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686Z"})])])],-1)])])),a[199]||(a[199]=Q("mjx-assistive-mml",{unselectable:"on",display:"inline"},[Q("math",{xmlns:"http://www.w3.org/1998/Math/MathML"},[Q("mi",{mathvariant:"bold-italic"},"V")])],-1))]),a[218]||(a[218]=t("，使其每一列都是一个特征向量",-1))]),Q("mjx-container",G2,[(l(),T("svg",X2,[...a[219]||(a[219]=[s('',1)])])),a[220]||(a[220]=Q("mjx-assistive-mml",{unselectable:"on",display:"block"},[Q("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[Q("mi",{mathvariant:"bold-italic"},"V"),Q("mo",null,"="),Q("mo",{stretchy:"false"},"["),Q("msup",null,[Q("mi",{mathvariant:"bold-italic"},"v"),Q("mrow",{"data-mjx-texclass":"ORD"},[Q("mo",{stretchy:"false"},"("),Q("mn",null,"1"),Q("mo",{stretchy:"false"},")")])]),Q("mo",null,","),Q("msup",null,[Q("mi",{mathvariant:"bold-italic"},"v"),Q("mrow",{"data-mjx-texclass":"ORD"},[Q("mo",{stretchy:"false"},"("),Q("mn",null,"2"),Q("mo",{stretchy:"false"},")")])]),Q("mo",null,","),Q("mo",null,"⋯"),Q("mo",null,","),Q("msup",null,[Q("mi",{mathvariant:"bold-italic"},"v"),Q("mrow",{"data-mjx-texclass":"ORD"},[Q("mo",{stretchy:"false"},"("),Q("mi",null,"n"),Q("mo",{stretchy:"false"},")")])]),Q("mo",{stretchy:"false"},"]")])],-1))]),a[319]||(a[319]=Q("p",null,"并且将特征值组成一个对角矩阵",-1)),Q("mjx-container",E2,[(l(),T("svg",N2,[...a[221]||(a[221]=[s('',1)])])),a[222]||(a[222]=Q("mjx-assistive-mml",{unselectable:"on",display:"block"},[Q("math",{xmlns:"http://www.w3.org/1998/Math/MathML",display:"block"},[Q("mi",{mathvariant:"bold"},"Λ"),Q("mo",null,"="),Q("mtext",null,"diag"),Q("mrow",{"data-mjx-texclass":"ORD"},[Q("mo",{stretchy:"false"},"["),Q("msub",null,[Q("mi",null,"λ"),Q("mn",null,"1")]),Q("mo",null,","),Q("msub",null,[Q("mi",null,"λ"),Q("mn",null,"2")]),Q("mo",null,","),Q("mo",null,"⋯"),Q("mo",null,","),Q("msub",null,[Q("mi",null,"λ"),Q("mi",null,"n")]),Q("mo",{stretchy:"false"},"]")])])],-1))]),Q("p",null,[a[225]||(a[225]=t("那么方阵",-1)),Q("mjx-container",q2,[(l(),T("svg",I2,[...a[223]||(a[223]=[Q("g",{stroke:"currentColor",fill:"currentColor","stroke-width":"0",transform:"scale(1,-1)"},[Q("g",{"data-mml-node":"math"},[Q("g",{"data-mml-node":"mi"},[Q("path",{"data-c":"1D468",d:"M65 0Q45 0 45 18Q48 52 61 60Q65 62 81 62Q155 62 165 74Q166 74 265 228T465 539T569 699Q576 707 583 709T611 711T637 710T649 700Q650 697 695 380L741 63L784 62H827Q839 50 839 45L835 29Q831 9 827 5T806 0Q803 0 790 0T743 1T657 2Q585 2 547 1T504 0Q481 0 481 17Q484 54 497 60Q501 62 541 62Q580 62 580 63Q580 68 573 121T564 179V181H308L271 124Q236 69 236 67T283 62H287Q316 62 316 46Q316 26 307 8Q302 3 295 0L262 1Q242 2 168 2Q119 2 93 1T65 0ZM537 372Q533 402 528 435T521 486T518 504V505Q517 505 433 375L348 244L451 243Q555 243 555 244L537 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# 定义一个 3x3 矩阵
A = np.array([[3, 4, 7],
[0, 2, 5],
[1, 9, 9]])
# 计算特征值、特征向量
eigenvalues, eigenvectors = np.linalg.eig(A)
print("特征值：")
print(eigenvalues)
print("\\n特征向量（每一列是一个）：")
print(eigenvectors)
A = np.array([[1, 2, 3],
[4, 5, 6]])
U, S, VT = np.linalg.svd(A)
print("U:\\n", U)
print("S（奇异值）:\\n", S)
print("VT:\\n", VT)
print(f"VT[0]={VT[0]}, VT[1]={VT[1]}, VT[2]={VT[2]}")