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begin
require "#{File.expand_path(File.dirname(__FILE__))}/../lib/multidim/conjugate_gradient.rb"
rescue LoadError
require "multidim/conjugate_gradient"
end
describe Minimization::FletcherReeves do
before :all do
@n = 3
@limit = 100
@epsilon = 1e-6
@p = Array.new(@n)
@start_point = Array.new(@n)
0.upto(@n - 1) do |i|
@p[i] = rand(@limit)
end
0.upto(@n - 1) do |i|
@start_point[i] = rand(@limit)
end
# fletcher_reeves example 1
puts @p.inspect
f = proc{ |x| (x[0] - @p[0])**2 + (x[1] - @p[1])**2 + (x[2] - @p[2])**2 }
fd = proc{ |x| [ 2 * (x[0] - @p[0]) , 2 * (x[1] - @p[1]) , 2 * (x[2] - @p[2]) ] }
@min1 = Minimization::FletcherReeves.minimize(f, fd, @start_point)
# fletcher_reeves example 2
@k = rand(@limit)
f2 = proc{ |x| ( @p[0]*x[0] + @p[1]*x[1] + @p[2]*x[2] )**2 + @k}
fd2 = proc{ |x|
r0 = ( @p[0]*x[0] + @p[1]*x[1] + @p[2]*x[2] ) * 2 * @p[0]
r1 = ( @p[0]*x[0] + @p[1]*x[1] + @p[2]*x[2] ) * 2 * @p[1]
r2 = ( @p[0]*x[0] + @p[1]*x[1] + @p[2]*x[2] ) * 2 * @p[2]
[r0, r1, r2]
}
@min2 = Minimization::FletcherReeves.minimize(f2, fd2, @start_point)
# fletcher_reeves example 3 : unidimensional
f3 = proc{ |x| ( (x[0] - @p[0])**2 + @k ) }
fd3 = proc{ |x| [ (x[0] - @p[0]) * 2 ] }
starting_point_3 = [rand(@limit)]
@min3 = Minimization::FletcherReeves.minimize(f3, fd3, starting_point_3)
end
it "#x_minimum be close to expected in example 1 :fletcher_reeves" do
0.upto(@n - 1) do |i|
expect(@min1.x_minimum[i]).to be_within(@epsilon).of(@p[i])
end
end
it "#f_minimum be close to expected in example 1 :fletcher_reeves" do
expect(@min1.f_minimum).to be_within(@epsilon).of(0)
end
it "#f_minimum be close to expected in example 2 :fletcher_reeves" do
expect(@min2.f_minimum).to be_within(@epsilon).of(@k)
end
it "#x_minimum be close to expected in example 3 :fletcher_reeves" do
expect(@min3.x_minimum[0]).to be_within(@epsilon).of(@p[0])
end
it "#f_minimum be close to expected in example 3 :fletcher_reeves" do
expect(@min3.f_minimum).to be_within(@epsilon).of(@k)
end
end
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