org.tensorics.core.tensor.lang

Class TensorStructurals

java.lang.Object

org.tensorics.core.tensor.lang.TensorStructurals

public final class TensorStructurals
extends java.lang.Object

Structural manipulations of tensors (which do not require any knowledge about the mathematical behaviour of the elements).

Method Summary

Modifier and Type	Method and Description
static <S> OngoingCompletion<S>	complete(Tensor<S> tensor)
static <V> Tensor<V>	completeWith(Tensor<V> tensor, Tensor<V> second)
static <S> void	consumeScalars(Tensor<S> tensor, java.util.function.BiConsumer<Position,S> consumer)
static <S> void	consumeScalars(Tensor<S> tensor, java.util.function.Consumer<S> consumer)
static <V> OngoingTensorFiltering<V>	filter(Tensor<V> tensor)
static <S> OngoingFlattening<S>	flatten(Tensor<S> tensor)
static <V> OngoingTensorManipulation<V>	from(Tensor<V> tensor)
static <E> Tensor<E>	merge(java.lang.Iterable<Tensor<E>> tensors) Merges the given set of the Tensors based on information in their context and dimensions.
static <V> Tensor<V>	mergeContextIntoShape(Tensor<V> tensor)
static <V> OngoingResamplingStart<V>	resample(Tensor<V> tensor) Starting point for a fluent clause to describe resampling in one or more dimensions.
static <V> Tensor<V>	setContext(Tensor<V> tensor, Position context)
static <S> Tensor<S>	stripContext(Tensor<S> tensor)
static <S,T> Tensor<T>	transformEntries(Tensor<S> tensor, java.util.function.Function<java.util.Map.Entry<Position,S>,T> function)
static <S,T> Tensor<T>	transformScalars(Tensor<S> tensor, java.util.function.BiFunction<Position,S,T> function)
static <S,T> Tensor<T>	transformScalars(Tensor<S> tensor, java.util.function.Function<S,T> function)

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Method Detail

from

public static <V> OngoingTensorManipulation<V> from(Tensor<V> tensor)

merge

public static <E> Tensor<E> merge(java.lang.Iterable<Tensor<E>> tensors)

Merges the given set of the Tensors based on information in their context and dimensions. This operation is only possible, if the following preconditions are fulfilled:

• The contexts of all the tensors have THE SAME dimensions AND
• The dimensions of all the tensors are THE SAME (first found tensor dimension is taken as an reference)

Parameters:

tensors - to be merged.

Returns:

a merged tensor.

Throws:

java.lang.IllegalArgumentException - if zero or one tensor is put to be merged OR if tensors dimensionality and their context positions dimensionality is not equal OR tensor context is empty.

flatten

public static <S> OngoingFlattening<S> flatten(Tensor<S> tensor)

complete

public static final <S> OngoingCompletion<S> complete(Tensor<S> tensor)

transformEntries

public static <S,T> Tensor<T> transformEntries(Tensor<S> tensor,
                                               java.util.function.Function<java.util.Map.Entry<Position,S>,T> function)

transformScalars

public static <S,T> Tensor<T> transformScalars(Tensor<S> tensor,
                                               java.util.function.Function<S,T> function)

transformScalars

public static <S,T> Tensor<T> transformScalars(Tensor<S> tensor,
                                               java.util.function.BiFunction<Position,S,T> function)

consumeScalars

public static <S> void consumeScalars(Tensor<S> tensor,
                                      java.util.function.Consumer<S> consumer)

consumeScalars

public static <S> void consumeScalars(Tensor<S> tensor,
                                      java.util.function.BiConsumer<Position,S> consumer)

stripContext

public static final <S> Tensor<S> stripContext(Tensor<S> tensor)

mergeContextIntoShape

public static final <V> Tensor<V> mergeContextIntoShape(Tensor<V> tensor)

setContext

public static final <V> Tensor<V> setContext(Tensor<V> tensor,
                                             Position context)

filter

public static final <V> OngoingTensorFiltering<V> filter(Tensor<V> tensor)

completeWith

public static final <V> Tensor<V> completeWith(Tensor<V> tensor,
                                               Tensor<V> second)

resample

public static final <V> OngoingResamplingStart<V> resample(Tensor<V> tensor)

Starting point for a fluent clause to describe resampling in one or more dimensions. This resampling only provides options which do not require the knowledge of a field. For example:

 
 Tensoric<V> resampled = resample(aTensor)
                           .repeat(Integer.class)
                           .then().repeat(String.class)
                           .toTensoric();
 
 

Note: The order of the options is important, as the resampling will be performed in the given order!

For options which require a field, (e.g. linear interpolation), see the version in TensorSupport.resample(Tensor).

Parameters:

tensor - the tensor to be resampled

Returns:

an object to define further the strategy for resampling.