TTTensor documentation mostly done

Makefile

@@ -232,7 +232,7 @@ clean:
 	rm -fr build
 	-rm -f $(TEST_NAME)
 	-rm -f include/xerus.h.gch
-	-rm -r doc/html
+	make -C doc clean
 
 
 

doc/Makefile

@@ -16,7 +16,9 @@ doc: .FORCE parseDoxytags findDoxytag
 	jekyll build --source jekyll/ --destination html/
 
 clean:
-	-rm -r html
+	-rm -rf html
+	-rm -f parseDoxytags findDoxytag
+	-rm -f xerus.tags xerus.tagfile
 
 serve: .FORCE parseDoxytags findDoxytag
 	-mkdir html

doc/jekyll/_includes/examples/qtt.cpp

@@ -25,10 +25,8 @@ int main() {
 	std::cout << "ttA ranks: " << ttA.ranks() << std::endl;
 	
 	// the right hand side of the equation both as Tensor and in (Q)TT format
-	auto b = xerus::Tensor::ones({512});
-	
-	b.reinterpret_dimensions(std::vector<size_t>(9, 2));
-	xerus::TTTensor ttb(b);
+	auto b = xerus::Tensor::ones(std::vector<size_t>(9, 2));
+	auto ttb = xerus::TTTensor::ones(b.dimensions);
 	
 	// construct a random initial guess of rank 3 for the ALS algorithm
 	xerus::TTTensor ttx = xerus::TTTensor::random(std::vector<size_t>(9, 2), std::vector<size_t>(8, 3));

doc/jekyll/_includes/examples/qtt.py

@@ -23,9 +23,8 @@ ttA = xe.TTOperator(A)
 print("ttA ranks:", ttA.ranks())
 
 # the right hand side of the equation both as Tensor and in (Q)TT format
-b = xe.Tensor.ones([512])
-b.reinterpret_dimensions([2,]*9)
-ttb = xe.TTTensor(b)
+b = xe.Tensor.ones([2,]*9)
+ttb = xe.TTTensor.ones(b.dimensions)
 
 # construct a random initial guess of rank 3 for the ALS algorithm
 ttx = xe.TTTensor.random([2,]*9, [3,]*8)

doc/jekyll/_plugins/tabs.rb

@@ -20,6 +20,8 @@ class TabsConverter < Converter
 			.gsub('<p>__dangerEnd</p>', "</div>")
 			.gsub('<p>__warnStart</p>', "<div class=\"alert alert-warning\">")
 			.gsub('<p>__warnEnd</p>', "</div>")
+			.gsub('<p>__infoStart</p>', "<div class=\"alert alert-info\">")
+			.gsub('<p>__infoEnd</p>', "</div>")
 			.gsub('__breakFix1</a></p>', "")
 			.gsub('<p>__breakFix2', "</a>")
 			.gsub('__version', %x( git describe --tags --always --abbrev=0 ) )

doc/jekyll/_posts/1000-12-10-quickstart.md

@@ -90,22 +90,18 @@ print("ttA ranks:", ttA.ranks())
 ~~~
 __tabsEnd
 
-For the right-hand-side we perform similar operations to obtain a QTT decomposed vector $ b_i = 1 \forall i $.
-As the generating function needs no index information, we create a `[]()->double` lambda function:
+For the right-hand-side we will take a simple tensor that is equal to 1 at every position. As this is a commonly used tensor,
+we can simply use the named constructor provide dby `xerus`.
 
 __tabsStart
 ~~~ cpp
-auto b = xerus::Tensor::ones({512});
-
-b.reinterpret_dimensions(std::vector<size_t>(9, 2));
-xerus::TTTensor ttb(b);
+auto b = xerus::Tensor::ones(std::vector<size_t>(9, 2));
+auto ttb = xerus::TTTensor::ones(b.dimensions);
 ~~~
 __tabsMid
 ~~~ python
-b = xerus.Tensor.ones([512])
-
-b.reinterpret_dimensions([2,]*9)
-ttb = xerus.TTTensor(b)
+b = xerus.Tensor.ones([2,]*9)
+ttb = xerus.TTTensor.ones(b.dimensions)
 ~~~
 __tabsEnd
 

doc/jekyll/_posts/2000-12-20-building_xerus.md

@@ -33,6 +33,8 @@ To build the python bindings you will furthermore need the python development he
 dnf install python2-numpy python-devel
 ~~~
 
+To build the documentation (i.e. this homepage) locally you will need `jekyll` and `doxygen`.
+
 After downloading the source it is necessary to set a number of options that are somewhat individual to your system and needs. All following build steps assume that these
 options are set in the `config.mk` file. Reasonable default values can be found in the `config.mk.default` file.
 

doc/jekyll/index.md

@@ -23,7 +23,7 @@ The key features include:
 * Full python bindings with very similar syntax for easy transitions from and to c++.
 * Calculation with tensors of arbitrary orders using an intuitive Einstein-like notation `A(i,j) = B(i,k,l) * C(k,j,l);`.
 * Full implementation of the Tensor-Train decompositions (MPS) with all neccessary capabilities (including Algorithms like ALS, ADF and CG).
-* Lazy evaluation of multiple tensor contractions featuring heuristics to find the most effective contraction order.
+* Lazy evaluation of (multiple) tensor contractions featuring heuristics to automatically find efficient contraction orders.
 * Direct integration of the `blas` and `lapack`, as high performance linear algebra backends.
 * Fast sparse tensor calculation by usage of the `suiteSparse` sparse matrix capabilities.
 * Capabilites to handle arbitrary Tensor Networks.

include/xerus/ttNetwork.h

@@ -159,7 +159,7 @@ namespace xerus {
 		 * @brief Random constructs a TTNetwork with the given dimensions and ranks limited by the given rank. 
 		 * @details The entries of the componend tensors are sampled independendly using the provided random generator and distribution.
 		 * @param _dimensions the dimensions of the to be created TTNetwork.
-		 * @param _ranks the maximal allowed rank. 
+		 * @param _rank the maximal allowed rank. 
 		 * @param _rnd the random engine to be passed to the constructor of the component tensors.
 		 * @param _dist the random distribution to be passed to the constructor of the component tensors.
 		 */