Teetool Probabilistic modelling of trajectories

Functions
test_gp Namespace Reference

## 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 ()

## Function Documentation

## § produce_cluster_data()

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)

## § test_cluster_data()

def test_gp.test_cluster_data | ( |  | ) |

    check if cluster_data is as expected

## § test_expectation_maximization()

def test_gp.test_expectation_maximization | ( |  | ) |

## § test_maximum_likelihood()

def test_gp.test_maximum_likelihood | ( |  | ) |

    check if maximum likelihood behaves as expected

## § test_mix_bern()

def test_gp.test_mix_bern | ( |  | ) |

    compare different methods

## § test_mix_rbf()

def test_gp.test_mix_rbf | ( |  | ) |

    compare different methods

## § test_resampling()

def test_gp.test_resampling | ( |  | ) |

    check if resampling behaves as expected

## § test_subfunctions()

def test_gp.test_subfunctions | ( |  | ) |

    various

## § test_subfunctions_EM()

def test_gp.test_subfunctions_EM | ( |  | ) |

    test subfunctions that contribute to the EM algorithm