Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1207#discussion_r15739002
 Diff: mllib/src/main/scala/org/apache/spark/mllib/feature/Normalizer.scala 
@@ 0,0 +1,108 @@
+/*
+ * 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/LICENSE2.0
+ *
+ * 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.Infinity, normalizes samples using sum(abs(vector).^p)^(1/p)
as norm.
+ * For p = Double.Infinity, max(abs(vector)) will be used as norm for normalization.
+ * For p = Double.NegativeInfinity, min(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  p == Double.NegativeInfinity)
+
+ /**
+ * 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 distance = 0.0
+
+ if (p >= 1.0) {
+ distance = vector.toBreeze.norm(p)
+ } else if (p == Double.NegativeInfinity) {
+ // Breeze norm doesn't work when p = NegativeInfinity
+ var min = Double.PositiveInfinity
+ vector.toBreeze match {
+ case dv: BDV[Double] => {
+ var i = 0
+ while (i < dv.length) {
+ if (Math.abs(dv(i)) < min) min = Math.abs(Math.abs(dv(i)))
+ i += 1
+ }
+ }
+ case sv: BSV[Double] => {
+ if (sv.index.length != sv.length) {
+ // Find a zero element in sparse vector, don't need going into the loop.
+ min = 0.0
+ } else {
+ var i = 0
+ while (i < sv.index.length) {
+ if (Math.abs(sv.data(i)) < min) min = Math.abs(Math.abs(sv.data(i)))
+ i += 1
+ }
+ }
+ }
+ case v: Any =>
+ throw new IllegalArgumentException("Do not support vector type " + v.getClass)
+ }
+ distance = if (min != Double.PositiveInfinity) min else 0.0
+ }
+
+ if (distance != 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 reuse it to save memory.
+ vector.toBreeze match {
+ case dv: BDV[Double] => Vectors.fromBreeze(dv :/ distance)
+ case sv: BSV[Double] => {
 End diff 
`=> {` > `=>` and remove the closing `}` below

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