## regrswh += ENET(descr="ENET()")
# glmnet from R via RPy
if externals.exists('glmnet'):
from mvpa.clfs.glmnet import GLMNET_C, GLMNET_R
clfswh += GLMNET_C(descr="GLMNET_C()")
regrswh += GLMNET_R(descr="GLMNET_R()")
# LDA/QDA
clfswh += LDA(descr='LDA()')
clfswh += QDA(descr='QDA()')
if externals.exists('skl'):
from scikits.learn.lda import LDA as sklLDA
from mvpa.clfs.skl.base import SKLLearnerAdapter
clfswh += SKLLearnerAdapter(sklLDA(), tags=['lda', 'linear', 'multiclass', 'binary'],
descr='scikits.learn.LDA()_adapter')
# kNN
clfswh += kNN(k=5, descr="kNN(k=5)")
clfswh += kNN(k=5, voting='majority', descr="kNN(k=5, voting='majority')")
clfswh += \
FeatureSelectionClassifier(
kNN(),
SensitivityBasedFeatureSelection(
SMLRWeights(SMLR(lm=1.0, implementation="C"),
postproc=maxofabs_sample()),
RangeElementSelector(mode='select')),
descr="kNN on SMLR(lm=1) non-0")
## regrswh += ENET(descr="ENET()")
# glmnet from R via RPy
if externals.exists('glmnet'):
from mvpa.clfs.glmnet import GLMNET_C, GLMNET_R
clfswh += GLMNET_C(descr="GLMNET_C()")
regrswh += GLMNET_R(descr="GLMNET_R()")
# LDA/QDA
clfswh += LDA(descr='LDA()')
clfswh += QDA(descr='QDA()')
if externals.exists('skl'):
from scikits.learn.lda import LDA as sklLDA
from mvpa.clfs.skl.base import SKLLearnerAdapter
clfswh += SKLLearnerAdapter(sklLDA(),
tags=['lda', 'linear', 'multiclass', 'binary'],
descr='skl.LDA()')
if externals.versions['skl'] >= '0.8':
from scikits.learn.pls import PLSRegression as sklPLSRegression
# somewhat silly use of PLS, but oh well
regrswh += SKLLearnerAdapter(sklPLSRegression(n_components=1),
tags=['linear', 'regression'],
enforce_dim=1,
descr='skl.PLSRegression_1d()')
if externals.versions['skl'] >= '0.6.0':
from scikits.learn.linear_model import \
LARS as sklLARS, LassoLARS as sklLassoLARS
_lars_tags = ['lars', 'linear', 'regression', 'does_feature_selection']
# glmnet from R via RPy
if externals.exists("glmnet"):
from mvpa2.clfs.glmnet import GLMNET_C, GLMNET_R
clfswh += GLMNET_C(descr="GLMNET_C()")
regrswh += GLMNET_R(descr="GLMNET_R()")
# LDA/QDA
clfswh += LDA(descr="LDA()")
clfswh += QDA(descr="QDA()")
if externals.exists("skl"):
from scikits.learn.lda import LDA as sklLDA
from mvpa2.clfs.skl.base import SKLLearnerAdapter
clfswh += SKLLearnerAdapter(sklLDA(), tags=["lda", "linear", "multiclass", "binary"], descr="skl.LDA()")
if externals.versions["skl"] >= "0.8":
from scikits.learn.pls import PLSRegression as sklPLSRegression
# somewhat silly use of PLS, but oh well
regrswh += SKLLearnerAdapter(
sklPLSRegression(n_components=1),
tags=["linear", "regression"],
enforce_dim=1,
descr="skl.PLSRegression_1d()",
)
if externals.versions["skl"] >= "0.6.0":
from scikits.learn.linear_model import LARS as sklLARS, LassoLARS as sklLassoLARS