/* IterTreeExplorer(Test) - verify tree expanding and backtracking iterator Copyright (C) Lumiera.org 2017, Hermann Vosseler This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. * *****************************************************/ /** @file iter-tree-explorer-test.cpp ** The \ref IterTreeExplorer_test covers and demonstrates a generic mechanism ** to expand and evaluate tree like structures. In its current shape (as of 2017), ** it can be seen as an preliminary step towards retrofitting IterExplorer into ** a framework of building blocks for tree expanding and backtracking evaluations. ** Due to the nature of Lumiera's design, we repeatedly encounter this kind of ** algorithms, when it comes to matching configuration and parametrisation against ** a likewise hierarchical and rules based model. To keep the code base maintainable, ** we deem it crucial to reduce the inherent complexity in such algorithms by clearly ** separate the _mechanics of evaluation_ from the actual logic of the target domain. ** ** Similar to IterExplorer_test, the his test relies on a demonstration setup featuring ** a custom encapsulated state type: we rely on a counter with start and end value, ** embedded into an iterator. Basically, this running counter, when iterated, generates ** a sequence of numbers start ... end. ** So -- conceptually -- this counting iterator can be thought to represent this ** sequence of numbers. Note that this is a kind of abstract or conceptual ** representation, not a factual representation of the sequence in memory. ** The whole point is _not to represent_ this sequence in runtime state at once, ** rather to pull and expand it on demand. ** ** All these tests work by first defining these _functional structures_, which just ** yields an iterator entity. We get the whole structure it conceptually defines ** only if we "pull" this iterator until exhaustion -- which is precisely what ** the test does to verify proper operation. Real world code of course would ** just not proceed in this way, like pulling everything from such an iterator. ** Often, the very reason we're using such a setup is the ability to represent ** infinite structures. Like e.g. the evaluation graph of video passed through ** a complex processing pipeline. */ #include "lib/test/run.hpp" #include "lib/test/test-helper.hpp" #include "lib/iter-adapter-stl.hpp" #include "lib/format-cout.hpp" #include "lib/format-util.hpp" #include "lib/util.hpp" #include "lib/iter-tree-explorer.hpp" #include "lib/meta/trait.hpp" #include #include #include namespace lib { namespace test{ using ::Test; using util::isnil; using util::isSameObject; using lib::iter_stl::eachElm; using lumiera::error::LUMIERA_ERROR_ITER_EXHAUST; using std::vector; using std::string; namespace { // test substrate: simple number sequence iterator /** * This iteration _"state core" type_ describes * a sequence of numbers yet to be delivered. */ class State { uint p,e; public: State(uint start =0, uint end =0) : p(start) , e(end) { } friend bool checkPoint (State const& st) { return st.p > st.e; } friend uint& yield (State const& st) { return util::unConst(checkPoint(st)? st.p : st.e); } friend void iterNext (State & st) { if (not checkPoint(st)) return; --st.p; } }; /** * A straight ascending number sequence as basic test iterator. * The tests will dress up this source sequence in various ways. */ class NumberSequence : public IterStateWrapper { public: explicit NumberSequence(uint end = 0) : IterStateWrapper (State(0,end)) { } NumberSequence(uint start, uint end) : IterStateWrapper (State(start,end)) { } }; inline NumberSequence seq (uint end) { return NumberSequence(end); } inline NumberSequence seq (uint start, uint end) { return NumberSequence(start, end); } NumberSequence NIL_Sequence; /** Diagnostic helper: "squeeze out" the given iterator * and join all the elements yielded into a string */ template inline string materialise (II&& ii) { return util::join (std::forward (ii), "-"); } template inline void pullOut (II & ii) { while (ii) { cout << *ii; if (++ii) cout << "-"; } cout << endl; } } // (END) test helpers /*******************************************************************//** * @test use a simple source iterator yielding numbers * to build various functional evaluation structures, * based on the \ref IterExplorer template. * - the [state adapter](\ref verifyStateAdapter() ) * iterator construction pattern * - helper to [chain iterators](\ref verifyChainedIterators() ) * - building [tree exploring structures](\ref verifyDepthFirstExploration()) * - the [monadic nature](\ref verifyMonadOperator()) of IterExplorer * - a [recursively self-integrating](\ref verifyRecrusiveSelfIntegration()) * evaluation pattern * * ## Explanation * * Both this test and the IterExplorer template might be bewildering * and cryptic, unless you know the *Monad design pattern*. Monads are * heavily used in functional programming, actually they originate * from Category Theory. Basically, Monad is a pattern where we * combine several computation steps in a specific way; but instead * of intermingling the individual computation steps and their * combination, the goal is to isolate and separate the _mechanics * of combination_, so we can focus on the actual _computation steps:_ * The mechanics of combination are embedded into the Monad type, * which acts as a kind of container, holding some entities * to be processed. The actual processing steps are then * fed to the monad as "function object" parameters. * * Using the monad pattern is well suited when both the mechanics of * combination and the individual computation steps tend to be complex. * In such a situation, it is beneficial to develop and test both * in isolation. The IterExplorer template applies this pattern * to the task of processing a source sequence. Typically we use * this in situations where we can't afford building elaborate * data structures in (global) memory, but rather strive at * doing everything on-the-fly. A typical example is the * processing of a variably sized data set without * using heap memory for intermediary results. * * @see TreeExplorer * @see IterAdapter */ class IterTreeExplorer_test : public Test { virtual void run (Arg) { verify_wrappedState(); verify_wrappedIterator(); verify_mapOperation(); verify_expandOperation(); verify_expandMapCombination(); verify_depthFirstExploration(); demonstrate_LayeredEvaluation(); } /** @test without using any extra functionality, * TreeExplorer just wraps an iterable state. */ void verify_wrappedState() { auto ii = treeExplore (State{5,0}); CHECK (!isnil (ii)); CHECK (5 == *ii); ++ii; CHECK (4 == *ii); pullOut(ii); CHECK ( isnil (ii)); CHECK (!ii); VERIFY_ERROR (ITER_EXHAUST, *ii ); VERIFY_ERROR (ITER_EXHAUST, ++ii ); ii = treeExplore (State{5}); CHECK (materialise(ii) == "5-4-3-2-1"); ii = treeExplore (State{7,4}); CHECK (materialise(ii) == "7-6-5"); ii = treeExplore (State{}); CHECK ( isnil (ii)); CHECK (!ii); } /** @test TreeExplorer is able to wrap any _Lumiera Forward Iterator_ */ void verify_wrappedIterator() { vector numz{1,-2,3,-5,8,-13}; auto ii = eachElm(numz); CHECK (!isnil (ii)); CHECK (1 == *ii); ++ii; CHECK (-2 == *ii); auto jj = treeExplore(ii); CHECK (!isnil (jj)); CHECK (-2 == *jj); ++jj; CHECK (3 == *jj); // we passed a LValue-Ref, thus a copy was made CHECK (-2 == *ii); CHECK (materialise(ii) == "-2-3--5-8--13"); CHECK (materialise(jj) == "3--5-8--13"); // can adapt STL container automatically auto kk = treeExplore(numz); CHECK (!isnil (kk)); CHECK (1 == *kk); CHECK (materialise(kk) == "1--2-3--5-8--13"); } /** @test pipe each result through a transformation function */ void verify_mapOperation() { UNIMPLEMENTED("map function onto the results"); } /** @test use a preconfigured "expand" functor to recurse into children */ void verify_expandOperation() { UNIMPLEMENTED("expand children"); } /** @test combie the recursion into children with a tail mapping operation */ void verify_expandMapCombination() { UNIMPLEMENTED("combine child expansion and result mapping"); } /** @test use a preconfigured exploration scheme to expand depth-first until exhaustion */ void verify_depthFirstExploration() { UNIMPLEMENTED("preconfigured repeated depth-first expansion"); } /** @test Demonstration how to build complex algorithms by layered tree expanding iteration * @remarks this is the actual use case which inspired the design of TreeExplorer */ void demonstrate_LayeredEvaluation() { UNIMPLEMENTED("build algorithm by layering iterator evaluation"); } }; LAUNCHER (IterTreeExplorer_test, "unit common"); }} // namespace lib::test