@@ -144,7 +144,7 @@ object TypedSimilarity extends Serializable {
144144 * key: document,
145145 * value: (word, documentsWithWord)
146146 * return: Edge of similarity between words measured by documents
147- * See: http ://arxiv.org/pdf/1206.2082v2.pdf
147+ * See: https ://arxiv.org/pdf/1206.2082v2.pdf
148148 */
149149 def discoCosineSimilarity [N : Ordering ](smallG : Grouped [N , (N , Int )],
150150 bigG : Grouped [N , (N , Int )], oversample : Double ): TypedPipe [Edge [N , Double ]] = {
@@ -181,7 +181,7 @@ object TypedSimilarity extends Serializable {
181181 * key: document,
182182 * value: (word, word weight in the document, norm of the word)
183183 * return: Edge of similarity between words measured by documents
184- * See: http ://stanford.edu/~rezab/papers/dimsum.pdf
184+ * See: https ://stanford.edu/~rezab/papers/dimsum.pdf
185185 */
186186 def dimsumCosineSimilarity [N : Ordering ](smallG : Grouped [N , (N , Double , Double )],
187187 bigG : Grouped [N , (N , Double , Double )], oversample : Double ): TypedPipe [Edge [N , Double ]] = {
@@ -236,7 +236,7 @@ class ExactInCosine[N](reducers: Int = -1)(implicit override val nodeOrdering: O
236236 * minCos: the minimum cosine similarity you care about accuracy for
237237 * delta: the error on the approximated cosine (e.g. 0.05 = 5%)
238238 * boundedProb: the probability we have larger than delta error
239- * see: http ://arxiv.org/pdf/1206.2082v2.pdf for more details
239+ * see: https ://arxiv.org/pdf/1206.2082v2.pdf for more details
240240 */
241241class DiscoInCosine [N ](minCos : Double , delta : Double , boundedProb : Double , reducers : Int = - 1 )(implicit override val nodeOrdering : Ordering [N ]) extends TypedSimilarity [N , InDegree , Double ] {
242242
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