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Diffstat (limited to 'python/pyspark/mllib/tests.py')
| -rw-r--r-- | python/pyspark/mllib/tests.py | 26 |
1 files changed, 26 insertions, 0 deletions
diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 61e0cf5d90..42aa228737 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -167,6 +167,32 @@ class ListTests(PySparkTestCase): # TODO: Allow small numeric difference. self.assertTrue(array_equal(c1, c2)) + def test_gmm(self): + from pyspark.mllib.clustering import GaussianMixture + data = self.sc.parallelize([ + [1, 2], + [8, 9], + [-4, -3], + [-6, -7], + ]) + clusters = GaussianMixture.train(data, 2, convergenceTol=0.001, + maxIterations=100, seed=56) + labels = clusters.predict(data).collect() + self.assertEquals(labels[0], labels[1]) + self.assertEquals(labels[2], labels[3]) + + def test_gmm_deterministic(self): + from pyspark.mllib.clustering import GaussianMixture + x = range(0, 100, 10) + y = range(0, 100, 10) + data = self.sc.parallelize([[a, b] for a, b in zip(x, y)]) + clusters1 = GaussianMixture.train(data, 5, convergenceTol=0.001, + maxIterations=100, seed=63) + clusters2 = GaussianMixture.train(data, 5, convergenceTol=0.001, + maxIterations=100, seed=63) + for c1, c2 in zip(clusters1.weights, clusters2.weights): + self.assertEquals(round(c1, 7), round(c2, 7)) + def test_classification(self): from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes from pyspark.mllib.tree import DecisionTree, RandomForest, GradientBoostedTrees |