diff options
Diffstat (limited to 'python')
| -rw-r--r-- | python/pyspark/mllib/clustering.py | 2 | ||||
| -rw-r--r-- | python/pyspark/mllib/tests.py | 12 |
2 files changed, 13 insertions, 1 deletions
diff --git a/python/pyspark/mllib/clustering.py b/python/pyspark/mllib/clustering.py index c9e6f1dec6..48daa87e82 100644 --- a/python/pyspark/mllib/clustering.py +++ b/python/pyspark/mllib/clustering.py @@ -346,7 +346,7 @@ class GaussianMixture(object): if initialModel.k != k: raise Exception("Mismatched cluster count, initialModel.k = %s, however k = %s" % (initialModel.k, k)) - initialModelWeights = initialModel.weights + initialModelWeights = list(initialModel.weights) initialModelMu = [initialModel.gaussians[i].mu for i in range(initialModel.k)] initialModelSigma = [initialModel.gaussians[i].sigma for i in range(initialModel.k)] java_model = callMLlibFunc("trainGaussianMixtureModel", rdd.map(_convert_to_vector), diff --git a/python/pyspark/mllib/tests.py b/python/pyspark/mllib/tests.py index 6ed03e3582..97fed7662e 100644 --- a/python/pyspark/mllib/tests.py +++ b/python/pyspark/mllib/tests.py @@ -475,6 +475,18 @@ class ListTests(MLlibTestCase): for c1, c2 in zip(clusters1.weights, clusters2.weights): self.assertEqual(round(c1, 7), round(c2, 7)) + def test_gmm_with_initial_model(self): + from pyspark.mllib.clustering import GaussianMixture + data = self.sc.parallelize([ + (-10, -5), (-9, -4), (10, 5), (9, 4) + ]) + + gmm1 = GaussianMixture.train(data, 2, convergenceTol=0.001, + maxIterations=10, seed=63) + gmm2 = GaussianMixture.train(data, 2, convergenceTol=0.001, + maxIterations=10, seed=63, initialModel=gmm1) + self.assertAlmostEqual((gmm1.weights - gmm2.weights).sum(), 0.0) + def test_classification(self): from pyspark.mllib.classification import LogisticRegressionWithSGD, SVMWithSGD, NaiveBayes from pyspark.mllib.tree import DecisionTree, DecisionTreeModel, RandomForest,\ |