|
Teetool
Probabilistic modelling of trajectories
|
Functions | |
| def | produce_cluster_data () |
| def | test_cluster_data () |
| def | test_mix_rbf () |
| def | test_mix_bern () |
| def | test_resampling () |
| def | test_maximum_likelihood () |
| def | test_expectation_maximization () |
| def | test_subfunctions_EM () |
| def | test_subfunctions () |
| def test_gp.produce_cluster_data | ( | ) |
returns some sample cluster data with known properties
output:
cluster_data - 5 trajectories, constant y (2nd dim), moving from left to right (1st dim)
| def test_gp.test_cluster_data | ( | ) |
check if cluster_data is as expected
| def test_gp.test_expectation_maximization | ( | ) |
| def test_gp.test_maximum_likelihood | ( | ) |
check if maximum likelihood behaves as expected
| def test_gp.test_mix_bern | ( | ) |
compare different methods
| def test_gp.test_mix_rbf | ( | ) |
compare different methods
| def test_gp.test_resampling | ( | ) |
check if resampling behaves as expected
| def test_gp.test_subfunctions | ( | ) |
various
| def test_gp.test_subfunctions_EM | ( | ) |
test subfunctions that contribute to the EM algorithm
1.8.12