本文整理了Java中zemberek.core.logging.Log类的一些代码示例，展示了Log类的具体用法。这些代码示例主要来源于Github/Stackoverflow/Maven等平台，是从一些精选项目中提取出来的代码，具有较强的参考意义，能在一定程度帮忙到你。Log类的具体详情如下：
包路径：zemberek.core.logging.Log
类名称：Log

Log介绍

[英]A convenient Log class.
[中]一个方便的日志类。

代码示例

代码示例来源：origin: ahmetaa/zemberek-nlp

public static void simpleSentenceBoundaryDetector() {
 String input =
   "Prof. Dr. Veli Davul açıklama yaptı. Kimse %6.5 lik enflasyon oranını beğenmemiş!" +
     " Oysa maçta ikinci olmuştuk... Değil mi?";
 Log.info("Paragraph = " + input);
 TurkishSentenceExtractor extractor = TurkishSentenceExtractor.DEFAULT;
 List<String> sentences = extractor.fromParagraph(input);
 Log.info("Sentences:");
 for (String sentence : sentences) {
  Log.info(sentence);
 }
}

代码示例来源：origin: ahmetaa/zemberek-nlp

public Builder setCondition(Condition _condition) {
 if (condition != null) {
  Log.warn("Condition was already set.");
 }
 this.condition = _condition;
 return this;
}

代码示例来源：origin: ahmetaa/zemberek-nlp

public synchronized void initializeStaticCache(Function<String, WordAnalysis> analysisProvider) {
 if (staticCacheDisabled || staticCacheInitialized) {
  return;
 }
 new Thread(() -> {
  try {
   Stopwatch stopwatch = Stopwatch.createStarted();
   List<String> words = TextIO.loadLinesFromResource(MOST_USED_WORDS_FILE);
   Log.debug("File read in %d ms.", stopwatch.elapsed(TimeUnit.MILLISECONDS));
   int size = Math.min(STATIC_CACHE_CAPACITY, words.size());
   for (int i = 0; i < size; i++) {
    String word = words.get(i);
    staticCache.put(word, analysisProvider.apply(word));
   }
   Log.debug("Static cache initialized with %d most frequent words", size);
   Log.debug("Initialization time: %d ms.", stopwatch.elapsed(TimeUnit.MILLISECONDS));
  } catch (IOException e) {
   Log.error("Could not read most frequent words list, static cache is disabled.");
   e.printStackTrace();
  }
 }).start();
 staticCacheInitialized = true;
}

代码示例来源：origin: ahmetaa/zemberek-nlp

Log.info("Loading from: " + idFile);
if (!idFile.exists()) {
 Log.warn("Cannot find n-gram id file " + idFile.getAbsolutePath());
 continue;
  Log.warn("Cannot load key file as flat array. Too much index values.");
  continue;

代码示例来源：origin: ahmetaa/zemberek-nlp

Log.debug("Changing log base from " + DEFAULT_LOG_BASE + " to " + logBase);
changeLogBase(logBase);
this.stupidBackoffLogAlpha = (float) (Math.log(stupidBackoffAlpha) / Math.log(logBase));
Log.debug("Applying unigram smoothing with unigram weight: " + unigramWeight);
applyUnigramSmoothing(unigramWeight);
Log.debug("Lm will use stupid back off with alpha value: " + stupidBackoffAlpha);
 Log.warn("Ngram id file directory %s does not exist. Continue without loading.",
   ngramKeyFileDir);
} else {
 Log.info("Loading actual n-gram id data.");
 this.ngramIds = new NgramIds(this.order, ngramKeyFileDir, mphfs);

代码示例来源：origin: ahmetaa/zemberek-nlp

File tmp = Files.createTempDir();
Splitter splitter = new Splitter(file, tmp, chunkBits);
Log.info("Gram count: " + splitter.gramCount);
Log.info("Segment count: " + splitter.pageCount);
Log.info("Average segment size: " + (1 << splitter.pageBit));
Log.info("Segmenting File...");
splitter.split();
int bucketBits = splitter.pageBit - 2;
for (int i = 0; i < splitter.pageCount; i++) {
 final ByteGramProvider keySegment = splitter.getKeySegment(i);
 Log.debug("Segment key count: " + keySegment.keyAmount());
 Log.debug("Segment bucket ratio: " + ((double) keySegment.keyAmount() / (1 << bucketBits)));
 total += keySegment.keyAmount();
 MultiLevelMphf mphf = MultiLevelMphf.generate(keySegment);
 Log.info("MPHF is generated for segment %d with %d keys. Average bits per key: %.3f",
   i,
   mphf.size(),
Log.debug("Total processed keys:" + total);
int maxMask = (1 << splitter.maxBit) - 1;
int bucketMask = (1 << bucketBits) - 1;

代码示例来源：origin: ahmetaa/zemberek-nlp

for (String word : inputVocabulary) {
 if (vocabularyIndexMap.containsKey(word)) {
  Log.warn("Language model vocabulary has duplicate item: " + word);
  continue;
   Log.warn(
     "Unknown word was already defined as %s but another matching token exist in the input vocabulary: %s",
     unknownWord, word);
   Log.warn(
     "Sentence start index was already defined as %s but another matching token exist in the input vocabulary: %s",
     sentenceStart, word);
   Log.warn(
     "Sentence end index was already defined as %s but another matching token exist in the input vocabulary: %s",
     sentenceEnd, word);
 cleanVocab.add(unknownWord);
 vocabularyIndexMap.put(unknownWord, indexCounter++);
 Log.debug("Necessary special token " + unknownWord
   + " was not found in the vocabulary, it is added explicitly");
 cleanVocab.add(sentenceStart);
 vocabularyIndexMap.put(sentenceStart, indexCounter++);
 Log.debug("Vocabulary does not contain sentence start token, it is added explicitly.");
 cleanVocab.add(sentenceEnd);
 vocabularyIndexMap.put(sentenceEnd, indexCounter);
 Log.debug("Vocabulary does not contain sentence end token, it is added explicitly.");

代码示例来源：origin: ahmetaa/zemberek-nlp

public static Quantizer getQuantizer(File file, int bitCount, QuantizerType quantizerType)
  throws IOException {
 BinaryFloatFileReader reader = new BinaryFloatFileReader(file);
 try (DataInputStream dis = reader.getStream()) {
  dis.skipBytes(4); // skip the count.
  LookupCalculator lookupCalc = new LookupCalculator(bitCount);
  for (int i = 0; i < reader.count; i++) {
   double d = dis.readFloat();
   lookupCalc.add(d);
   if (Log.isDebug() && i % 500000 == 0) {
    Log.debug("Values added to value histogram = %d", i);
   }
  }
  return lookupCalc.getQuantizer(quantizerType);
 }
}

代码示例来源：origin: ahmetaa/zemberek-nlp

@Override
 public void uncaughtException(Thread t, Throwable e) {
  Log.error("Exception occurred in thread :" + t.getName(), e);
  e.printStackTrace();
 }
}

代码示例来源：origin: ahmetaa/zemberek-nlp

Log.info("Generating models for:" + Arrays.toString(languages));
  modelFileMap.remove(l);
 } else {
  Log.warn("Cannot find count model file for language " + language);
 Log.info("Generating garbage model from remaining count models.");
 CharNgramCountModel garbageModel = new CharNgramCountModel("unk", order);
 for (File file : modelFileMap.values()) {

代码示例来源：origin: ahmetaa/zemberek-nlp

if (Log.isDebug()) {
 Log.debug("Failed key Count:%d " , failedKeyCount);

代码示例来源：origin: ahmetaa/zemberek-nlp

public static void simpleTokenization() {
 Log.info("Simple tokenization returns a list of token strings.");
 TurkishTokenizer tokenizer = TurkishTokenizer.DEFAULT;
 String input = "İstanbul'a, merhaba!";
 Log.info("Input = " + input);
 Log.info("Tokenization list = " +
   Joiner.on("|").join(tokenizer.tokenizeToStrings("İstanbul'a, merhaba!")));
}

代码示例来源：origin: ahmetaa/zemberek-nlp

Log.info("Amount of words to include using include file: %d", wordsToInclude.size());
Set<String> wordsToExclude = getWordsFromFile(excludeFile);
Log.info("Amount of words to exclude using exclude file: %d", wordsToExclude.size());
 Log.warn(
   "There are matching words in both include and exclude files: " + intersection.toString());
Log.info("Processing corpus: %s", corpus);
try (BufferedReader reader = new BufferedReader(
  new InputStreamReader(new FileInputStream(corpus), "utf-8"))) {
   Log.info("%d lines processed. Vocabulary Size: %d", count, histogram.size());
 Log.info("A total of %d lines have been processed. Vocabulary Size: %d", count,
   histogram.size());
  top = histogram.size();
 } else {
  Log.info("Top %d words will be used.", top);
 Log.info("Coverage: %.3f",
   100d * ((double) histogram.totalCount(mostFrequent)) / histogram.totalCount());
 Log.info("Total size of vocabulary: %d", result.size());
 if (ordered) {
  Log.info("Sorting file with word order.");
  result.sort(collator);
 Log.info("Saving to vocabulary file: %s", outFile);

代码示例来源：origin: ahmetaa/zemberek-nlp

public MorphemeState addIncoming(MorphemeTransition... suffixTransitions) {
 for (MorphemeTransition suffixTransition : suffixTransitions) {
  if (incoming.contains(suffixTransition)) {
   Log.warn("Incoming transition %s already exist in %s", suffixTransition, this);
  }
  incoming.add(suffixTransition);
 }
 return this;
}

代码示例来源：origin: ahmetaa/zemberek-nlp

ArpaToBinaryConverter(File dir, int fractionDigitCount)
  throws FileNotFoundException {
 Log.info("Generating multi file uncompressed language model from Arpa file in directory: %s",
   dir.getAbsolutePath());
 this.dir = dir;
 if (fractionDigitCount >= 0) {
  fractionMultiplier = Math.pow(10, fractionDigitCount);
 } else {
  fractionMultiplier = 0;
 }
 start = System.currentTimeMillis();
}

代码示例来源：origin: ahmetaa/zemberek-nlp

static void countTokens(Path... paths) throws IOException {
 for (Path path : paths) {
  List<String> lines = TextIO.loadLines(path);
  Histogram<String> hw = new Histogram<>();
  Histogram<String> hl = new Histogram<>();
  for (String l : lines) {
   for (String s : l.split("[\\s]+")) {
    if (s.contains("__label__")) {
     if(s.contains("-")) {
      Log.warn(l);
     }
     hl.add(s);
    } else {
     hw.add(s);
    }
   }
  }
  Log.info("There are %d lines, %d words, %d labels in %s",
    lines.size(),
    hw.size(),
    hl.size(),
    path);
 }
}

代码示例来源：origin: ahmetaa/zemberek-nlp

public MorphemeState addOutgoing(MorphemeTransition... suffixTransitions) {
 for (MorphemeTransition suffixTransition : suffixTransitions) {
  if (outgoing.contains(suffixTransition)) {
   Log.warn("Outgoing transition %s already exist in %s", suffixTransition, this);
  }
  outgoing.add(suffixTransition);
 }
 return this;
}

代码示例来源：origin: ahmetaa/zemberek-nlp

void info() {
 Log.info("There are %d sentences and %d tokens.",
   sentences.size(),
   sentences.stream().mapToInt(SentenceAnalysis::size).sum());
}

代码示例来源：origin: ahmetaa/zemberek-nlp

Log.info("Processing.. %d found.", batchResult.acceptedSentences.size());
for (String sentence : toProcess) {
  Log.warn("Sentence [%s] contains word(s) that all analyses are ignored.",
    r.sentence);
  continue;

代码示例来源：origin: ahmetaa/zemberek-nlp

/**
 * @param index Word for the index. if index is out of bounds, <UNK> is returned with warning.
 * Note that Vocabulary may contain <UNK> token as well.
 */
public String getWord(int index) {
 if (index < 0 || index >= vocabulary.size()) {
  Log.warn("Out of bounds word index is used:" + index);
  return unknownWord;
 }
 return vocabulary.get(index);
}