def __init__(self,
nu=0.5,
C=1.0,
kernel='rbf',
degree=3,
gamma='auto_deprecated',
coef0=0.0,
shrinking=True,
tol=0.001,
cache_size=200,
verbose=False,
max_iter=(-1)):
self._hyperparams = {
'nu': nu,
'C': C,
'kernel': kernel,
'degree': degree,
'gamma': gamma,
'coef0': coef0,
'shrinking': shrinking,
'tol': tol,
'cache_size': cache_size,
'verbose': verbose,
'max_iter': max_iter
}
self._wrapped_model = SKLModel(**self._hyperparams)
def fit(self, X, y=None):
self._sklearn_model = SKLModel(**self._hyperparams)
if (y is not None):
self._sklearn_model.fit(X, y)
else:
self._sklearn_model.fit(X)
return self
def __init__(self,
C=1.0,
kernel='rbf',
degree=3,
gamma='auto_deprecated',
coef0=0.0,
shrinking=True,
probability=False,
tol=0.001,
cache_size=200,
class_weight='balanced',
verbose=False,
max_iter=(-1),
decision_function_shape='ovr',
random_state=None):
self._hyperparams = {
'C': C,
'kernel': kernel,
'degree': degree,
'gamma': gamma,
'coef0': coef0,
'shrinking': shrinking,
'probability': probability,
'tol': tol,
'cache_size': cache_size,
'class_weight': class_weight,
'verbose': verbose,
'max_iter': max_iter,
'decision_function_shape': decision_function_shape,
'random_state': random_state
}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self, penalty='l2', loss='squared_hinge', dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight='balanced', verbose=0, random_state=None, max_iter=1000):
self._hyperparams = {
'penalty': penalty,
'loss': loss,
'dual': dual,
'tol': tol,
'C': C,
'multi_class': multi_class,
'fit_intercept': fit_intercept,
'intercept_scaling': intercept_scaling,
'class_weight': class_weight,
'verbose': verbose,
'random_state': random_state,
'max_iter': max_iter}
self._wrapped_model = SKLModel(**self._hyperparams)
def __init__(self,
epsilon=0.0,
tol=0.0001,
C=1.0,
loss='epsilon_insensitive',
fit_intercept=True,
intercept_scaling=1.0,
dual=True,
verbose=0,
random_state=None,
max_iter=1000):
self._hyperparams = {
'epsilon': epsilon,
'tol': tol,
'C': C,
'loss': loss,
'fit_intercept': fit_intercept,
'intercept_scaling': intercept_scaling,
'dual': dual,
'verbose': verbose,
'random_state': random_state,
'max_iter': max_iter
}
self._wrapped_model = SKLModel(**self._hyperparams)