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require 'rubygems'
require 'opentox-ruby'
require 'test/unit'
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_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.transform(d1), logas.vs
assert_equal logas.restore(logas.vs), d1
logas = OpenTox::Transform::LogAutoScale.new(d2)
assert_equal logas.vs, d2la
assert_equal logas.transform(d2), d2la
assert_equal logas.restore(logas.vs), d2
logas = OpenTox::Transform::LogAutoScale.new(d3)
assert_equal logas.vs, d3la
assert_equal logas.transform(d3), logas.vs
assert_equal logas.restore(logas.vs), d3
logas = OpenTox::Transform::LogAutoScale.new(d4)
assert_equal logas.vs, d4la
assert_equal logas.transform(d4), logas.vs
assert_equal logas.restore(logas.vs), d4
logas = OpenTox::Transform::LogAutoScale.new(d5)
assert_equal logas.vs, d5la
assert_equal logas.transform(d5), logas.vs
assert_equal logas.restore(logas.vs), d5
}
end
end
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