diff --git a/bob/pad/base/algorithm/PadLDA.py b/bob/pad/base/algorithm/PadLDA.py index 91dc05d01f64d978e586804772cc030c704bc04b..d84efc455c82f951083d3dcbc38da0404cb7c088 100644 --- a/bob/pad/base/algorithm/PadLDA.py +++ b/bob/pad/base/algorithm/PadLDA.py @@ -8,8 +8,8 @@ class PadLDA(LDA): """Wrapper for bob.bio.base.algorithm.LDA, Here, LDA is used in a PAD context. This means that the feature - will be projected on a two-dimensional subspace, where the two - dimensions represents the real and attack classes. + will be projected on a single dimension subspace, which acts as a score + For more details, you may want to have a look at `bob.learn.linear Documentation`_ diff --git a/bob/pad/base/test/test_algorithms.py b/bob/pad/base/test/test_algorithms.py index f73b0f06789b94205070e7bce7fda49993bd09ce..1e4d9106aa67f6f99e0734cc3dd0d97d80f987a8 100644 --- a/bob/pad/base/test/test_algorithms.py +++ b/bob/pad/base/test/test_algorithms.py @@ -13,6 +13,7 @@ import bob.pad.base from bob.pad.base.algorithm import SVM from bob.pad.base.algorithm import OneClassGMM from bob.pad.base.algorithm import MLP +from bob.pad.base.algorithm import PadLDA import random @@ -231,8 +232,4 @@ def test_LDA(): lda = PadLDA() lda.train_projector(training_features, '/tmp/lda.hdf5') - - real_sample = real_array[0] - prob = lda.project(real_sample) - assert prob[0] > prob[1] - + assert lda.machine.shape == (2, 1)