(self)
| 52 | return _fn |
| 53 | |
| 54 | def setUp(self): |
| 55 | np.random.seed(3) |
| 56 | random_seed_lib.set_random_seed(2) |
| 57 | self.num_centers = 2 |
| 58 | self.num_dims = 2 |
| 59 | self.num_points = 4000 |
| 60 | self.batch_size = self.num_points |
| 61 | self.true_centers = self.make_random_centers(self.num_centers, |
| 62 | self.num_dims) |
| 63 | self.points, self.assignments = self.make_random_points( |
| 64 | self.true_centers, self.num_points) |
| 65 | |
| 66 | # Use initial means from kmeans (just like scikit-learn does). |
| 67 | clusterer = kmeans.KMeansClustering(num_clusters=self.num_centers) |
| 68 | clusterer.fit(input_fn=lambda: (constant_op.constant(self.points), None), |
| 69 | steps=30) |
| 70 | self.initial_means = clusterer.clusters() |
| 71 | |
| 72 | @staticmethod |
| 73 | def make_random_centers(num_centers, num_dims): |
nothing calls this directly
no test coverage detected