The following example illustrates an aggregate operation using * {@link Stream} and {@link DoubleStream}, computing the sum of the weights of the * red widgets: *
{@code
* double sum = widgets.stream()
* .filter(w -> w.getColor() == RED)
* .mapToDouble(w -> w.getWeight())
* .sum();
* }
* See the class documentation for {@link Stream} and the package documentation
* for java.util.stream for additional
* specification of streams, stream operations, stream pipelines, and
* parallelism.
* @since 1.8
* @see Stream
* @see java.util.stream
*/
// @ts-ignore
interface DoubleStream extends java.util.stream.BaseStreamThis is an intermediate * operation. * @param predicate a non-interfering, * stateless * predicate to apply to each element to determine if it * should be included * @return the new stream */ // @ts-ignore filter(predicate: java.util.function$.DoublePredicate): java.util.stream.DoubleStream /** * Returns a stream consisting of the results of applying the given * function to the elements of this stream. *
This is an intermediate * operation. * @param mapper a non-interfering, * stateless * function to apply to each element * @return the new stream */ // @ts-ignore map(mapper: java.util.function$.DoubleUnaryOperator): java.util.stream.DoubleStream /** * Returns an object-valued {@code Stream} consisting of the results of * applying the given function to the elements of this stream. *
This is an
* intermediate operation.
* @param the element type of the new stream
* @param mapper a non-interfering,
* stateless
* function to apply to each element
* @return the new stream
*/
// @ts-ignore
mapToObj(mapper: java.util.function$.DoubleFunction This is an intermediate
* operation.
* @param mapper a non-interfering,
* stateless
* function to apply to each element
* @return the new stream
*/
// @ts-ignore
mapToInt(mapper: java.util.function$.DoubleToIntFunction): java.util.stream.IntStream
/**
* Returns a {@code LongStream} consisting of the results of applying the
* given function to the elements of this stream.
* This is an intermediate
* operation.
* @param mapper a non-interfering,
* stateless
* function to apply to each element
* @return the new stream
*/
// @ts-ignore
mapToLong(mapper: java.util.function$.DoubleToLongFunction): java.util.stream.LongStream
/**
* Returns a stream consisting of the results of replacing each element of
* this stream with the contents of a mapped stream produced by applying
* the provided mapping function to each element. Each mapped stream is
* {@link java.util.stream.BaseStream#close() closed} after its contents
* have been placed into this stream. (If a mapped stream is {@code null}
* an empty stream is used, instead.)
* This is an intermediate
* operation.
* @param mapper a non-interfering,
* stateless
* function to apply to each element which produces a
* {#code DoubleStream} of new values
* @return the new stream
* @see Stream#flatMap(Function)
*/
// @ts-ignore
flatMap(mapper: java.util.function$.DoubleFunction This is a stateful
* intermediate operation.
* @return the result stream
*/
// @ts-ignore
distinct(): java.util.stream.DoubleStream
/**
* Returns a stream consisting of the elements of this stream in sorted
* order. The elements are compared for equality according to
* {@link java.lang.Double#compare(double, double)}.
* This is a stateful
* intermediate operation.
* @return the result stream
*/
// @ts-ignore
sorted(): java.util.stream.DoubleStream
/**
* Returns a stream consisting of the elements of this stream, additionally
* performing the provided action on each element as elements are consumed
* from the resulting stream.
* This is an intermediate
* operation.
* For parallel stream pipelines, the action may be called at
* whatever time and in whatever thread the element is made available by the
* upstream operation. If the action modifies shared state,
* it is responsible for providing the required synchronization.
* @apiNote This method exists mainly to support debugging, where you want
* to see the elements as they flow past a certain point in a pipeline:
* This is a short-circuiting
* stateful intermediate operation.
* @apiNote While {#code limit()} is generally a cheap operation on sequential
* stream pipelines, it can be quite expensive on ordered parallel pipelines,
* especially for large values of {@code maxSize}, since {@code limit(n)}
* is constrained to return not just any n elements, but the
* first n elements in the encounter order. Using an unordered
* stream source (such as {@link #generate(DoubleSupplier)}) or removing the
* ordering constraint with {@link #unordered()} may result in significant
* speedups of {@code limit()} in parallel pipelines, if the semantics of
* your situation permit. If consistency with encounter order is required,
* and you are experiencing poor performance or memory utilization with
* {@code limit()} in parallel pipelines, switching to sequential execution
* with {@link #sequential()} may improve performance.
* @param maxSize the number of elements the stream should be limited to
* @return the new stream
* @throws IllegalArgumentException if {#code maxSize} is negative
*/
// @ts-ignore
limit(maxSize: number /*long*/): java.util.stream.DoubleStream
/**
* Returns a stream consisting of the remaining elements of this stream
* after discarding the first {@code n} elements of the stream.
* If this stream contains fewer than {@code n} elements then an
* empty stream will be returned.
* This is a stateful
* intermediate operation.
* @apiNote While {#code skip()} is generally a cheap operation on sequential
* stream pipelines, it can be quite expensive on ordered parallel pipelines,
* especially for large values of {@code n}, since {@code skip(n)}
* is constrained to skip not just any n elements, but the
* first n elements in the encounter order. Using an unordered
* stream source (such as {@link #generate(DoubleSupplier)}) or removing the
* ordering constraint with {@link #unordered()} may result in significant
* speedups of {@code skip()} in parallel pipelines, if the semantics of
* your situation permit. If consistency with encounter order is required,
* and you are experiencing poor performance or memory utilization with
* {@code skip()} in parallel pipelines, switching to sequential execution
* with {@link #sequential()} may improve performance.
* @param n the number of leading elements to skip
* @return the new stream
* @throws IllegalArgumentException if {#code n} is negative
*/
// @ts-ignore
skip(n: number /*long*/): java.util.stream.DoubleStream
/**
* Performs an action for each element of this stream.
* This is a terminal
* operation.
* For parallel stream pipelines, this operation does not
* guarantee to respect the encounter order of the stream, as doing so
* would sacrifice the benefit of parallelism. For any given element, the
* action may be performed at whatever time and in whatever thread the
* library chooses. If the action accesses shared state, it is
* responsible for providing the required synchronization.
* @param action a
* non-interfering action to perform on the elements
*/
// @ts-ignore
forEach(action: java.util.function$.DoubleConsumer): void
/**
* Performs an action for each element of this stream, guaranteeing that
* each element is processed in encounter order for streams that have a
* defined encounter order.
* This is a terminal
* operation.
* @param action a
* non-interfering action to perform on the elements
* @see #forEach(DoubleConsumer)
*/
// @ts-ignore
forEachOrdered(action: java.util.function$.DoubleConsumer): void
/**
* Returns an array containing the elements of this stream.
* This is a terminal
* operation.
* @return an array containing the elements of this stream
*/
// @ts-ignore
toArray(): number /*double*/[]
/**
* Performs a reduction on the
* elements of this stream, using the provided identity value and an
* associative
* accumulation function, and returns the reduced value. This is equivalent
* to:
* The {@code identity} value must be an identity for the accumulator
* function. This means that for all {@code x},
* {@code accumulator.apply(identity, x)} is equal to {@code x}.
* The {@code accumulator} function must be an
* associative function.
* This is a terminal
* operation.
* @apiNote Sum, min, max, and average are all special cases of reduction.
* Summing a stream of numbers can be expressed as:
*
* While this may seem a more roundabout way to perform an aggregation
* compared to simply mutating a running total in a loop, reduction
* operations parallelize more gracefully, without needing additional
* synchronization and with greatly reduced risk of data races.
* @param identity the identity value for the accumulating function
* @param op an associative,
* non-interfering,
* stateless
* function for combining two values
* @return the result of the reduction
* @see #sum()
* @see #min()
* @see #max()
* @see #average()
*/
// @ts-ignore
reduce(identity: number /*double*/, op: java.util.function$.DoubleBinaryOperator): number /*double*/
/**
* Performs a reduction on the
* elements of this stream, using an
* associative accumulation
* function, and returns an {@code OptionalDouble} describing the reduced
* value, if any. This is equivalent to:
* The {@code accumulator} function must be an
* associative function.
* This is a terminal
* operation.
* @param op an associative,
* non-interfering,
* stateless
* function for combining two values
* @return the result of the reduction
* @see #reduce(double, DoubleBinaryOperator)
*/
// @ts-ignore
reduce(op: java.util.function$.DoubleBinaryOperator): java.util.OptionalDouble
/**
* Performs a mutable
* reduction operation on the elements of this stream. A mutable
* reduction is one in which the reduced value is a mutable result container,
* such as an {@code ArrayList}, and elements are incorporated by updating
* the state of the result rather than by replacing the result. This
* produces a result equivalent to:
* Like {@link #reduce(double, DoubleBinaryOperator)}, {@code collect}
* operations can be parallelized without requiring additional
* synchronization.
* This is a terminal
* operation.
* @param If any stream element is a NaN or the sum is at any point a NaN
* then the sum will be NaN.
* The value of a floating-point sum is a function both
* of the input values as well as the order of addition
* operations. The order of addition operations of this method is
* intentionally not defined to allow for implementation
* flexibility to improve the speed and accuracy of the computed
* result.
* In particular, this method may be implemented using compensated
* summation or other technique to reduce the error bound in the
* numerical sum compared to a simple summation of {@code double}
* values.
* This is a terminal
* operation.
* @apiNote Elements sorted by increasing absolute magnitude tend
* to yield more accurate results.
* @return the sum of elements in this stream
*/
// @ts-ignore
sum(): number /*double*/
/**
* Returns an {@code OptionalDouble} describing the minimum element of this
* stream, or an empty OptionalDouble if this stream is empty. The minimum
* element will be {@code Double.NaN} if any stream element was NaN. Unlike
* the numerical comparison operators, this method considers negative zero
* to be strictly smaller than positive zero. This is a special case of a
* reduction and is
* equivalent to:
* This is a terminal
* operation.
* @return an {#code OptionalDouble} containing the minimum element of this
* stream, or an empty optional if the stream is empty
*/
// @ts-ignore
min(): java.util.OptionalDouble
/**
* Returns an {@code OptionalDouble} describing the maximum element of this
* stream, or an empty OptionalDouble if this stream is empty. The maximum
* element will be {@code Double.NaN} if any stream element was NaN. Unlike
* the numerical comparison operators, this method considers negative zero
* to be strictly smaller than positive zero. This is a
* special case of a
* reduction and is
* equivalent to:
* This is a terminal
* operation.
* @return an {#code OptionalDouble} containing the maximum element of this
* stream, or an empty optional if the stream is empty
*/
// @ts-ignore
max(): java.util.OptionalDouble
/**
* Returns the count of elements in this stream. This is a special case of
* a reduction and is
* equivalent to:
* This is a terminal operation.
* @return the count of elements in this stream
*/
// @ts-ignore
count(): number /*long*/
/**
* Returns an {@code OptionalDouble} describing the arithmetic
* mean of elements of this stream, or an empty optional if this
* stream is empty.
* If any recorded value is a NaN or the sum is at any point a NaN
* then the average will be NaN.
* The average returned can vary depending upon the order in
* which values are recorded.
* This method may be implemented using compensated summation or
* other technique to reduce the error bound in the {@link #sum
* numerical sum} used to compute the average.
* The average is a special case of a reduction.
* This is a terminal
* operation.
* @apiNote Elements sorted by increasing absolute magnitude tend
* to yield more accurate results.
* @return an {#code OptionalDouble} containing the average element of this
* stream, or an empty optional if the stream is empty
*/
// @ts-ignore
average(): java.util.OptionalDouble
/**
* Returns a {@code DoubleSummaryStatistics} describing various summary data
* about the elements of this stream. This is a special
* case of a reduction.
* This is a terminal
* operation.
* @return a {#code DoubleSummaryStatistics} describing various summary data
* about the elements of this stream
*/
// @ts-ignore
summaryStatistics(): java.util.DoubleSummaryStatistics
/**
* Returns whether any elements of this stream match the provided
* predicate. May not evaluate the predicate on all elements if not
* necessary for determining the result. If the stream is empty then
* {@code false} is returned and the predicate is not evaluated.
* This is a short-circuiting
* terminal operation.
* @apiNote This method evaluates the existential quantification of the
* predicate over the elements of the stream (for some x P(x)).
* @param predicate a non-interfering,
* stateless
* predicate to apply to elements of this stream
* @return {#code true} if any elements of the stream match the provided
* predicate, otherwise {@code false}
*/
// @ts-ignore
anyMatch(predicate: java.util.function$.DoublePredicate): boolean
/**
* Returns whether all elements of this stream match the provided predicate.
* May not evaluate the predicate on all elements if not necessary for
* determining the result. If the stream is empty then {@code true} is
* returned and the predicate is not evaluated.
* This is a short-circuiting
* terminal operation.
* @apiNote This method evaluates the universal quantification of the
* predicate over the elements of the stream (for all x P(x)). If the
* stream is empty, the quantification is said to be vacuously
* satisfied and is always {#code true} (regardless of P(x)).
* @param predicate a non-interfering,
* stateless
* predicate to apply to elements of this stream
* @return {#code true} if either all elements of the stream match the
* provided predicate or the stream is empty, otherwise {@code false}
*/
// @ts-ignore
allMatch(predicate: java.util.function$.DoublePredicate): boolean
/**
* Returns whether no elements of this stream match the provided predicate.
* May not evaluate the predicate on all elements if not necessary for
* determining the result. If the stream is empty then {@code true} is
* returned and the predicate is not evaluated.
* This is a short-circuiting
* terminal operation.
* @apiNote This method evaluates the universal quantification of the
* negated predicate over the elements of the stream (for all x ~P(x)). If
* the stream is empty, the quantification is said to be vacuously satisfied
* and is always {#code true}, regardless of P(x).
* @param predicate a non-interfering,
* stateless
* predicate to apply to elements of this stream
* @return {#code true} if either no elements of the stream match the
* provided predicate or the stream is empty, otherwise {@code false}
*/
// @ts-ignore
noneMatch(predicate: java.util.function$.DoublePredicate): boolean
/**
* Returns an {@link OptionalDouble} describing the first element of this
* stream, or an empty {@code OptionalDouble} if the stream is empty. If
* the stream has no encounter order, then any element may be returned.
* This is a short-circuiting
* terminal operation.
* @return an {#code OptionalDouble} describing the first element of this
* stream, or an empty {@code OptionalDouble} if the stream is empty
*/
// @ts-ignore
findFirst(): java.util.OptionalDouble
/**
* Returns an {@link OptionalDouble} describing some element of the stream,
* or an empty {@code OptionalDouble} if the stream is empty.
* This is a short-circuiting
* terminal operation.
* The behavior of this operation is explicitly nondeterministic; it is
* free to select any element in the stream. This is to allow for maximal
* performance in parallel operations; the cost is that multiple invocations
* on the same source may not return the same result. (If a stable result
* is desired, use {@link #findFirst()} instead.)
* @return an {#code OptionalDouble} describing some element of this stream,
* or an empty {@code OptionalDouble} if the stream is empty
* @see #findFirst()
*/
// @ts-ignore
findAny(): java.util.OptionalDouble
/**
* Returns a {@code Stream} consisting of the elements of this stream,
* boxed to {@code Double}.
* This is an intermediate
* operation.
* @return a {#code Stream} consistent of the elements of this stream,
* each boxed to a {@code Double}
*/
// @ts-ignore
boxed(): java.util.stream.Stream The first element (position {@code 0}) in the {@code DoubleStream}
* will be the provided {@code seed}. For {@code n > 0}, the element at
* position {@code n}, will be the result of applying the function {@code f}
* to the element at position {@code n - 1}.
* @param seed the initial element
* @param f a function to be applied to to the previous element to produce
* a new element
* @return a new sequential {#code DoubleStream}
*/
// @ts-ignore
iterate(seed: number /*double*/, f: java.util.function$.DoubleUnaryOperator): java.util.stream.DoubleStream
/**
* Returns an infinite sequential unordered stream where each element is
* generated by the provided {@code DoubleSupplier}. This is suitable for
* generating constant streams, streams of random elements, etc.
* @param s the {#code DoubleSupplier} for generated elements
* @return a new infinite sequential unordered {#code DoubleStream}
*/
// @ts-ignore
generate(s: java.util.function$.DoubleSupplier): java.util.stream.DoubleStream
/**
* Creates a lazily concatenated stream whose elements are all the
* elements of the first stream followed by all the elements of the
* second stream. The resulting stream is ordered if both
* of the input streams are ordered, and parallel if either of the input
* streams is parallel. When the resulting stream is closed, the close
* handlers for both input streams are invoked.
* @implNote Use caution when constructing streams from repeated concatenation.
* Accessing an element of a deeply concatenated stream can result in deep
* call chains, or even {#code StackOverflowException}.
* @param a the first stream
* @param b the second stream
* @return the concatenation of the two input streams
*/
// @ts-ignore
concat(a: java.util.stream.DoubleStream, b: java.util.stream.DoubleStream): java.util.stream.DoubleStream
}
}
}
}
{#code
* DoubleStream.of(1, 2, 3, 4)
* .filter(e -> e > 2)
* .peek(e -> System.out.println("Filtered value: " + e))
* .map(e -> e * e)
* .peek(e -> System.out.println("Mapped value: " + e))
* .sum();
* }
* @param action a
* non-interfering action to perform on the elements as
* they are consumed from the stream
* @return the new stream
*/
// @ts-ignore
peek(action: java.util.function$.DoubleConsumer): java.util.stream.DoubleStream
/**
* Returns a stream consisting of the elements of this stream, truncated
* to be no longer than {@code maxSize} in length.
* {@code
* double result = identity;
* for (double element : this stream)
* result = accumulator.applyAsDouble(result, element)
* return result;
* }
* but is not constrained to execute sequentially.
* {#code
* double sum = numbers.reduce(0, (a, b) -> a+b);
* }
*
* or more compactly:
*
* {@code
* double sum = numbers.reduce(0, Double::sum);
* }
*
* {@code
* boolean foundAny = false;
* double result = null;
* for (double element : this stream) {
* if (!foundAny) {
* foundAny = true;
* result = element;
* }
* else
* result = accumulator.applyAsDouble(result, element);
* }
* return foundAny ? OptionalDouble.of(result) : OptionalDouble.empty();
* }
* but is not constrained to execute sequentially.
* {@code
* R result = supplier.get();
* for (double element : this stream)
* accumulator.accept(result, element);
* return result;
* }
* {@code
* return reduce(0, Double::sum);
* }
* However, since floating-point summation is not exact, the above
* code is not necessarily equivalent to the summation computation
* done by this method.
* {@code
* return reduce(Double::min);
* }
* {@code
* return reduce(Double::max);
* }
* {@code
* return mapToLong(e -> 1L).sum();
* }
*