(一)运行结果
orsci: 奇异值分解svd ... http://www.orsci.cn
X =
rowCount = 5 colCount = 7
0 10 20 30 40 50 60
2 12 22 32 42 52 62
4 14 24 34 44 54 64
6 16 26 36 46 56 66
8 18 28 38 48 58 68
svd成功标记:1
U =
rowCount = 5 colCount = 5
-0.407295 -0.658871 0.596548 0.10174 -0.18379
-0.426827 -0.343247 -0.42681 -0.162737 0.700964
-0.44636 -0.027623 -0.475794 -0.3699 -0.660904
-0.465892 0.288001 -0.154176 0.821049 -0.0459215
-0.485425 0.603625 0.460232 -0.390152 0.189652
S =
rowCount = 5 colCount = 7
233.813 0 0 0 0 0 0
0 8.46789 0 0 0 0 0
0 0 2.04753e-014 0 0 0 0
0 0 0 4.61561e-015 0 0 0
0 0 0 0 5.87633e-016 0 0
V =
rowCount = 7 colCount = 7
-0.0398518 0.680219 -0.715495 0.0309774 -0.146736
-0.00497478 0.0355614
-0.135304 0.517114 0.459817 -0.156459 0.272747
0.363861 0.521142
-0.230757 0.35401 0.34527 0.0932267 0.035483 -0.821335
-0.132951
-0.326209 0.190905 0.138332 -0.431914 0.0164508
0.304723 -0.747215
-0.421662 0.0278004 0.208011 0.52369 -0.665568 0.2465
0.0128577
-0.517114 -0.135304 -0.218208 0.471885 0.656533
0.0887109 -0.0713435
-0.612567 -0.298409 -0.217726 -0.531406 -0.16891
-0.177486 0.381949
U * S * V.T() =
rowCount = 5 colCount = 7
-8.70902e-015 10 20 30 40 50 60
2 12 22 32 42 52 62
4 14 24 34 44 54 64
6 16 26 36 46 56 66
8 18 28 38 48 58 68
X-U*S*V.T() =
rowCount = 5 colCount = 7
8.70902e-015 -8.88178e-015 -7.10543e-015 0 -7.10543e-015 -7.10543
e-015 -7.10543e-015
-3.19744e-014 -7.10543e-015 -7.10543e-015 -1.42109e-014 -1.42109e-014
-1.42109e-014 -2.13163e-014
-4.88498e-014 -5.32907e-015 -1.06581e-014 -1.42109e-014 -2.13163e-014
-2.84217e-014 -2.84217e-014
-5.9508e-014 -1.06581e-014 -1.42109e-014 -2.13163e-014 -2.84217e-014
-4.9738e-014 -4.26326e-014
-5.86198e-014 -3.55271e-015 -7.10543e-015 -7.10543e-015 -2.13163e-014
-3.55271e-014 -4.26326e-014
奇异值向量:
233.813 8.46789 2.04753e-014 4.61561e-015 5.87633e-016
press any key to stop...
(二)说明:
(1)矩阵奇异值分解,有着较多应用,svd(...)和svds(...)可以完成奇异值分解。
(2)关于向量与矩阵,请参看书籍:姜维. 《数据分析与数据挖掘》、《数据分析与数据挖掘实践》。
(6)orsci包支持向量和矩阵计算,可下载配套软件orsci-art应用。 |