func TestWeightedQuadtricFit(t *testing.T) { var n int = 19 data := MakeData(0.1, 2.0, 0.1) X := matrix.MatrixAlloc(n, 3) y := vector.VectorAlloc(n) w := vector.VectorAlloc(n) c := vector.VectorAlloc(3) cov := matrix.MatrixAlloc(3, 3) for i := 0; i < n; i++ { xi := data[i*3] yi := data[i*3+1] ei := data[i*3+2] fmt.Printf("%g %g +/- %g\n", xi, yi, ei) matrix.Set(X, i, 0, 1.0) matrix.Set(X, i, 1, xi) matrix.Set(X, i, 2, xi*xi) vector.Set(y, i, yi) vector.Set(w, i, 1.0/(ei*ei)) } work := multifit.LinearAlloc(n, 3) _, chisq := multifit.Wlinear(X, w, y, c, cov, work) fmt.Printf("# best fit: Y = %g + %g X + %g X^2\n", vector.Get(c, 0), vector.Get(c, 1), vector.Get(c, 2)) fmt.Printf("# covariance matrix:\n") fmt.Printf("[ %+.5e, %+.5e, %+.5e \n", matrix.Get(cov, 0, 0), matrix.Get(cov, 0, 1), matrix.Get(cov, 0, 2)) fmt.Printf(" %+.5e, %+.5e, %+.5e \n", matrix.Get(cov, 1, 0), matrix.Get(cov, 1, 1), matrix.Get(cov, 1, 2)) fmt.Printf(" %+.5e, %+.5e, %+.5e ]\n", matrix.Get(cov, 2, 0), matrix.Get(cov, 2, 1), matrix.Get(cov, 2, 2)) fmt.Printf("# chisq = %g\n", chisq) }
func (h *GslHistogram2d) Slice(xofs, xlength, yofs, ylength int) *matrix.GslMatrix { dim := h.Dim() if xlength+xofs > dim[0] || ylength+yofs > dim[1] { gogsl.Error("histogram slice out of range", gogsl.GSL_ERANGE) return nil } // bin[i * ny + j] ptrOfs := xofs*dim[1] + yofs ptr := C.histogram2d_get_value_ptr((*C.gsl_histogram2d)(unsafe.Pointer(h.Ptr())), C.size_t(ptrOfs)) dimlen := dim[0]*dim[1] - ptrOfs srcHdr := &reflect.SliceHeader{Data: uintptr(ptr), Len: dimlen, Cap: dimlen} src := *(*[]float64)(unsafe.Pointer(srcHdr)) view := matrix.ViewArrayWithTda(src, xlength, ylength, dim[1]) dst := matrix.MatrixAlloc(xlength, ylength) matrix.Memcpy(dst, view.Matrix()) return dst }
func TestEigen(t *testing.T) { data := []float64{1.0, 1 / 2.0, 1 / 3.0, 1 / 4.0, 1 / 2.0, 1 / 3.0, 1 / 4.0, 1 / 5.0, 1 / 3.0, 1 / 4.0, 1 / 5.0, 1 / 6.0, 1 / 4.0, 1 / 5.0, 1 / 6.0, 1 / 7.0} m := matrix.ViewArray(data, 4, 4) eval := vector.VectorAlloc(4) evec := matrix.MatrixAlloc(4, 4) w := eigen.SymmvAlloc(4) eigen.Symmv(m.Matrix(), eval, evec, w) eigen.SymmvSort(eval, evec, eigen.EIGEN_SORT_ABS_ASC) for i := 0; i < 4; i++ { evalI := vector.Get(eval, i) evecI := matrix.Column(evec, i) fmt.Printf("eigenvalue = %g\n", evalI) fmt.Printf("eigenvector = \n") vector.Fprintf(os.Stdout, evecI.Vector(), "%g") } }
func TestRobust(t *testing.T) { var p int = 2 // linear fit var a float64 = 1.45 // data slope var b float64 = 3.88 // data intercept var n int = 20 X := matrix.MatrixAlloc(n, p) x := vector.VectorAlloc(n) y := vector.VectorAlloc(n) c := vector.VectorAlloc(p) cOls := vector.VectorAlloc(p) cov := matrix.MatrixAlloc(p, p) r := rng.RngAlloc(rng.DefaultRngType()) // generate linear dataset for i := 0; i < n-3; i++ { dx := 10.0 / (float64(n) - 1.0) ei := rng.Uniform(r) xi := -5.0 + float64(i)*dx yi := a*xi + b vector.Set(x, i, xi) vector.Set(y, i, yi+ei) } // add a few outliers vector.Set(x, n-3, 4.7) vector.Set(y, n-3, -8.3) vector.Set(x, n-2, 3.5) vector.Set(y, n-2, -6.7) vector.Set(x, n-1, 4.1) vector.Set(y, n-1, -6.0) // construct design matrix X for linear fit for i := 0; i < n; i++ { xi := vector.Get(x, i) matrix.Set(X, i, 0, 1.0) matrix.Set(X, i, 1, xi) } // perform robust and OLS fit DoFit(multifit.GSL_MULTIFIT_ROBUST_OLS, X, y, cOls, cov) DoFit(multifit.GSL_MULTIFIT_ROBUST_BISQUARE, X, y, c, cov) // output data and model for i := 0; i < n; i++ { xi := vector.Get(x, i) yi := vector.Get(y, i) v := matrix.Row(X, i).Vector() _, yRob, _ := multifit.RobustEst(v, c, cov) _, yOls, _ := multifit.RobustEst(v, cOls, cov) fmt.Printf("%g %g %g %g\n", xi, yi, yRob, yOls) } fmt.Printf("# best fit: Y = %g + %g X\n", vector.Get(c, 0), vector.Get(c, 1)) fmt.Printf("# covariance matrix:\n") fmt.Printf("# [ %+.5e, %+.5e\n", matrix.Get(cov, 0, 0), matrix.Get(cov, 0, 1)) fmt.Printf("# %+.5e, %+.5e\n", matrix.Get(cov, 1, 0), matrix.Get(cov, 1, 1)) }
func TestBspline(t *testing.T) { var n int = 200 var ncoeffs int = 12 var nbreak int = ncoeffs - 2 rng.EnvSetup() r := rng.RngAlloc(rng.DefaultRngType()) // allocate a cubic bspline workspace (k = 4) bw := bspline.Alloc(4, nbreak) B := vector.VectorAlloc(ncoeffs) x := vector.VectorAlloc(n) y := vector.VectorAlloc(n) X := matrix.MatrixAlloc(n, ncoeffs) c := vector.VectorAlloc(ncoeffs) w := vector.VectorAlloc(n) cov := matrix.MatrixAlloc(ncoeffs, ncoeffs) mw := multifit.LinearAlloc(n, ncoeffs) fmt.Printf("#m=0,S=0\n") // this is the data to be fitted for i := 0; i < n; i++ { xi := (15.0 / (float64(n) - 1)) * float64(i) yi := math.Cos(xi) * math.Exp(-0.1*xi) sigma := 0.1 * yi dy := randist.Gaussian(r, sigma) vector.Set(x, i, xi) vector.Set(y, i, yi+dy) vector.Set(w, i, 1.0/(sigma*sigma)) fmt.Printf("%f %f\n", xi, yi+dy) } // use uniform breakpoints on [0, 15] bspline.KnotsUniform(0.0, 15.0, bw) // construct the fit matrix X for i := 0; i < n; i++ { xi := vector.Get(x, i) // compute B_j(xi) for all j bspline.Eval(xi, B, bw) // fill in row i of X for j := 0; j < ncoeffs; j++ { matrix.Set(X, i, j, vector.Get(B, j)) } } // do the fit _, chisq := multifit.Wlinear(X, w, y, c, cov, mw) dof := float64(n - ncoeffs) tss := stats.Wtss(w.Data_(), w.Stride(), y.Data_(), y.Stride(), n) rsq := 1.0 - chisq/tss fmt.Printf("chisq/dof = %e, Rsq = %f\n", chisq/dof, rsq) fmt.Printf("#m=1,S=0\n") for xi := 0.0; xi < 15.0; xi += 0.1 { bspline.Eval(xi, B, bw) _, yi, _ := multifit.LinearEst(B, c, cov) fmt.Printf("%f %f\n", xi, yi) } }