Mallet topic model example can not compile -

i want compile mallet in java (instead using command line), include jar in project, , cite code of example from: http://mallet.cs.umass.edu/topics-devel.php, however, when run code, there error :

exception in thread "main" java.lang.noclassdeffounderror: gnu/trove/tobjectinthashmap     @ cc.mallet.types.alphabet.<init>(alphabet.java:51)     @ cc.mallet.types.alphabet.<init>(alphabet.java:70)     @ cc.mallet.pipe.tokensequence2featuresequence.<init>    (tokensequence2featuresequence.java:35) @ mallet.topicmodel.main(topicmodel.java:25) caused by: java.lang.classnotfoundexception: gnu.trove.tobjectinthashmap @ java.net.urlclassloader$1.run(unknown source) @ java.net.urlclassloader$1.run(unknown source) @ java.security.accesscontroller.doprivileged(native method) @ java.net.urlclassloader.findclass(unknown source) @ java.lang.classloader.loadclass(unknown source) @ sun.misc.launcher$appclassloader.loadclass(unknown source) @ java.lang.classloader.loadclass(unknown source) ... 4 more 

i not sure causes error. help?

package mallet;  import cc.mallet.util.*; import cc.mallet.types.*; import cc.mallet.pipe.*; import cc.mallet.pipe.iterator.*; import cc.mallet.topics.*;  import java.util.*; import java.util.regex.*; import java.io.*;  public class topicmodel {  public static void main(string[] args) throws exception {      string filepath = "d:/ap.txt";     // begin importing documents text feature sequences     arraylist<pipe> pipelist = new arraylist<pipe>();      // pipes: lowercase, tokenize, remove stopwords, map features     pipelist.add( new charsequencelowercase() );     pipelist.add( new charsequence2tokensequence(pattern.compile("\\p{l}[\\p{l}\\p{p}]+\\p{l}")) );     pipelist.add( new tokensequenceremovestopwords(new file("stoplists/en.txt"), "utf-8", false, false, false) );     pipelist.add( new tokensequence2featuresequence() );      instancelist instances = new instancelist (new serialpipes(pipelist));      reader filereader = new inputstreamreader(new fileinputstream(new file(filepath)), "utf-8");     instances.addthrupipe(new csviterator (filereader, pattern.compile("^(\\s*)[\\s,]*(\\s*)[\\s,]*(.*)$"),                                            3, 2, 1)); // data, label, name fields      // create model 100 topics, alpha_t = 0.01, beta_w = 0.01     //  note first parameter passed sum on topics, while     //  second      int numtopics = 100;     paralleltopicmodel model = new paralleltopicmodel(numtopics, 1.0, 0.01);      model.addinstances(instances);      // use 2 parallel samplers, each @ 1 half corpus , combine     //  statistics after every iteration.     model.setnumthreads(2);      // run model 50 iterations , stop (this testing only,      //  real applications, use 1000 2000 iterations)     model.setnumiterations(50);     model.estimate();      // show words , topics in first instance      // data alphabet maps word ids strings     alphabet dataalphabet = instances.getdataalphabet();      featuresequence tokens = (featuresequence) model.getdata().get(0).instance.getdata();     labelsequence topics = model.getdata().get(0).topicsequence;      formatter out = new formatter(new stringbuilder(), locale.us);     (int position = 0; position < tokens.getlength(); position++) {         out.format("%s-%d ", dataalphabet.lookupobject(tokens.getindexatposition(position)), topics.getindexatposition(position));     }     system.out.println(out);      // estimate topic distribution of first instance,      //  given current gibbs state.     double[] topicdistribution = model.gettopicprobabilities(0);      // array of sorted sets of word id/count pairs     arraylist<treeset<idsorter>> topicsortedwords = model.getsortedwords();      // show top 5 words in topics proportions first document     (int topic = 0; topic < numtopics; topic++) {         iterator<idsorter> iterator = topicsortedwords.get(topic).iterator();          out = new formatter(new stringbuilder(), locale.us);         out.format("%d\t%.3f\t", topic, topicdistribution[topic]);         int rank = 0;         while (iterator.hasnext() && rank < 5) {             idsorter idcountpair = iterator.next();             out.format("%s (%.0f) ", dataalphabet.lookupobject(idcountpair.getid()), idcountpair.getweight());             rank++;         }         system.out.println(out);     }      // create new instance high probability of topic 0     stringbuilder topiczerotext = new stringbuilder();     iterator<idsorter> iterator = topicsortedwords.get(0).iterator();      int rank = 0;     while (iterator.hasnext() && rank < 5) {         idsorter idcountpair = iterator.next();         topiczerotext.append(dataalphabet.lookupobject(idcountpair.getid()) + " ");         rank++;     }      // create new instance named "test instance" empty target , source fields.     instancelist testing = new instancelist(instances.getpipe());     testing.addthrupipe(new instance(topiczerotext.tostring(), null, "test instance", null));      topicinferencer inferencer = model.getinferencer();     double[] testprobabilities = inferencer.getsampleddistribution(testing.get(0), 10, 1, 5);     system.out.println("0\t" + testprobabilities[0]); } 

}

i solved problem. firstly, tried import trove3.1 in eclipse not work. then, noticed in mallet folder, there "lib" folder, included jar files in eclipse. bingo! works.