def __init__(self,
n_columns=10,
n_rows=10,
initialcodebook=None,
kerneltype=0,
maptype="planar",
gridtype="rectangular",
compactsupport=False,
neighborhood="gaussian",
epochs=10,
radius0=0,
radiusN=1,
radiuscooling="linear",
scale0=0.1,
scaleN=0.01,
scalecooling="linear"):
self.som = Somoclu(n_columns, n_rows, initialcodebook, kerneltype,
maptype, gridtype, compactsupport, neighborhood,
epochs, radius0, radiusN, radiuscooling, scale0,
scaleN, scalecooling)
class SOM(BaseEstimator):
_estimator_type = "classifier"
def __init__(self, n_columns=10, n_rows=10, initialcodebook=None, kerneltype=0, maptype="planar",
gridtype="rectangular", compactsupport=False, neighborhood="gaussian", epochs=10,
radius0=0, radiusN=1, radiuscooling="linear", scale0=0.1, scaleN=0.01, scalecooling="linear"):
self.som = Somoclu(n_columns, n_rows, initialcodebook, kerneltype, maptype, gridtype, compactsupport,
neighborhood, epochs, radius0, radiusN, radiuscooling, scale0, scaleN, scalecooling)
def fit(self, X, y=None):
self.som.train(X, y)
return self
def predict(self, X):
# print([self.classes_[self.km.predict(x)] for x in X])
return np.array([self.som.predict(x) for x in X])
class SOM(BaseEstimator):
_estimator_type = "classifier"
def __init__(self,
n_columns=10,
n_rows=10,
initialcodebook=None,
kerneltype=0,
maptype="planar",
gridtype="rectangular",
compactsupport=False,
neighborhood="gaussian",
epochs=10,
radius0=0,
radiusN=1,
radiuscooling="linear",
scale0=0.1,
scaleN=0.01,
scalecooling="linear"):
self.som = Somoclu(n_columns, n_rows, initialcodebook, kerneltype,
maptype, gridtype, compactsupport, neighborhood,
epochs, radius0, radiusN, radiuscooling, scale0,
scaleN, scalecooling)
def fit(self, X, y=None):
self.som.train(X, y)
return self
def predict(self, X):
# print([self.classes_[self.km.predict(x)] for x in X])
return np.array([self.som.predict(x) for x in X])
def __init__(self, n_columns=10, n_rows=10, initialcodebook=None, kerneltype=0, maptype="planar",
gridtype="rectangular", compactsupport=False, neighborhood="gaussian", epochs=10,
radius0=0, radiusN=1, radiuscooling="linear", scale0=0.1, scaleN=0.01, scalecooling="linear"):
self.som = Somoclu(n_columns, n_rows, initialcodebook, kerneltype, maptype, gridtype, compactsupport,
neighborhood, epochs, radius0, radiusN, radiuscooling, scale0, scaleN, scalecooling)
