Tensorics is a java framework which uses a tensor as a central object. A tensor represents a set of values placed in an N-dimensional space. Wherever you are tempted to use maps of maps, a tensor might be a good choice ;-) Tensorics provides methods to create, transform and performing calculations with those tensors.
One way to describe a tensor within tensorics is to imagine it as a map from a position within a multidimensional to values of any type. To illustrate this, let’s assume a tensor of double values:
Tensor<Double> aTensor; /* initialization omitted for the moment */;
then it can be transformed into a map by:
Map<Position, Double> map = Tensorics.mapFrom(aTensor);
Here, 'Position' is the type of keys in this map and is itself composed of a fixed number (N) of partial keys. N is the number of dimensions of the tensor. The partial keys are identified by their types. For example, if we would assume that our tensor would have two dimensions (String.class and Integer.class), then a valid position for this tensor could be e.g. created like this:
Position position = Position.of("kaifox", 1);where the two coordinates within the 2-dimensional space (where N=2 in this case) are "kaifox" (dimension String.class) and 1 (dimension Integer.class).
To most common way to create tensors is to use builders (in the following example we assume a static import of Tensorics.*):
Tensor<Double> aTensor = builder(String.class, Integer.class) // the dimension of the tensor
.put(at("kaifox", 1), 1.5)
.put(at("kaifox", 2), 2.5)
.put(at("michi",1), 3.5)
.put(at("andrea", 2), 4.5)
.build();
Tensors built in such a way are always immutable objects. The values can then be simply accessed through get methods:
Double value = aTensor.get("kaifox", 1); // would return 1.5
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Note
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A main difference to a usual map is here that get() methods on tensors would throw a NoSuchElementException if a set of coordinates i provided for which no value is stored in the tensor, while a get() method of a map would silently return null.
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Tensorics provides a big featureset of operations to work with such tensors, for example:
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Structural operations (extractions, slicing) for any value type of tensors
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Fluent API for all operations on scalars and tensors.
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Mathematical operations based on algebraic structures (e.g. Field). Mathematical operations are possible any value for which those structures are defined. A default implementation is currently provided for doubles.
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Physical Quantities (value - unit pair), currently by the use of jscience.
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Tensors of quantities.
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Error and Validity propagation for quantities and tensors of quantities.
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Scripting of all functionality with deferred execution, which opens the possibilities for parallel processing and massive distribution of calculations.