apunkt/GPy
1
0
Fork 0
mirror of https://github.com/SheffieldML/GPy.git synced 2026-05-11 04:52:37 +02:00
Code Issues Projects Releases Packages Wiki Activity Actions

[stochastics] added optimization for a few runs

Browse source
This commit is contained in:
mzwiessele 2016-03-08 10:01:07 +00:00
parent e4a4344334
commit eaf20a952e
1 changed files with 8 additions and 0 deletions
Download patch file Download diff file Expand all files Collapse all files

8
GPy/testing/minibatch_tests.py
View file

@ -132,15 +132,23 @@ class SparseGPMinibatchTest(unittest.TestCase):
Q = Z.shape[1] Q = Z.shape[1]
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=False) m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=False)
assert(m.checkgrad()) assert(m.checkgrad())
m.optimize(max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=True) m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=True, stochastic=True)
assert(m.checkgrad()) assert(m.checkgrad())
m.optimize(max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=False) m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=False)
assert(m.checkgrad()) assert(m.checkgrad())
m.optimize(max_iters=10)
assert(m.checkgrad())
m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=True) m = GPy.models.sparse_gp_minibatch.SparseGPMiniBatch(self.X, self.Y, Z, GPy.kern.RBF(Q)+GPy.kern.Matern32(Q)+GPy.kern.Bias(Q), GPy.likelihoods.Gaussian(), missing_data=False, stochastic=True)
assert(m.checkgrad()) assert(m.checkgrad())
m.optimize(max_iters=10)
assert(m.checkgrad())
def test_predict_missing_data(self): def test_predict_missing_data(self):
m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=True, batchsize=self.Y.shape[1]) m = GPy.models.bayesian_gplvm_minibatch.BayesianGPLVMMiniBatch(self.Y, self.Q, X_variance=False, missing_data=True, stochastic=True, batchsize=self.Y.shape[1])