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On Tue, Feb 19, 2008 at 09:32:16AM +0900, Lionel Bouton wrote:
> Philipp Hofmann wrote:
>> Here is my solution. Although it uses a brute force approach if the
>> first attempts (driven by obvious constraints) of determining the
>> circle fail, it scales on larger sets, because the given equal
>> distibution of random points form an approximate square on these
>> sets.
>
> Wow, that was awfully fast for the random distribution, it takes more
> time to display the result than computing it even on 10000 points !
>
> On badly shaped point clouds it suffers greatly, I couldn't get it to
> compute the circle for 1000 points if I feed it with :

What you call a 'badly shaped point clouds' is in fact an approximate
circle. It is true that this is the worst case for convex hull based
approaches because in this case the convex hull has lots of points.

It would be nice to have an algorithm that produces random
distibutions that form arbitrary polygons. But I guess that's a task
for another quiz.

As I mentioned in one of my comments in the code, right before it goes
into brute force, I prefer using Clarkson's probabilistic aproach here.

So here is my improved version, still based on convex hulls but also
able to deal with circular shaped distributions.

I confident it doesn't do any harm to your poor laptop this time. ;)

> ...

g phil

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class Point < Struct.new(:x, :y)

# this distribution is the worst case for convex hull
# based algorithms, because it leads to an approximate
# circle, therefore the convex hull has lots of points
def self.random
#Point.new(rand, rand)
ro  and / 2
theta  ath::PI * 2 * rand
Point.new(ro * Math::cos(theta) + 0.5, ro * Math::sin(theta) + 0.5)
end

def to_s
"(#{x}, #{y})"
end

def distance(p)
Math.sqrt((x-p.x)**2+(y-p.y)**2)
end

def middle(p)
Point.new((self.x+p.x)/2, (self.y+p.y)/2)
end
end

def to_s
end
end

def generate_samples(n)
(1..n).map { Point.random }
end

# ----- clarkson's probabilistic approach

def encircle(points)
# this in arbitrary set threshold
return previous_encircle(points) if points.size < 100
circle  il
pot  }
points.each { |p| pot[p]   }
finished  alse
until finished
# select 13 random points
selection  ]
sum  ot.values.inject { |sum, s| sum +  }
13.times { selection << rand(sum) }
selection.uniq!
while selection.size < 13
selection << rand(sum)
selection.uniq!
end
selection.sort!
thirteen  ]
sum
threshold  election.shift
pot.each do |p, s|
sum +
if threshold < um
thirteen << p
break if selection.empty?
threshold  election.shift
end
end
# use previous method on selection of thirteen
circle  revious_encircle(thirteen)
# double chance of every outsider
finished  rue
pot.each do |p, chance|
if !thirteen.include?(p) && p.distance(circle.center) > circle.radius
pot[p]  hance * 2
finished  alse
end
end
end
circle
end

# --- convex hull based approach

def previous_encircle(points)

# only one point? then we're done
return Circle.new(points.first, 0) if points.size

# firstly, we calculate the convex hull
# this will reduce points to calculate
# especially for larger sets
hull  onvexHull::convex_hull(points)
#draw_hull(hull.clone, :lightgray) if VISUALIZE

# find the two spots on the hull with the larges distance
# these will make a good starting point
distances0  }
list_of_points  ull.clone
until list_of_points.empty?
p1  ist_of_points.shift
list_of_points.each do |p2|
distances0[[p1, p2]]  1.distance(p2)
end
end
max0  istances0.values.max
p0, p1  istances0.invert[max0]
circle0  ircle.new(p0.middle(p1), max0/2)
hull.delete(p0)
hull.delete(p1)

# we're done if the hull only had two points
return circle0 if hull.empty?
# find the point that has the largest
# distance to the center of circle0
distances1  }
hull.each do |point|
distances1[point]  ircle0.center.distance(point)
end
max1  istances1.values.max
# if the farest point from center is
# closer then the radius we're done
return circle0 if max1 < ircle0.radius

p2  istances1.invert[max1]
circle1  OLE::circle_of(p0, p1, p2)
hull.delete(p2)
# find the point that has the largest
# distance to the center of circle1
distances2  }
hull.each do |point|
distances2[point]  ircle1.center.distance(point)
end
max2  istances2.values.max
# if the farest point from center is in the circle we're done
return circle1 if max2 < ircle1.radius

# ok, now we fall back to brute force on the convex hull
# we can do this because the random distribution given
# leads to an approximate square and therefore to convex hulls
# with very few points, if this weren't the case we should
# uses clarksons probabilistic approach here, especcially
# for larger sets of points.
hull.concat([p0, p1, p2])
sets_of_three  ubSet::subsets(hull, 3)
solutions  ]
sets_of_three.each do |subset|
circle2  OLE::circle_of(*subset)
distances3  }
hull.each do |p3|
distances3[p3]  ircle2.center.distance(p3) unless subset.include?(p3)
end
max3  istances3.values.max
solutions << circle2 if max3 < ircle2.radius
end

circle3  olutions.sort_by { |circle| circle.radius }.first
return circle3

end

# --- calculate subsets

module SubSet

# i admit this could definitly be improved
def self.subsets(set, ranksl)
ranks || ..set.size
ranks  anks..ranks unless ranks.is_a?(Range)
sets  ll_subsets(set)
sets.select { |s| ranks.include?(s.size) }
end

def self.all_subsets(set)
return [[]] if set.empty?
set  et.clone
first  et.shift
sets  ll_subsets(set)
sets.concat(sets.collect { |s| [first] + s })
return sets
end

end

# --- calculate the convex hull

module ConvexHull

# after graham & andrew
def self.convex_hull(points)
lop  oints.sort_by { |p| p.x }
left  op.shift
right  op.pop
lower, upper  left], [left]
lower_hull, upper_hull  ], []
det_func  eterminant_function(left, right)
until lop.empty?
p  op.shift
( det_func.call(p) < 0 ? lower : upper ) << p
end
lower << right
until lower.empty?
lower_hull << lower.shift
while (lower_hull.size > ) &&
!convex?(lower_hull.last(3), true)
last  ower_hull.pop
lower_hull.pop
lower_hull << last
end
end
upper << right
until upper.empty?
upper_hull << upper.shift
while (upper_hull.size > ) &&
!convex?(upper_hull.last(3), false)
last  pper_hull.pop
upper_hull.pop
upper_hull << last
end
end
upper_hull.shift
upper_hull.pop
lower_hull + upper_hull.reverse
end

private

def self.determinant_function(p0, p1)
proc { |p| ((p0.x-p1.x)*(p.y-p1.y))-((p.x-p1.x)*(p0.y-p1.y)) }
end

def self.convex?(list_of_three, lower)
p0, p1, p2  ist_of_three
(determinant_function(p0, p2).call(p1) > 0) ^ lower
end

end

# --- calculate a circle out of three given points

module SOLE

def self.circle_of(*three_points)
m  ole_matrix_for_circle(three_points)
solve_sole!(m)
x  [1].last
y  [2].last
r  ath.sqrt(x*x + y*y - m[0].last)
Circle.new(Point.new(x, y), r)
end

private

def self.sole_matrix_for_circle(points)
matrix  ]
c
points.each do |p|
matrix[c]  1]
matrix[c] << -2 * p.x
matrix[c] << -2 * p.y
matrix[c] << -p.x**2 - p.y**2
c +
end
matrix
end

# solve system of linear equations
def self.solve_sole!(matrix)
num_equations  atrix.size
num_variables  atrix.first.size
(num_variables-1).times do |j|
q  atrix[j][j]
if q 0
for i in j+1..num_equations-1 do
if matrix[i][j] !
for k in 0..num_variables-1 do
matrix[j][k] + atrix[i][k]
end
q  atrix[j][j]
break
end
end
end
if q !
for k in 0..num_variables-1
matrix[j][k]  atrix[j][k] / q
end
end
for i in 0..num_equations-1
if i !
q  atrix[i][j]
for k in 0..num_variables-1
matrix[i][k] - atrix[j][k] * q
end
end
end
end
end

end

# ----- visualize

def scale(value)
\$size / 6 + value * \$size / 1.5
end

\$gc.stroke_color(color.to_s) if color
end

def draw_point(center, colorů±se)
\$gc.stroke_color(color.to_s) if color
\$gc.circle(scale(center.x), scale(center.y), scale(center.x), scale(center.y)+2)
end

def draw_line(p0, p1, colorů±se)
\$gc.stroke_color(color.to_s) if color
\$gc.line(scale(p0.x), scale(p0.y), scale(p1.x), scale(p1.y))
end

def draw_hull(points, colorů±se)
first  oints.shift
prev  irst
until points.empty?
p  oints.shift
draw_line(prev, p, color)
prev
end
draw_line(prev, first, color)
end

# ----- if executed directly

if __FILE__ \$0

VISUALIZE  rue

if VISUALIZE
require 'RMagick'
\$size  00
\$gc  agick::Draw.new
\$gc.fill_opacity(0)
\$gc.stroke_width(2)
\$gc.stroke_opacity(1)
end

samples  enerate_samples(ARGV[0].to_i)
circle  ncircle(samples)
puts circle

if VISUALIZE
samples.each { |p| draw_point(p, :black) }