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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. I'm keeping this around for now so that we can
compare results. |
source code
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_unsparsify_weights(self,
samples,
weights)
Unsparsify weights via least squares regression. |
source code
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Inherited from base.Classifier :
__repr__ ,
__str__ ,
clone ,
isTrained ,
predict ,
repredict ,
retrain ,
summary ,
train ,
trained ,
untrain
Inherited from misc.state.ClassWithCollections :
__getattribute__ ,
__new__ ,
__setattr__ ,
reset
Inherited from object :
__delattr__ ,
__format__ ,
__hash__ ,
__reduce__ ,
__reduce_ex__ ,
__sizeof__ ,
__subclasshook__
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_clf_internals = ['smlr', 'linear', 'has_sensitivity', 'binary...
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(True, allowedtype= 'bool', doc= ""...
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implementation = Parameter(_DEFAULT_IMPLEMENTATION, allowedtyp...
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seed = Parameter(None, allowedtype= 'None or int', doc= """See...
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unsparsify = Parameter(False, allowedtype= 'bool', doc= """***...
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std_to_keep = Parameter(2.0, allowedtype= 'float', doc= """Sta...
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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 :
_DEV__doc__ ,
feature_ids ,
predicting_time ,
predictions ,
regression ,
retrainable ,
trained_dataset ,
trained_labels ,
trained_nsamples ,
training_confusion ,
training_time ,
values
Inherited from misc.state.ClassWithCollections :
descr
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