Example #1
0
def test_pybrain_params():
    check_params(PyBrainClassifier,
                 layers=[1, 2],
                 epochs=5,
                 use_rprop=True,
                 hiddenclass=['LinearLayer'])
    check_params(PyBrainRegressor,
                 layers=[1, 2],
                 epochs=5,
                 etaplus=1.3,
                 hiddenclass=['LinearLayer'],
                 learningrate=0.1)
Example #2
0
def test_neurolab_params():
    check_params(NeurolabClassifier,
                 layers=[1, 2],
                 epochs=5,
                 trainf='blah',
                 cn=2,
                 omnomnom=4)
    check_params(NeurolabRegressor,
                 layers=[1, 2],
                 epochs=5,
                 trainf='blah',
                 cn=2,
                 omnomnom=4)
Example #3
0
def test_theanets_params():
    check_params(TheanetsClassifier,
                 layers=[1, 2],
                 scaler=False,
                 trainers=[{}, {
                     'optimize': 'nag'
                 }])
    check_params(TheanetsRegressor,
                 layers=[1, 2],
                 scaler=False,
                 trainers=[{}, {
                     'optimize': 'nag'
                 }])
Example #4
0
def test_theanets_params():
    check_params(TheanetsClassifier,
                 layers=[1, 2],
                 scaler=False,
                 trainers=[{}, {
                     'algo': 'nag',
                     'learning_rate': 0.1
                 }])
    check_params(TheanetsRegressor,
                 layers=[1, 2],
                 scaler=False,
                 trainers=[{}, {
                     'algo': 'nag',
                     'learning_rate': 0.1
                 }])
Example #5
0
def test_pybrain_params():
    check_params(PyBrainClassifier, layers=[1, 2], epochs=5, use_rprop=True, hiddenclass=['LinearLayer'])
    check_params(PyBrainRegressor, layers=[1, 2], epochs=5, etaplus=1.3, hiddenclass=['LinearLayer'], learningrate=0.1)
Example #6
0
def test_neurolab_params():
    check_params(NeurolabClassifier, layers=[1, 2], epochs=5, trainf='blah', cn=2, omnomnom=4)
    check_params(NeurolabRegressor, layers=[1, 2], epochs=5, trainf='blah', cn=2, omnomnom=4)
Example #7
0
def test_theanets_params():
    check_params(TheanetsClassifier, layers=[1, 2], scaler=False, trainers=[{}, {'optimize': 'nag'}])
    check_params(TheanetsRegressor, layers=[1, 2], scaler=False, trainers=[{}, {'optimize': 'nag'}])
Example #8
0
def test_theanets_params():
    check_params(TheanetsClassifier, layers=[1, 2], scaler=False, trainers=[{}, {'algo': 'nag', 'learning_rate': 0.1}])
    check_params(TheanetsRegressor, layers=[1, 2], scaler=False, trainers=[{}, {'algo': 'nag', 'learning_rate': 0.1}])