851484
+1.000000	0.901144	4.621899
+1.000000	0.958476	4.580768
+1.000000	0.274561	3.620992
+1.000000	0.394396	3.580501
+1.000000	0.872480	4.618706
+1.000000	0.409932	3.676867
+1.000000	0.908969	4.641845
+1.000000	0.166819	3.175939
+1.000000	0.665016	4.264980
+1.000000	0.263727	3.558448
+1.000000	0.231214	3.436632
+1.000000	0.552928	3.831052
+1.000000	0.047744	3.182853
+1.000000	0.365746	3.498906
+1.000000	0.495002	3.946833
+1.000000	0.493466	3.900583
+1.000000	0.792101	4.238522
+1.000000	0.769660	4.233080
+1.000000	0.251821	3.521557
+1.000000	0.181951	3.203344
+1.000000	0.808177	4.278105
+1.000000	0.334116	3.555705
+1.000000	0.338630	3.502661
+1.000000	0.452584	3.859776
+1.000000	0.694770	4.275956
+1.000000	0.590902	3.916191
+1.000000	0.307928	3.587961
+1.000000	0.148364	3.183004
+1.000000	0.702180	4.225236
+1.000000	0.721544	4.231083
+1.000000	0.666886	4.240544
+1.000000	0.124931	3.222372
+1.000000	0.618286	4.021445
+1.000000	0.381086	3.567479
+1.000000	0.385643	3.562580
+1.000000	0.777175	4.262059
+1.000000	0.116089	3.208813
+1.000000	0.115487	3.169825
+1.000000	0.663510	4.193949
+1.000000	0.254884	3.491678
+1.000000	0.993888	4.533306
+1.000000	0.295434	3.550108
+1.000000	0.952523	4.636427
+1.000000	0.307047	3.557078
+1.000000	0.277261	3.552874
+1.000000	0.279101	3.494159
+1.000000	0.175724	3.206828
+1.000000	0.156383	3.195266
+1.000000	0.733165	4.221292
+1.000000	0.848142	4.413372
+1.000000	0.771184	4.184347
+1.000000	0.429492	3.742878
+1.000000	0.162176	3.201878
+1.000000	0.917064	4.648964
+1.000000	0.315044	3.510117
+1.000000	0.201473	3.274434
+1.000000	0.297038	3.579622
+1.000000	0.336647	3.489244
+1.000000	0.666109	4.237386
+1.000000	0.583888	3.913749
+1.000000	0.085031	3.228990
+1.000000	0.687006	4.286286
+1.000000	0.949655	4.628614
+1.000000	0.189912	3.239536
+1.000000	0.844027	4.457997
+1.000000	0.333288	3.513384
+1.000000	0.427035	3.729674
+1.000000	0.466369	3.834274
+1.000000	0.550659	3.811155
+1.000000	0.278213	3.598316
+1.000000	0.918769	4.692514
+1.000000	0.886555	4.604859
+1.000000	0.569488	3.864912
+1.000000	0.066379	3.184236
+1.000000	0.335751	3.500796
+1.000000	0.426863	3.743365
+1.000000	0.395746	3.622905
+1.000000	0.694221	4.310796
+1.000000	0.272760	3.583357
+1.000000	0.503495	3.901852
+1.000000	0.067119	3.233521
+1.000000	0.038326	3.105266
+1.000000	0.599122	3.865544
+1.000000	0.947054	4.628625
+1.000000	0.671279	4.231213
+1.000000	0.434811	3.791149
+1.000000	0.509381	3.968271
+1.000000	0.749442	4.253910
+1.000000	0.058014	3.194710
+1.000000	0.482978	3.996503
+1.000000	0.466776	3.904358
+1.000000	0.357767	3.503976
+1.000000	0.949123	4.557545
+1.000000	0.417320	3.699876
+1.000000	0.920461	4.613614
+1.000000	0.156433	3.140401
+1.000000	0.656662	4.206717
+1.000000	0.616418	3.969524
+1.000000	0.853428	4.476096
+1.000000	0.133295	3.136528
+1.000000	0.693007	4.279071
+1.000000	0.178449	3.200603
+1.000000	0.199526	3.299012
+1.000000	0.073224	3.209873
+1.000000	0.286515	3.632942
+1.000000	0.182026	3.248361
+1.000000	0.621523	3.995783
+1.000000	0.344584	3.563262
+1.000000	0.398556	3.649712
+1.000000	0.480369	3.951845
+1.000000	0.153350	3.145031
+1.000000	0.171846	3.181577
+1.000000	0.867082	4.637087
+1.000000	0.223855	3.404964
+1.000000	0.528301	3.873188
+1.000000	0.890192	4.633648
+1.000000	0.106352	3.154768
+1.000000	0.917886	4.623637
+1.000000	0.014855	3.078132
+1.000000	0.567682	3.913596
+1.000000	0.068854	3.221817
+1.000000	0.603535	3.938071
+1.000000	0.532050	3.880822
+1.000000	0.651362	4.176436
+1.000000	0.901225	4.648161
+1.000000	0.204337	3.332312
+1.000000	0.696081	4.240614
+1.000000	0.963924	4.532224
+1.000000	0.981390	4.557105
+1.000000	0.987911	4.610072
+1.000000	0.990947	4.636569
+1.000000	0.736021	4.229813
+1.000000	0.253574	3.500860
+1.000000	0.674722	4.245514
+1.000000	0.939368	4.605182
+1.000000	0.235419	3.454340
+1.000000	0.110521	3.180775
+1.000000	0.218023	3.380820
+1.000000	0.869778	4.565020
+1.000000	0.196830	3.279973
+1.000000	0.958178	4.554241
+1.000000	0.972673	4.633520
+1.000000	0.745797	4.281037
+1.000000	0.445674	3.844426
+1.000000	0.470557	3.891601
+1.000000	0.549236	3.849728
+1.000000	0.335691	3.492215
+1.000000	0.884739	4.592374
+1.000000	0.918916	4.632025
+1.000000	0.441815	3.756750
+1.000000	0.116598	3.133555
+1.000000	0.359274	3.567919
+1.000000	0.814811	4.363382
+1.000000	0.387125	3.560165
+1.000000	0.982243	4.564305
+1.000000	0.780880	4.215055
+1.000000	0.652565	4.174999
+1.000000	0.870030	4.586640
+1.000000	0.604755	3.960008
+1.000000	0.255212	3.529963
+1.000000	0.730546	4.213412
+1.000000	0.493829	3.908685
+1.000000	0.257017	3.585821
+1.000000	0.833735	4.374394
+1.000000	0.070095	3.213817
+1.000000	0.527070	3.952681
+1.000000	0.116163	3.129283