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add MRD for regression benchmark

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Zhenwen Dai 2015-08-27 17:38:45 +01:00
parent 8f78431983
commit 9564287cf3
2 changed files with 31 additions and 3 deletions
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30
benchmarks/regression/methods.py
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@ -54,13 +54,41 @@ class GP_RBF(RegressionMethod):
def _fit(self, train_data):
inputs, labels = train_data
self.model = GPy.models.GPRegression(inputs, labels,kernel=GPy.kern.RBF(inputs.shape[-1],ARD=True) +GPy.kern.Linear(inputs.shape[1], ARD=True) + GPy.kern.White(inputs.shape[1],0.01) )
self.model = GPy.models.GPRegression(inputs, labels,kernel=GPy.kern.RBF(inputs.shape[-1],ARD=True) +GPy.kern.Linear(inputs.shape[1], ARD=True) )
self.model.likelihood.variance[:] = labels.var()*0.01
self.model.optimize()
return True
def _predict(self, test_data):
return self.model.predict(test_data)[0]
class SparseGP_RBF(RegressionMethod):
name = 'SparseGP_RBF'
def _fit(self, train_data):
inputs, labels = train_data
self.model = GPy.models.SparseGPRegression(inputs, labels,kernel=GPy.kern.RBF(inputs.shape[-1],ARD=True) +GPy.kern.Linear(inputs.shape[1], ARD=True) ,num_inducing=100)
self.model.likelihood.variance[:] = labels.var()*0.01
self.model.optimize()
return True
def _predict(self, test_data):
return self.model.predict(test_data)[0]
# class MRD_RBF(RegressionMethod):
# name = 'MRD_RBF'
#
# def _fit(self, train_data):
# inputs, labels = train_data
# Q = 5
# self.model = GPy.models.MRD([inputs, labels],Q,kernel=GPy.kern.RBF(Q,ARD=True),num_inducing=50)
# self.model.Y0.likelihood.variance[:] = inputs.var()*0.01
# self.model.Y1.likelihood.variance[:] = labels.var()*0.01
# self.model.optimize()
# return True
#
# def _predict(self, test_data):
# return self.model.predict(self.model.Y0.infer_newX(test_data)[0])[0]
class SVIGP_RBF(RegressionMethod):
name = 'SVIGP_RBF'