org.nd4j.linalg.dataset.api.DataSet Maven / Gradle / Ivy
package org.nd4j.linalg.dataset.api;
import com.google.common.base.Function;
import org.apache.commons.math3.random.RandomGenerator;
import org.nd4j.linalg.api.ndarray.INDArray;
import org.nd4j.linalg.dataset.SplitTestAndTrain;
import org.nd4j.linalg.indexing.conditions.Condition;
import java.io.Serializable;
import java.util.Iterator;
import java.util.List;
/**
* Created by agibsonccc on 8/26/14.
*/
public interface DataSet extends Iterable,Serializable {
INDArray getFeatures();
void apply(Condition condition,Function function);
void setFeatures(INDArray features);
void setLabels(INDArray labels);
org.nd4j.linalg.dataset.DataSet copy();
org.nd4j.linalg.dataset.DataSet reshape(int rows, int cols);
void multiplyBy(double num);
void divideBy(int num);
void shuffle();
void squishToRange(double min, double max);
void scaleMinAndMax(double min,double max);
void scale();
void addFeatureVector(INDArray toAdd);
void addFeatureVector(INDArray feature, int example);
void normalize();
void binarize();
void binarize(double cutoff);
void normalizeZeroMeanZeroUnitVariance();
int numInputs();
void validate();
int outcome();
void setNewNumberOfLabels(int labels);
void setOutcome(int example, int label);
org.nd4j.linalg.dataset.DataSet get(int i);
org.nd4j.linalg.dataset.DataSet get(int[] i);
List> batchBy(int num);
org.nd4j.linalg.dataset.DataSet filterBy(int[] labels);
void filterAndStrip(int[] labels);
List dataSetBatches(int num);
List> sortAndBatchByNumLabels();
List> batchByNumLabels();
List asList();
SplitTestAndTrain splitTestAndTrain(int numHoldout);
INDArray getLabels();
INDArray getFeatureMatrix();
void sortByLabel();
void addRow(org.nd4j.linalg.dataset.DataSet d, int i);
INDArray exampleSums();
INDArray exampleMaxs();
INDArray exampleMeans();
org.nd4j.linalg.dataset.DataSet sample(int numSamples);
org.nd4j.linalg.dataset.DataSet sample(int numSamples, RandomGenerator rng);
org.nd4j.linalg.dataset.DataSet sample(int numSamples, boolean withReplacement);
org.nd4j.linalg.dataset.DataSet sample(int numSamples, RandomGenerator rng, boolean withReplacement);
void roundToTheNearest(int roundTo);
int numOutcomes();
int numExamples();
List getLabelNames();
void setLabelNames(List labelNames);
List getColumnNames();
void setColumnNames(List columnNames);
@Override
Iterator iterator();
}