Github user dorx commented on a diff in the pull request:
https://github.com/apache/spark/pull/1367#discussion_r14836715
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/stat/correlation/Correlation.scala
---
@@ -0,0 +1,121 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.mllib.stat.correlation
+
+import org.apache.spark.mllib.linalg.{DenseVector, Matrix, Vector}
+import org.apache.spark.rdd.RDD
+
+/**
+ * New correlation algorithms should implement this trait
+ */
+trait Correlation {
+
+ /**
+ * Compute correlation for two datasets.
+ */
+ def computeCorrelation(x: RDD[Double], y: RDD[Double]): Double
+
+ /**
+ * Compute the correlation matrix S, for the input matrix, where S(i, j)
is the correlation
+ * between column i and j.
+ */
+ def computeCorrelationMatrix(X: RDD[Vector]): Matrix
+
+ /**
+ * Combine the two input RDD[Double]s into an RDD[Vector] and compute
the correlation using the
+ * correlation implementation for RDD[Vector]
+ */
+ def computeCorrelationWithMatrixImpl(x: RDD[Double], y: RDD[Double]):
Double = {
+ val mat: RDD[Vector] = x.zip(y).mapPartitions({ iter =>
+ iter.map {case(xi, yi) => new DenseVector(Array(xi, yi))}
+ }, preservesPartitioning = true)
+ computeCorrelationMatrix(mat)(0, 1)
+ }
+
+}
+
+/**
+ * Delegates computation to the specific correlation object based on the
input method name
+ *
+ * Currently supported correlations: pearson, spearman.
+ * After new correlation algorithms are added, please update the
documentation here and in
+ * Statistics.scala for the correlation APIs.
+ *
+ * Cases are ignored when doing method matching. We also allow initials,
e.g. "P" for "pearson", as
+ * long as initials are unique in the supported set of correlation
algorithms. In addition, a
+ * supported method name has to be a substring of the input method name
for it to be matched (e.g.
+ * "spearmansrho" will be matched to "spearman")
+ *
+ * Maintains the default correlation type, pearson
+ */
+object Correlations {
--- End diff --
The reason I named it Correlations isn't to copy Vectors but to signify the
fact that this is more of a delegator object that maintains all supported
correlations.
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