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docs/services/docs/markdown/virtdata-dev/concepts.md

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-# Virtual Dataset Concepts
-
-VirtData is a library for the flexible management and expressive use of
-procedural generation libraries. It is a reincarnation of a previous project.
-This version of the idea starts by focusing directly on usage aspects and
-extension points rather than the big idea.
-
-### Procedural Generation
-
-Procedural generation is a general class of methods for taking a set of inputs
-and modifying them in a predictable way to generate content which appears random
-but is actually deterministic. For example, some games use procedural generation
-to take a single value known as the "seed" to generate an apparently rich and
-interesting world.
-
-### Apparently Random RNGs
-
-Sequences of values produced by RNGs (more properly called PRNGs) are not
-actually random, even though they may pass certain tests for randomness. In
-practice, the combination of these two properties is quite valuable for testing
-and data synthesis. Having a stream of data that is measurably random by some
-meaningful standard, but which is configurable and reusable allows for test to
-be replayed, for example.
-
-### Apparently Random Samples
-
-Just as RNGs can appear random when the are not truly, statistical distributions
-which rely on them can also appear random. Uniform random number generators over
-the unit interval [0,1.0) are a common input to virtual sampling methods. This
-means that if you can configure the RNG stream that you feed into your virtual
-sampling methods, you can simulate a repeatable sequence from a known
-distribution.
-
-### Data Mapping Functions
-
-The data mapping functions are the core building block of virtdata. They are the
-functional logic that powers all procedural generation. Data mapping functions
-are generally pure functions. This simply means that a generator function will
-always provide the same result given the same input. All top-level mapping
-functions all take a long value as their input, and produce a result based on
-their parameterized type.
-
-##### Combining RNGs and Data Mapping Functions
-
-Because pure functions play such a key part in procedural generation techniques,
-the terms "data mapping function", "data mapper" and "data mapping library" will
-be more common in the library than "generator". Conceptually, mapping functions
-to not generate anything. It makes more sense to think of mapping data from one
-domain to another. Even so, the data that is yielded by mapping functions can
-appear quite realistic.
-
-Because good RNGs do generally contain internal state, they aren't purely
-functional. This means that in some cases -- those in which you need to have
-random access to a virtual data set, hash functions make more sense. This
-toolkit allows you to choose between the two in some cases. However, it
-generally favors using hashing and pure-function approaches where possible. Even
-the statistical curve simulations do this.
-
-### Data Mapper Library
-
-Data Mapping functions are packaged into libraries which can be loaded by the
-virtdata-user component of the project. Each library has a name, a function
-resolver, and a set of functions that can be instantiated via the function
-resolver.
-
-### Function Resolver
-
-Each library must implement its own function resolver. This is because each
-library may have a different way of naming, finding, creating or managing
-function generator instances. For the user, the description of a generator is
-simply a string. What the generator library does with it is
-implementation-specific. This means that some generator libraries may simply
-have constructor signatures as function specifiers, and others may go as far as
-implementing their own DSL. The basic contract for a function resolver is that
-you pass it a string describing what you want, and it provides a generator
-function in return.
-
-#### Bindings Template
-
-It is often useful to have a template that describes a set of generator
-functions that can be reused across many threads or other application scopes. A
-bindings template is a way to capture the requested generator functions for
-re-use, with actual scope instantiation of the generator functions controlled by
-the usage point. For example, in a JEE app, you may have a bindings template in
-the application scope, and a set of actual bindings within each request (thread
-scope).
-