code stringlengths 73 34.1k | label stringclasses 1
value |
|---|---|
public Record predict(String text) {
TrainingParameters trainingParameters = (TrainingParameters) knowledgeBase.getTrainingParameters();
Dataframe testDataset = new Dataframe(knowledgeBase.getConfiguration());
testDataset.add(
new Record(
new AssociativeArray... | java |
public ClassificationMetrics validate(Dataframe testDataset) {
logger.info("validate()");
predict(testDataset);
ClassificationMetrics vm = new ClassificationMetrics(testDataset);
return vm;
} | java |
public ClassificationMetrics validate(Map<Object, URI> datasets) {
TrainingParameters trainingParameters = (TrainingParameters) knowledgeBase.getTrainingParameters();
//build the testDataset
Dataframe testDataset = Dataframe.Builder.parseTextFiles(
datasets,
Abst... | java |
protected final String createKnowledgeBaseName(String storageName, String separator) {
return storageName + separator + getClass().getSimpleName();
} | java |
public static <T> Set<Set<T>> combinations(Set<T> elements, int subsetSize) {
return combinationsStream(elements, subsetSize).collect(Collectors.toSet());
} | java |
public static <T> Stream<Set<T>> combinationsStream(Set<T> elements, int subsetSize) {
if (subsetSize == 0) {
return Stream.of(new HashSet<>());
}
else if (subsetSize <= elements.size()) {
Set<T> remainingElements = elements;
Iterator<T> it = remainingElement... | java |
public static <T> Iterator<T[]> combinationsIterator(final T[] elements, final int subsetSize) {
return new Iterator<T[]>() {
/**
* The index on the combination array.
*/
private int r = 0;
/**
* The index on the elements array.
... | java |
public static <T> Stream<T> stream(Spliterator<T> spliterator, boolean parallel) {
return StreamSupport.<T>stream(spliterator, parallel);
} | java |
public static <T> Stream<T> stream(Stream<T> stream, boolean parallel) {
if(parallel) {
return stream.parallel();
}
else {
return stream.sequential();
}
} | java |
public static Method findMethod(Object obj, String methodName, Object... params) {
Class<?>[] classArray = new Class<?>[params.length];
for (int i = 0; i < params.length; i++) {
classArray[i] = params[i].getClass();
}
try {
//look on all the public, protected, def... | java |
@Override
public Map<String, Double> extract(final String text) {
Map<Integer, String> ID2word = new HashMap<>(); //ID=>Kwd
Map<Integer, Double> ID2occurrences = new HashMap<>(); //ID=>counts/scores
Map<Integer, Integer> position2ID = new LinkedHashMap<>(); //word position=>ID maintain the o... | java |
public static FlatDataCollection weightedSampling(AssociativeArray weightedTable, int n, boolean withReplacement) {
FlatDataList sampledIds = new FlatDataList();
double sumOfFrequencies = Descriptives.sum(weightedTable.toFlatDataCollection());
int populationN = weightedTable.size();
... | java |
public static double xbarVariance(double variance, int sampleN, int populationN) {
if(populationN<=0 || sampleN<=0 || sampleN>populationN) {
throw new IllegalArgumentException("All the parameters must be positive and sampleN smaller than populationN.");
}
double xbarVariance... | java |
public static double xbarStd(double std, int sampleN) {
return Math.sqrt(xbarVariance(std*std, sampleN, Integer.MAX_VALUE));
} | java |
public static double xbarStd(double std, int sampleN, int populationN) {
return Math.sqrt(xbarVariance(std*std, sampleN, populationN));
} | java |
public static double pbarVariance(double pbar, int sampleN, int populationN) {
if(populationN<=0 || sampleN<=0 || sampleN>populationN) {
throw new IllegalArgumentException("All the parameters must be positive and sampleN smaller than populationN.");
}
double f = (double)sampleN/popul... | java |
public static double pbarStd(double pbar, int sampleN) {
return Math.sqrt(pbarVariance(pbar, sampleN, Integer.MAX_VALUE));
} | java |
public static double pbarStd(double pbar, int sampleN, int populationN) {
return Math.sqrt(pbarVariance(pbar, sampleN, populationN));
} | java |
public static int minimumSampleSizeForMaximumXbarStd(double maximumXbarStd, double populationStd, int populationN) {
if(populationN<=0) {
throw new IllegalArgumentException("The populationN parameter must be positive.");
}
double minimumSampleN = 1.0/(Math.pow(maximumXbarStd... | java |
public static int minimumSampleSizeForGivenDandMaximumRisk(double d, double aLevel, double populationStd) {
return minimumSampleSizeForGivenDandMaximumRisk(d, aLevel, populationStd, Integer.MAX_VALUE);
} | java |
public static int minimumSampleSizeForGivenDandMaximumRisk(double d, double aLevel, double populationStd, int populationN) {
if(populationN<=0 || aLevel<=0 || d<=0) {
throw new IllegalArgumentException("All the parameters must be positive.");
}
double a = 1.0 - aLevel/2.0;
... | java |
public static double shinglerSimilarity(String text1, String text2, int w) {
preprocessDocument(text1);
preprocessDocument(text2);
NgramsExtractor.Parameters parameters = new NgramsExtractor.Parameters();
parameters.setMaxCombinations(w);
parameters.setMaxDistanceBetweenKwds(0);... | java |
private void bigMapInitializer(StorageEngine storageEngine) {
//get all the fields from all the inherited classes
for(Field field : ReflectionMethods.getAllFields(new LinkedList<>(), this.getClass())){
//if the field is annotated with BigMap
if (field.isAnnotationPresent(BigMap.c... | java |
private void initializeBigMapField(StorageEngine storageEngine, Field field) {
field.setAccessible(true);
try {
BigMap a = field.getAnnotation(BigMap.class);
field.set(this, storageEngine.getBigMap(field.getName(), a.keyClass(), a.valueClass(), a.mapType(), a.storageHint(), a.co... | java |
public Trainable put(String key, Trainable value) {
return bundle.put(key, value);
} | java |
public void setParallelized(boolean parallelized) {
for(Trainable t : bundle.values()) {
if (t !=null && t instanceof Parallelizable) {
((Parallelizable)t).setParallelized(parallelized);
}
}
} | java |
public static <T extends Trainable, TP extends Parameterizable> T create(TP trainingParameters, Configuration configuration) {
try {
Class<T> aClass = (Class<T>) trainingParameters.getClass().getEnclosingClass();
Constructor<T> constructor = aClass.getDeclaredConstructor(trainingParamete... | java |
public static <T extends Trainable> T load(Class<T> aClass, String storageName, Configuration configuration) {
try {
Constructor<T> constructor = aClass.getDeclaredConstructor(String.class, Configuration.class);
constructor.setAccessible(true);
return constructor.newInstance(... | java |
public static double combination(int n, int k) {
if(n<k) {
throw new IllegalArgumentException("The n can't be smaller than k.");
}
double combinations=1.0;
double lowerBound = n-k;
for(int i=n;i>lowerBound;i--) {
combinations *= i/(i-lowerBound);
}... | java |
private StorageType getStorageTypeFromName(String name) {
for(Map.Entry<StorageType, DB> entry : storageRegistry.entrySet()) {
DB storage = entry.getValue();
if(isOpenStorage(storage) && storage.exists(name)) {
return entry.getKey();
}
}
... | java |
private void closeStorageRegistry() {
for(DB storage : storageRegistry.values()) {
if(isOpenStorage(storage)) {
storage.close();
}
}
storageRegistry.clear();
} | java |
private boolean blockedStorageClose(StorageType storageType) {
DB storage = storageRegistry.get(storageType);
if(isOpenStorage(storage)) {
storage.commit();
//find the underlying engine
Engine e = storage.getEngine();
while (EngineWrapper.class.isAssignab... | java |
@Override
public List<String> tokenize(String text) {
List<String> tokens = new ArrayList<>(Arrays.asList(text.split("[\\p{Z}\\p{C}]+")));
return tokens;
} | java |
public static Map.Entry<Object, Object> maxMin(DataTable2D payoffMatrix) {
if(payoffMatrix.isValid()==false) {
throw new IllegalArgumentException("The payoff matrix does not have a rectangular format.");
}
AssociativeArray minPayoffs = new AssociativeArray();
for(Map... | java |
public static Map.Entry<Object, Object> maxMax(DataTable2D payoffMatrix) {
if(payoffMatrix.isValid()==false) {
throw new IllegalArgumentException("The payoff matrix does not have a rectangular format.");
}
Double maxMaxPayoff = Double.NEGATIVE_INFINITY;
Object maxMax... | java |
public static Map.Entry<Object, Object> savage(DataTable2D payoffMatrix) {
if(payoffMatrix.isValid()==false) {
throw new IllegalArgumentException("The payoff matrix does not have a rectangular format.");
}
//Deep clone the payoffMatrix to avoid modifying its original values
... | java |
public static Map.Entry<Object, Object> laplace(DataTable2D payoffMatrix) {
if(payoffMatrix.isValid()==false) {
throw new IllegalArgumentException("The payoff matrix does not have a rectangular format.");
}
//http://orms.pef.czu.cz/text/game-theory/DecisionTheory.html
... | java |
public static Map.Entry<Object, Object> hurwiczAlpha(DataTable2D payoffMatrix, double alpha) {
if(payoffMatrix.isValid()==false) {
throw new IllegalArgumentException("The payoff matrix does not have a rectangular format.");
}
AssociativeArray minPayoffs = new AssociativeArra... | java |
public static Map.Entry<Object, Object> maximumLikelihood(DataTable2D payoffMatrix, AssociativeArray eventProbabilities) {
if(payoffMatrix.isValid()==false) {
throw new IllegalArgumentException("The payoff matrix does not have a rectangular format.");
}
Map.Entry<Object, Obj... | java |
public static Map.Entry<Object, Object> bayes(DataTable2D payoffMatrix, AssociativeArray eventProbabilities) {
if(payoffMatrix.isValid()==false) {
throw new IllegalArgumentException("The payoff matrix does not have a rectangular format.");
}
AssociativeArray expectedPayoffs ... | java |
private static DataTable2D bivariateMatrix(Dataframe dataSet, BivariateType type) {
DataTable2D bivariateMatrix = new DataTable2D();
//extract values of first variable
Map<Object, TypeInference.DataType> columnTypes = dataSet.getXDataTypes();
Object[] allVariables = columnTypes.... | java |
public static <K> void updateWeights(double l1, double l2, double learningRate, Map<K, Double> weights, Map<K, Double> newWeights) {
L2Regularizer.updateWeights(l2, learningRate, weights, newWeights);
L1Regularizer.updateWeights(l1, learningRate, weights, newWeights);
} | java |
public static <K> double estimatePenalty(double l1, double l2, Map<K, Double> weights) {
double penalty = 0.0;
penalty += L2Regularizer.estimatePenalty(l2, weights);
penalty += L1Regularizer.estimatePenalty(l1, weights);
return penalty;
} | java |
public static int substr_count(final String string, final String substring) {
if(substring.length()==1) {
return substr_count(string, substring.charAt(0));
}
int count = 0;
int idx = 0;
while ((idx = string.indexOf(substring, idx)) != -1) {
++idx;... | java |
public static int substr_count(final String string, final char character) {
int count = 0;
int n = string.length();
for(int i=0;i<n;i++) {
if(string.charAt(i)==character) {
++count;
}
}
return count;
} | java |
public static String preg_replace(String regex, String replacement, String subject) {
Pattern p = Pattern.compile(regex);
return preg_replace(p, replacement, subject);
} | java |
public static String preg_replace(Pattern pattern, String replacement, String subject) {
Matcher m = pattern.matcher(subject);
StringBuffer sb = new StringBuffer(subject.length());
while(m.find()){
m.appendReplacement(sb, replacement);
}
m.appendTail(sb);
ret... | java |
public static int preg_match(String regex, String subject) {
Pattern p = Pattern.compile(regex);
return preg_match(p, subject);
} | java |
public static int preg_match(Pattern pattern, String subject) {
int matches=0;
Matcher m = pattern.matcher(subject);
while(m.find()){
++matches;
}
return matches;
} | java |
public static double round(double d, int i) {
double multiplier = Math.pow(10, i);
return Math.round(d*multiplier)/multiplier;
} | java |
public static double log(double d, double base) {
if(base==1.0 || base<=0.0) {
throw new IllegalArgumentException("Invalid base for logarithm.");
}
return Math.log(d)/Math.log(base);
} | java |
public static <K,V> Map<V,K> array_flip(Map<K,V> map) {
Map<V,K> flipped = new HashMap<>();
for(Map.Entry<K,V> entry : map.entrySet()) {
flipped.put(entry.getValue(), entry.getKey());
}
return flipped;
} | java |
public static <T> void shuffle(T[] array, Random rnd) {
//Implementing Fisher-Yates shuffle
T tmp;
for (int i = array.length - 1; i > 0; --i) {
int index = rnd.nextInt(i + 1);
tmp = array[index];
array[index] = array[i];
array[i] = tmp... | java |
public static <T extends Comparable<T>> Integer[] asort(T[] array) {
return _asort(array, false);
} | java |
public static <T extends Comparable<T>> Integer[] arsort(T[] array) {
return _asort(array, true);
} | java |
public static <T> void arrangeByIndex(T[] array, Integer[] indexes) {
if(array.length != indexes.length) {
throw new IllegalArgumentException("The length of the two arrays must match.");
}
//sort the array based on the indexes
for(int i=0;i<array.length;i++) {
... | java |
public static double[] array_clone(double[] a) {
if(a == null) {
return a;
}
return Arrays.copyOf(a, a.length);
} | java |
public static double[][] array_clone(double[][] a) {
if(a == null) {
return a;
}
double[][] copy = new double[a.length][];
for(int i=0;i<a.length;i++) {
copy[i] = Arrays.copyOf(a[i], a[i].length);
}
return copy;
} | java |
public static AssociativeArray sum(DataTable2D classifierClassProbabilityMatrix) {
AssociativeArray combinedClassProbabilities = new AssociativeArray();
for(Map.Entry<Object, AssociativeArray> entry : classifierClassProbabilityMatrix.entrySet()) {
//Object classifier = entry.getKey... | java |
public static AssociativeArray median(DataTable2D classifierClassProbabilityMatrix) {
AssociativeArray combinedClassProbabilities = new AssociativeArray();
//extract all the classes first
for(Map.Entry<Object, AssociativeArray> entry : classifierClassProbabilityMatrix.entrySet()) {
... | java |
public static AssociativeArray majorityVote(DataTable2D classifierClassProbabilityMatrix) {
AssociativeArray combinedClassProbabilities = new AssociativeArray();
//extract all the classes first
for(Map.Entry<Object, AssociativeArray> entry : classifierClassProbabilityMatrix.entrySet())... | java |
public AssociativeArray2D getWordProbabilitiesPerTopic() {
AssociativeArray2D ptw = new AssociativeArray2D();
ModelParameters modelParameters = knowledgeBase.getModelParameters();
TrainingParameters trainingParameters = knowledgeBase.getTrainingParameters();
//initializ... | java |
private <K> void increase(Map<K, Integer> map, K key) {
map.put(key, map.getOrDefault(key, 0)+1);
} | java |
private <K> void decrease(Map<K, Integer> map, K key) {
map.put(key, map.getOrDefault(key, 0)-1);
} | java |
protected <T extends Serializable> Map<String, Object> preSerializer(T serializableObject) {
Map<String, Object> objReferences = new HashMap<>();
for(Field field : ReflectionMethods.getAllFields(new LinkedList<>(), serializableObject.getClass())) {
if (field.isAnnotationPresent(BigMap.class... | java |
protected <T extends Serializable> void postSerializer(T serializableObject, Map<String, Object> objReferences) {
for(Field field : ReflectionMethods.getAllFields(new LinkedList<>(), serializableObject.getClass())) {
String fieldName = field.getName();
Object ref = objReferences.remove(... | java |
protected <T extends Serializable> void postDeserializer(T serializableObject) {
Method method = null;
for(Field field : ReflectionMethods.getAllFields(new LinkedList<>(), serializableObject.getClass())) {
if (field.isAnnotationPresent(BigMap.class)) { //look only for BigMaps
... | java |
public static boolean isActive(Enum obj) {
Enum value = ACTIVE_SWITCHES.get((Class)obj.getClass());
return value != null && value == obj;
} | java |
public boolean isValid() {
int totalNumberOfColumns = 0;
Set<Object> columns = new HashSet<>();
for(Map.Entry<Object, AssociativeArray> entry : internalData.entrySet()) {
AssociativeArray row = entry.getValue();
if(columns.isEmpty()) {
//this is executed o... | java |
protected Object getSelectedClassFromClassScores(AssociativeArray predictionScores) {
Map.Entry<Object, Object> maxEntry = MapMethods.selectMaxKeyValue(predictionScores);
return maxEntry.getKey();
} | java |
public static FlatDataCollection randomSampling(FlatDataList idList, int n, boolean randomizeRecords) {
FlatDataList sampledIds = new FlatDataList();
int populationN = idList.size();
Object[] keys = idList.toArray();
if(randomizeRecords) {
PHPMethods.<Object... | java |
private CL getFromClusterMap(int clusterId, Map<Integer, CL> clusterMap) {
CL c = clusterMap.get(clusterId);
if(c.getFeatureIds() == null) {
c.setFeatureIds(knowledgeBase.getModelParameters().getFeatureIds()); //fetch the featureIds from model parameters object
}
return c;
... | java |
protected String getDirectory() {
//get the default filepath of the permanet storage file
String directory = storageConfiguration.getDirectory();
if(directory == null || directory.isEmpty()) {
directory = System.getProperty("java.io.tmpdir"); //write them to the tmp directory
... | java |
protected Path getRootPath(String storageName) {
return Paths.get(getDirectory() + File.separator + storageName);
} | java |
protected boolean deleteIfExistsRecursively(Path path) throws IOException {
try {
return Files.deleteIfExists(path);
}
catch (DirectoryNotEmptyException ex) {
//do recursive delete
Files.walkFileTree(path, new SimpleFileVisitor<Path>() {
@Overr... | java |
protected boolean deleteDirectory(Path path, boolean cleanParent) throws IOException {
boolean pathExists = deleteIfExistsRecursively(path);
if(pathExists && cleanParent) {
cleanEmptyParentDirectory(path.getParent());
return true;
}
return false;
} | java |
private void cleanEmptyParentDirectory(Path path) throws IOException {
Path normPath = path.normalize();
if(normPath.equals(Paths.get(getDirectory()).normalize()) || normPath.equals(Paths.get(System.getProperty("java.io.tmpdir")).normalize())) { //stop if we reach the output or temporary directory
... | java |
protected boolean moveDirectory(Path src, Path target) throws IOException {
if(Files.exists(src)) {
createDirectoryIfNotExists(target.getParent());
deleteDirectory(target, false);
Files.move(src, target);
cleanEmptyParentDirectory(src.getParent());
ret... | java |
protected boolean createDirectoryIfNotExists(Path path) throws IOException {
if(!Files.exists(path)) {
Files.createDirectories(path);
return true;
}
else {
return false;
}
} | java |
public static double simpleMovingAverage(FlatDataList flatDataList, int N) {
double SMA=0;
int counter=0;
for(int i=flatDataList.size()-1;i>=0;--i) {
double Yti = flatDataList.getDouble(i);
if(counter>=N) {
break;
}
SMA+=Yti; //pos... | java |
public static double weightedMovingAverage(FlatDataList flatDataList, int N) {
double WMA=0;
double denominator=0.0;
int counter=0;
for(int i=flatDataList.size()-1;i>=0;--i) {
double Yti = flatDataList.getDouble(i);
if(counter>=N) {
b... | java |
public static double simpleExponentialSmoothing(FlatDataList flatDataList, double a) {
double EMA=0;
int count=0;
for(int i=flatDataList.size()-1;i>=0;--i) {
double Yti = flatDataList.getDouble(i);
EMA+=a*Math.pow(1-a,count)*Yti;
++count;
}
r... | java |
public static Double largest(Iterator<Double> elements, int k) {
Iterator<Double> oppositeElements = new Iterator<Double>() {
/** {@inheritDoc} */
@Override
public boolean hasNext() {
return elements.hasNext();
}
/** {@inhe... | java |
public static double nBar(TransposeDataList clusterIdList) {
int populationM = clusterIdList.size();
double nBar = 0.0;
for(Map.Entry<Object, FlatDataList> entry : clusterIdList.entrySet()) {
nBar += (double)entry.getValue().size()/populationM;
}
return nBar... | java |
public static TransposeDataCollection randomSampling(TransposeDataList clusterIdList, int sampleM) {
TransposeDataCollection sampledIds = new TransposeDataCollection();
Object[] selectedClusters = clusterIdList.keySet().toArray();
PHPMethods.<Object>shuffle(selectedClusters);
... | java |
public static String tokenizeSmileys(String text) {
for(Map.Entry<String, String> smiley : SMILEYS_MAPPING.entrySet()) {
text = text.replaceAll(smiley.getKey(), smiley.getValue());
}
return text;
} | java |
public static String unifyTerminators(String text) {
text = text.replaceAll("[\",:;()\\-]+", " "); // Replace commas, hyphens, quotes etc (count them as spaces)
text = text.replaceAll("[\\.!?]", "."); // Unify terminators
text = text.replaceAll("\\.[\\. ]+", "."); // Check for duplicated termina... | java |
public static String removeAccents(String text) {
text = Normalizer.normalize(text, Normalizer.Form.NFD);
text = text.replaceAll("[\\p{InCombiningDiacriticalMarks}]", "");
return text;
} | java |
public static String clear(String text) {
text = StringCleaner.tokenizeURLs(text);
text = StringCleaner.tokenizeSmileys(text);
text = StringCleaner.removeAccents(text);
text = StringCleaner.removeSymbols(text);
text = StringCleaner.removeExtraSpaces(text);
return... | java |
public static double getScoreValue(DataTable2D dataTable) {
AssociativeArray result = getScore(dataTable);
double score = result.getDouble("score");
return score;
} | java |
public static LPResult solve(double[] linearObjectiveFunction, List<LPSolver.LPConstraint> linearConstraintsList, boolean nonNegative, boolean maximize) {
int m = linearConstraintsList.size();
List<LinearConstraint> constraints = new ArrayList<>(m);
for(LPSolver.LPConstraint constraint : linear... | java |
public final Object get2d(Object key1, Object key2) {
AssociativeArray tmp = internalData.get(key1);
if(tmp == null) {
return null;
}
return tmp.internalData.get(key2);
} | java |
public final Object put2d(Object key1, Object key2, Object value) {
AssociativeArray tmp = internalData.get(key1);
if(tmp == null) {
internalData.put(key1, new AssociativeArray());
}
return internalData.get(key1).internalData.put(key2, value);
} | java |
public static String joinURL(Map<URLParts, String> urlParts) {
try {
URI uri = new URI(urlParts.get(URLParts.PROTOCOL), urlParts.get(URLParts.AUTHORITY), urlParts.get(URLParts.PATH), urlParts.get(URLParts.QUERY), urlParts.get(URLParts.REF));
return uri.toString();
}
catc... | java |
public static Map<DomainParts, String> splitDomain(String domain) {
Map<DomainParts, String> domainParts = null;
String[] dottedParts = domain.trim().toLowerCase(Locale.ENGLISH).split("\\.");
if(dottedParts.length==2) {
domainParts = new HashMap<>();
dom... | java |
public static double fleschKincaidReadingEase(String strText) {
strText = cleanText(strText);
return PHPMethods.round((206.835 - (1.015 * averageWordsPerSentence(strText)) - (84.6 * averageSyllablesPerWord(strText))), 1);
} | java |
public static double fleschKincaidGradeLevel(String strText) {
strText = cleanText(strText);
return PHPMethods.round(((0.39 * averageWordsPerSentence(strText)) + (11.8 * averageSyllablesPerWord(strText)) - 15.59), 1);
} | java |
public static double gunningFogScore(String strText) {
strText = cleanText(strText);
return PHPMethods.round(((averageWordsPerSentence(strText) + percentageWordsWithThreeSyllables(strText)) * 0.4), 1);
} | java |
public static double colemanLiauIndex(String strText) {
strText = cleanText(strText);
int intWordCount = wordCount(strText);
return PHPMethods.round( ( (5.89 * (letterCount(strText) / (double)intWordCount)) - (0.3 * (sentenceCount(strText) / (double)intWordCount)) - 15.8 ), 1);
} | java |
public static double smogIndex(String strText) {
strText = cleanText(strText);
return PHPMethods.round(1.043 * Math.sqrt((wordsWithThreeSyllables(strText) * (30.0 / sentenceCount(strText))) + 3.1291), 1);
} | java |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.