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106 lines
2.6 KiB
JavaScript
106 lines
2.6 KiB
JavaScript
import approx from '../../../../tools/approx'
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import math from '../../../../src/main'
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describe('slu', function () {
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it('should decompose matrix, 4 x 4, natural ordering (order=0), partial pivoting', function () {
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const m = math.sparse(
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[
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[4.5, 0, 3.2, 0],
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[3.1, 2.9, 0, 0.9],
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[0, 1.7, 3, 0],
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[3.5, 0.4, 0, 1]
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])
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// partial pivoting
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const r = math.slu(m, 0, 1)
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// verify M[p,q]=L*U
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approx.deepEqual(_permute(m, r.p, r.q).valueOf(), math.multiply(r.L, r.U).valueOf())
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})
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it('should decompose matrix, 4 x 4, amd(A+A\') (order=1)', function () {
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const m = math.sparse(
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[
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[4.5, 0, 3.2, 0],
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[3.1, 2.9, 0, 0.9],
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[0, 1.7, 3, 0],
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[3.5, 0.4, 0, 1]
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])
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// partial pivoting
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const r = math.slu(m, 1, 1)
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// verify M[p,q]=L*U
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approx.deepEqual(_permute(m, r.p, r.q).valueOf(), math.multiply(r.L, r.U).valueOf())
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})
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it('should decompose matrix, 4 x 4, amd(A\'*A) (order=2), partial pivoting', function () {
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const m = math.sparse(
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[
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[4.5, 0, 3.2, 0],
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[3.1, 2.9, 0, 0.9],
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[0, 1.7, 3, 0],
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[3.5, 0.4, 0, 1]
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])
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// partial pivoting
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const r = math.slu(m, 2, 1)
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// verify M[p,q]=L*U
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approx.deepEqual(_permute(m, r.p, r.q).valueOf(), math.multiply(r.L, r.U).valueOf())
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})
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it('should decompose matrix, 4 x 4, amd(A\'*A) (order=3), partial pivoting', function () {
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const m = math.sparse(
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[
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[4.5, 0, 3.2, 0],
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[3.1, 2.9, 0, 0.9],
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[0, 1.7, 3, 0],
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[3.5, 0.4, 0, 1]
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])
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// partial pivoting
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const r = math.slu(m, 3, 1)
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// verify M[p,q]=L*U
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approx.deepEqual(_permute(m, r.p, r.q).valueOf(), math.multiply(r.L, r.U).valueOf())
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})
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/**
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* C = A(p,q) where p is the row permutation vector and q the column permutation vector.
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*/
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function _permute (A, pinv, q) {
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// matrix arrays
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const values = A._values
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const index = A._index
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const ptr = A._ptr
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const size = A._size
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// columns
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const n = size[1]
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// c arrays
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const cvalues = []
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const cindex = []
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const cptr = []
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// loop columns
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for (let k = 0; k < n; k++) {
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cptr[k] = cindex.length
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// column in C
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const j = q ? (q[k]) : k
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// values in column j
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for (let t = ptr[j]; t < ptr[j + 1]; t++) {
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cvalues.push(values[t])
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cindex.push(pinv ? (pinv[index[t]]) : index[t])
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}
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}
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cptr[n] = cindex.length
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// return matrix
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return new math.type.SparseMatrix({
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values: cvalues,
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index: cindex,
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ptr: cptr,
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size: size,
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datatype: A._datatype
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})
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}
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})
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