diff options
Diffstat (limited to 'tensorflow/contrib/factorization/python/ops/clustering_ops.py')
| -rw-r--r-- | tensorflow/contrib/factorization/python/ops/clustering_ops.py | 11 |
1 files changed, 6 insertions, 5 deletions
diff --git a/tensorflow/contrib/factorization/python/ops/clustering_ops.py b/tensorflow/contrib/factorization/python/ops/clustering_ops.py index 23137e0a97..84e80791f4 100644 --- a/tensorflow/contrib/factorization/python/ops/clustering_ops.py +++ b/tensorflow/contrib/factorization/python/ops/clustering_ops.py @@ -41,11 +41,12 @@ from tensorflow.python.platform import resource_loader _clustering_ops = loader.load_op_library( resource_loader.get_path_to_datafile('_clustering_ops.so')) -# Euclidean distance between vectors U and V is defined as ||U - V||_F which is -# the square root of the sum of the absolute squares of the elements difference. +# Euclidean distance between vectors U and V is defined as \\(||U - V||_F\\) +# which is the square root of the sum of the absolute squares of the elements +# difference. SQUARED_EUCLIDEAN_DISTANCE = 'squared_euclidean' # Cosine distance between vectors U and V is defined as -# 1 - (U \dot V) / (||U||_F ||V||_F) +# \\(1 - (U \dot V) / (||U||_F ||V||_F)\\) COSINE_DISTANCE = 'cosine' RANDOM_INIT = 'random' @@ -472,8 +473,8 @@ class KMeans(object): # Locally compute the sum of inputs mapped to each id. # For a cluster with old cluster value x, old count n, and with data # d_1,...d_k newly assigned to it, we recompute the new value as - # x += (sum_i(d_i) - k * x) / (n + k). - # Compute sum_i(d_i), see comment above. + # \\(x += (sum_i(d_i) - k * x) / (n + k)\\). + # Compute \\(sum_i(d_i)\\), see comment above. cluster_center_updates = math_ops.unsorted_segment_sum( inp, unique_idx, num_unique_cluster_idx) # Shape to enable broadcasting count_updates and learning_rate to inp. |