def _fit(self, image, dot, tags, boxConstraints = []):
img = self.normalize(image)
if type(boxConstraints) is dict:
boxConstraints["boxFeatures"] = self.normalize(boxConstraints["boxFeatures"])
numFeatures = img.shape[1]
if self._method == "RandomForest":
from sklearn.ensemble import RandomForestRegressor as RFR
regressor = RFR(n_estimators=self._ntrees,max_depth=self._maxdepth)
regressor.fit(img, dot)
elif self._method == "svrBoxed-gurobi":
regressor = RegressorGurobi(C = self._C, epsilon = self._epsilon)
regressor.fit(img, dot, tags, self.getOldBoxConstraints(boxConstraints, numFeatures
))
#elif self._method == "svrBoxed-gurobi":
# regressor = RegressorGurobi(C = self._C, epsilon = self._epsilon)
# regressor.fit(img, dot, tags, self.getOldBoxConstraints(boxConstraints, numFeatures
# ))
elif self._method == "BoxedRegressionGurobi":
regressor = RegressorC(C = self._C, epsilon = self._epsilon)
regressor.fitgurobi(img, dot, tags, boxConstraints)
elif self._method == "BoxedRegressionCplex":
regressor = RegressorC(C = self._C, epsilon = self._epsilon)
regressor.fitcplex(img, dot, tags, boxConstraints)
return regressor
def _fit(self, img, dot, tags, boxConstraints=[]):
numFeatures = img.shape[1]
if self._method == "RandomForest":
from sklearn.ensemble import RandomForestRegressor as RFR
regressor = RFR(n_estimators=self._ntrees,
max_depth=self._maxdepth)
regressor.fit(img, dot)
elif self._method == "svrBoxed-gurobi":
regressor = RegressorGurobi(C=self._C, epsilon=self._epsilon)
regressor.fit(
img, dot, tags,
self.getOldBoxConstraints(boxConstraints, numFeatures))
#elif self._method == "svrBoxed-gurobi":
# regressor = RegressorGurobi(C = self._C, epsilon = self._epsilon)
# regressor.fit(img, dot, tags, self.getOldBoxConstraints(boxConstraints, numFeatures
# ))
elif self._method == "BoxedRegressionGurobi":
regressor = RegressorC(C=self._C, epsilon=self._epsilon)
regressor.fitgurobi(img, dot, tags, boxConstraints)
elif self._method == "BoxedRegressionCplex":
regressor = RegressorC(C=self._C, epsilon=self._epsilon)
regressor.fitcplex(img, dot, tags, boxConstraints)
return regressor
