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ee.Array.eigen
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Calcola gli autovettori e gli autovalori reali di un array 2D quadrato di A righe e A colonne. Restituisce una matrice con A righe e A+1 colonne, in cui ogni riga contiene un autovalore nella prima colonna e l'autovettore corrispondente nelle restanti A colonne. Le righe sono ordinate per autovalore, in ordine decrescente.
Questa implementazione utilizza DecompositionFactory.eig() da https://ejml.org.
| Utilizzo | Resi |
|---|
Array.eigen() | Array |
| Argomento | Tipo | Dettagli |
|---|
questo: input | Array | Un array bidimensionale quadrato da cui calcolare la decomposizione agli autovalori. |
Esempi
Editor di codice (JavaScript)
print(ee.Array([[0, 0], [0, 0]]).eigen()); // [[0,0,1],[0,1,0]]
print(ee.Array([[1, 0], [0, 0]]).eigen()); // [[1,1,0],[0,0,1]]
print(ee.Array([[0, 1], [0, 0]]).eigen()); // [[0,0,1],[0,1,0]]
print(ee.Array([[0, 0], [1, 0]]).eigen()); // [[0,-1,0],[0,0,-1]]
print(ee.Array([[0, 0], [0, 1]]).eigen()); // [[1,0,1],[0,1,0]]
print(ee.Array([[1, 1], [0, 0]]).eigen()); // [[1,1,0],[0,-1/√2,1/√2]]
print(ee.Array([[0, 0], [1, 1]]).eigen()); // [[1,0,-1],[0,-1/√2,1/√2]]]
print(ee.Array([[1, 0], [1, 0]]).eigen()); // [[1,1/√2,1/√2],[0,0,1]]
print(ee.Array([[1, 0], [0, 1]]).eigen()); // [[1,1,0],[1,0,1]]
print(ee.Array([[0, 1], [1, 0]]).eigen()); // [[1,1/√2,1/√2],[-1,1/√2,-1/√2]]
print(ee.Array([[0, 1], [0, 1]]).eigen()); // [[1,1/√2,1/√2],[0,1,0]]
print(ee.Array([[1, 1], [1, 0]]).eigen()); // [[1.62,0.85,0.53],[-0.62,0.53]]
print(ee.Array([[1, 1], [0, 1]]).eigen()); // [[1,0,1],[1,1,0]]
print(ee.Array([[1, 0], [1, 1]]).eigen()); // [[1,-1,0],[1,0,-1]]
// [[1.62,-0.53,-0.85],[-0.62,-0.85,0.53]]
print(ee.Array([[0, 1], [1, 1]]).eigen());
print(ee.Array([[1, 1], [1, 1]]).eigen()); // [[2,1/√2,1/√2],[0,1/√2,-1/√2]]
var matrix = ee.Array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]]);
print(matrix.eigen()); // [[1,1,0,0],[1,0,1,0],[1,0,0,1]]
var matrix = ee.Array([
[2, 0, 0],
[0, 3, 0],
[0, 0, 4]]);
print(matrix.eigen()); // [[4,0,0,1],[3,0,1,0],[2,1,0,0]]
matrix = ee.Array([
[1, 0, 0],
[0, 0, 0],
[0, 0, 0]]);
print(matrix.eigen()); // [[1,1,0,0],[0,0,1,0],[0,0,0,1]]
matrix = ee.Array([
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]]);
// [[3,-0.58,-0.58,-0.58],[0,0,-1/√2,1/√2],[0,-0.82,0.41,0.41]]
print(matrix.eigen());
Configurazione di Python
Consulta la pagina
Ambiente Python per informazioni sull'API Python e sull'utilizzo di
geemap per lo sviluppo interattivo.
import ee
import geemap.core as geemap
Colab (Python)
display(ee.Array([[0, 0], [0, 0]]).eigen()) # [[0, 0, 1], [0, 1, 0]]
display(ee.Array([[1, 0], [0, 0]]).eigen()) # [[1, 1, 0], [0,0,1]]
display(ee.Array([[0, 1], [0, 0]]).eigen()) # [[0, 0, 1], [0, 1, 0]]
display(ee.Array([[0, 0], [1, 0]]).eigen()) # [[0, -1, 0], [0, 0, -1]]
display(ee.Array([[0, 0], [0, 1]]).eigen()) # [[1, 0, 1], [0, 1, 0]]
# [[1, 1, 0], [0, -1/√2, 1/√2]]
display(ee.Array([[1, 1], [0, 0]]).eigen())
# [[1, 0, -1], [0, -1/√2, 1/√2]]]
display(ee.Array([[0, 0], [1, 1]]).eigen())
# [[1, 1/√2, 1/√2], [0, 0, 1]]
display(ee.Array([[1, 0], [1, 0]]).eigen())
display(ee.Array([[1, 0], [0, 1]]).eigen()) # [[1, 1, 0], [1, 0, 1]]
# [[1, 1/√2, 1/√2], [-1, 1/√2, -1/√2]]
display(ee.Array([[0, 1], [1, 0]]).eigen())
# [[1, 1/√2, 1/√2], [0, 1, 0]]
display(ee.Array([[0, 1], [0, 1]]).eigen())
# [[1.62, 0.85, 0.53], [-0.62, 0.53]]
display(ee.Array([[1, 1], [1, 0]]).eigen())
display(ee.Array([[1, 1], [0, 1]]).eigen()) # [[1, 0, 1], [1, 1, 0]]
display(ee.Array([[1, 0], [1, 1]]).eigen()) # [[1, -1, 0], [1, 0, -1]]
# [[1.62, -0.53, -0.85], [-0.62, -0.85, 0.53]]
display(ee.Array([[0, 1], [1, 1]]).eigen())
# [[2, 1/√2, 1/√2], [0, 1/√2, -1/√2]]
display(ee.Array([[1, 1], [1, 1]]).eigen())
matrix = ee.Array([
[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
display(matrix.eigen()) # [[1, 1, 0, 0], [1, 0, 1, 0], [1, 0, 0, 1]]
matrix = ee.Array([
[2, 0, 0],
[0, 3, 0],
[0, 0, 4]])
display(matrix.eigen()) # [[4, 0, 0, 1], [3, 0, 1, 0], [2, 1, 0, 0]]
matrix = ee.Array([
[1, 0, 0],
[0, 0, 0],
[0, 0, 0]])
display(matrix.eigen()) # [[1, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]
matrix = ee.Array([
[1, 1, 1],
[1, 1, 1],
[1, 1, 1]])
# [[3, -0.58, -0.58, -0.58], [0, 0, -1/√2, 1/√2], [0, -0.82, 0.41, 0.41]]
display(matrix.eigen())
Salvo quando diversamente specificato, i contenuti di questa pagina sono concessi in base alla licenza Creative Commons Attribution 4.0, mentre gli esempi di codice sono concessi in base alla licenza Apache 2.0. Per ulteriori dettagli, consulta le norme del sito di Google Developers. Java è un marchio registrato di Oracle e/o delle sue consociate.
Ultimo aggiornamento 2025-07-26 UTC.
[null,null,["Ultimo aggiornamento 2025-07-26 UTC."],[[["\u003cp\u003eComputes the real eigenvectors and eigenvalues of a 2D square array.\u003c/p\u003e\n"],["\u003cp\u003eReturns an array where each row represents an eigenvalue and its corresponding eigenvector.\u003c/p\u003e\n"],["\u003cp\u003eEigenvalues are sorted in descending order within the output array.\u003c/p\u003e\n"],["\u003cp\u003eUtilizes the \u003ccode\u003eDecompositionFactory.eig()\u003c/code\u003e method from the EJML library for computation.\u003c/p\u003e\n"],["\u003cp\u003eAccepts a single argument: the input 2D square array.\u003c/p\u003e\n"]]],["The `eigen()` function computes the eigenvalues and eigenvectors of a square 2D array. It takes a square 2D array as input and returns a new array where each row represents an eigenvalue and its corresponding eigenvector. The first column of each row contains the eigenvalue, and the remaining columns contain the eigenvector components. The rows are sorted in descending order by eigenvalue. It uses `DecompositionFactory.eig()` for its core calculations.\n"],null,["# ee.Array.eigen\n\nComputes the real eigenvectors and eigenvalues of a square 2D array of A rows and A columns. Returns an array with A rows and A+1 columns, where each row contains an eigenvalue in the first column, and the corresponding eigenvector in the remaining A columns. The rows are sorted by eigenvalue, in descending order.\n\n\u003cbr /\u003e\n\nThis implementation uses DecompositionFactory.eig() from https://ejml.org.\n\n| Usage | Returns |\n|-----------------|---------|\n| Array.eigen`()` | Array |\n\n| Argument | Type | Details |\n|---------------|-------|------------------------------------------------------------------------|\n| this: `input` | Array | A square, 2D array from which to compute the eigenvalue decomposition. |\n\nExamples\n--------\n\n### Code Editor (JavaScript)\n\n```javascript\nprint(ee.Array([[0, 0], [0, 0]]).eigen()); // [[0,0,1],[0,1,0]]\n\nprint(ee.Array([[1, 0], [0, 0]]).eigen()); // [[1,1,0],[0,0,1]]\nprint(ee.Array([[0, 1], [0, 0]]).eigen()); // [[0,0,1],[0,1,0]]\nprint(ee.Array([[0, 0], [1, 0]]).eigen()); // [[0,-1,0],[0,0,-1]]\nprint(ee.Array([[0, 0], [0, 1]]).eigen()); // [[1,0,1],[0,1,0]]\n\nprint(ee.Array([[1, 1], [0, 0]]).eigen()); // [[1,1,0],[0,-1/√2,1/√2]]\nprint(ee.Array([[0, 0], [1, 1]]).eigen()); // [[1,0,-1],[0,-1/√2,1/√2]]]\n\nprint(ee.Array([[1, 0], [1, 0]]).eigen()); // [[1,1/√2,1/√2],[0,0,1]]\nprint(ee.Array([[1, 0], [0, 1]]).eigen()); // [[1,1,0],[1,0,1]]\nprint(ee.Array([[0, 1], [1, 0]]).eigen()); // [[1,1/√2,1/√2],[-1,1/√2,-1/√2]]\nprint(ee.Array([[0, 1], [0, 1]]).eigen()); // [[1,1/√2,1/√2],[0,1,0]]\n\nprint(ee.Array([[1, 1], [1, 0]]).eigen()); // [[1.62,0.85,0.53],[-0.62,0.53]]\nprint(ee.Array([[1, 1], [0, 1]]).eigen()); // [[1,0,1],[1,1,0]]\nprint(ee.Array([[1, 0], [1, 1]]).eigen()); // [[1,-1,0],[1,0,-1]]\n// [[1.62,-0.53,-0.85],[-0.62,-0.85,0.53]]\nprint(ee.Array([[0, 1], [1, 1]]).eigen());\n\nprint(ee.Array([[1, 1], [1, 1]]).eigen()); // [[2,1/√2,1/√2],[0,1/√2,-1/√2]]\n\nvar matrix = ee.Array([\n [1, 0, 0],\n [0, 1, 0],\n [0, 0, 1]]);\nprint(matrix.eigen()); // [[1,1,0,0],[1,0,1,0],[1,0,0,1]]\n\nvar matrix = ee.Array([\n [2, 0, 0],\n [0, 3, 0],\n [0, 0, 4]]);\nprint(matrix.eigen()); // [[4,0,0,1],[3,0,1,0],[2,1,0,0]]\n\nmatrix = ee.Array([\n [1, 0, 0],\n [0, 0, 0],\n [0, 0, 0]]);\nprint(matrix.eigen()); // [[1,1,0,0],[0,0,1,0],[0,0,0,1]]\n\nmatrix = ee.Array([\n [1, 1, 1],\n [1, 1, 1],\n [1, 1, 1]]);\n// [[3,-0.58,-0.58,-0.58],[0,0,-1/√2,1/√2],[0,-0.82,0.41,0.41]]\nprint(matrix.eigen());\n```\nPython setup\n\nSee the [Python Environment](/earth-engine/guides/python_install) page for information on the Python API and using\n`geemap` for interactive development. \n\n```python\nimport ee\nimport geemap.core as geemap\n```\n\n### Colab (Python)\n\n```python\ndisplay(ee.Array([[0, 0], [0, 0]]).eigen()) # [[0, 0, 1], [0, 1, 0]]\n\ndisplay(ee.Array([[1, 0], [0, 0]]).eigen()) # [[1, 1, 0], [0,0,1]]\ndisplay(ee.Array([[0, 1], [0, 0]]).eigen()) # [[0, 0, 1], [0, 1, 0]]\ndisplay(ee.Array([[0, 0], [1, 0]]).eigen()) # [[0, -1, 0], [0, 0, -1]]\ndisplay(ee.Array([[0, 0], [0, 1]]).eigen()) # [[1, 0, 1], [0, 1, 0]]\n\n# [[1, 1, 0], [0, -1/√2, 1/√2]]\ndisplay(ee.Array([[1, 1], [0, 0]]).eigen())\n\n# [[1, 0, -1], [0, -1/√2, 1/√2]]]\ndisplay(ee.Array([[0, 0], [1, 1]]).eigen())\n\n# [[1, 1/√2, 1/√2], [0, 0, 1]]\ndisplay(ee.Array([[1, 0], [1, 0]]).eigen())\ndisplay(ee.Array([[1, 0], [0, 1]]).eigen()) # [[1, 1, 0], [1, 0, 1]]\n\n# [[1, 1/√2, 1/√2], [-1, 1/√2, -1/√2]]\ndisplay(ee.Array([[0, 1], [1, 0]]).eigen())\n\n# [[1, 1/√2, 1/√2], [0, 1, 0]]\ndisplay(ee.Array([[0, 1], [0, 1]]).eigen())\n\n# [[1.62, 0.85, 0.53], [-0.62, 0.53]]\ndisplay(ee.Array([[1, 1], [1, 0]]).eigen())\ndisplay(ee.Array([[1, 1], [0, 1]]).eigen()) # [[1, 0, 1], [1, 1, 0]]\ndisplay(ee.Array([[1, 0], [1, 1]]).eigen()) # [[1, -1, 0], [1, 0, -1]]\n\n# [[1.62, -0.53, -0.85], [-0.62, -0.85, 0.53]]\ndisplay(ee.Array([[0, 1], [1, 1]]).eigen())\n\n# [[2, 1/√2, 1/√2], [0, 1/√2, -1/√2]]\ndisplay(ee.Array([[1, 1], [1, 1]]).eigen())\n\nmatrix = ee.Array([\n [1, 0, 0],\n [0, 1, 0],\n [0, 0, 1]])\ndisplay(matrix.eigen()) # [[1, 1, 0, 0], [1, 0, 1, 0], [1, 0, 0, 1]]\n\nmatrix = ee.Array([\n [2, 0, 0],\n [0, 3, 0],\n [0, 0, 4]])\ndisplay(matrix.eigen()) # [[4, 0, 0, 1], [3, 0, 1, 0], [2, 1, 0, 0]]\n\nmatrix = ee.Array([\n [1, 0, 0],\n [0, 0, 0],\n [0, 0, 0]])\ndisplay(matrix.eigen()) # [[1, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]]\n\nmatrix = ee.Array([\n [1, 1, 1],\n [1, 1, 1],\n [1, 1, 1]])\n# [[3, -0.58, -0.58, -0.58], [0, 0, -1/√2, 1/√2], [0, -0.82, 0.41, 0.41]]\ndisplay(matrix.eigen())\n```"]]
