tech.tablesaw.api.FloatColumn Maven / Gradle / Ivy
package tech.tablesaw.api;
import static com.google.common.base.Preconditions.checkArgument;
import it.unimi.dsi.fastutil.floats.*;
import java.nio.ByteBuffer;
import java.util.HashSet;
import java.util.Iterator;
import java.util.Set;
import java.util.stream.Stream;
import tech.tablesaw.columns.AbstractColumnParser;
import tech.tablesaw.columns.Column;
import tech.tablesaw.columns.numbers.FloatColumnType;
import tech.tablesaw.columns.numbers.NumberColumnFormatter;
import tech.tablesaw.selection.BitmapBackedSelection;
import tech.tablesaw.selection.Selection;
/** A column that contains float values */
public class FloatColumn extends NumberColumn {
protected final FloatArrayList data;
private FloatColumn(String name, FloatArrayList data) {
super(FloatColumnType.instance(), name, FloatColumnType.DEFAULT_PARSER);
setPrintFormatter(NumberColumnFormatter.floatingPointDefault());
this.data = data;
}
/** {@inheritDoc} */
@Override
public String getString(int row) {
final float value = getFloat(row);
return getPrintFormatter().format(value);
}
/** {@inheritDoc} */
@Override
public int valueHash(int rowNumber) {
return Float.hashCode(getFloat(rowNumber));
}
/** {@inheritDoc} */
@Override
public boolean equals(int rowNumber1, int rowNumber2) {
return getFloat(rowNumber1) == getFloat(rowNumber2);
}
public static FloatColumn create(String name) {
return new FloatColumn(name, new FloatArrayList());
}
public static FloatColumn create(String name, float... arr) {
return new FloatColumn(name, new FloatArrayList(arr));
}
public static FloatColumn create(String name, int initialSize) {
FloatColumn column = new FloatColumn(name, new FloatArrayList(initialSize));
for (int i = 0; i < initialSize; i++) {
column.appendMissing();
}
return column;
}
public static FloatColumn create(String name, Float[] arr) {
FloatColumn column = create(name);
for (Float val : arr) {
column.append(val);
}
return column;
}
public static FloatColumn create(String name, Stream stream) {
FloatColumn column = create(name);
stream.forEach(column::append);
return column;
}
/** {@inheritDoc} */
@Override
public FloatColumn createCol(String name, int initialSize) {
return create(name, initialSize);
}
/** {@inheritDoc} */
@Override
public FloatColumn createCol(String name) {
return create(name);
}
/** {@inheritDoc} */
@Override
public Float get(int index) {
float result = getFloat(index);
return isMissingValue(result) ? null : result;
}
public static boolean valueIsMissing(float value) {
return FloatColumnType.valueIsMissing(value);
}
/** {@inheritDoc} */
@Override
public FloatColumn subset(int[] rows) {
final FloatColumn c = this.emptyCopy();
for (final int row : rows) {
c.append(getFloat(row));
}
return c;
}
public Selection isNotIn(final float... numbers) {
final Selection results = new BitmapBackedSelection();
results.addRange(0, size());
results.andNot(isIn(numbers));
return results;
}
public Selection isIn(final float... numbers) {
final Selection results = new BitmapBackedSelection();
final FloatRBTreeSet doubleSet = new FloatRBTreeSet(numbers);
for (int i = 0; i < size(); i++) {
if (doubleSet.contains(getFloat(i))) {
results.add(i);
}
}
return results;
}
/** {@inheritDoc} */
@Override
public int size() {
return data.size();
}
/** {@inheritDoc} */
@Override
public void clear() {
data.clear();
}
/** {@inheritDoc} */
@Override
public FloatColumn unique() {
final FloatSet values = new FloatOpenHashSet();
for (int i = 0; i < size(); i++) {
values.add(getFloat(i));
}
final FloatColumn column = FloatColumn.create(name() + " Unique values");
for (float value : values) {
column.append(value);
}
return column;
}
/** {@inheritDoc} */
@Override
public FloatColumn top(int n) {
FloatArrayList top = new FloatArrayList();
float[] values = data.toFloatArray();
FloatArrays.parallelQuickSort(values, FloatComparators.OPPOSITE_COMPARATOR);
for (int i = 0; i < n && i < values.length; i++) {
top.add(values[i]);
}
return new FloatColumn(name() + "[Top " + n + "]", top);
}
/** {@inheritDoc} */
@Override
public FloatColumn bottom(final int n) {
FloatArrayList bottom = new FloatArrayList();
float[] values = data.toFloatArray();
FloatArrays.parallelQuickSort(values);
for (int i = 0; i < n && i < values.length; i++) {
bottom.add(values[i]);
}
return new FloatColumn(name() + "[Bottoms " + n + "]", bottom);
}
/** {@inheritDoc} */
@Override
public FloatColumn lag(int n) {
final int srcPos = n >= 0 ? 0 : -n;
final float[] dest = new float[size()];
final int destPos = Math.max(n, 0);
final int length = n >= 0 ? size() - n : size() + n;
for (int i = 0; i < size(); i++) {
dest[i] = FloatColumnType.missingValueIndicator();
}
float[] array = data.toFloatArray();
System.arraycopy(array, srcPos, dest, destPos, length);
return new FloatColumn(name() + " lag(" + n + ")", new FloatArrayList(dest));
}
/** {@inheritDoc} */
@Override
public FloatColumn removeMissing() {
FloatColumn result = copy();
result.clear();
FloatListIterator iterator = data.iterator();
while (iterator.hasNext()) {
final float v = iterator.nextFloat();
if (!isMissingValue(v)) {
result.append(v);
}
}
return result;
}
public FloatColumn append(float i) {
data.add(i);
return this;
}
/** {@inheritDoc} */
@Override
public FloatColumn append(Float val) {
if (val == null) {
appendMissing();
} else {
append(val.floatValue());
}
return this;
}
/** {@inheritDoc} */
@Override
public FloatColumn copy() {
FloatColumn copy = new FloatColumn(name(), data.clone());
copy.setPrintFormatter(getPrintFormatter());
copy.locale = locale;
return copy;
}
/** {@inheritDoc} */
@Override
public Iterator iterator() {
return data.iterator();
}
public float[] asFloatArray() {
return data.toFloatArray();
}
/** {@inheritDoc} */
@Override
public Float[] asObjectArray() {
final Float[] output = new Float[size()];
for (int i = 0; i < size(); i++) {
if (!isMissing(i)) {
output[i] = getFloat(i);
} else {
output[i] = null;
}
}
return output;
}
/** {@inheritDoc} */
@Override
public int compare(Float o1, Float o2) {
return Float.compare(o1, o2);
}
/** {@inheritDoc} */
@Override
public FloatColumn set(int i, Float val) {
return val == null ? setMissing(i) : set(i, (float) val);
}
public FloatColumn set(int i, float val) {
data.set(i, val);
return this;
}
/** {@inheritDoc} */
@Override
public Column set(int row, String stringValue, AbstractColumnParser parser) {
return set(row, parser.parseFloat(stringValue));
}
/** {@inheritDoc} */
@Override
public FloatColumn append(final Column column) {
checkArgument(
column.type() == this.type(),
"Column '%s' has type %s, but column '%s' has type %s.",
name(),
type(),
column.name(),
column.type());
final FloatColumn numberColumn = (FloatColumn) column;
final int size = numberColumn.size();
for (int i = 0; i < size; i++) {
append(numberColumn.getFloat(i));
}
return this;
}
/** {@inheritDoc} */
@Override
public FloatColumn append(Column column, int row) {
checkArgument(
column.type() == this.type(),
"Column '%s' has type %s, but column '%s' has type %s.",
name(),
type(),
column.name(),
column.type());
return append(((FloatColumn) column).getFloat(row));
}
/** {@inheritDoc} */
@Override
public FloatColumn set(int row, Column column, int sourceRow) {
checkArgument(
column.type() == this.type(),
"Column '%s' has type %s, but column '%s' has type %s.",
name(),
type(),
column.name(),
column.type());
return set(row, ((FloatColumn) column).getFloat(sourceRow));
}
/** {@inheritDoc} */
@Override
public byte[] asBytes(int rowNumber) {
return ByteBuffer.allocate(FloatColumnType.instance().byteSize())
.putFloat(getFloat(rowNumber))
.array();
}
/** {@inheritDoc} */
@Override
public int countUnique() {
FloatSet uniqueElements = new FloatOpenHashSet();
for (int i = 0; i < size(); i++) {
uniqueElements.add(getFloat(i));
}
return uniqueElements.size();
}
/** {@inheritDoc} */
@Override
public double getDouble(int row) {
float value = data.getFloat(row);
if (isMissingValue(value)) {
return FloatColumnType.missingValueIndicator();
}
return value;
}
/**
* Returns a float representation of the data at the given index. Some precision may be lost, and
* if the value is to large to be cast to a float, an exception is thrown.
*
* @throws ClassCastException if the value can't be cast to ta float
*/
public float getFloat(int row) {
return data.getFloat(row);
}
public boolean isMissingValue(float value) {
return FloatColumnType.valueIsMissing(value);
}
/** {@inheritDoc} */
@Override
public boolean isMissing(int rowNumber) {
return isMissingValue(getFloat(rowNumber));
}
/** {@inheritDoc} */
@Override
public FloatColumn setMissing(int i) {
return set(i, FloatColumnType.missingValueIndicator());
}
/** {@inheritDoc} */
@Override
public void sortAscending() {
data.sort(FloatComparators.NATURAL_COMPARATOR);
}
/** {@inheritDoc} */
@Override
public void sortDescending() {
data.sort(FloatComparators.OPPOSITE_COMPARATOR);
}
/** {@inheritDoc} */
@Override
public FloatColumn appendMissing() {
return append(FloatColumnType.missingValueIndicator());
}
/** {@inheritDoc} */
@Override
public FloatColumn appendObj(Object obj) {
if (obj == null) {
return appendMissing();
}
if (obj instanceof Float) {
return append((float) obj);
}
throw new IllegalArgumentException("Could not append " + obj.getClass());
}
/** {@inheritDoc} */
@Override
public FloatColumn appendCell(final String value) {
try {
return append(parser().parseFloat(value));
} catch (final NumberFormatException e) {
throw new NumberFormatException(
"Error adding value to column " + name() + ": " + e.getMessage());
}
}
/** {@inheritDoc} */
@Override
public FloatColumn appendCell(final String value, AbstractColumnParser parser) {
try {
return append(parser.parseFloat(value));
} catch (final NumberFormatException e) {
throw new NumberFormatException(
"Error adding value to column " + name() + ": " + e.getMessage());
}
}
/** {@inheritDoc} */
@Override
public String getUnformattedString(final int row) {
final float value = getFloat(row);
if (FloatColumnType.valueIsMissing(value)) {
return "";
}
return String.valueOf(value);
}
/**
* 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() {
LongColumn result = LongColumn.create(name());
for (float d : data) {
if (FloatColumnType.valueIsMissing(d)) {
result.appendMissing();
} else {
result.append((long) d);
}
}
return result;
}
/**
* 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() {
IntColumn result = IntColumn.create(name());
for (float d : data) {
if (FloatColumnType.valueIsMissing(d)) {
result.appendMissing();
} else {
result.append((int) d);
}
}
return result;
}
/**
* 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 ShortColumn asShortColumn() {
ShortColumn result = ShortColumn.create(name());
for (float d : data) {
if (FloatColumnType.valueIsMissing(d)) {
result.appendMissing();
} else {
result.append((short) d);
}
}
return result;
}
/**
* Returns a new DoubleColumn containing a value for each value in this column.
*
*
No information is lost in converting from the floats to doubles
*
*
A missing value in the receiver is converted to a missing value in the result
*/
@Override
public DoubleColumn asDoubleColumn() {
DoubleColumn result = DoubleColumn.create(name());
for (float d : data) {
if (FloatColumnType.valueIsMissing(d)) {
result.appendMissing();
} else {
result.append(d);
}
}
return result;
}
/** {@inheritDoc} */
@Override
public Set asSet() {
return new HashSet<>(unique().asList());
}
}