Github user javadba commented on the pull request:
https://github.com/apache/spark/pull/4254#issuecomment-71932032
Hi,
We have suggestion here: to separate the creation/definition of the input
graph from the PIC:
val G = PIC.createGaussianAffinityMatrix(sc, vertices) // Create the
affinity matrix in a Graph structure
val (ccenters, estCollected) = PIC.run(sc, G, nClusters, nIterations)
// Run PIC
The new signatures are:
Take a Graph input instead of the input vertices:
def run(sc: SparkContext,
G: Graph[Double, Double],
nClusters: Int,
nIterations: Int = defaultIterations,
sigma: Double = defaultSigma,
minAffinity: Double = defaultMinAffinity,
nRuns: Int = defaultKMeansRuns)
And here is the new method for creating the input Graph:
def createGaussianAffinityMatrix(sc: SparkContext,
points: Points,
sigma: Double = defaultSigma,
minAffinity: Double = defaultMinAffinity)
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