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[climin] added tests and install directions for travis

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mzwiessele 2016-03-08 10:23:05 +00:00
parent eaf20a952e
commit 88a9b92c80
2 changed files with 5 additions and 4 deletions
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1
.travis.yml
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@ -30,6 +30,7 @@ install:
- source install_retry.sh - source install_retry.sh
- pip install codecov - pip install codecov
- pip install pypandoc - pip install pypandoc
- pip install git+git://github.com/BRML/climin.git
- python setup.py develop - python setup.py develop
script: script:

8
GPy/testing/minibatch_tests.py
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@ -132,22 +132,22 @@ 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) m.optimize('adadelta', max_iters=10)
assert(m.checkgrad()) 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) m.optimize('rprop', max_iters=10)
assert(m.checkgrad()) 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) m.optimize('rprop', max_iters=10)
assert(m.checkgrad()) 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) m.optimize('adadelta', max_iters=10)
assert(m.checkgrad()) assert(m.checkgrad())
def test_predict_missing_data(self): def test_predict_missing_data(self):