Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1207#discussion_r15739934
--- Diff:
mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala ---
@@ -0,0 +1,77 @@
+/*
+ * 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.feature
+
+import breeze.linalg.{DenseVector => BDV, SparseVector => BSV}
+
+import org.apache.spark.annotation.DeveloperApi
+import org.apache.spark.mllib.linalg.{Vector, Vectors}
+
+/**
+ * :: DeveloperApi ::
+ * Normalizes samples individually to unit L^p^ norm
+ *
+ * For any 1 <= p < Double.PositiveInfinity, normalizes samples using
+ * sum(abs(vector).^p^)^(1/p)^ as norm.
+ *
+ * For p = Double.PositiveInfinity, max(abs(vector)) will be used as norm
for normalization.
+ *
+ * @param p Normalization in L^p^ space, p = 2 by default.
+ */
+@DeveloperApi
+class Normalizer(p: Double) extends VectorTransformer {
+
+ def this() = this(2)
+
+ require(p >= 1.0)
+
+ /**
+ * Applies unit length normalization on a vector.
+ *
+ * @param vector vector to be normalized.
+ * @return normalized vector. If the norm of the input is zero, it will
return the input vector.
+ */
+ override def transform(vector: Vector): Vector = {
+ var norm = vector.toBreeze.norm(p)
+
+ if (norm != 0.0) {
+ // For dense vector, we've to allocate new memory for new output
vector.
+ // However, for sparse vector, the `index` array will not be changed,
+ // so we can re-use it to save memory.
+ vector.toBreeze match {
+ case dv: BDV[Double] => Vectors.fromBreeze(dv :/ norm)
+ case sv: BSV[Double] =>
+ val output = new BSV[Double](sv.index, sv.data.clone(),
sv.length)
+ var i = 0
+ while (i < output.data.length) {
+ output.data(i) /= norm
+ i += 1
+ }
+ Vectors.fromBreeze(output)
+ case v: Any =>
--- End diff --
minor: `: Any` can be removed
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