def __init__(self, arg = None, **args) :
Classifier.__init__(self)
self.trainingDirectory = None
self.testingDirectory = None
self.maxSize = 2e6
self.numTrees = 200
self.numFeatures = 0
if arg.__class__ == self.__class__ :
other = arg
self.numTrees = other.numTrees
self.numFeatures = other.numFeatures
if 'numTrees' in args :
self.numTrees = args['numTrees']
if 'numFeatures' in args :
self.numFeatures = args['numFeatures']
def __init__(self, arg=None, **args):
Classifier.__init__(self)
self.trainingDirectory = None
self.testingDirectory = None
self.maxSize = 2e6
self.numTrees = 200
self.numFeatures = 0
if arg.__class__ == self.__class__:
other = arg
self.numTrees = other.numTrees
self.numFeatures = other.numFeatures
if 'numTrees' in args:
self.numTrees = args['numTrees']
if 'numFeatures' in args:
self.numFeatures = args['numFeatures']
def __init__(self, arg = None, **args):
"""
:Parameters:
- `arg` - another SVM object or a kernel object; if no argument is given
the kernel function of the training dataset is used
:Keywords:
- `C` - the svm C parameter
- `Cmode` - the way the C parameter is used; values: 'equal', 'classProb',
'fromData'.
In 'equal' mode C is set to be the same for both classes
In 'classProb' mode each class is assigned a C value that is
proportional to the size of the other class. This results in
margin error costs being proportional to the ratio of the
sizes of the two classes.
This is useful for datasets with an unbalanced class distribution.
In 'fromData' the value of C for each pattern is taken from the
'C' attribute of the training data.
- `optimizer` - which optimizer to use. the options are:
'libsvm' -- run libsvm
'liblinear' -- use liblinear (linear svm only)
in this case you have the option to set the loss function - either 'l1' or 'l2'
'mysmo' - use the PyML native optmizer (based on libsvm)
'gist' - use a gist-like optimizer.
- `loss` - when using liblinear set this to 'l1' or 'l2' (default: 'l1')
- `cacheSize` - size of the kernel cache (in MB).
"""
Classifier.__init__(self, arg, **args)
self.kernel = None
if arg.__class__ == self.__class__ :
if arg.kernel is not None :
self.kernel = arg.kernel.__class__(arg.kernel)
elif hasattr(arg, 'type') and arg.type == 'kernel' :
self.kernel = arg.__class__(arg)
elif arg is not None :
raise ValueError, 'unknown type of argument'
def __init__(self, arg = None, **args):
"""
:Parameters:
- `arg` - another SVM object or a kernel object; if no argument is given
the kernel function of the training dataset is used
:Keywords:
- `C` - the svm C parameter
- `Cmode` - the way the C parameter is used; values: 'equal', 'classProb',
'fromData'.
In 'equal' mode C is set to be the same for both classes
In 'classProb' mode each class is assigned a C value that is
proportional to the size of the other class. This results in
margin error costs being proportional to the ratio of the
sizes of the two classes.
This is useful for datasets with an unbalanced class distribution.
In 'fromData' the value of C for each pattern is taken from the
'C' attribute of the training data.
- `optimizer` - which optimizer to use. the options are:
'libsvm' -- run libsvm
'liblinear' -- use liblinear (linear svm only)
in this case you have the option to set the loss function - either 'l1' or 'l2'
'mysmo' - use the PyML native optmizer (based on libsvm)
'gist' - use a gist-like optimizer.
- `loss` - when using liblinear set this to 'l1' or 'l2' (default: 'l1')
- `cacheSize` - size of the kernel cache (in MB).
"""
Classifier.__init__(self, arg, **args)
self.kernel = None
if arg.__class__ == self.__class__ :
if arg.kernel is not None :
self.kernel = arg.kernel.__class__(arg.kernel)
elif hasattr(arg, 'type') and arg.type == 'kernel' :
self.kernel = arg.__class__(arg)
elif arg is not None :
raise ValueError, 'unknown type of argument'
def __init__(self, arg=None, **args):
Classifier.__init__(self, arg, **args)
if self.regression:
self.resultsObject = resultsObjects.RegressionResults
self.classify = self.decisionFunc
def __init__(self, arg = None, **args) :
Classifier.__init__(self, arg, **args)
def __init__(self, arg = None, **args) :
Classifier.__init__(self, arg, **args)
if self.regression :
self.resultsObject = resultsObjects.RegressionResults
self.classify = self.decisionFunc
