update docs

Author: 9il

README.md

@@ -11,17 +11,25 @@ Generic Numerical Library for Science and Machine Learning.
 Contents
 --------
 
- - `mir.ndslice` [Multidimensional Random Access Ranges and Arrays](http://dlang.org/phobos-prerelease/std_experimental_ndslice.html)
- - `mir.las.sum` Functions and Output Ranges for Summation Algorithms. Works with user-defined types.
-  - Precise algorithm: improved analog of Python's `fsum`
-  - Pairwise algorithm: fast version for Input Ranges
-  - Kahan, KBN, and KB2 algorithms
- - `mir.combinatorics` Combinations, combinations with repeats, cartesian power, permutations.
+- `mir.ndslice` [Multidimensional Random Access Ranges and Arrays](http://dlang.org/phobos-prerelease/std_experimental_ndslice.html)
+- `mir.sparse` Sparse Tensors
+ - `Sparse` - DOK format
+ - Different ranges for COO format
+ - `CompressedTensor` - CSR/CSC formats
+- `mir.sparse.blas` - Sparse BLAS for `CompressedTensor`
+- `mir.model.lda.hoffman` - Online variational Bayes for latent Dirichlet allocation (Online VB LDA) for sparse documents. LDA is used for topic modeling.
+- `mir.combinatorics` Combinations, combinations with repeats, cartesian power, permutations.
+- `mir.las.sum` Functions and Output Ranges for Summation Algorithms. Works with user-defined types.
+ - Precise algorithm: improved analog of Python's `fsum`
+ - Pairwise algorithm: fast version for Input Ranges
+ - Kahan, KBN, and KB2 algorithms
+- `mir.blas` - this is slow snail, it is for experiments with BLAS API. Don't use for now.
+
 
 ### In progress
 
- - `mir.sparse` Sparse Tensors (see sparse branch and `v0.15.1-beta2`+)
-  -  `mir.sparse.blas` Spars BLAS
+ - `mir.random` - non-uniform RNGs.
+ - `mir.blas` - BLAS in D.
 
 Documentation
 -------------

index.d

@@ -1,23 +1,4 @@
 /**
 Numerical library and mirror for upcoming numeric packages for the Dlang standard library.
-
-$(DL
-	$(DT $(LINK2 http://dlang.org/phobos-prerelease/std_experimental_ndslice.html, ndslice package)
-		$(DD Multidimensional Random Access Ranges and Arrays)
-	)
-	$(DT $(DPMODULE2 las, sum)
-		$(DD Functions and Output Ranges for Summation Algorithms. Works with user-defined types.)
-		$(DD Precise algorithm: improved analog of Python's `fsum`)
-        $(DD Pairwise algorithm: fast version for Input Ranges)
-        $(DD Kahan, KBN, and KB2 algorithms)
-	)
-	$(DT $(DPMODULE2 combinatorics, package)
-		$(DD $(DPREF2 combinatorics, package, permutations))
-		$(DD $(DPREF2 combinatorics, package, cartesianPower))
-		$(DD $(DPREF2 combinatorics, package, combinations))
-		$(DD $(DPREF2 combinatorics, package, combinationsRepeat))
-	)
-)
-
 */
 module mir;

modules.ddoc

@@ -6,3 +6,4 @@ MODULES =
 	$(MODULE mir.sparse.blas.gemv)
 	$(MODULE mir.sparse.blas.gemm)
 	$(MODULE mir.combinatorics.package)
+	$(MODULE mir.model.lda.hoffman)

source/mir/sparse/package.d

@@ -1,9 +1,6 @@
 /++
 $(H2 Sparse Tensors)
 
-This is a submodule of $(LINK2 mir_ndslice.html, mir.ndslice).
-
-
 License:   $(WEB www.boost.org/LICENSE_1_0.txt, Boost License 1.0).
 
 Authors:   Ilya Yaroshenko
@@ -84,7 +81,7 @@ Sparse Slice in Dictionary of Keys (DOK) format.
 alias Sparse(size_t N, T) = Slice!(N, SparseMap!T);
 
 /++
-SparseMap is a range, which is used internally by $(LREF Sparse).
+`SparseMap` is used internally by `Slice` type to represent $(LREF Sparse).
 +/
 struct SparseMap(T)
 {
@@ -197,7 +194,7 @@ private sizediff_t cmpCoo(size_t N)(const auto ref size_t[N] a, const auto ref s
 }
 
 /++
-Returns unsorted range of (coordinate, values) pairs.
+Returns unsorted forward range of (coordinate, value) pairs.
 Params:
 	slice = sparse slice with pure structure. Any operations on structure of a slice are not allowed.
 +/
@@ -256,7 +253,7 @@ pure unittest
 }
 
 /++
-Returns unsorted range of coordinates.
+Returns unsorted forward range of coordinates.
 Params:
 	slice = sparse slice with pure structure. Any operations on structure of a slice are not allowed.
 +/
@@ -312,7 +309,7 @@ pure unittest
 }
 
 /++
-Returns unsorted range of values.
+Returns unsorted forward range of values.
 Params:
 	slice = sparse slice with pure structure. Any operations on structure of a slice are not allowed.
 +/
@@ -459,7 +456,7 @@ unittest
 }
 
 /++
-Returns compressed tensor with changed element type.
+Returns compressed tensor with different element type.
 +/
 CompressedTensor!(N, V, I, J)
 	compressWithType
@@ -553,7 +550,7 @@ CompressedTensor!(N, V, I, J)
 
 
 /++
-Re-compress already compressed tensor. Makes it consequent in memory.
+Re-compresses a compressed tensor. Makes all values, indexes and pointers consequent in memory.
 +/
 CompressedTensor!(N + 1, V, I, J)
 	recompress
@@ -590,7 +587,7 @@ CompressedTensor!(N + 1, V, I, J)
 	return m.sliced(slice.shape);
 }
 
-/// Compresstion of compressed tensor
+///
 unittest
 {
 	auto slice = slice!double(5, 8);
@@ -618,10 +615,16 @@ unittest
 }
 
 /++
+`CompressedTensor!(N, T, I, J)` is  `Slice!(N - 1, CompressedMap!(T, I, J))`.
+
+See_also: $(LREF CompressedMap)
 +/
 alias CompressedTensor(size_t N, T, I = uint, J = size_t) = Slice!(N - 1, CompressedMap!(T, I, J));
 
 /++
+Compressed array is just a structure of values array and indexes array.
+
+See_also: $(LREF CompressedTensor), $(LREF CompressedMap)
 +/
 struct CompressedArray(T, I = uint)
 	if (is(I : size_t) && isUnsigned!I)
@@ -636,6 +639,7 @@ struct CompressedArray(T, I = uint)
 }
 
 /++
+`CompressedMap` is used internally by `Slice` type to represent $(LREF CompressedTensor).
 +/
 struct CompressedMap(T, I = uint, J = size_t)
 	if (is(I : size_t) && isUnsigned!I && is(J : size_t) && isUnsigned!J && I.sizeof <= J.sizeof)