diff options
Diffstat (limited to 'tensorflow/contrib/factorization/python/ops/kmeans.py')
| -rw-r--r-- | tensorflow/contrib/factorization/python/ops/kmeans.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/tensorflow/contrib/factorization/python/ops/kmeans.py b/tensorflow/contrib/factorization/python/ops/kmeans.py index 38faca119d..bfe338c9f9 100644 --- a/tensorflow/contrib/factorization/python/ops/kmeans.py +++ b/tensorflow/contrib/factorization/python/ops/kmeans.py @@ -374,11 +374,11 @@ class KMeansClustering(estimator.Estimator): than `num_clusters`, a TensorFlow runtime error occurs. distance_metric: The distance metric used for clustering. One of: * `KMeansClustering.SQUARED_EUCLIDEAN_DISTANCE`: Euclidean distance - between vectors `u` and `v` is defined as `||u - v||_2` which is - the square root of the sum of the absolute squares of the elements' - difference. + between vectors `u` and `v` is defined as `\\(||u - v||_2\\)` + which is the square root of the sum of the absolute squares of + the elements' difference. * `KMeansClustering.COSINE_DISTANCE`: Cosine distance between vectors - `u` and `v` is defined as `1 - (u . v) / (||u||_2 ||v||_2)`. + `u` and `v` is defined as `\\(1 - (u . v) / (||u||_2 ||v||_2)\\)`. random_seed: Python integer. Seed for PRNG used to initialize centers. use_mini_batch: A boolean specifying whether to use the mini-batch k-means algorithm. See explanation above. |