VARIABLE_SERIALIZER = new VariableSerializer();
/**
* Indexes of values.
*/
private final int[] indexes;
/**
* Stored values.
*/
private final double[] values;
/**
* The dimensionality of this feature vector.
*/
private int dimensionality;
/**
* Direct constructor.
*
* @param indexes Indexes Must be sorted!
* @param values Associated value.
* @param dimensionality "true" dimensionality
*/
public SparseDoubleVector(int[] indexes, double[] values, int dimensionality) {
super();
this.indexes = indexes;
this.values = values;
this.dimensionality = dimensionality;
}
/**
* Provides a SparseDoubleVector consisting of double values according to the
* specified mapping of indices and values.
*
* @param values the values to be set as values of the real vector
* @param dimensionality the dimensionality of this feature vector
* @throws IllegalArgumentException if the given dimensionality is too small
* to cover the given values (i.e., the maximum index of any value not
* zero is bigger than the given dimensionality)
*/
public SparseDoubleVector(TIntDoubleMap values, int dimensionality) throws IllegalArgumentException {
if (values.size() > dimensionality) {
throw new IllegalArgumentException("values.size() > dimensionality!");
}
this.indexes = new int[values.size()];
this.values = new double[values.size()];
// Import and sort the indexes
{
TIntDoubleIterator iter = values.iterator();
for (int i = 0; iter.hasNext(); i++) {
iter.advance();
this.indexes[i] = iter.key();
}
Arrays.sort(this.indexes);
}
// Import the values accordingly
{
for (int i = 0; i < values.size(); i++) {
this.values[i] = values.get(this.indexes[i]);
}
}
this.dimensionality = dimensionality;
final int maxdim = getMaxDim();
if (maxdim > dimensionality) {
throw new IllegalArgumentException("Given dimensionality " + dimensionality + " is too small w.r.t. the given values (occurring maximum: " + maxdim + ").");
}
}
/**
* Get the maximum dimensionality.
*
* @return the maximum dimensionality seen
*/
private int getMaxDim() {
if (this.indexes.length == 0) {
return 0;
} else {
return this.indexes[this.indexes.length - 1];
}
}
/**
* Provides a SparseDoubleVector consisting of double values according to the
* specified mapping of indices and values.
*
* @param values the values to be set as values of the real vector
* @throws IllegalArgumentException if the given dimensionality is too small
* to cover the given values (i.e., the maximum index of any value not
* zero is bigger than the given dimensionality)
*/
public SparseDoubleVector(double[] values) throws IllegalArgumentException {
this.dimensionality = values.length;
// Count the number of non-zero entries
int size = 0;
{
for (int i = 0; i < values.length; i++) {
if (values[i] != 0.0f) {
size++;
}
}
}
this.indexes = new int[size];
this.values = new double[size];
// Copy the values
{
int pos = 0;
for (int i = 0; i < values.length; i++) {
double value = values[i];
if (value != 0.0f) {
this.indexes[pos] = i;
this.values[pos] = value;
pos++;
}
}
}
}
@Override
public int getDimensionality() {
return dimensionality;
}
/**
* Sets the dimensionality to the new value.
*
*
* @param dimensionality the new dimensionality
* @throws IllegalArgumentException if the given dimensionality is too small
* to cover the given values (i.e., the maximum index of any value not
* zero is bigger than the given dimensionality)
*/
@Override
public void setDimensionality(int dimensionality) throws IllegalArgumentException {
final int maxdim = getMaxDim();
if (maxdim > dimensionality) {
throw new IllegalArgumentException("Given dimensionality " + dimensionality + " is too small w.r.t. the given values (occurring maximum: " + maxdim + ").");
}
this.dimensionality = dimensionality;
}
@Override
@Deprecated
public Double getValue(int dimension) {
int pos = Arrays.binarySearch(this.indexes, dimension);
if (pos >= 0) {
return values[pos];
} else {
return 0.0;
}
}
@Override
public double doubleValue(int dimension) {
int pos = Arrays.binarySearch(this.indexes, dimension);
if (pos >= 0) {
return values[pos];
} else {
return 0.0;
}
}
@Override
public long longValue(int dimension) {
int pos = Arrays.binarySearch(this.indexes, dimension);
if (pos >= 0) {
return (long) values[pos];
} else {
return 0;
}
}
@Override
public Vector getColumnVector() {
return new Vector(getValues());
}
/**
*
* Provides a String representation of this SparseDoubleVector as suitable for
* {@link de.lmu.ifi.dbs.elki.datasource.parser.SparseNumberVectorLabelParser}
* .
*
*
*
* The returned String is a single line with entries separated by
* {@link AbstractNumberVector#ATTRIBUTE_SEPARATOR}. The first entry gives the
* number of values actually not zero. Following entries are pairs of Integer
* and Double where the Integer gives the index of the dimensionality and the
* Double gives the corresponding value.
*
*
*
* Example: a vector (0,1.2,1.3,0)T would result in the String
* 2 2 1.2 3 1.3
*
*
* @return a String representation of this SparseDoubleVector
*/
@Override
public String toString() {
StringBuilder featureLine = new StringBuilder();
featureLine.append(this.indexes.length);
for (int i = 0; i < this.indexes.length; i++) {
featureLine.append(ATTRIBUTE_SEPARATOR);
featureLine.append(this.indexes[i]);
featureLine.append(ATTRIBUTE_SEPARATOR);
featureLine.append(this.values[i]);
}
return featureLine.toString();
}
/**
* Returns an array consisting of the values of this feature vector.
*
* @return an array consisting of the values of this feature vector
*/
private double[] getValues() {
double[] vals = new double[dimensionality];
for (int i = 0; i < indexes.length; i++) {
vals[this.indexes[i]] = this.values[i];
}
return vals;
}
/**
* Factory class.
*
* @author Erich Schubert
*
* @apiviz.has SparseDoubleVector
*/
public static class Factory extends AbstractNumberVector.Factory implements SparseNumberVector.Factory {
@Override
public SparseDoubleVector newFeatureVector(A array, ArrayAdapter adapter) {
int dim = adapter.size(array);
double[] values = new double[dim];
for (int i = 0; i < dim; i++) {
values[i] = adapter.get(array, i);
}
// TODO: improve efficiency
return new SparseDoubleVector(values);
}
@Override
public SparseDoubleVector newNumberVector(A array, NumberArrayAdapter adapter) {
int dim = adapter.size(array);
double[] values = new double[dim];
for (int i = 0; i < dim; i++) {
values[i] = adapter.getDouble(array, i);
}
// TODO: improve efficiency
return new SparseDoubleVector(values);
}
@Override
public SparseDoubleVector newNumberVector(TIntDoubleMap values, int maxdim) {
return new SparseDoubleVector(values, maxdim);
}
@Override
public ByteBufferSerializer getDefaultSerializer() {
return VARIABLE_SERIALIZER;
}
@Override
public Class getRestrictionClass() {
return SparseDoubleVector.class;
}
/**
* Parameterization class.
*
* @author Erich Schubert
*
* @apiviz.exclude
*/
public static class Parameterizer extends AbstractParameterizer {
@Override
protected SparseDoubleVector.Factory makeInstance() {
return FACTORY;
}
}
}
@Override
public BitSet getNotNullMask() {
BitSet b = new BitSet();
for (int key : indexes) {
b.set(key);
}
return b;
}
/**
* Empty map.
*/
public static final TIntDoubleMap EMPTYMAP = new TUnmodifiableIntDoubleMap(new TIntDoubleHashMap());
/**
* Serialization class using VarInt encodings.
*
* @author Erich Schubert
*
* @apiviz.uses SparseDoubleVector - - «serializes»
*/
public static class VariableSerializer implements ByteBufferSerializer {
@Override
public SparseDoubleVector fromByteBuffer(ByteBuffer buffer) throws IOException {
final int dimensionality = ByteArrayUtil.readUnsignedVarint(buffer);
final int nonzero = ByteArrayUtil.readUnsignedVarint(buffer);
final int[] dims = new int[nonzero];
final double[] values = new double[nonzero];
for (int i = 0; i < nonzero; i++) {
dims[i] = ByteArrayUtil.readUnsignedVarint(buffer);
values[i] = buffer.getDouble();
}
return new SparseDoubleVector(dims, values, dimensionality);
}
@Override
public void toByteBuffer(ByteBuffer buffer, SparseDoubleVector vec) throws IOException {
ByteArrayUtil.writeUnsignedVarint(buffer, vec.dimensionality);
ByteArrayUtil.writeUnsignedVarint(buffer, vec.values.length);
for (int i = 0; i < vec.values.length; i++) {
ByteArrayUtil.writeUnsignedVarint(buffer, vec.indexes[i]);
buffer.putDouble(vec.values[i]);
}
}
@Override
public int getByteSize(SparseDoubleVector vec) {
int sum = 0;
sum += ByteArrayUtil.getUnsignedVarintSize(vec.dimensionality);
sum += ByteArrayUtil.getUnsignedVarintSize(vec.values.length);
for (int d : vec.indexes) {
sum += ByteArrayUtil.getUnsignedVarintSize(d);
}
sum += vec.values.length * ByteArrayUtil.SIZE_DOUBLE;
return sum;
}
}
}