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require 'rubygems'
require 'opentox-ruby'
require 'test/unit'
class Float
def round_to(x)
(self * 10**x).round.to_f / 10**x
end
end
class TransformTest < Test::Unit::TestCase
#def test_mlr
# 2.times {
# n_prop = [ [1,1], [2,2], [3,3] ] # erste WH
# acts = [ 3,2,3 ] # should yield a constant y=2.8
# sims = [ 4,2,4 ] # move constant closer to 3.0
# q_prop = [0.5,0.5] # extrapolation
# params={:n_prop => n_prop, :q_prop => q_prop, :sims => sims, :acts => acts}
#
# prediction = OpenTox::Algorithm::Neighbors.mlr(params)
# assert_in_delta prediction, 2.8, 1.0E-10 # small deviations, don't know why
#
# q_prop = [1.5,1.5] # interpolation
# prediction = OpenTox::Algorithm::Neighbors.mlr(params)
# assert_in_delta prediction, 2.8, 1.0E-10 # small deviations, don't know why
# }
#end
def test_pca
d = GSL::Matrix.alloc([1.0, -5, 1.1, 2.0, -5, 1.9, 3.0, -5, 3.3], 3, 3) # 2nd col is const -5, gets removed
rd = GSL::Matrix.alloc([1.0, 1.1, 1.9, 2.0, 3.1, 3.2], 3, 2)
td = GSL::Matrix.alloc([-1.4142135623731, -0.14142135623731, 1.5556349186104],3,1)
ev = GSL::Matrix.alloc([0.707106781186548, 0.707106781186548], 2, 1)
# Lossy
2.times do # repeat to ensure idempotency
pca = OpenTox::Transform::PCA.new(d, 0.05)
assert_equal pca.data_matrix, d
assert_equal pca.data_transformed_matrix, td
assert_equal pca.transform(d), td
assert_equal pca.eigenvector_matrix, ev
assert_equal pca.restore, rd
end
rd = GSL::Matrix.alloc([1.0, 1.1, 2.0, 1.9, 3.0, 3.3], 3, 2) # 2nd col of d is const -5, gets removed on rd
td = GSL::Matrix.alloc([-1.4142135623731, -7.84962372879505e-17, -0.14142135623731, -0.14142135623731, 1.5556349186104, 0.141421356237309],3,2)
ev = GSL::Matrix.alloc([0.707106781186548, -0.707106781186548, 0.707106781186548, 0.707106781186548], 2, 2)
# Lossless
2.times do
pca = OpenTox::Transform::PCA.new(d, 0.0)
assert_equal pca.data_matrix, d
assert_equal pca.data_transformed_matrix, td
assert_equal pca.transform(d), td
assert_equal pca.eigenvector_matrix, ev
assert_equal pca.restore, rd
end
rd = GSL::Matrix.alloc([1.0, 1.1, 1.9, 2.0, 3.1, 3.2], 3, 2)
td = GSL::Matrix.alloc([-1.4142135623731, -0.14142135623731, 1.5556349186104],3,1)
ev = GSL::Matrix.alloc([0.707106781186548, 0.707106781186548], 2, 1)
# Lossy, but using maxcols constraint
2.times do
pca = OpenTox::Transform::PCA.new(d, 0.0, 1) # 1 column
assert_equal pca.data_matrix, d
assert_equal pca.data_transformed_matrix, td
assert_equal pca.transform(d), td
assert_equal pca.eigenvector_matrix, ev
assert_equal pca.restore, rd
end
end
def test_svd
m = GSL::Matrix[
[5,5,0,5],
[5,0,3,4],
[3,4,0,3],
[0,0,5,3],
[5,4,4,5],
[5,4,5,5]
]
foo = GSL::Matrix[[5,5,3,0,5,5]]
bar = GSL::Matrix[[5,4,5,5]]
# AutoScale (mean and center) to improve on representation
nr_cases, nr_features = m.size1, m.size2
(0..nr_features-1).each { |i|
autoscaler = OpenTox::Transform::AutoScale.new(m.col(i))
m.col(i)[0..nr_cases-1] = autoscaler.vs
bar.col(i)[0..0] = autoscaler.transform bar.col(i)
}
autoscaler = OpenTox::Transform::AutoScale.new(foo.transpose.col(0))
foo = GSL::Matrix[autoscaler.vs]
#puts
#puts m.to_a.collect { |r| r.collect{ |v| sprintf("%.2f", v) }.join(", ") }.join("\n")
#puts
#puts foo.to_a.collect { |r| r.collect{ |v| sprintf("%.2f", v) }.join(", ") }.join("\n")
#puts
#puts bar.to_a.collect { |r| r.collect{ |v| sprintf("%.2f", v) }.join(", ") }.join("\n")
# run SVD
svd = OpenTox::Algorithm::Transform::SVD.new m, 0.2
#puts
#puts svd.restore.to_a.collect { |r| r.collect{ |v| sprintf("%.2f", v) }.join(", ") }.join("\n")
#puts
#puts svd.data_transformed_matrix.to_a.collect { |r| r.collect{ |v| sprintf("%.2f", v) }.join(", ") }.join("\n")
# instance transform
bar = svd.transform bar # alias for svd.transform_instance bar
sim = []
svd.uk.each_row { |x|
sim << OpenTox::Algorithm::Similarity.cosine_num(x,bar.row(0))
}
# # # NO AUTOSCALE
#assert_equal sim[0].round_to(3), 0.346
#assert_equal sim[1].round_to(3), 0.966
#assert_equal sim[2].round_to(3), 0.282
#assert_equal sim[3].round_to(3), 0.599
#assert_equal sim[4].round_to(3), 0.975
#assert_equal sim[5].round_to(3), 1.000
# # # AUTOSCALE
assert_equal sim[0].round_to(3), -0.115
assert_equal sim[1].round_to(3), 0.425
assert_equal sim[2].round_to(3), -0.931
assert_equal sim[3].round_to(3), -0.352
assert_equal sim[4].round_to(3), 0.972
assert_equal sim[5].round_to(3), 1.000
# feature transform, only for demonstration of concept
foo = svd.transform_feature foo
sim = []
svd.vk.each_row { |x|
sim << OpenTox::Algorithm::Similarity.cosine_num(x,foo.row(0))
}
# # # NO AUTOSCALE
#assert_equal sim[0].round_to(3), 1.000
#assert_equal sim[1].round_to(3), 0.874
#assert_equal sim[2].round_to(3), 0.064
#assert_equal sim[3].round_to(3), 0.895
# # # AUTOSCALE
assert_equal sim[0].round_to(3), 1.000
assert_equal sim[1].round_to(3), 0.705
assert_equal sim[2].round_to(3), 0.023
assert_equal sim[3].round_to(3), 0.934
end
def test_logas
d1 = [ 1,2,3 ].to_gv
d2 = [ -1,0,1 ].to_gv
d3 = [ -2,3,8 ].to_gv
d4 = [ -20,30,80 ].to_gv
d5 = [ 0.707, 0.7071].to_gv
d1la = [ -1.31668596949013, 0.211405021140643, 1.10528094834949 ].to_gv
d2la = d1la
d3la = [ -1.37180016053906, 0.388203523926062, 0.983596636612997 ].to_gv
d4la = [ -1.40084731572532, 0.532435269814955, 0.868412045910369 ].to_gv
d5la = [ -1.0, 1.0 ].to_gv
2.times {
logas = OpenTox::Transform::LogAutoScale.new(d1)
assert_equal logas.vs, d1la
assert_equal logas.restore(logas.vs), d1
logas = OpenTox::Transform::LogAutoScale.new(d2)
assert_equal logas.vs, d2la
assert_equal logas.restore(logas.vs), d2
logas = OpenTox::Transform::LogAutoScale.new(d3)
assert_equal logas.vs, d3la
assert_equal logas.restore(logas.vs), d3
logas = OpenTox::Transform::LogAutoScale.new(d4)
assert_equal logas.vs, d4la
assert_equal logas.restore(logas.vs), d4
logas = OpenTox::Transform::LogAutoScale.new(d5)
assert_equal logas.vs, d5la
assert_equal logas.restore(logas.vs), d5
}
end
end
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