本文整理汇总了Java中cc.mallet.pipe.iterator.FileIterator类的典型用法代码示例。如果您正苦于以下问题：Java FileIterator类的具体用法？Java FileIterator怎么用？Java FileIterator使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
FileIterator类属于cc.mallet.pipe.iterator包，在下文中一共展示了FileIterator类的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Java代码示例。
示例1: main
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
public static void main(String[] args) {
String htmldir = args[0];
Pipe pipe = new SerialPipes(new Pipe[] { new Input2CharSequence(),
new CharSequenceRemoveHTML() });
InstanceList list = new InstanceList(pipe);
list.addThruPipe(new FileIterator(htmldir, FileIterator.STARTING_DIRECTORIES));
for (int index = 0; index < list.size(); index++) {
Instance inst = list.get(index);
System.err.println(inst.getData());
}
}
示例2: testThree
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
public void testThree ()
{
InstanceList il = new InstanceList (
new SerialPipes(new Pipe[] {
new Target2Label(),
new CharSequence2TokenSequence(),
new TokenSequenceLowercase(),
new TokenSequenceRemoveStopwords(),
new TokenSequence2FeatureSequence(),
new FeatureSequence2FeatureVector()
}));
Iterator<Instance> pi = new FileIterator(new File("foo/bar"), null, Pattern.compile("^([^/]*)/"));
il.addThruPipe (pi);
}
示例3: testIncrementallyTrainedGrowingAlphabets
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
public void testIncrementallyTrainedGrowingAlphabets()
{
System.out.println("testIncrementallyTrainedGrowingAlphabets");
String[] args = new String[] {
"src/cc/mallet/classify/tests/NaiveBayesData/learn/a",
"src/cc/mallet/classify/tests/NaiveBayesData/learn/b"
};
File[] directories = new File[args.length];
for (int i = 0; i < args.length; i++)
directories[i] = new File (args[i]);
SerialPipes instPipe =
// MALLET pipeline for converting instances to feature vectors
new SerialPipes(new Pipe[] {
new Target2Label(),
new Input2CharSequence(),
//SKIP_HEADER only works for Unix
//new CharSubsequence(CharSubsequence.SKIP_HEADER),
new CharSequence2TokenSequence(),
new TokenSequenceLowercase(),
new TokenSequenceRemoveStopwords(),
new TokenSequence2FeatureSequence(),
new FeatureSequence2FeatureVector() });
InstanceList instList = new InstanceList(instPipe);
instList.addThruPipe(new
FileIterator(directories, FileIterator.STARTING_DIRECTORIES));
System.out.println("Training 1");
NaiveBayesTrainer trainer = new NaiveBayesTrainer();
NaiveBayes classifier = trainer.trainIncremental(instList);
//instList.getDataAlphabet().stopGrowth();
// incrementally train...
String[] t2directories = {
"src/cc/mallet/classify/tests/NaiveBayesData/learn/b"
};
System.out.println("data alphabet size " + instList.getDataAlphabet().size());
System.out.println("target alphabet size " + instList.getTargetAlphabet().size());
InstanceList instList2 = new InstanceList(instPipe);
instList2.addThruPipe(new
FileIterator(t2directories, FileIterator.STARTING_DIRECTORIES));
System.out.println("Training 2");
System.out.println("data alphabet size " + instList2.getDataAlphabet().size());
System.out.println("target alphabet size " + instList2.getTargetAlphabet().size());
NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2);
}
示例4: testIncrementallyTrained
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
public void testIncrementallyTrained()
{
System.out.println("testIncrementallyTrained");
String[] args = new String[] {
"src/cc/mallet/classify/tests/NaiveBayesData/learn/a",
"src/cc/mallet/classify/tests/NaiveBayesData/learn/b"
};
File[] directories = new File[args.length];
for (int i = 0; i < args.length; i++)
directories[i] = new File (args[i]);
SerialPipes instPipe =
// MALLET pipeline for converting instances to feature vectors
new SerialPipes(new Pipe[] {
new Target2Label(),
new Input2CharSequence(),
//SKIP_HEADER only works for Unix
//new CharSubsequence(CharSubsequence.SKIP_HEADER),
new CharSequence2TokenSequence(),
new TokenSequenceLowercase(),
new TokenSequenceRemoveStopwords(),
new TokenSequence2FeatureSequence(),
new FeatureSequence2FeatureVector() });
InstanceList instList = new InstanceList(instPipe);
instList.addThruPipe(new
FileIterator(directories, FileIterator.STARTING_DIRECTORIES));
System.out.println("Training 1");
NaiveBayesTrainer trainer = new NaiveBayesTrainer();
NaiveBayes classifier = (NaiveBayes) trainer.trainIncremental(instList);
Classification initialClassification = classifier.classify("Hello Everybody");
Classification initial2Classification = classifier.classify("Goodbye now");
System.out.println("Initial Classification = ");
initialClassification.print();
initial2Classification.print();
System.out.println("data alphabet " + classifier.getAlphabet());
System.out.println("label alphabet " + classifier.getLabelAlphabet());
// incrementally train...
String[] t2directories = {
"src/cc/mallet/classify/tests/NaiveBayesData/learn/b"
};
System.out.println("data alphabet size " + instList.getDataAlphabet().size());
System.out.println("target alphabet size " + instList.getTargetAlphabet().size());
InstanceList instList2 = new InstanceList(instPipe);
instList2.addThruPipe(new
FileIterator(t2directories, FileIterator.STARTING_DIRECTORIES));
System.out.println("Training 2");
System.out.println("data alphabet size " + instList2.getDataAlphabet().size());
System.out.println("target alphabet size " + instList2.getTargetAlphabet().size());
NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2);
}
示例5: testEmptyStringBug
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
public void testEmptyStringBug()
{
System.out.println("testEmptyStringBug");
String[] args = new String[] {
"src/cc/mallet/classify/tests/NaiveBayesData/learn/a",
"src/cc/mallet/classify/tests/NaiveBayesData/learn/b"
};
File[] directories = new File[args.length];
for (int i = 0; i < args.length; i++)
directories[i] = new File (args[i]);
SerialPipes instPipe =
// MALLET pipeline for converting instances to feature vectors
new SerialPipes(new Pipe[] {
new Target2Label(),
new Input2CharSequence(),
//SKIP_HEADER only works for Unix
//new CharSubsequence(CharSubsequence.SKIP_HEADER),
new CharSequence2TokenSequence(),
new TokenSequenceLowercase(),
new TokenSequenceRemoveStopwords(),
new TokenSequence2FeatureSequence(),
new FeatureSequence2FeatureVector() });
InstanceList instList = new InstanceList(instPipe);
instList.addThruPipe(new
FileIterator(directories, FileIterator.STARTING_DIRECTORIES));
System.out.println("Training 1");
NaiveBayesTrainer trainer = new NaiveBayesTrainer();
NaiveBayes classifier = (NaiveBayes) trainer.trainIncremental(instList);
Classification initialClassification = classifier.classify("Hello Everybody");
Classification initial2Classification = classifier.classify("Goodbye now");
System.out.println("Initial Classification = ");
initialClassification.print();
initial2Classification.print();
System.out.println("data alphabet " + classifier.getAlphabet());
System.out.println("label alphabet " + classifier.getLabelAlphabet());
// test
String[] t2directories = {
"src/cc/mallet/classify/tests/NaiveBayesData/learn/b"
};
System.out.println("data alphabet size " + instList.getDataAlphabet().size());
System.out.println("target alphabet size " + instList.getTargetAlphabet().size());
InstanceList instList2 = new InstanceList(instPipe);
instList2.addThruPipe(new
FileIterator(t2directories, FileIterator.STARTING_DIRECTORIES, true));
System.out.println("Training 2");
System.out.println("data alphabet size " + instList2.getDataAlphabet().size());
System.out.println("target alphabet size " + instList2.getTargetAlphabet().size());
NaiveBayes classifier2 = (NaiveBayes) trainer.trainIncremental(instList2);
Classification secondClassification = classifier.classify("Goodbye now");
secondClassification.print();
}
示例6: main
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
public static void main (String[] args) throws IOException {
CommandOption
.setSummary(Text2Clusterings.class,
"A tool to convert a list of text files to a Clusterings.");
CommandOption.process(Text2Clusterings.class, args);
if (classDirs.value.length == 0) {
logger
.warning("You must include --input DIR1 DIR2 ...' in order to specify a"
+ "list of directories containing the documents for each class.");
System.exit(-1);
}
Clustering[] clusterings = new Clustering[classDirs.value.length];
int fi = 0;
for (int i = 0; i < classDirs.value.length; i++) {
Alphabet fieldAlph = new Alphabet();
Alphabet valueAlph = new Alphabet();
File directory = new File(classDirs.value[i]);
File[] subdirs = getSubDirs(directory);
Alphabet clusterAlph = new Alphabet();
InstanceList instances = new InstanceList(new Noop());
TIntArrayList labels = new TIntArrayList();
for (int j = 0; j < subdirs.length; j++) {
ArrayList<File> records = new FileIterator(subdirs[j]).getFileArray();
int label = clusterAlph.lookupIndex(subdirs[j].toString());
for (int k = 0; k < records.size(); k++) {
if (fi % 100 == 0) System.out.print(fi);
else if (fi % 10 == 0) System.out.print(".");
if (fi % 1000 == 0 && fi > 0) System.out.println();
System.out.flush();
fi++;
File record = records.get(k);
labels.add(label);
instances.add(new Instance(new Record(fieldAlph, valueAlph, parseFile(record)),
new Integer(label), record.toString(),
record.toString()));
}
}
clusterings[i] =
new Clustering(instances, subdirs.length, labels.toNativeArray());
}
logger.info("\nread " + fi + " objects in " + clusterings.length + " clusterings.");
try {
ObjectOutputStream oos =
new ObjectOutputStream(new FileOutputStream(outputFile.value));
oos.writeObject(new Clusterings(clusterings));
oos.close();
} catch (Exception e) {
logger.warning("Exception writing clustering to file " + outputFile.value
+ " " + e);
e.printStackTrace();
}
}
示例7: main
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
public static void main (String[] args) throws IOException {
CommandOption
.setSummary(Text2Clusterings.class,
"A tool to convert a list of text files to a Clusterings.");
CommandOption.process(Text2Clusterings.class, args);
if (classDirs.value.length == 0) {
logger
.warning("You must include --input DIR1 DIR2 ...' in order to specify a"
+ "list of directories containing the documents for each class.");
System.exit(-1);
}
Clustering[] clusterings = new Clustering[classDirs.value.length];
int fi = 0;
for (int i = 0; i < classDirs.value.length; i++) {
Alphabet fieldAlph = new Alphabet();
Alphabet valueAlph = new Alphabet();
File directory = new File(classDirs.value[i]);
File[] subdirs = getSubDirs(directory);
Alphabet clusterAlph = new Alphabet();
InstanceList instances = new InstanceList(new Noop());
TIntArrayList labels = new TIntArrayList();
for (int j = 0; j < subdirs.length; j++) {
ArrayList<File> records = new FileIterator(subdirs[j]).getFileArray();
int label = clusterAlph.lookupIndex(subdirs[j].toString());
for (int k = 0; k < records.size(); k++) {
if (fi % 100 == 0) System.out.print(fi);
else if (fi % 10 == 0) System.out.print(".");
if (fi % 1000 == 0 && fi > 0) System.out.println();
System.out.flush();
fi++;
File record = records.get(k);
labels.add(label);
instances.add(new Instance(new Record(fieldAlph, valueAlph, parseFile(record)),
new Integer(label), record.toString(),
record.toString()));
}
}
clusterings[i] =
new Clustering(instances, subdirs.length, labels.toArray());
}
logger.info("\nread " + fi + " objects in " + clusterings.length + " clusterings.");
try {
ObjectOutputStream oos =
new ObjectOutputStream(new FileOutputStream(outputFile.value));
oos.writeObject(new Clusterings(clusterings));
oos.close();
} catch (Exception e) {
logger.warning("Exception writing clustering to file " + outputFile.value
+ " " + e);
e.printStackTrace();
}
}
示例8: getInstanceList
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
/**
*
* @param data_dir
* @return
*/
public InstanceList getInstanceList(String data_dir) {
InstanceList instances = new InstanceList(getPipe());
instances.addThruPipe(new FileIterator(new File[] { new File(data_dir) }, FileIterator.STARTING_DIRECTORIES, true));
return instances;
}
示例9: main
import cc.mallet.pipe.iterator.FileIterator; //导入依赖的package包/类
public static void main (String[] args) throws IOException {
CommandOption
.setSummary(Text2Clusterings.class,
"A tool to convert a list of text files to a Clusterings.");
CommandOption.process(Text2Clusterings.class, args);
if (classDirs.value.length == 0) {
logger
.warning("You must include --input DIR1 DIR2 ...' in order to specify a"
+ "list of directories containing the documents for each class.");
System.exit(-1);
}
Clustering[] clusterings = new Clustering[classDirs.value.length];
int fi = 0;
for (int i = 0; i < classDirs.value.length; i++) {
Alphabet fieldAlph = new Alphabet();
Alphabet valueAlph = new Alphabet();
File directory = new File(classDirs.value[i]);
File[] subdirs = getSubDirs(directory);
Alphabet clusterAlph = new Alphabet();
InstanceList instances = new InstanceList(new Noop());
IntArrayList labels = new IntArrayList();
for (int j = 0; j < subdirs.length; j++) {
ArrayList<File> records = new FileIterator(subdirs[j]).getFileArray();
int label = clusterAlph.lookupIndex(subdirs[j].toString());
for (int k = 0; k < records.size(); k++) {
if (fi % 100 == 0) System.out.print(fi);
else if (fi % 10 == 0) System.out.print(".");
if (fi % 1000 == 0 && fi > 0) System.out.println();
System.out.flush();
fi++;
File record = records.get(k);
labels.add(label);
instances.add(new Instance(new Record(fieldAlph, valueAlph, parseFile(record)),
new Integer(label), record.toString(),
record.toString()));
}
}
clusterings[i] =
new Clustering(instances, subdirs.length, labels.toArray());
}
logger.info("\nread " + fi + " objects in " + clusterings.length + " clusterings.");
try {
ObjectOutputStream oos =
new ObjectOutputStream(new FileOutputStream(outputFile.value));
oos.writeObject(new Clusterings(clusterings));
oos.close();
} catch (Exception e) {
logger.warning("Exception writing clustering to file " + outputFile.value
+ " " + e);
e.printStackTrace();
}
}