Nothing
# Adjust graphical parameters
opar <- par(ask = dev.interactive(orNone = TRUE))
# Creation of new loca.p object
o <- loca.p(x = c(-1, 1, 0), y = c(0, 0, 1))
# Summaring and printing the object
print(o)
# Plot the demand points
plot(o)
# Evaluation at point (0, .5)
distsum(o, x=0, y=.5)
# Contour plot the objective function
contour(o)
# 3D plot of the objective function
persp(o)
# 3D nice plot
persp(o, col=cm.colors(10000), border=FALSE, shade=TRUE, theta=50, phi=5, ltheta=135)
# Another 3D plot
persp(o, col=cm.colors(10000), border=FALSE, shade=TRUE, theta=50, phi=5, ltheta=135, lphi=90)
# Plots with a background image
file = system.file('img', 'spain_provinces.png', package='orloca')
img = readPNG(file)
plot(loca.p(x=.55, y=.62), img=img, xlim=c(0,1), ylim=c(0,1), xleft=0, ybottom=0, xright=1, ytop=1)
contour(loca.p(x=.55, y=.62), img=img, xmin=0, ymin=0, xmax=1, ymax=1, xleft=0, ybottom=0, xright=1, ytop=1)
# Find the minimum
distsummin(o)
# New random loca.p object with 10 demand point
p <- rloca.p(10)
# Find the minimun
sol <- distsummin(p)
# Show it
sol
# Eval the function at minimun
distsum(p, sol[1], sol[2])
# Timing the algorithm
system.time(distsummin(p))
# Restoring graphical parameters
par(opar)
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