@@ -88,11 +88,12 @@ class PowerIterationClustering private[clustering] (
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/**
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* Run the PIC algorithm.
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*
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- * @param similarities an RDD of (i, j, s_ij_) tuples representing the affinity matrix, which is
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- * the matrix A in the PIC paper. The similarity s_ij_ must be nonnegative.
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- * This is a symmetric matrix and hence s_ij_ = s_ji_. For any (i, j) with
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- * nonzero similarity, there should be either (i, j, s_ij_) or (j, i, s_ji_)
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- * in the input. Tuples with i = j are ignored, because we assume s_ij_ = 0.0.
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+ * @param similarities an RDD of (i, j, s,,ij,,) tuples representing the affinity matrix, which is
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+ * the matrix A in the PIC paper. The similarity s,,ij,, must be nonnegative.
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+ * This is a symmetric matrix and hence s,,ij,, = s,,ji,,. For any (i, j) with
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+ * nonzero similarity, there should be either (i, j, s,,ij,,) or
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+ * (j, i, s,,ji,,) in the input. Tuples with i = j are ignored, because we
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+ * assume s,,ij,, = 0.0.
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*
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* @return a [[PowerIterationClusteringModel ]] that contains the clustering result
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*/
@@ -109,7 +110,7 @@ class PowerIterationClustering private[clustering] (
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* Runs the PIC algorithm.
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*
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* @param w The normalized affinity matrix, which is the matrix W in the PIC paper with
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- * w_ij_ = a_ij_ / d_ii_ as its edge properties and the initial vector of the power
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+ * w,,ij,, = a,,ij,, / d,,ii,, as its edge properties and the initial vector of the power
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* iteration as its vertex properties.
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*/
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private def pic (w : Graph [Double , Double ]): PowerIterationClusteringModel = {
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