def __init__(self,
n_neighbors=5,
weights='uniform',
metric="dtw",
metric_params=None):
KNeighborsClassifier.__init__(self,
n_neighbors=n_neighbors,
weights=weights,
algorithm='brute')
self.metric = metric
self.metric_params = metric_params
def __init__(self):
KNeighborsClassifier.__init__(
self,
n_neighbors=5,
weights='distance',
algorithm='brute',
leaf_size='40',
p=2,
metric='minkowski',
metric_params=None,
n_jobs=1,
)
self.__name = 'k-NN'
def __init__(self,
n_neighbors=5,
weights='uniform',
metric='dtw',
metric_params=None,
n_jobs=None,
verbose=0):
KNeighborsClassifier.__init__(self,
n_neighbors=n_neighbors,
weights=weights,
algorithm='brute')
self.metric = metric
self.metric_params = metric_params
self.n_jobs = n_jobs
self.verbose = verbose
def __init__(self,
n_neighbors=5,
weights='uniform',
algorithm='auto',
leaf_size=30,
p=2,
metric='minkowski',
metric_params=None,
n_jobs=1,
**kwargs):
leaf_size = int(leaf_size)
n_neighbors = int(n_neighbors)
_KNeighborsClassifier.__init__(self, n_neighbors, weights, algorithm,
leaf_size, p, metric, metric_params,
n_jobs, **kwargs)
BaseWrapperClf.__init__(self)
def __init__(self,
imbalance_upsampling=None,
class_weight=None,
random_state=1,
n_neighbors=5,
method=None,
log=None):
"""
:param imbalance_upsampling: Use upsampling to compensate imbalance
:param class_weight: Use class_weight to compensate imbalance
:param random_state: Random state
:param n_neighbors: Number of neighbor samples to use
:param method: Ensemble method
:param log: Log
"""
MlModelCommon.__init__(self,
imbalance_upsampling=imbalance_upsampling,
class_weight=class_weight,
method=method,
log=log)
#
# class_weight is not supported for KNN.
#
if method == "Bagging":
model = KNeighborsClassifier(n_neighbors=n_neighbors,
metric="minkowski")
self.ensemble_method = BaggingClassifier(base_estimator=model,
n_estimators=10,
random_state=random_state)
elif method == "Adaptive Boosting":
model = KNeighborsClassifier(n_neighbors=n_neighbors,
metric="minkowski")
self.ensemble_method = AdaBoostClassifier(
base_estimator=model,
n_estimators=10,
random_state=random_state)
else:
self.ensemble_method = None
KNeighborsClassifier.__init__(self,
n_neighbors=n_neighbors,
metric="minkowski")
def __init__(self, k = 'all'):
if k != None:
MutableKNeighborsClassifier.K_features = k
KNeighborsClassifier.__init__(self,n_neighbors=7, metric='cosine',algorithm='brute', weights = 'distance')
def __init__(self, data=None, nb_class=2, labels=None, k_neighbors=8):
KNeighborsClassifier.__init__(self, n_neighbors=k_neighbors)
self.data = data
self.labels = labels
self.nb_class = nb_class
self.k_neighbors = k_neighbors
def __init__(self, pmml, n_jobs=None):
PMMLBaseClassifier.__init__(self, pmml)
KNeighborsClassifier.__init__(self, n_jobs=n_jobs)
PMMLBaseKNN.__init__(self)
KNeighborsClassifier.fit(self, self._X, self._y)
def __init__(self, n_neighbors=1, weights='uniform', metric="dtw", metric_params=None, variables_size=1, multivariate_output=None):
KNeighborsClassifier.__init__(self, n_neighbors=n_neighbors, weights=weights, algorithm='brute')
self.metric = metric
self.metric_params = metric_params
self.variables_size = variables_size
self.multivariate_output = multivariate_output
def __init__(self,k=5):
self.k=k
KNeighborsClassifier.__init__(self,n_neighbors=k)
def __init__(self,threshold=1,ll_ranking=False,**kwargs):
kN.__init__(self,**kwargs)
BaseClassifier.__init__(self,threshold=threshold,ll_ranking=ll_ranking)
