From 88a9b92c80402f1288e9192a6987acd8562d199b Mon Sep 17 00:00:00 2001 From: mzwiessele Date: Tue, 8 Mar 2016 10:23:05 +0000 Subject: [PATCH] [climin] added tests and install directions for travis --- .travis.yml | 1 + GPy/testing/minibatch_tests.py | 8 ++++---- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index 0e9efae1..f4c38549 100644 --- a/.travis.yml +++ b/.travis.yml @@ -30,6 +30,7 @@ install: - source install_retry.sh - pip install codecov - pip install pypandoc +- pip install git+git://github.com/BRML/climin.git - python setup.py develop script: diff --git a/GPy/testing/minibatch_tests.py b/GPy/testing/minibatch_tests.py index 7b39af95..d217cb16 100644 --- a/GPy/testing/minibatch_tests.py +++ b/GPy/testing/minibatch_tests.py @@ -132,22 +132,22 @@ class SparseGPMinibatchTest(unittest.TestCase): 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) assert(m.checkgrad()) - m.optimize(max_iters=10) + m.optimize('adadelta', 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) assert(m.checkgrad()) - m.optimize(max_iters=10) + m.optimize('rprop', 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) assert(m.checkgrad()) - m.optimize(max_iters=10) + m.optimize('rprop', 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) assert(m.checkgrad()) - m.optimize(max_iters=10) + m.optimize('adadelta', max_iters=10) assert(m.checkgrad()) def test_predict_missing_data(self):