def fit(self, chips):
"""Train a support vector machine model.
Parameters
----------
chips: list
Contains a stored array of Chip objects
"""
X = [chip.LCT.values() for chip in chips]
gnd = [chip.gnd for chip in chips]
if grid_search:
grid = { 'C': [1, 5, 10, 50, 100], \
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1] }
print 'SVM: Grid search using parameter grid: ', grid
self.model = GridSearchCV(SVC(kernel='rbf'), grid, n_jobs=4, \
fit_params={'class_weight': {1 : 1, -1 : 1}})
else:
self.model = SVC()
self.scale_factors, Xstd = standardize(X)
self.model.fit(Xstd, gnd)
def fit(self, chips):
"""Train a support vector machine model.
Parameters
----------
chips: list
Contains a stored array of Chip objects
"""
X = [chip.LCT.values() for chip in chips]
gnd = [chip.gnd for chip in chips]
if grid_search:
grid = { 'C': [1, 5, 10, 50, 100], \
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1] }
print 'SVM: Grid search using parameter grid: ', grid
self.model = GridSearchCV(SVC(kernel='rbf'), grid, n_jobs=4, \
fit_params={'class_weight': {1 : 1, -1 : 1}})
else:
self.model = SVC()
self.scale_factors, Xstd = standardize(X)
self.model.fit(Xstd, gnd)
def predict(self, chip):
"""Use the trained SVM model to predict.
Parameters:
----------
chip: chip model object
Contains a chip's test data
"""
X = standardize(chip.LCT.values(), self.scale_factors)
return self.model.predict(X)
def predict(self, chip):
"""Use the trained SVM model to predict.
Parameters:
----------
chip: chip model object
Contains a chip's test data
"""
X = standardize(chip.LCT.values(), self.scale_factors)
return self.model.predict(X)
