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svd, matrix+*double sped up, pointerToMatrix fixed
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scottsievert
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Jul 15, 2014
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,39 @@ | ||
| // | ||
| // svd.m | ||
| // swix | ||
| // | ||
| // Created by Scott Sievert on 7/15/14. | ||
| // Copyright (c) 2014 com.scott. All rights reserved. | ||
| // | ||
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| #import <Foundation/Foundation.h> | ||
| #import <Accelerate/Accelerate.h> | ||
| #import <stdint.h> | ||
| #import "swix-Bridging-Header.h" // for copy_objc | ||
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| void svd_objc(double * xx, int m, int n, double* sigma, double* vt, double* u){ | ||
| // adapted from http://stackoverflow.com/questions/5047503/lapack-svd-singular-value-decomposition | ||
| // Setup a buffer to hold the singular values: | ||
| int lda = m; | ||
| int numberOfSingularValues = m < n ? m : n; | ||
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| // Workspace and status variables: | ||
| double workSize; | ||
| double *work = &workSize; | ||
| int lwork = -1; | ||
| int *iwork = malloc(8*numberOfSingularValues); | ||
| int info = 0; | ||
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| // Call dgesdd_ with lwork = -1 to query optimal workspace size: | ||
| dgesdd_("A", &m, &n, xx, &lda, sigma, u, &m, vt, &n, work, &lwork, iwork, &info); | ||
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| // Optimal workspace size is returned in work[0]. | ||
| lwork = workSize; | ||
| work = malloc(lwork * sizeof *work); | ||
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| // Call dgesdd_ to do the actual computation: | ||
| dgesdd_("A", &m, &n, xx, &lda, sigma, u, &m, vt, &n, work, &lwork, iwork, &info); | ||
| free(work); | ||
| } | ||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,36 @@ | ||
| // | ||
| // twoD-complex-math.swift | ||
| // swix | ||
| // | ||
| // Created by Scott Sievert on 7/15/14. | ||
| // Copyright (c) 2014 com.scott. All rights reserved. | ||
| // | ||
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| import Foundation | ||
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| func svd(x: matrix2d) -> (matrix2d, matrix, matrix2d){ | ||
| // 2014-7-15: almost have this working. to change by tomorrow. | ||
| var (m, n) = x.shape | ||
| var nS = m < n ? m : n | ||
| var sigma = zeros(nS) | ||
| var vt = zeros((n,n)) | ||
| var u = zeros((m,m)) | ||
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| var xx = zeros_like(x) | ||
| xx.flat = x.flat | ||
| xx = transpose(xx) | ||
| var xP = matrixToPointer(xx.flat) | ||
| var sP = matrixToPointer(sigma) | ||
| var vP = matrixToPointer(vt.flat) | ||
| var uP = matrixToPointer(u.flat) | ||
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| println(x) | ||
| svd_objc(xP, m.cint, n.cint, sP, vP, uP); | ||
| if m > n {vt = transpose(vt)} | ||
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| println(sigma) | ||
| println(vt) | ||
| println(u) | ||
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| return (zeros((2,2)), zeros(2), zeros((2,2))) | ||
| } |
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