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/*
* Licensed to the Apache Software Foundation (ASF) under one or more
* contributor license agreements. See the NOTICE file distributed with
* this work for additional information regarding copyright ownership.
* The ASF licenses this file to You under the Apache License, Version 2.0
* (the "License"); you may not use this file except in compliance with
* the License. You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*/
package org.apache.ignite.ml.structures;
import java.io.Externalizable;
import java.io.IOException;
import java.io.ObjectInput;
import java.io.ObjectOutput;
import java.util.Arrays;
import org.apache.ignite.ml.math.primitives.vector.Vector;
/**
* Class for set of vectors. This is a base class in hierarchy of datasets.
*/
public class Dataset<Row extends DatasetRow> implements Externalizable {
/** Data to keep. */
protected Row[] data;
/** Metadata to identify feature. */
protected FeatureMetadata[] meta;
/** Amount of instances. */
protected int rowSize;
/** Amount of attributes in each vector. */
protected int colSize;
/**
* Default constructor (required by Externalizable).
*/
public Dataset() {}
/**
* Creates new Dataset by given data.
*
* @param data Given data. Should be initialized with one vector at least.
* @param meta Feature's metadata.
*/
public Dataset(Row[] data, FeatureMetadata[] meta) {
this.data = data;
this.meta = meta;
}
/**
* Creates new Dataset by given data.
*
* @param data Given data. Should be initialized with one vector at least.
* @param featureNames Column names.
* @param colSize Amount of observed attributes in each vector.
*/
public Dataset(Row[] data, String[] featureNames, int colSize) {
this(data.length, colSize, featureNames);
assert data != null;
this.data = data;
}
/**
* Creates new Dataset by given data.
*
* @param data Should be initialized with one vector at least.
* @param colSize Amount of observed attributes in each vector.
*/
public Dataset(Row[] data, int colSize) {
this(data, null, colSize);
}
/**
* Creates new Dataset by given data.
*
* @param data Should be initialized with one vector at least.
*/
public Dataset(Row[] data) {
this.data = data;
this.rowSize = data.length;
}
/**
* Creates new Dataset and initialized with empty data structure.
*
* @param rowSize Amount of instances. Should be > 0.
* @param colSize Amount of attributes. Should be > 0
* @param featureNames Column names.
*/
public Dataset(int rowSize, int colSize, String[] featureNames) {
assert rowSize > 0;
assert colSize > 0;
if (featureNames == null)
generateFeatureNames();
else {
assert colSize == featureNames.length;
convertStringNamesToFeatureMetadata(featureNames);
}
this.rowSize = rowSize;
this.colSize = colSize;
}
/** */
protected void convertStringNamesToFeatureMetadata(String[] featureNames) {
this.meta = new FeatureMetadata[featureNames.length];
for (int i = 0; i < featureNames.length; i++)
this.meta[i] = new FeatureMetadata(featureNames[i]);
}
/** */
protected void generateFeatureNames() {
String[] featureNames = new String[colSize];
for (int i = 0; i < colSize; i++)
featureNames[i] = "f_" + i;
convertStringNamesToFeatureMetadata(featureNames);
}
/**
* Returns feature name for column with given index.
*
* @param i The given index.
* @return Feature name.
*/
public String getFeatureName(int i) {
return meta[i].name();
}
/** */
public DatasetRow[] data() {
return data;
}
/** */
public void setData(Row[] data) {
this.data = data;
}
/** */
public FeatureMetadata[] meta() {
return meta;
}
/** */
public void setMeta(FeatureMetadata[] meta) {
this.meta = meta;
}
/**
* Gets amount of attributes.
*
* @return Amount of attributes in each Labeled Vector.
*/
public int colSize() {
return colSize;
}
/**
* Gets amount of observation.
*
* @return Amount of rows in dataset.
*/
public int rowSize() {
return rowSize;
}
/**
* Retrieves Labeled Vector by given index.
*
* @param idx Index of observation.
* @return Labeled features.
*/
public Row getRow(int idx) {
return data[idx];
}
/**
* Get the features.
*
* @param idx Index of observation.
* @return Vector with features.
*/
public Vector features(int idx) {
assert idx < rowSize;
assert data != null;
assert data[idx] != null;
return data[idx].features();
}
/** {@inheritDoc} */
@Override public boolean equals(Object o) {
if (this == o)
return true;
if (o == null || getClass() != o.getClass())
return false;
Dataset that = (Dataset)o;
return rowSize == that.rowSize && colSize == that.colSize && Arrays.equals(data, that.data) && Arrays.equals(meta, that.meta);
}
/** {@inheritDoc} */
@Override public int hashCode() {
int res = Arrays.hashCode(data);
res = 31 * res + Arrays.hashCode(meta);
res = 31 * res + rowSize;
res = 31 * res + colSize;
return res;
}
/** {@inheritDoc} */
@Override public void writeExternal(ObjectOutput out) throws IOException {
out.writeObject(data);
out.writeObject(meta);
out.writeInt(rowSize);
out.writeInt(colSize);
}
/** {@inheritDoc} */
@Override public void readExternal(ObjectInput in) throws IOException, ClassNotFoundException {
data = (Row[]) in.readObject();
meta = (FeatureMetadata[]) in.readObject();
rowSize = in.readInt();
colSize = in.readInt();
}
}