def __init__(self,
svm_type=TYPE_SVC,
kernel=KERNEL_RBF,
svr_epsilon=0.1,
nu=0.5,
random_seed=None,
validation_size=0.33,
**kwargs):
'''
Create an svm.
Make sure you choose "classifacition" or "regression". Other parameters control
features of the SVM.
also passes keyword args to VectorClassifier
'''
#TODO: Document constructor
#TODO: Add an option to specify SVM parameters directly and disable automatic tuning.
self.svm = None
self.svm_type = svm_type
self.kernel = kernel
self.epsilon = svr_epsilon
self.nu = nu
self.random_seed = random_seed
self.validation_size = validation_size
if svm_type in (TYPE_C_SVC, TYPE_NU_SVC):
VectorClassifier.__init__(self, TYPE_MULTICLASS, **kwargs)
else:
VectorClassifier.__init__(self, TYPE_REGRESSION, **kwargs)
def __init__(self,
training_size=0.67, # The fraction of the data to use for training
validation_size=None, # The fraction of the data to use for training
kernels= [RBF(gamma=2**i) for i in range(-15,4)],
lams = [2.0**i for i in range(-8,9)],
random_seed = None,
**kwargs):
if isinstance(lams,list):
self.lams = lams
else:
self.lams = [lams]
if isinstance(kernels,list):
self.kernels = kernels
else:
self.kernels = [kernels]
self.training_size = training_size
self.validation_size = validation_size
# set durring training
self.mse = None
self.lam = None
self.kernel = None
self.training_info = []
self.rng = random.Random(random_seed)
VectorClassifier.__init__(self,TYPE_REGRESSION,**kwargs)
def __init__(self, order=2, **kwargs):
#FIXME: DOcument this code
'''
This class fits a polynomial to a function of 2 variables.
'''
self.order = order
self.x = None
VectorClassifier.__init__(self, TYPE_REGRESSION, **kwargs)
def __init__(self,order=2,**kwargs):
#FIXME: DOcument this code
'''
This class fits a polynomial to a function of 2 variables.
'''
self.order = order
self.x = None
VectorClassifier.__init__(self,TYPE_REGRESSION,**kwargs)
def __init__(self, svm_type=TYPE_SVC, kernel=KERNEL_RBF, svr_epsilon=0.1, nu = 0.5, random_seed=None, validation_size=0.33,**kwargs):
'''
Create an svm.
Make sure you choose "classifacition" or "regression". Other parameters control
features of the SVM.
also passes keyword args to VectorClassifier
'''
#TODO: Document constructor
#TODO: Add an option to specify SVM parameters directly and disable automatic tuning.
self.svm = None
self.svm_type = svm_type
self.kernel = kernel
self.epsilon=svr_epsilon
self.nu = nu
self.random_seed = random_seed
self.validation_size = validation_size
if svm_type in (TYPE_C_SVC,TYPE_NU_SVC):
VectorClassifier.__init__(self,TYPE_MULTICLASS,**kwargs)
else:
VectorClassifier.__init__(self,TYPE_REGRESSION,**kwargs)
def __init__(self,lam=0.1,**kwargs):
self.lam = lam
VectorClassifier.__init__(self,TYPE_REGRESSION,**kwargs)
