This is an implementation of the SMLR algorithm published in
Krishnapuram et al. (2005, IEEE Transactions on Pattern Analysis
and Machine Intelligence). Be sure to cite that article if you
use this for your work.
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_pythonStepwiseRegression(self,
w,
X,
XY,
Xw,
E,
auto_corr,
lambda_over_2_auto_corr,
S,
M,
maxiter,
convergence_tol,
resamp_decay,
min_resamp,
verbose,
seed=None)
The (much slower) python version of the stepwise
regression. |
source code
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Inherited from base.Classifier :
__str__ ,
isTrained ,
predict ,
regression ,
train ,
train2predict ,
trained ,
untrain
Inherited from misc.state.Parametrized :
__repr__
Inherited from misc.state.Stateful :
__getattribute__ ,
__setattr__ ,
reset
Inherited from object :
__delattr__ ,
__hash__ ,
__new__ ,
__reduce__ ,
__reduce_ex__
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_clf_internals = ['smlr', 'linear', 'has_sensitivity', 'multic...
Describes some specifics about the classifier -- is that it is
doing regression for instance....
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lm = Parameter(.1, min= 1e-10, allowedtype= 'float', doc= """T...
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convergence_tol = Parameter(1e-3, min= 1e-10, max= 1.0, allowe...
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resamp_decay = Parameter(0.5, allowedtype= 'float', min= 0.0, ...
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min_resamp = Parameter(0.001, allowedtype= 'float', min= 1e-10...
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maxiter = Parameter(10000, allowedtype= 'int', min= 1, doc= ""...
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has_bias = Parameter(True, allowedtype= 'bool', doc= """Whethe...
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fit_all_weights = Parameter(False, allowedtype= 'bool', doc= "...
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implementation = Parameter("C", allowedtype= 'basestring', cho...
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seed = Parameter(None, allowedtype= 'None or int', doc= """See...
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biases = property(lambda self: self.__biases)
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weights = property(lambda self: self.__weights)
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Inherited from base.Classifier :
feature_ids ,
predicting_time ,
predictions ,
retrainable ,
trained_dataset ,
trained_labels ,
training_confusion ,
training_time ,
values
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