curvefit | R Documentation |
Polynomial fitting of parametrized points on 2D curves, also requiring to meet some points exactly.
curvefit(u, x, y, n, U = NULL, V = NULL)
u |
the parameter vector. |
x, y |
x-, y-coordinates for each parameter value. |
n |
order of the polynomials, the same in x- and y-dirction. |
U |
parameter values where points will be fixed. |
V |
matrix with two columns and |
This function will attempt to fit two polynomials to parametrized curve
points using the linear least squares approach with linear equality
constraints in lsqlin
. The requirement to meet exactly some fixed
points is interpreted as a linear equality constraint.
Returns a list with 4 components, xp
and yp
coordinates of
the fitted points, and px
and py
the coefficients of the
fitting polynomials in x- and y-direction.
In the same manner, derivatives/directions could be prescribed at certain points.
circlefit
, lsqlin
## Approximating half circle arc with small perturbations
N <- 50
u <- linspace(0, pi, N)
x <- cos(u) + 0.05 * randn(1, N)
y <- sin(u) + 0.05 * randn(1, N)
n <- 8
cfit1 <- curvefit(u, x, y, n)
## Not run:
plot(x, y, col = "darkgray", pch = 19, asp = 1)
xp <- cfit1$xp; yp <- cfit1$yp
lines(xp, yp, col="blue")
grid()
## End(Not run)
## Fix the end points at t = 0 and t = pi
U <- c(0, pi)
V <- matrix(c(1, 0, -1, 0), 2, 2, byrow = TRUE)
cfit2 <- curvefit(u, x, y, n, U, V)
## Not run:
xp <- cfit2$xp; yp <- cfit2$yp
lines(xp, yp, col="red")
## End(Not run)
## Not run:
## Archimedian spiral
n <- 8
u <- linspace(0, 3*pi, 50)
a <- 1.0
x <- as.matrix(a*u*cos(u))
y <- as.matrix(a*u*sin(u))
plot(x, y, type = "p", pch = 19, col = "darkgray", asp = 1)
lines(x, y, col = "darkgray", lwd = 3)
cfit <- curvefit(u, x, y, n)
px <- c(cfit$px); py <- c(cfit$py)
v <- linspace(0, 3*pi, 200)
xs <- polyval(px, v)
ys <- polyval(py, v)
lines(xs, ys, col = "navy")
grid()
## End(Not run)
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