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128,600 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/TransformProcess.java | TransformProcess.transformRawStringsToInputList | public List<Writable> transformRawStringsToInputList(List<String> values) {
List<Writable> ret = new ArrayList<>();
if (values.size() != initialSchema.numColumns())
throw new IllegalArgumentException(
String.format("Number of values %d does not match the number of input c... | java | public List<Writable> transformRawStringsToInputList(List<String> values) {
List<Writable> ret = new ArrayList<>();
if (values.size() != initialSchema.numColumns())
throw new IllegalArgumentException(
String.format("Number of values %d does not match the number of input c... | [
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128,601 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPoolingUtils.java | KerasPoolingUtils.mapPoolingType | public static PoolingType mapPoolingType(String className, KerasLayerConfiguration conf)
throws UnsupportedKerasConfigurationException {
PoolingType poolingType;
if (className.equals(conf.getLAYER_CLASS_NAME_MAX_POOLING_2D()) ||
className.equals(conf.getLAYER_CLASS_NAME_MAX_P... | java | public static PoolingType mapPoolingType(String className, KerasLayerConfiguration conf)
throws UnsupportedKerasConfigurationException {
PoolingType poolingType;
if (className.equals(conf.getLAYER_CLASS_NAME_MAX_POOLING_2D()) ||
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128,602 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/layers/pooling/KerasPoolingUtils.java | KerasPoolingUtils.mapGlobalPoolingDimensions | public static int[] mapGlobalPoolingDimensions(String className, KerasLayerConfiguration conf)
throws UnsupportedKerasConfigurationException {
int[] dimensions;
if (className.equals(conf.getLAYER_CLASS_NAME_GLOBAL_MAX_POOLING_1D()) ||
className.equals(conf.getLAYER_CLASS_NAME... | java | public static int[] mapGlobalPoolingDimensions(String className, KerasLayerConfiguration conf)
throws UnsupportedKerasConfigurationException {
int[] dimensions;
if (className.equals(conf.getLAYER_CLASS_NAME_GLOBAL_MAX_POOLING_1D()) ||
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128,603 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/ui/HtmlSequencePlotting.java | HtmlSequencePlotting.createHtmlSequencePlotFile | public static void createHtmlSequencePlotFile(String title, Schema schema, List<List<Writable>> sequence,
File output) throws Exception {
String s = createHtmlSequencePlots(title, schema, sequence);
FileUtils.writeStringToFile(output, s, StandardCharsets.UTF_8);
} | java | public static void createHtmlSequencePlotFile(String title, Schema schema, List<List<Writable>> sequence,
File output) throws Exception {
String s = createHtmlSequencePlots(title, schema, sequence);
FileUtils.writeStringToFile(output, s, StandardCharsets.UTF_8);
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128,604 | deeplearning4j/deeplearning4j | datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/transform/RandomCropTransform.java | RandomCropTransform.doTransform | @Override
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// ensure that transform is valid
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throw new Un... | java | @Override
protected ImageWritable doTransform(ImageWritable image, Random random) {
if (image == null) {
return null;
}
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if (image.getFrame().imageHeight < outputHeight || image.getFrame().imageWidth < outputWidth)
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128,605 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/TimeSeriesUtils.java | TimeSeriesUtils.movingAverage | public static INDArray movingAverage(INDArray toAvg, int n) {
INDArray ret = Nd4j.cumsum(toAvg);
INDArrayIndex[] ends = new INDArrayIndex[] {NDArrayIndex.interval(n, toAvg.columns())};
INDArrayIndex[] begins = new INDArrayIndex[] {NDArrayIndex.interval(0, toAvg.columns() - n, false)};
IN... | java | public static INDArray movingAverage(INDArray toAvg, int n) {
INDArray ret = Nd4j.cumsum(toAvg);
INDArrayIndex[] ends = new INDArrayIndex[] {NDArrayIndex.interval(n, toAvg.columns())};
INDArrayIndex[] begins = new INDArrayIndex[] {NDArrayIndex.interval(0, toAvg.columns() - n, false)};
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128,606 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-cuda/src/main/java/org/nd4j/jita/allocator/utils/AllocationUtils.java | AllocationUtils.buildAllocationShape | public static AllocationShape buildAllocationShape(DataBuffer buffer) {
AllocationShape shape = new AllocationShape();
shape.setDataType(buffer.dataType());
shape.setLength(buffer.length());
return shape;
} | java | public static AllocationShape buildAllocationShape(DataBuffer buffer) {
AllocationShape shape = new AllocationShape();
shape.setDataType(buffer.dataType());
shape.setLength(buffer.length());
return shape;
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128,607 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/AnalyzeSpark.java | AnalyzeSpark.sampleFromColumn | public static List<Writable> sampleFromColumn(int count, String columnName, Schema schema,
JavaRDD<List<Writable>> data) {
int colIdx = schema.getIndexOfColumn(columnName);
JavaRDD<Writable> ithColumn = data.map(new SelectColumnFunction(colIdx));
return ithColumn.takeSample(... | java | public static List<Writable> sampleFromColumn(int count, String columnName, Schema schema,
JavaRDD<List<Writable>> data) {
int colIdx = schema.getIndexOfColumn(columnName);
JavaRDD<Writable> ithColumn = data.map(new SelectColumnFunction(colIdx));
return ithColumn.takeSample(... | [
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128,608 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/AnalyzeSpark.java | AnalyzeSpark.sample | public static List<List<Writable>> sample(int count, JavaRDD<List<Writable>> data) {
return data.takeSample(false, count);
} | java | public static List<List<Writable>> sample(int count, JavaRDD<List<Writable>> data) {
return data.takeSample(false, count);
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128,609 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/AnalyzeSpark.java | AnalyzeSpark.sampleSequence | public static List<List<List<Writable>>> sampleSequence(int count, JavaRDD<List<List<Writable>>> data) {
return data.takeSample(false, count);
} | java | public static List<List<List<Writable>>> sampleSequence(int count, JavaRDD<List<List<Writable>>> data) {
return data.takeSample(false, count);
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128,610 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/AnalyzeSpark.java | AnalyzeSpark.sampleMostFrequentFromColumn | public static Map<Writable, Long> sampleMostFrequentFromColumn(int nMostFrequent, String columnName, Schema schema,
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int columnIdx = schema.getIndexOfColumn(columnName);
JavaPairRDD<Writable, Long> keyedByWritable = data.mapToPair(new ColumnToKeyPairTr... | java | public static Map<Writable, Long> sampleMostFrequentFromColumn(int nMostFrequent, String columnName, Schema schema,
JavaRDD<List<Writable>> data) {
int columnIdx = schema.getIndexOfColumn(columnName);
JavaPairRDD<Writable, Long> keyedByWritable = data.mapToPair(new ColumnToKeyPairTr... | [
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128,611 | deeplearning4j/deeplearning4j | datavec/datavec-spark/src/main/java/org/datavec/spark/transform/AnalyzeSpark.java | AnalyzeSpark.min | public static Writable min(JavaRDD<List<Writable>> allData, String columnName, Schema schema){
int columnIdx = schema.getIndexOfColumn(columnName);
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int columnIdx = schema.getIndexOfColumn(columnName);
JavaRDD<Writable> col = allData.map(new SelectColumnFunction(columnIdx));
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128,612 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/EvaluationUtils.java | EvaluationUtils.precision | public static double precision(long tpCount, long fpCount, double edgeCase) {
//Edge case
if (tpCount == 0 && fpCount == 0) {
return edgeCase;
}
return tpCount / (double) (tpCount + fpCount);
} | java | public static double precision(long tpCount, long fpCount, double edgeCase) {
//Edge case
if (tpCount == 0 && fpCount == 0) {
return edgeCase;
}
return tpCount / (double) (tpCount + fpCount);
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128,613 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/EvaluationUtils.java | EvaluationUtils.recall | public static double recall(long tpCount, long fnCount, double edgeCase) {
//Edge case
if (tpCount == 0 && fnCount == 0) {
return edgeCase;
}
return tpCount / (double) (tpCount + fnCount);
} | java | public static double recall(long tpCount, long fnCount, double edgeCase) {
//Edge case
if (tpCount == 0 && fnCount == 0) {
return edgeCase;
}
return tpCount / (double) (tpCount + fnCount);
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128,614 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/EvaluationUtils.java | EvaluationUtils.falsePositiveRate | public static double falsePositiveRate(long fpCount, long tnCount, double edgeCase) {
//Edge case
if (fpCount == 0 && tnCount == 0) {
return edgeCase;
}
return fpCount / (double) (fpCount + tnCount);
} | java | public static double falsePositiveRate(long fpCount, long tnCount, double edgeCase) {
//Edge case
if (fpCount == 0 && tnCount == 0) {
return edgeCase;
}
return fpCount / (double) (fpCount + tnCount);
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128,615 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/EvaluationUtils.java | EvaluationUtils.falseNegativeRate | public static double falseNegativeRate(long fnCount, long tpCount, double edgeCase) {
//Edge case
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return edgeCase;
}
return fnCount / (double) (fnCount + tpCount);
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//Edge case
if (fnCount == 0 && tpCount == 0) {
return edgeCase;
}
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128,616 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/EvaluationUtils.java | EvaluationUtils.fBeta | public static double fBeta(double beta, long tp, long fp, long fn) {
double prec = tp / ((double) tp + fp);
double recall = tp / ((double) tp + fn);
return fBeta(beta, prec, recall);
} | java | public static double fBeta(double beta, long tp, long fp, long fn) {
double prec = tp / ((double) tp + fp);
double recall = tp / ((double) tp + fn);
return fBeta(beta, prec, recall);
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128,617 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/EvaluationUtils.java | EvaluationUtils.fBeta | public static double fBeta(double beta, double precision, double recall) {
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double numerator = (1 + beta * beta) * precision * recall;
double denominator = beta * beta * precision + recall;
return numerator / denominator;
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128,618 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/EvaluationUtils.java | EvaluationUtils.matthewsCorrelation | public static double matthewsCorrelation(long tp, long fp, long fn, long tn) {
double numerator = ((double) tp) * tn - ((double) fp) * fn;
double denominator = Math.sqrt(((double) tp + fp) * (tp + fn) * (tn + fp) * (tn + fn));
return numerator / denominator;
} | java | public static double matthewsCorrelation(long tp, long fp, long fn, long tn) {
double numerator = ((double) tp) * tn - ((double) fp) * fn;
double denominator = Math.sqrt(((double) tp + fp) * (tp + fn) * (tn + fp) * (tn + fn));
return numerator / denominator;
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128,619 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/graphmapper/tf/TFGraphMapper.java | TFGraphMapper.nodesForIf | public IfImportState nodesForIf(NodeDef from, GraphDef graph) {
//Assume we start with a switch statement
int currNodeIndex = graph.getNodeList().indexOf(from);
val trueDefName = from.getInput(1);
val falseDefName = from.getInput(0);
val scopeId = UUID.randomUUID().toString();
... | java | public IfImportState nodesForIf(NodeDef from, GraphDef graph) {
//Assume we start with a switch statement
int currNodeIndex = graph.getNodeList().indexOf(from);
val trueDefName = from.getInput(1);
val falseDefName = from.getInput(0);
val scopeId = UUID.randomUUID().toString();
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128,620 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java | SDRNN.gru | public List<SDVariable> gru(GRUCellConfiguration configuration) {
GRUCell c = new GRUCell(sd, configuration);
return Arrays.asList(c.outputVariables());
} | java | public List<SDVariable> gru(GRUCellConfiguration configuration) {
GRUCell c = new GRUCell(sd, configuration);
return Arrays.asList(c.outputVariables());
} | [
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@return | [
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128,621 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java | SDRNN.sru | public SDVariable sru(String baseName, SRUConfiguration configuration) {
return new SRU(sd, configuration).outputVariables(baseName)[0];
} | java | public SDVariable sru(String baseName, SRUConfiguration configuration) {
return new SRU(sd, configuration).outputVariables(baseName)[0];
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@return | [
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128,622 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java | SDRNN.sruCell | public SDVariable sruCell(String baseName, SRUCellConfiguration configuration) {
return new SRUCell(sd, configuration).outputVariables(baseName)[0];
} | java | public SDVariable sruCell(String baseName, SRUCellConfiguration configuration) {
return new SRUCell(sd, configuration).outputVariables(baseName)[0];
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@param baseName the base name to use for the output variables
@param configuration the configuration for the sru cell
@return | [
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128,623 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/util/OutputLayerUtil.java | OutputLayerUtil.validateOutputLayerForClassifierEvaluation | public static void validateOutputLayerForClassifierEvaluation(Layer outputLayer, Class<? extends IEvaluation> classifierEval){
if(outputLayer instanceof Yolo2OutputLayer){
throw new IllegalStateException("Classifier evaluation using " + classifierEval.getSimpleName() + " class cannot be applied for ... | java | public static void validateOutputLayerForClassifierEvaluation(Layer outputLayer, Class<? extends IEvaluation> classifierEval){
if(outputLayer instanceof Yolo2OutputLayer){
throw new IllegalStateException("Classifier evaluation using " + classifierEval.getSimpleName() + " class cannot be applied for ... | [
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This is used to try and catch invalid evaluation - i.e., trying to use classifier evaluation on a regression model.
This method won't catch all possible invalid cases, but should catch some common problems.
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128,624 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/dataset/BalanceMinibatches.java | BalanceMinibatches.balance | public void balance() {
if (!rootDir.exists())
rootDir.mkdirs();
if (!rootSaveDir.exists())
rootSaveDir.mkdirs();
if (paths == null)
paths = Maps.newHashMap();
if (labelRootDirs == null)
labelRootDirs = Lists.newArrayList();
for (... | java | public void balance() {
if (!rootDir.exists())
rootDir.mkdirs();
if (!rootSaveDir.exists())
rootSaveDir.mkdirs();
if (paths == null)
paths = Maps.newHashMap();
if (labelRootDirs == null)
labelRootDirs = Lists.newArrayList();
for (... | [
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128,625 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-scaleout/spark/dl4j-spark-nlp-java8/src/main/java/org/deeplearning4j/spark/models/paragraphvectors/SparkParagraphVectors.java | SparkParagraphVectors.fitMultipleFiles | public void fitMultipleFiles(JavaPairRDD<String, String> documentsRdd) {
/*
All we want here, is to transform JavaPairRDD into JavaRDD<Sequence<VocabWord>>
*/
validateConfiguration();
broadcastEnvironment(new JavaSparkContext(documentsRdd.context()));
JavaRDD<Seque... | java | public void fitMultipleFiles(JavaPairRDD<String, String> documentsRdd) {
/*
All we want here, is to transform JavaPairRDD into JavaRDD<Sequence<VocabWord>>
*/
validateConfiguration();
broadcastEnvironment(new JavaSparkContext(documentsRdd.context()));
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128,626 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/memory/abstracts/DummyWorkspace.java | DummyWorkspace.alloc | @Override
public PagedPointer alloc(long requiredMemory, MemoryKind kind, DataType dataType, boolean initialize) {
throw new UnsupportedOperationException("DummyWorkspace shouldn't be used for allocation");
} | java | @Override
public PagedPointer alloc(long requiredMemory, MemoryKind kind, DataType dataType, boolean initialize) {
throw new UnsupportedOperationException("DummyWorkspace shouldn't be used for allocation");
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128,627 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/memory/abstracts/DummyWorkspace.java | DummyWorkspace.notifyScopeEntered | @Override
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parentWorkspace = Nd4j.getMemoryManager().getCurrentWorkspace();
Nd4j.getMemoryManager().setCurrentWorkspace(null);
return this;
} | java | @Override
public MemoryWorkspace notifyScopeEntered() {
parentWorkspace = Nd4j.getMemoryManager().getCurrentWorkspace();
Nd4j.getMemoryManager().setCurrentWorkspace(null);
return this;
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128,628 | deeplearning4j/deeplearning4j | nd4j/nd4j-parameter-server-parent/nd4j-parameter-server-node/src/main/java/org/nd4j/parameterserver/distributed/logic/completion/Clipboard.java | Clipboard.nextCandidate | public VoidAggregation nextCandidate() {
VoidAggregation result = completedQueue.poll();
// removing aggregation from tracking table
if (result != null) {
completedCounter.decrementAndGet();
unpin(result.getOriginatorId(), result.getTaskId());
}
return r... | java | public VoidAggregation nextCandidate() {
VoidAggregation result = completedQueue.poll();
// removing aggregation from tracking table
if (result != null) {
completedCounter.decrementAndGet();
unpin(result.getOriginatorId(), result.getTaskId());
}
return r... | [
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128,629 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.yield | private List<String> yield(List<String> labels) {
labels.add(label);
for (Tree t : children()) {
labels.addAll(t.yield());
}
return labels;
} | java | private List<String> yield(List<String> labels) {
labels.add(label);
for (Tree t : children()) {
labels.addAll(t.yield());
}
return labels;
} | [
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128,630 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.isPreTerminal | public boolean isPreTerminal() {
if (children == null && label != null && !label.equals("TOP"))
children = new ArrayList<>();
if (children != null && children.size() == 1) {
Tree child = children.get(0);
return child != null && child.isLeaf();
}
return... | java | public boolean isPreTerminal() {
if (children == null && label != null && !label.equals("TOP"))
children = new ArrayList<>();
if (children != null && children.size() == 1) {
Tree child = children.get(0);
return child != null && child.isLeaf();
}
return... | [
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128,631 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.depth | public int depth() {
if (isLeaf()) {
return 0;
}
int maxDepth = 0;
List<Tree> kids = children();
for (Tree kid : kids) {
int curDepth = kid.depth();
if (curDepth > maxDepth) {
maxDepth = curDepth;
}
}
... | java | public int depth() {
if (isLeaf()) {
return 0;
}
int maxDepth = 0;
List<Tree> kids = children();
for (Tree kid : kids) {
int curDepth = kid.depth();
if (curDepth > maxDepth) {
maxDepth = curDepth;
}
}
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128,632 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.depth | public int depth(Tree node) {
Tree p = node.parent(this);
if (this == node) {
return 0;
}
if (p == null) {
return -1;
}
int depth = 1;
while (this != p) {
p = p.parent(this);
depth++;
}
return depth;
... | java | public int depth(Tree node) {
Tree p = node.parent(this);
if (this == node) {
return 0;
}
if (p == null) {
return -1;
}
int depth = 1;
while (this != p) {
p = p.parent(this);
depth++;
}
return depth;
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128,633 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.parent | public Tree parent(Tree root) {
List<Tree> kids = root.children();
return traverse(root, kids, this);
} | java | public Tree parent(Tree root) {
List<Tree> kids = root.children();
return traverse(root, kids, this);
} | [
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128,634 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.traverse | private static Tree traverse(Tree parent, List<Tree> kids, Tree node) {
for (Tree kid : kids) {
if (kid == node) {
return parent;
}
Tree ret = node.parent(kid);
if (ret != null) {
return ret;
}
}
return ... | java | private static Tree traverse(Tree parent, List<Tree> kids, Tree node) {
for (Tree kid : kids) {
if (kid == node) {
return parent;
}
Tree ret = node.parent(kid);
if (ret != null) {
return ret;
}
}
return ... | [
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128,635 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.ancestor | public Tree ancestor(int height, Tree root) {
if (height < 0) {
throw new IllegalArgumentException("ancestor: height cannot be negative");
}
if (height == 0) {
return this;
}
Tree par = parent(root);
if (par == null) {
return null;
... | java | public Tree ancestor(int height, Tree root) {
if (height < 0) {
throw new IllegalArgumentException("ancestor: height cannot be negative");
}
if (height == 0) {
return this;
}
Tree par = parent(root);
if (par == null) {
return null;
... | [
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128,636 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.errorSum | public double errorSum() {
if (isLeaf()) {
return 0.0;
} else if (isPreTerminal()) {
return error();
} else {
double error = 0.0;
for (Tree child : children()) {
error += child.errorSum();
}
return error() + ... | java | public double errorSum() {
if (isLeaf()) {
return 0.0;
} else if (isPreTerminal()) {
return error();
} else {
double error = 0.0;
for (Tree child : children()) {
error += child.errorSum();
}
return error() + ... | [
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128,637 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.getLeaves | @SuppressWarnings("unchecked")
public <T extends Tree> List<T> getLeaves(List<T> list) {
if (isLeaf()) {
list.add((T) this);
} else {
for (Tree kid : children()) {
kid.getLeaves(list);
}
}
return list;
} | java | @SuppressWarnings("unchecked")
public <T extends Tree> List<T> getLeaves(List<T> list) {
if (isLeaf()) {
list.add((T) this);
} else {
for (Tree kid : children()) {
kid.getLeaves(list);
}
}
return list;
} | [
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128,638 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/layers/feedforward/autoencoder/recursive/Tree.java | Tree.connect | public void connect(List<Tree> children) {
this.children = children;
for (Tree t : children)
t.setParent(this);
} | java | public void connect(List<Tree> children) {
this.children = children;
for (Tree t : children)
t.setParent(this);
} | [
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128,639 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/string/NDArrayStrings.java | NDArrayStrings.format | public String format(INDArray arr, boolean summarize) {
if(arr.isEmpty())
return EMPTY_ARRAY_STR;
this.scientificFormat = "0.";
int addPrecision = this.precision;
while (addPrecision > 0) {
this.scientificFormat += "#";
addPrecision -= 1;
}
... | java | public String format(INDArray arr, boolean summarize) {
if(arr.isEmpty())
return EMPTY_ARRAY_STR;
this.scientificFormat = "0.";
int addPrecision = this.precision;
while (addPrecision > 0) {
this.scientificFormat += "#";
addPrecision -= 1;
}
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128,640 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-modelimport/src/main/java/org/deeplearning4j/nn/modelimport/keras/utils/KerasLossUtils.java | KerasLossUtils.mapLossFunction | public static LossFunctions.LossFunction mapLossFunction(String kerasLoss, KerasLayerConfiguration conf)
throws UnsupportedKerasConfigurationException {
LossFunctions.LossFunction dl4jLoss;
if (kerasLoss.equals(conf.getKERAS_LOSS_MEAN_SQUARED_ERROR()) ||
kerasLoss.equals(conf... | java | public static LossFunctions.LossFunction mapLossFunction(String kerasLoss, KerasLayerConfiguration conf)
throws UnsupportedKerasConfigurationException {
LossFunctions.LossFunction dl4jLoss;
if (kerasLoss.equals(conf.getKERAS_LOSS_MEAN_SQUARED_ERROR()) ||
kerasLoss.equals(conf... | [
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128,641 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/SequenceVectors.java | SequenceVectors.buildVocab | public void buildVocab() {
val constructor = new VocabConstructor.Builder<T>().addSource(iterator, minWordFrequency)
.setTargetVocabCache(vocab).fetchLabels(trainSequenceVectors).setStopWords(stopWords)
.enableScavenger(enableScavenger).setEntriesLimit(vocabLimi... | java | public void buildVocab() {
val constructor = new VocabConstructor.Builder<T>().addSource(iterator, minWordFrequency)
.setTargetVocabCache(vocab).fetchLabels(trainSequenceVectors).setStopWords(stopWords)
.enableScavenger(enableScavenger).setEntriesLimit(vocabLimi... | [
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128,642 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/inverse/InvertMatrix.java | InvertMatrix.invert | public static INDArray invert(INDArray arr, boolean inPlace) {
if(arr.rank() == 2 && arr.length() == 1){
//[1,1] edge case. Matrix inversion: [x] * [1/x] = [1]
if(inPlace){
return arr.rdivi(1.0);
} else {
return arr.rdiv(1.0);
}
... | java | public static INDArray invert(INDArray arr, boolean inPlace) {
if(arr.rank() == 2 && arr.length() == 1){
//[1,1] edge case. Matrix inversion: [x] * [1/x] = [1]
if(inPlace){
return arr.rdivi(1.0);
} else {
return arr.rdiv(1.0);
}
... | [
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128,643 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/inverse/InvertMatrix.java | InvertMatrix.pinvert | public static INDArray pinvert(INDArray arr, boolean inPlace) {
// TODO : do it natively instead of relying on commons-maths
RealMatrix realMatrix = CheckUtil.convertToApacheMatrix(arr);
QRDecomposition decomposition = new QRDecomposition(realMatrix, 0);
DecompositionSolver solver = de... | java | public static INDArray pinvert(INDArray arr, boolean inPlace) {
// TODO : do it natively instead of relying on commons-maths
RealMatrix realMatrix = CheckUtil.convertToApacheMatrix(arr);
QRDecomposition decomposition = new QRDecomposition(realMatrix, 0);
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128,644 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/inverse/InvertMatrix.java | InvertMatrix.pLeftInvert | public static INDArray pLeftInvert(INDArray arr, boolean inPlace) {
try {
final INDArray inv = invert(arr.transpose().mmul(arr), inPlace).mmul(arr.transpose());
if (inPlace) arr.assign(inv);
return inv;
} catch (SingularMatrixException e) {
throw new IllegalArgume... | java | public static INDArray pLeftInvert(INDArray arr, boolean inPlace) {
try {
final INDArray inv = invert(arr.transpose().mmul(arr), inPlace).mmul(arr.transpose());
if (inPlace) arr.assign(inv);
return inv;
} catch (SingularMatrixException e) {
throw new IllegalArgume... | [
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128,645 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/dropout/Dropout.java | Dropout.initializeHelper | protected void initializeHelper(DataType dataType){
String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
if("CUDA".equalsIgnoreCase(backend)) {
try {
helper = Class.forName("org.deeplearning4j.nn.layers.dropout.CudnnDropoutHelper")
... | java | protected void initializeHelper(DataType dataType){
String backend = Nd4j.getExecutioner().getEnvironmentInformation().getProperty("backend");
if("CUDA".equalsIgnoreCase(backend)) {
try {
helper = Class.forName("org.deeplearning4j.nn.layers.dropout.CudnnDropoutHelper")
... | [
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128,646 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/graph/huffman/GraphHuffman.java | GraphHuffman.buildTree | public void buildTree(int[] vertexDegree) {
PriorityQueue<Node> pq = new PriorityQueue<>();
for (int i = 0; i < vertexDegree.length; i++)
pq.add(new Node(i, vertexDegree[i], null, null));
while (pq.size() > 1) {
Node left = pq.remove();
Node right = pq.remove... | java | public void buildTree(int[] vertexDegree) {
PriorityQueue<Node> pq = new PriorityQueue<>();
for (int i = 0; i < vertexDegree.length; i++)
pq.add(new Node(i, vertexDegree[i], null, null));
while (pq.size() > 1) {
Node left = pq.remove();
Node right = pq.remove... | [
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128,647 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/learning/AdaDeltaUpdater.java | AdaDeltaUpdater.applyUpdater | @Override
public void applyUpdater(INDArray gradient, int iteration, int epoch) {
if (msg == null || msdx == null)
throw new IllegalStateException("Updater has not been initialized with view state");
double rho = config.getRho();
double epsilon = config.getEpsilon();
//... | java | @Override
public void applyUpdater(INDArray gradient, int iteration, int epoch) {
if (msg == null || msdx == null)
throw new IllegalStateException("Updater has not been initialized with view state");
double rho = config.getRho();
double epsilon = config.getEpsilon();
//... | [
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128,648 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/schema/Schema.java | Schema.sameTypes | public boolean sameTypes(Schema schema) {
if (schema.numColumns() != numColumns())
return false;
for (int i = 0; i < schema.numColumns(); i++) {
if (getType(i) != schema.getType(i))
return false;
}
return true;
} | java | public boolean sameTypes(Schema schema) {
if (schema.numColumns() != numColumns())
return false;
for (int i = 0; i < schema.numColumns(); i++) {
if (getType(i) != schema.getType(i))
return false;
}
return true;
} | [
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128,649 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/schema/Schema.java | Schema.getIndexOfColumn | public int getIndexOfColumn(String columnName) {
Integer idx = columnNamesIndex.get(columnName);
if (idx == null)
throw new NoSuchElementException("Unknown column: \"" + columnName + "\"");
return idx;
} | java | public int getIndexOfColumn(String columnName) {
Integer idx = columnNamesIndex.get(columnName);
if (idx == null)
throw new NoSuchElementException("Unknown column: \"" + columnName + "\"");
return idx;
} | [
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@return the index of the given column name
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128,650 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/schema/Schema.java | Schema.hasColumn | public boolean hasColumn(String columnName) {
Integer idx = columnNamesIndex.get(columnName);
return idx != null;
} | java | public boolean hasColumn(String columnName) {
Integer idx = columnNamesIndex.get(columnName);
return idx != null;
} | [
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@param columnName Name to see if the column exists
@return True if a column exists for that name, false otherwise | [
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128,651 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/schema/Schema.java | Schema.fromJson | public static Schema fromJson(String json) {
try{
return JsonMappers.getMapper().readValue(json, Schema.class);
} catch (Exception e){
//TODO better exceptions
throw new RuntimeException(e);
}
} | java | public static Schema fromJson(String json) {
try{
return JsonMappers.getMapper().readValue(json, Schema.class);
} catch (Exception e){
//TODO better exceptions
throw new RuntimeException(e);
}
} | [
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128,652 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/schema/Schema.java | Schema.fromYaml | public static Schema fromYaml(String yaml) {
try{
return JsonMappers.getMapperYaml().readValue(yaml, Schema.class);
} catch (Exception e){
//TODO better exceptions
throw new RuntimeException(e);
}
} | java | public static Schema fromYaml(String yaml) {
try{
return JsonMappers.getMapperYaml().readValue(yaml, Schema.class);
} catch (Exception e){
//TODO better exceptions
throw new RuntimeException(e);
}
} | [
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128,653 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/schema/Schema.java | Schema.infer | public static Schema infer(List<Writable> record) {
Schema.Builder builder = new Schema.Builder();
for (int i = 0; i < record.size(); i++) {
if (record.get(i) instanceof DoubleWritable)
builder.addColumnDouble(String.valueOf(i));
else if (record.get(i) instanceof ... | java | public static Schema infer(List<Writable> record) {
Schema.Builder builder = new Schema.Builder();
for (int i = 0; i < record.size(); i++) {
if (record.get(i) instanceof DoubleWritable)
builder.addColumnDouble(String.valueOf(i));
else if (record.get(i) instanceof ... | [
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128,654 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/evaluation/classification/EvaluationCalibration.java | EvaluationCalibration.getReliabilityDiagram | public ReliabilityDiagram getReliabilityDiagram(int classIdx) {
INDArray totalCountBins = rDiagBinTotalCount.getColumn(classIdx);
INDArray countPositiveBins = rDiagBinPosCount.getColumn(classIdx);
double[] meanPredictionBins = rDiagBinSumPredictions.getColumn(classIdx).castTo(DataType.DOUBLE)
... | java | public ReliabilityDiagram getReliabilityDiagram(int classIdx) {
INDArray totalCountBins = rDiagBinTotalCount.getColumn(classIdx);
INDArray countPositiveBins = rDiagBinPosCount.getColumn(classIdx);
double[] meanPredictionBins = rDiagBinSumPredictions.getColumn(classIdx).castTo(DataType.DOUBLE)
... | [
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128,655 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-backend-impls/nd4j-native/src/main/java/org/nd4j/linalg/cpu/nativecpu/NDArray.java | NDArray.unsafeDuplication | @Override
public INDArray unsafeDuplication() {
WorkspaceUtils.assertValidArray(this, "Cannot duplicate array");
if (isView())
return this.dup(this.ordering());
DataBuffer rb = Nd4j.getMemoryManager().getCurrentWorkspace() == null ? Nd4j.getDataBufferFactory().createSame(this.da... | java | @Override
public INDArray unsafeDuplication() {
WorkspaceUtils.assertValidArray(this, "Cannot duplicate array");
if (isView())
return this.dup(this.ordering());
DataBuffer rb = Nd4j.getMemoryManager().getCurrentWorkspace() == null ? Nd4j.getDataBufferFactory().createSame(this.da... | [
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128,656 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/api/loader/FileBatch.java | FileBatch.forFiles | public static FileBatch forFiles(List<File> files) throws IOException {
List<String> origPaths = new ArrayList<>(files.size());
List<byte[]> bytes = new ArrayList<>(files.size());
for (File f : files) {
bytes.add(FileUtils.readFileToByteArray(f));
origPaths.add(f.toURI().... | java | public static FileBatch forFiles(List<File> files) throws IOException {
List<String> origPaths = new ArrayList<>(files.size());
List<byte[]> bytes = new ArrayList<>(files.size());
for (File f : files) {
bytes.add(FileUtils.readFileToByteArray(f));
origPaths.add(f.toURI().... | [
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128,657 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/parallel/MultiBoolean.java | MultiBoolean.set | public void set(boolean value, int entry) {
if (entry > numEntries || entry < 0)
throw new ND4JIllegalStateException(
"Entry index given (" + entry + ")in is higher then configured one (" + numEntries + ")");
if (oneTime && this.timeTracker.get(entry))
... | java | public void set(boolean value, int entry) {
if (entry > numEntries || entry < 0)
throw new ND4JIllegalStateException(
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if (oneTime && this.timeTracker.get(entry))
... | [
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128,658 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/parallel/MultiBoolean.java | MultiBoolean.get | public boolean get(int entry) {
if (entry > numEntries || entry < 0)
throw new ND4JIllegalStateException(
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return (this.holder & 1 << (entry + 1)) != 0;
} | java | public boolean get(int entry) {
if (entry > numEntries || entry < 0)
throw new ND4JIllegalStateException(
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return (this.holder & 1 << (entry + 1)) != 0;
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128,659 | deeplearning4j/deeplearning4j | datavec/datavec-api/src/main/java/org/datavec/api/transform/condition/BooleanCondition.java | BooleanCondition.XOR | public static Condition XOR(Condition first, Condition second) {
return new BooleanCondition(Type.XOR, first, second);
} | java | public static Condition XOR(Condition first, Condition second) {
return new BooleanCondition(Type.XOR, first, second);
} | [
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128,660 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp/src/main/java/org/deeplearning4j/models/sequencevectors/graph/walkers/impl/RandomWalker.java | RandomWalker.reset | @Override
public void reset(boolean shuffle) {
this.position.set(0);
if (shuffle) {
logger.debug("Calling shuffle() on entries...");
// https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm
for (int i = order.length - 1; i > 0; i--) {
... | java | @Override
public void reset(boolean shuffle) {
this.position.set(0);
if (shuffle) {
logger.debug("Calling shuffle() on entries...");
// https://en.wikipedia.org/wiki/Fisher%E2%80%93Yates_shuffle#The_modern_algorithm
for (int i = order.length - 1; i > 0; i--) {
... | [
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128,661 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDValidation.java | SDValidation.validateInteger | protected static void validateInteger(String opName, SDVariable v) {
if (v == null)
return;
if (!v.dataType().isIntType())
throw new IllegalStateException("Cannot apply operation \"" + opName + "\" to variable \"" + v.getVarName() + "\" with non-integer data type " + v.dataType()... | java | protected static void validateInteger(String opName, SDVariable v) {
if (v == null)
return;
if (!v.dataType().isIntType())
throw new IllegalStateException("Cannot apply operation \"" + opName + "\" to variable \"" + v.getVarName() + "\" with non-integer data type " + v.dataType()... | [
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128,662 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDValidation.java | SDValidation.validateFloatingPoint | protected static void validateFloatingPoint(String opName, SDVariable v) {
if (v == null)
return;
if (!v.dataType().isFPType())
throw new IllegalStateException("Cannot apply operation \"" + opName + "\" to variable \"" + v.getVarName() + "\" with non-floating point data type " + ... | java | protected static void validateFloatingPoint(String opName, SDVariable v) {
if (v == null)
return;
if (!v.dataType().isFPType())
throw new IllegalStateException("Cannot apply operation \"" + opName + "\" to variable \"" + v.getVarName() + "\" with non-floating point data type " + ... | [
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128,663 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDValidation.java | SDValidation.validateBool | protected static void validateBool(String opName, SDVariable v) {
if (v == null)
return;
if (v.dataType() != DataType.BOOL)
throw new IllegalStateException("Cannot apply operation \"" + opName + "\" to variable \"" + v.getVarName() + "\" with non-boolean point data type " + v.dat... | java | protected static void validateBool(String opName, SDVariable v) {
if (v == null)
return;
if (v.dataType() != DataType.BOOL)
throw new IllegalStateException("Cannot apply operation \"" + opName + "\" to variable \"" + v.getVarName() + "\" with non-boolean point data type " + v.dat... | [
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128,664 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDValidation.java | SDValidation.validateBool | protected static void validateBool(String opName, SDVariable v1, SDVariable v2) {
if (v1.dataType() != DataType.BOOL || v2.dataType() != DataType.BOOL)
throw new IllegalStateException("Cannot perform operation \"" + opName + "\" on variables \"" + v1.getVarName() + "\" and \"" +
... | java | protected static void validateBool(String opName, SDVariable v1, SDVariable v2) {
if (v1.dataType() != DataType.BOOL || v2.dataType() != DataType.BOOL)
throw new IllegalStateException("Cannot perform operation \"" + opName + "\" on variables \"" + v1.getVarName() + "\" and \"" +
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128,665 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-data/deeplearning4j-utility-iterators/src/main/java/org/deeplearning4j/datasets/iterator/JointMultiDataSetIterator.java | JointMultiDataSetIterator.resetSupported | @Override
public boolean resetSupported() {
boolean sup = true;
for (val i: iterators)
if (!i.resetSupported()) {
sup = false;
break;
}
return sup;
} | java | @Override
public boolean resetSupported() {
boolean sup = true;
for (val i: iterators)
if (!i.resetSupported()) {
sup = false;
break;
}
return sup;
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128,666 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/DoubleArrayTrie.java | DoubleArrayTrie.read | public static DoubleArrayTrie read(InputStream input) throws IOException {
DoubleArrayTrie trie = new DoubleArrayTrie();
DataInputStream dis = new DataInputStream(new BufferedInputStream(input));
trie.compact = dis.readBoolean();
int baseCheckSize = dis.readInt(); // Read size of baseAr... | java | public static DoubleArrayTrie read(InputStream input) throws IOException {
DoubleArrayTrie trie = new DoubleArrayTrie();
DataInputStream dis = new DataInputStream(new BufferedInputStream(input));
trie.compact = dis.readBoolean();
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128,667 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/DoubleArrayTrie.java | DoubleArrayTrie.build | public void build(Trie trie) {
ProgressLog.begin("building " + (compact ? "compact" : "sparse") + " trie");
baseBuffer = IntBuffer.allocate(BASE_CHECK_INITIAL_SIZE);
baseBuffer.put(0, 1);
checkBuffer = IntBuffer.allocate(BASE_CHECK_INITIAL_SIZE);
tailBuffer = CharBuffer.allocate(... | java | public void build(Trie trie) {
ProgressLog.begin("building " + (compact ? "compact" : "sparse") + " trie");
baseBuffer = IntBuffer.allocate(BASE_CHECK_INITIAL_SIZE);
baseBuffer.put(0, 1);
checkBuffer = IntBuffer.allocate(BASE_CHECK_INITIAL_SIZE);
tailBuffer = CharBuffer.allocate(... | [
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128,668 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/DoubleArrayTrie.java | DoubleArrayTrie.matchTail | private int matchTail(int base, int index, String key) {
int positionInTailArr = base - TAIL_OFFSET;
int keyLength = key.length();
for (int i = 0; i < keyLength; i++) {
if (key.charAt(i) != tailBuffer.get(positionInTailArr + i)) {
return -1;
}
}
... | java | private int matchTail(int base, int index, String key) {
int positionInTailArr = base - TAIL_OFFSET;
int keyLength = key.length();
for (int i = 0; i < keyLength; i++) {
if (key.charAt(i) != tailBuffer.get(positionInTailArr + i)) {
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] | effce52f2afd7eeb53c5bcca699fcd90bd06822f | https://github.com/deeplearning4j/deeplearning4j/blob/effce52f2afd7eeb53c5bcca699fcd90bd06822f/deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/DoubleArrayTrie.java#L264-L274 |
128,669 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/trie/DoubleArrayTrie.java | DoubleArrayTrie.findBase | private int findBase(int index, List<Trie.Node> nodes) {
int base = baseBuffer.get(index);
if (base < 0) {
return base;
}
while (true) {
boolean collision = false; // already taken?
for (Trie.Node node : nodes) {
int nextIndex = index ... | java | private int findBase(int index, List<Trie.Node> nodes) {
int base = baseBuffer.get(index);
if (base < 0) {
return base;
}
while (true) {
boolean collision = false; // already taken?
for (Trie.Node node : nodes) {
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128,670 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/checkutil/CheckUtil.java | CheckUtil.checkAdd | public static boolean checkAdd(INDArray first, INDArray second, double maxRelativeDifference,
double minAbsDifference) {
RealMatrix rmFirst = convertToApacheMatrix(first);
RealMatrix rmSecond = convertToApacheMatrix(second);
INDArray result = first.add(second);
RealM... | java | public static boolean checkAdd(INDArray first, INDArray second, double maxRelativeDifference,
double minAbsDifference) {
RealMatrix rmFirst = convertToApacheMatrix(first);
RealMatrix rmSecond = convertToApacheMatrix(second);
INDArray result = first.add(second);
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128,671 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/checkutil/CheckUtil.java | CheckUtil.checkSubtract | public static boolean checkSubtract(INDArray first, INDArray second, double maxRelativeDifference,
double minAbsDifference) {
RealMatrix rmFirst = convertToApacheMatrix(first);
RealMatrix rmSecond = convertToApacheMatrix(second);
INDArray result = first.sub(second);
... | java | public static boolean checkSubtract(INDArray first, INDArray second, double maxRelativeDifference,
double minAbsDifference) {
RealMatrix rmFirst = convertToApacheMatrix(first);
RealMatrix rmSecond = convertToApacheMatrix(second);
INDArray result = first.sub(second);
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128,672 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/workspace/WorkspaceUtils.java | WorkspaceUtils.assertNoWorkspacesOpen | public static void assertNoWorkspacesOpen(String msg, boolean allowScopedOut) throws ND4JWorkspaceException {
if (Nd4j.getWorkspaceManager().anyWorkspaceActiveForCurrentThread()) {
MemoryWorkspace currWs = Nd4j.getMemoryManager().getCurrentWorkspace();
if(allowScopedOut && (currWs == nu... | java | public static void assertNoWorkspacesOpen(String msg, boolean allowScopedOut) throws ND4JWorkspaceException {
if (Nd4j.getWorkspaceManager().anyWorkspaceActiveForCurrentThread()) {
MemoryWorkspace currWs = Nd4j.getMemoryManager().getCurrentWorkspace();
if(allowScopedOut && (currWs == nu... | [
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128,673 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-uima/src/main/java/org/deeplearning4j/text/corpora/treeparser/HeadWordFinder.java | HeadWordFinder.findHead | public Tree findHead(Tree parentNode) {
Tree cursor = parentNode.getType().equals("TOP") ? parentNode.firstChild() : parentNode;
while (cursor.children() != null && !cursor.children().isEmpty())
cursor = findHead2(cursor);
return cursor;
} | java | public Tree findHead(Tree parentNode) {
Tree cursor = parentNode.getType().equals("TOP") ? parentNode.firstChild() : parentNode;
while (cursor.children() != null && !cursor.children().isEmpty())
cursor = findHead2(cursor);
return cursor;
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128,674 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nlp-parent/deeplearning4j-nlp-japanese/src/main/java/com/atilika/kuromoji/util/UnknownDictionaryEntryParser.java | UnknownDictionaryEntryParser.parse | public GenericDictionaryEntry parse(String entry) {
String[] fields = parseLine(entry);
String surface = fields[0];
short leftId = Short.parseShort(fields[1]);
short rightId = Short.parseShort(fields[2]);
short wordCost = Short.parseShort(fields[3]);
List<String> pos = ... | java | public GenericDictionaryEntry parse(String entry) {
String[] fields = parseLine(entry);
String surface = fields[0];
short leftId = Short.parseShort(fields[1]);
short rightId = Short.parseShort(fields[2]);
short wordCost = Short.parseShort(fields[3]);
List<String> pos = ... | [
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128,675 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.multiply | public Rational multiply(final BigInteger val) {
Rational val2 = new Rational(val, BigInteger.ONE);
return (multiply(val2));
} | java | public Rational multiply(final BigInteger val) {
Rational val2 = new Rational(val, BigInteger.ONE);
return (multiply(val2));
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128,676 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.multiply | public Rational multiply(final int val) {
BigInteger tmp = BigInteger.valueOf(val);
return multiply(tmp);
} | java | public Rational multiply(final int val) {
BigInteger tmp = BigInteger.valueOf(val);
return multiply(tmp);
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128,677 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.divide | public Rational divide(BigInteger val) {
Rational val2 = new Rational(val, BigInteger.ONE);
return (divide(val2));
} | java | public Rational divide(BigInteger val) {
Rational val2 = new Rational(val, BigInteger.ONE);
return (divide(val2));
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@return the value of this/val | [
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128,678 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.add | public Rational add(Rational val) {
BigInteger num = a.multiply(val.b).add(b.multiply(val.a));
BigInteger deno = b.multiply(val.b);
return (new Rational(num, deno));
} | java | public Rational add(Rational val) {
BigInteger num = a.multiply(val.b).add(b.multiply(val.a));
BigInteger deno = b.multiply(val.b);
return (new Rational(num, deno));
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128,679 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.add | public Rational add(BigInteger val) {
Rational val2 = new Rational(val, BigInteger.ONE);
return (add(val2));
} | java | public Rational add(BigInteger val) {
Rational val2 = new Rational(val, BigInteger.ONE);
return (add(val2));
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128,680 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.subtract | public Rational subtract(Rational val) {
Rational val2 = val.negate();
return (add(val2));
} | java | public Rational subtract(Rational val) {
Rational val2 = val.negate();
return (add(val2));
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128,681 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.subtract | public Rational subtract(BigInteger val) {
Rational val2 = new Rational(val, BigInteger.ONE);
return (subtract(val2));
} | java | public Rational subtract(BigInteger val) {
Rational val2 = new Rational(val, BigInteger.ONE);
return (subtract(val2));
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128,682 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.trunc | public BigInteger trunc() {
/* is already integer: return the numerator
*/
if (b.compareTo(BigInteger.ONE) == 0) {
return a;
} else {
return a.divide(b);
}
} | java | public BigInteger trunc() {
/* is already integer: return the numerator
*/
if (b.compareTo(BigInteger.ONE) == 0) {
return a;
} else {
return a.divide(b);
}
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128,683 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.doubleValue | public double doubleValue() {
/* To meet the risk of individual overflows of the exponents of
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* in a BigDecimal environment and converst the result.
*/
BigDecimal adivb = (new BigDecimal(a)).divide(new... | java | public double doubleValue() {
/* To meet the risk of individual overflows of the exponents of
* a separate invocation a.doubleValue() or b.doubleValue(), we divide first
* in a BigDecimal environment and converst the result.
*/
BigDecimal adivb = (new BigDecimal(a)).divide(new... | [
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128,684 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.floatValue | public float floatValue() {
BigDecimal adivb = (new BigDecimal(a)).divide(new BigDecimal(b), MathContext.DECIMAL128);
return adivb.floatValue();
} | java | public float floatValue() {
BigDecimal adivb = (new BigDecimal(a)).divide(new BigDecimal(b), MathContext.DECIMAL128);
return adivb.floatValue();
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128,685 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.BigDecimalValue | public BigDecimal BigDecimalValue(MathContext mc) {
/* numerator and denominator individually rephrased
*/
BigDecimal n = new BigDecimal(a);
BigDecimal d = new BigDecimal(b);
return n.divide(d, mc);
} | java | public BigDecimal BigDecimalValue(MathContext mc) {
/* numerator and denominator individually rephrased
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BigDecimal n = new BigDecimal(a);
BigDecimal d = new BigDecimal(b);
return n.divide(d, mc);
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128,686 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.toFString | public String toFString(int digits) {
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128,687 | deeplearning4j/deeplearning4j | nd4j/nd4j-common/src/main/java/org/nd4j/linalg/util/Rational.java | Rational.normalize | protected void normalize() {
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128,688 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-nn/src/main/java/org/deeplearning4j/nn/conf/layers/Layer.java | Layer.initializeConstraints | protected void initializeConstraints(Builder<?> builder) {
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128,689 | deeplearning4j/deeplearning4j | deeplearning4j/deeplearning4j-data/deeplearning4j-datasets/src/main/java/org/deeplearning4j/datasets/mnist/MnistLabelFile.java | MnistLabelFile.readLabels | public int[] readLabels(int num) throws IOException {
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for (int i = 0; i < num; i++)
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128,690 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java | BaseLevel1.asum | @Override
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... | java | @Override
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128,691 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java | BaseLevel1.iamax | @Override
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i... | java | @Override
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128,692 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java | BaseLevel1.iamin | @Override
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128,693 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java | BaseLevel1.swap | @Override
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... | java | @Override
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128,694 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java | BaseLevel1.copy | @Override
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128,695 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java | BaseLevel1.rotg | @Override
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128,696 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java | BaseLevel1.rot | @Override
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128,697 | deeplearning4j/deeplearning4j | nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java | BaseLevel1.rotmg | @Override
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if (X.isSparse()) {
Nd4j.getSparseBlasWrapper().level1().scal(N, alpha, X);
... | java | @Override
public void scal(long N, double alpha, INDArray X) {
if (Nd4j.getExecutioner().getProfilingMode() == OpExecutioner.ProfilingMode.ALL)
OpProfiler.getInstance().processBlasCall(false, X);
if (X.isSparse()) {
Nd4j.getSparseBlasWrapper().level1().scal(N, alpha, X);
... | [
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... | computes a vector by a scalar product.
@param N
@param alpha
@param X | [
"computes",
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"scalar",
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"."
] | effce52f2afd7eeb53c5bcca699fcd90bd06822f | https://github.com/deeplearning4j/deeplearning4j/blob/effce52f2afd7eeb53c5bcca699fcd90bd06822f/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/blas/impl/BaseLevel1.java#L419-L432 |
128,699 | deeplearning4j/deeplearning4j | datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/loader/ImageLoader.java | ImageLoader.asRowVector | public INDArray asRowVector(BufferedImage image) {
if (centerCropIfNeeded) {
image = centerCropIfNeeded(image);
}
image = scalingIfNeed(image, true);
if (channels == 3) {
return toINDArrayBGR(image).ravel();
}
int[][] ret = toIntArrayArray(image);
... | java | public INDArray asRowVector(BufferedImage image) {
if (centerCropIfNeeded) {
image = centerCropIfNeeded(image);
}
image = scalingIfNeed(image, true);
if (channels == 3) {
return toINDArrayBGR(image).ravel();
}
int[][] ret = toIntArrayArray(image);
... | [
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"i... | Convert an image in to a row vector
@param image the image to convert
@return the row vector based on a rastered
representation of the image | [
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] | effce52f2afd7eeb53c5bcca699fcd90bd06822f | https://github.com/deeplearning4j/deeplearning4j/blob/effce52f2afd7eeb53c5bcca699fcd90bd06822f/datavec/datavec-data/datavec-data-image/src/main/java/org/datavec/image/loader/ImageLoader.java#L131-L141 |
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