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From mateiz <...@git.apache.org>
Subject [GitHub] spark pull request: [SPARK-1752][MLLIB] Standardize text format fo...
Date Sat, 10 May 2014 05:13:16 GMT
Github user mateiz commented on a diff in the pull request:

    https://github.com/apache/spark/pull/685#discussion_r12502576
  
    --- Diff: mllib/src/main/scala/org/apache/spark/mllib/util/NumericParser.scala ---
    @@ -0,0 +1,153 @@
    +/*
    + * 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.util
    +
    +import scala.collection.mutable.{ArrayBuffer, ListBuffer}
    +
    +private[mllib] object NumericTokenizer {
    +  val NUMBER = -1
    +  val END = -2
    +}
    +
    +import NumericTokenizer._
    +
    +/**
    + * Simple tokenizer for a numeric structure consisting of three types:
    + *
    + *  - number: a double in Java's floating number format
    + *  - array: an array of numbers stored as `[v0,v1,...,vn]`
    + *  - tuple: a list of numbers, arrays, or tuples stored as `(...)`
    + *
    + * @param s input string
    + * @param start start index
    + * @param end end index
    + */
    +private[mllib] class NumericTokenizer(s: String, start: Int, end: Int) {
    --- End diff --
    
    Much of the logic here could perhaps be replaced by `new StringTokenizer(s.substring(start,
end), "[]()," true)`. StringTokenizer can be configured to return the tokens (namely '[',
',', etc) so it should be enough for parsing these data types.


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