Thanks, Burak,Yes, tab was an issue and I was able to get it working after
replacing that with space.
> Date: Wed, 17 Sep 2014 21:11:00 -0700
> From: bya...@stanford.edu
> To: ssti...@live.com
> CC: user@spark.apache.org
> Subject: Re: MLLib: LIBSVM issue
>
> Hi,
>
> The spacing between the inputs should be a single space, not a tab. I feel
> like your inputs have tabs between them instead of a single space. Therefore
> the parser
> cannot parse the input.
>
> Best,
> Burak
>
> ----- Original Message -----
> From: "Sameer Tilak" <ssti...@live.com>
> To: user@spark.apache.org
> Sent: Wednesday, September 17, 2014 7:25:10 PM
> Subject: MLLib: LIBSVM issue
>
> Hi All,We have a fairly large amount of sparse data. I was following the
> following instructions in the manual:
> Sparse dataIt is very common in practice to have sparse training data. MLlib
> supports reading training examples stored in LIBSVM format, which is the
> default format used by LIBSVM and LIBLINEAR. It is a text format in which
> each line represents a labeled sparse feature vector using the following
> format:label index1:value1 index2:value2 ...
> import org.apache.spark.mllib.regression.LabeledPointimport
> org.apache.spark.mllib.util.MLUtilsimport org.apache.spark.rdd.RDD
> val examples: RDD[LabeledPoint] = MLUtils.loadLibSVMFile(sc,
> "data/mllib/sample_libsvm_data.txt")
> I believe that I have formatted my data as per the required Libsvm format.
> Here is a snippet of that:
> 1 122:1 1693:1 1771:1 1974:1 2334:1
> 2378:1 2562:1 1 118:1 1389:1 1413:1 1454:1
> 1780:1 2562:1 5051:1 5417:1 5548:1
> 5798:1 5862:1 0 150:1 214:1 468:1 1013:1
> 1078:1 1092:1 1117:1 1489:1 1546:1
> 1630:1 1635:1 1827:1 2024:1 2215:1 2478:1
> 2761:1 5985:1 6115:1 6218:1 0 251:1
> 5578:1
> However,When I use MLUtils.loadLibSVMFile(sc, "path-to-data-file")I get the
> following error messages in mt spark-shell. Can someone please point me in
> right direction.
> java.lang.NumberFormatException: For input string: "150:1 214:1
> 468:1 1013:1 1078:1 1092:1 1117:1 1489:1
> 1546:1 1630:1 1635:1 1827:1 2024:1
> 2215:1 2478:1 2761:1 5985:1 6115:1 6218:1"
> at
> sun.misc.FloatingDecimal.readJavaFormatString(FloatingDecimal.java:1241)
> at java.lang.Double.parseDouble(Double.java:540) at
> scala.collection.immutable.StringLike$class.toDouble(StringLike.scala:232)
>
>
>
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