Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance. Project price only 1 $
You can buy this project and download/modify it how often you want.
package tech.tablesaw.api;
import com.google.common.base.Preconditions;
import it.unimi.dsi.fastutil.doubles.DoubleArrayList;
import it.unimi.dsi.fastutil.doubles.DoubleArrays;
import it.unimi.dsi.fastutil.doubles.DoubleComparator;
import it.unimi.dsi.fastutil.doubles.DoubleIterator;
import it.unimi.dsi.fastutil.doubles.DoubleListIterator;
import it.unimi.dsi.fastutil.doubles.DoubleOpenHashSet;
import it.unimi.dsi.fastutil.doubles.DoubleSet;
import it.unimi.dsi.fastutil.floats.FloatArrayList;
import it.unimi.dsi.fastutil.ints.IntArrayList;
import it.unimi.dsi.fastutil.longs.LongArrayList;
import it.unimi.dsi.fastutil.shorts.ShortArrayList;
import tech.tablesaw.columns.Column;
import tech.tablesaw.columns.AbstractColumnParser;
import tech.tablesaw.columns.numbers.DoubleColumnType;
import tech.tablesaw.columns.numbers.FloatColumnType;
import tech.tablesaw.columns.numbers.NumberFillers;
import tech.tablesaw.columns.numbers.fillers.DoubleRangeIterable;
import tech.tablesaw.selection.Selection;
import java.math.BigDecimal;
import java.nio.ByteBuffer;
import java.util.Comparator;
import java.util.Iterator;
import java.util.List;
import java.util.function.DoubleConsumer;
import java.util.function.DoublePredicate;
import java.util.function.DoubleSupplier;
import java.util.function.Function;
import java.util.function.Predicate;
import java.util.function.ToDoubleFunction;
public class DoubleColumn extends NumberColumn implements NumberFillers {
/**
* Compares two doubles, such that a sort based on this comparator would sort in descending order
*/
private final DoubleComparator descendingComparator = (o2, o1) -> (Double.compare(o1, o2));
private final DoubleArrayList data;
protected DoubleColumn(final String name, final DoubleArrayList data) {
super(DoubleColumnType.instance(), name);
this.data = data;
}
@Override
public String getString(final int row) {
final double value = getDouble(row);
if (DoubleColumnType.isMissingValue(value)) {
return "";
}
return String.valueOf(printFormatter.format(value));
}
@Override
public int size() {
return data.size();
}
@Override
public void clear() {
data.clear();
}
public DoubleColumn setMissing(int index) {
set(index, DoubleColumnType.missingValueIndicator());
return this;
}
protected DoubleColumn(final String name) {
super(DoubleColumnType.instance(), name);
this.data = new DoubleArrayList(DEFAULT_ARRAY_SIZE);
}
public static DoubleColumn create(final String name, final double[] arr) {
return new DoubleColumn(name, new DoubleArrayList(arr));
}
public static DoubleColumn create(final String name) {
return new DoubleColumn(name);
}
public static DoubleColumn create(final String name, final float[] arr) {
final double[] doubles = new double[arr.length];
for (int i = 0; i < arr.length; i++) {
doubles[i] = arr[i];
}
return new DoubleColumn(name, new DoubleArrayList(doubles));
}
public static DoubleColumn create(final String name, final int[] arr) {
final double[] doubles = new double[arr.length];
for (int i = 0; i < arr.length; i++) {
doubles[i] = arr[i];
}
return new DoubleColumn(name, new DoubleArrayList(doubles));
}
public static DoubleColumn create(final String name, final long[] arr) {
final double[] doubles = new double[arr.length];
for (int i = 0; i < arr.length; i++) {
doubles[i] = arr[i];
}
return new DoubleColumn(name, new DoubleArrayList(doubles));
}
public static DoubleColumn create(final String name, final List numberList) {
final double[] doubles = new double[numberList.size()];
for (int i = 0; i < numberList.size(); i++) {
doubles[i] = numberList.get(i).doubleValue();
}
return new DoubleColumn(name, new DoubleArrayList(doubles));
}
public static DoubleColumn create(final String name, final Number[] numbers) {
final double[] doubles = new double[numbers.length];
for (int i = 0; i < numbers.length; i++) {
doubles[i] = numbers[i].doubleValue();
}
return new DoubleColumn(name, new DoubleArrayList(doubles));
}
public static DoubleColumn create(final String name, final int initialSize) {
DoubleColumn column = new DoubleColumn(name);
for (int i = 0; i < initialSize; i++) {
column.appendMissing();
}
return column;
}
@Override
public DoubleColumn createCol(final String name, final int initialSize) {
return create(name, initialSize);
}
@Override
public DoubleColumn createCol(final String name) {
return create(name);
}
@Override
public Double get(int index) {
return getDouble(index);
}
@Override
public DoubleColumn subset(final int[] rows) {
final DoubleColumn c = this.emptyCopy();
for (final int row : rows) {
c.append(getDouble(row));
}
return c;
}
@Override
public DoubleColumn unique() {
final DoubleSet doubles = new DoubleOpenHashSet();
for (int i = 0; i < size(); i++) {
if (!isMissing(i)) {
doubles.add(getDouble(i));
}
}
final DoubleColumn column = DoubleColumn.create(name() + " Unique values");
doubles.forEach((DoubleConsumer) column::append);
return column;
}
@Override
public DoubleColumn top(int n) {
DoubleArrayList top = new DoubleArrayList();
double[] values = data.toDoubleArray();
DoubleArrays.parallelQuickSort(values, descendingComparator);
for (int i = 0; i < n && i < values.length; i++) {
top.add(values[i]);
}
return new DoubleColumn(name() + "[Top " + n + "]", top);
}
@Override
public DoubleColumn bottom(final int n) {
DoubleArrayList bottom = new DoubleArrayList();
double[] values = data.toDoubleArray();
DoubleArrays.parallelQuickSort(values);
for (int i = 0; i < n && i < values.length; i++) {
bottom.add(values[i]);
}
return new DoubleColumn(name() + "[Bottoms " + n + "]", bottom);
}
@Override
public DoubleColumn lag(int n) {
final int srcPos = n >= 0 ? 0 : 0 - n;
final double[] dest = new double[size()];
final int destPos = n <= 0 ? 0 : n;
final int length = n >= 0 ? size() - n : size() + n;
for (int i = 0; i < size(); i++) {
dest[i] = FloatColumnType.missingValueIndicator();
}
double[] array = data.toDoubleArray();
System.arraycopy(array, srcPos, dest, destPos, length);
return new DoubleColumn(name() + " lag(" + n + ")", new DoubleArrayList(dest));
}
@Override
public DoubleColumn removeMissing() {
DoubleColumn result = copy();
result.clear();
DoubleListIterator iterator = data.iterator();
while (iterator.hasNext()) {
double v = iterator.nextDouble();
if (!isMissingValue(v)) {
result.append(v);
}
}
return result;
}
/**
* Adds the given float to this column
*/
public DoubleColumn append(final float f) {
data.add(f);
return this;
}
/**
* Adds the given double to this column
*/
public DoubleColumn append(double d) {
data.add(d);
return this;
}
public DoubleColumn append(int i) {
data.add(i);
return this;
}
@Override
public DoubleColumn append(Double val) {
this.append(val.doubleValue());
return this;
}
public DoubleColumn append(Integer val) {
this.append(val.doubleValue());
return this;
}
@Override
public DoubleColumn emptyCopy() {
return (DoubleColumn) super.emptyCopy();
}
@Override
public DoubleColumn emptyCopy(final int rowSize) {
return (DoubleColumn) super.emptyCopy(rowSize);
}
@Override
public DoubleColumn copy() {
return new DoubleColumn(name(), data.clone());
}
@Override
public Iterator iterator() {
return (Iterator) data.iterator();
}
@Override
public Double[] asObjectArray() {
final Double[] output = new Double[size()];
for (int i = 0; i < size(); i++) {
output[i] = getDouble(i);
}
return output;
}
@Override
public int compare(Double o1, Double o2) {
return Double.compare(o1, o2);
}
@Override
public DoubleColumn set(int i, Double val) {
return set(i, (double) val);
}
public DoubleColumn set(int i, double val) {
data.set(i, val);
return this;
}
@Override
public DoubleColumn append(final Column column) {
Preconditions.checkArgument(column.type() == this.type());
final DoubleColumn numberColumn = (DoubleColumn) column;
final int size = numberColumn.size();
for (int i = 0; i < size; i++) {
append(numberColumn.getDouble(i));
}
return this;
}
@Override
public DoubleColumn append(Column column, int row) {
Preconditions.checkArgument(column.type() == this.type());
DoubleColumn doubleColumn = (DoubleColumn) column;
return append(doubleColumn.getDouble(row));
}
@Override
public DoubleColumn set(int row, Column column, int sourceRow) {
Preconditions.checkArgument(column.type() == this.type());
DoubleColumn doubleColumn = (DoubleColumn) column;
return set(row, doubleColumn.getDouble(sourceRow));
}
/**
* Maps the function across all rows, appending the results to a new NumberColumn
*
* @param fun function to map
* @return the NumberColumn with the results
*/
public DoubleColumn map(ToDoubleFunction fun) {
DoubleColumn result = DoubleColumn.create(name());
for (double t : this) {
try {
result.append(fun.applyAsDouble(t));
} catch (Exception e) {
result.appendMissing();
}
}
return result;
}
/**
* Returns a new NumberColumn with only those rows satisfying the predicate
*
* @param test the predicate
* @return a new NumberColumn with only those rows satisfying the predicate
*/
public DoubleColumn filter(DoublePredicate test) {
DoubleColumn result = DoubleColumn.create(name());
for (int i = 0; i < size(); i++) {
double d = getDouble(i);
if (test.test(d)) {
result.append(d);
}
}
return result;
}
@Override
public byte[] asBytes(int rowNumber) {
return ByteBuffer.allocate(DoubleColumnType.instance().byteSize()).putDouble(getDouble(rowNumber)).array();
}
@Override
public int countUnique() {
DoubleSet uniqueElements = new DoubleOpenHashSet();
for (int i = 0; i < size(); i++) {
if (!isMissing(i)) {
uniqueElements.add(getDouble(i));
}
}
return uniqueElements.size();
}
@Override
public double getDouble(int row) {
return data.getDouble(row);
}
public boolean isMissingValue(double value) {
return DoubleColumnType.isMissingValue(value);
}
@Override
public boolean isMissing(int rowNumber) {
return isMissingValue(getDouble(rowNumber));
}
@Override
public void sortAscending() {
DoubleArrays.parallelQuickSort(data.elements());
}
@Override
public void sortDescending() {
DoubleArrays.parallelQuickSort(data.elements(), descendingComparator);
}
@Override
public DoubleColumn appendMissing() {
return append(DoubleColumnType.missingValueIndicator());
}
@Override
public DoubleColumn appendObj(Object obj) {
if (obj == null) {
return appendMissing();
}
if (obj instanceof Double) {
return append((double) obj);
}
if (obj instanceof BigDecimal) {
return append(((BigDecimal) obj).doubleValue());
}
throw new IllegalArgumentException("Could not append " + obj.getClass());
}
@Override
public DoubleColumn appendCell(final String value) {
try {
return append(DoubleColumnType.DEFAULT_PARSER.parseDouble(value));
} catch (final NumberFormatException e) {
throw new NumberFormatException("Error adding value to column " + name() + ": " + e.getMessage());
}
}
@Override
public DoubleColumn appendCell(final String value, AbstractColumnParser parser) {
try {
return append(parser.parseDouble(value));
} catch (final NumberFormatException e) {
throw new NumberFormatException("Error adding value to column " + name() + ": " + e.getMessage());
}
}
@Override
public String getUnformattedString(final int row) {
final double value = getDouble(row);
if (DoubleColumnType.isMissingValue(value)) {
return "";
}
return String.valueOf(value);
}
// fillWith methods
@Override
public DoubleColumn fillWith(final DoubleIterator iterator) {
for (int r = 0; r < size(); r++) {
if (!iterator.hasNext()) {
break;
}
set(r, iterator.nextDouble());
}
return this;
}
@Override
public DoubleColumn fillWith(final DoubleRangeIterable iterable) {
DoubleIterator iterator = iterable.iterator();
for (int r = 0; r < size(); r++) {
if (!iterator.hasNext()) {
iterator = iterable.iterator();
if (!iterator.hasNext()) {
break;
}
}
set(r, iterator.nextDouble());
}
return this;
}
@Override
public DoubleColumn fillWith(final DoubleSupplier supplier) {
for (int r = 0; r < size(); r++) {
try {
set(r, supplier.getAsDouble());
} catch (final Exception e) {
break;
}
}
return this;
}
@Override
public DoubleColumn inRange(int start, int end) {
return (DoubleColumn) super.inRange(start, end);
}
@Override
public DoubleColumn where(Selection selection) {
return (DoubleColumn) super.where(selection);
}
@Override
public DoubleColumn lead(int n) {
return (DoubleColumn) super.lead(n);
}
@Override
public DoubleColumn setName(String name) {
return (DoubleColumn) super.setName(name);
}
@Override
public DoubleColumn filter(Predicate test) {
return (DoubleColumn) super.filter(test);
}
@Override
public DoubleColumn sorted(Comparator comp) {
return (DoubleColumn) super.sorted(comp);
}
@Override
public DoubleColumn map(Function fun) {
return (DoubleColumn) super.map(fun);
}
@Override
public DoubleColumn min(Column other) {
return (DoubleColumn) super.min(other);
}
@Override
public DoubleColumn max(Column other) {
return (DoubleColumn) super.max(other);
}
@Override
public DoubleColumn set(Selection condition, Column other) {
return (DoubleColumn) super.set(condition, other);
}
@Override
public DoubleColumn set(Selection rowSelection, Double newValue) {
return (DoubleColumn) super.set(rowSelection, newValue);
}
@Override
public DoubleColumn first(int numRows) {
return (DoubleColumn) super.first(numRows);
}
@Override
public DoubleColumn last(int numRows) {
return (DoubleColumn) super.last(numRows);
}
@Override
public DoubleColumn sampleN(int n) {
return (DoubleColumn) super.sampleN(n);
}
@Override
public DoubleColumn sampleX(double proportion) {
return (DoubleColumn) super.sampleX(proportion);
}
/**
* Returns a new LongColumn containing a value for each value in this column, truncating if necessary
*
* A narrowing primitive conversion such as this one may lose information about the overall magnitude of a
* numeric value and may also lose precision and range. Specifically, if the value is too small (a negative value
* of large magnitude or negative infinity), the result is the smallest representable value of type long.
*
* Similarly, if the value is too large (a positive value of large magnitude or positive infinity), the result is the
* largest representable value of type long.
*
* Despite the fact that overflow, underflow, or other loss of information may occur, a narrowing primitive
* conversion never results in a run-time exception.
*
* A missing value in the receiver is converted to a missing value in the result
*/
@Override
public LongColumn asLongColumn() {
LongArrayList values = new LongArrayList();
for (double d : data) {
values.add((long) d);
}
values.trim();
return LongColumn.create(this.name(), values.elements());
}
/**
* Returns a new IntColumn containing a value for each value in this column, truncating if necessary.
*
* A narrowing primitive conversion such as this one may lose information about the overall magnitude of a
* numeric value and may also lose precision and range. Specifically, if the value is too small (a negative value
* of large magnitude or negative infinity), the result is the smallest representable value of type int.
*
* Similarly, if the value is too large (a positive value of large magnitude or positive infinity), the result is the
* largest representable value of type int.
*
* Despite the fact that overflow, underflow, or other loss of information may occur, a narrowing primitive
* conversion never results in a run-time exception.
*
* A missing value in the receiver is converted to a missing value in the result
*/
@Override
public IntColumn asIntColumn() {
IntArrayList values = new IntArrayList();
for (double d : data) {
values.add((int) d);
}
values.trim();
return IntColumn.create(this.name(), values.elements());
}
/**
* Returns a new ShortColumn containing a value for each value in this column, truncating if necessary.
*
* A narrowing primitive conversion such as this one may lose information about the overall magnitude of a
* numeric value and may also lose precision and range. Specifically, if the value is too small (a negative value
* of large magnitude or negative infinity), the result is the smallest representable value of type int.
*
* Similarly, if the value is too large (a positive value of large magnitude or positive infinity), the result is the
* largest representable value of type short.
*
* Despite the fact that overflow, underflow, or other loss of information may occur, a narrowing primitive
* conversion never results in a run-time exception.
*
* A missing value in the receiver is converted to a missing value in the result
*/
@Override
public ShortColumn asShortColumn() {
ShortArrayList values = new ShortArrayList();
for (double d : data) {
values.add((short) d);
}
values.trim();
return ShortColumn.create(this.name(), values.elements());
}
/**
* Returns a new FloatColumn containing a value for each value in this column, truncating if necessary.
*
* A narrowing primitive conversion such as this one may lose information about the overall magnitude of a
* numeric value and may also lose precision and range. Specifically, if the value is too small (a negative value
* of large magnitude or negative infinity), the result is the smallest representable value of type float.
*
* Similarly, if the value is too large (a positive value of large magnitude or positive infinity), the result is the
* largest representable value of type float.
*
* Despite the fact that overflow, underflow, or other loss of information may occur, a narrowing primitive
* conversion never results in a run-time exception.
*
* A missing value in the receiver is converted to a missing value in the result
*/
@Override
public FloatColumn asFloatColumn() {
FloatArrayList values = new FloatArrayList();
for (double d : data) {
values.add((float) d);
}
values.trim();
return FloatColumn.create(this.name(), values.elements());
}
}
