(*
    Non-Negative Matrix Factorization algorithm implementation in Mathematica
    Copyright (C) 2013  Anton Antonov

    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 3 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, see <http://www.gnu.org/licenses/>.

	Written by Anton Antonov, 
	antononcube @ gmail . com,
	Windermere, Florida, USA.
*)

(*
    Mathematica is (C) Copyright 1988-2013 Wolfram Research, Inc.

    Protected by copyright law and international treaties.

    Unauthorized reproduction or distribution subject to severe civil
    and criminal penalties.

    Mathematica is a registered trademark of Wolfram Research, Inc.
*)

(* Version 1.0 *)
(* This package contains definitions for the application of Non-Negative Matrix Factorization (NNMF). *)
(* 
   The implementation follows the description of the hybrid algorithm GD-CLS (Gradient Descent with Constrained Least Squares) in the article:

     Shahnaz, F., Berry, M., Pauca, V., Plemmons, R., 2006.
     Document clustering using nonnegative matrix factorization. Information Processing & Management 42 (2), 373-386.

   In order to use NearestWords a nearest function has to be created over the column indices of the topic matrix.
   For example:

   {W, H} = NormalizeMatrixProduct[W, H];
   HNF = Nearest[Range[Dimensions[H][[2]]], DistanceFunction -> (Norm[H[[All, #1]] - H[[All, #2]]] &)]
   NearestWords[HNF, "agent", termsOfH, stemmingRules, 15]

*)

BeginPackage["NonNegativeMatrixFactorization`"];

NonNegativeMatrixFactorization::usage = "NonNegativeMatrixFactorization[V_?MatrixQ,k_Integer,opts] \
returns the pair of matrices {W,H} such that V = W H and \
the number of the columns of W and the number of rows of H are k. \
The method used is called Gradient Descent with Constrained Least Squares.";

GDCLS::usage = "Synonym of NonNegativeMatrixFactorization";

NonNegativeMatrixFactorizationGlobal::usage = "NonNegativeMatrixFactorizationGlobal[V_?MatrixQ,W_?MatrixQ,H_?MatrixQ,opts] \
continues the NNMF iterations over the matrices W and H \
in the execution context and returns {W,H} as a result.";

GDCLSGlobal::usage = "Synonym of NonNegativeMatrixFactorizationGlobal";

NormalizeMatrixProduct::usage = "NormalizeMatrixProduct[W_?MatrixQ,H_?MatrixQ] returns a pair of matrices {W1,H1} \
such that W1 H1 = W H and the norms of the columns of W1 are 1.";

LeftNormalizeMatrixProduct::usage = "Same as NormalizeMatrixProduct.";

RightNormalizeMatrixProduct::usage = "RightNormalizeMatrixProduct[W_?MatrixQ,H_?MatrixQ] returns a pair of matrices {W1,H1} \
such that W1 H1 = W H and the norms of the rows of H1 are 1.";

BasisVectorInterpretation::usage = "BasisVectorInterpretation[vec_?VectorQ,n_Integer,interpretationItems_List] \
takes the n largest coordinates of vec, finds the corresponding elements in interpretationItems, \
and returns a list of coordinate-item pairs.";

NearestWords::usage = "NearestWords[HNF, word, terms, stemmingRules, n] calculates a statistical thesaurus entry \
for a specified nearest function over the columns of a matrix of topics and a word.";

Begin["`Private`"];


(***********************************************************)
(* NonNegativeMatrixFactorization                          *)
(***********************************************************)


Clear[NonNegativeMatrixFactorization];

SyntaxInformation[NonNegativeMatrixFactorization] = { "ArgumentsPattern" -> { _, _, OptionsPattern[] } };

NonNegativeMatrixFactorization::ndim = "The second argument is expected to be a positive integer";

NonNegativeMatrixFactorization::nmsteps = "The value of the option MaxSteps is expected to be a positive integer";

NonNegativeMatrixFactorization::npreal = "The value of the option `1` is expected to be a positive real number or Automatic";

NonNegativeMatrixFactorization::nnorm = "The value of the option Normalization is expected to be one of \
Left, Right, True, False, None, or Automatic.";

Options[NonNegativeMatrixFactorization] = {
  "Epsilon" -> 10^-6.,
  MaxSteps -> 200,
  "NonNegative" -> True,
  "Normalization" -> Left,
  PrecisionGoal -> Automatic,
  "ProfilingPrints" -> False,
  "RegularizationParameter" -> 0.01
};

NonNegativeMatrixFactorization[V_?MatrixQ, k_?IntegerQ, opts : OptionsPattern[]] :=
    Block[{t, fls, A, W, H, T, m, n, b, diffNorm, normV, nSteps = 0,
      nonnegQ, normalization, maxSteps, eps, lbd, pgoal, PRINT},

      eps = OptionValue[NonNegativeMatrixFactorization, "Epsilon"];
      maxSteps = OptionValue[NonNegativeMatrixFactorization, MaxSteps];
      nonnegQ = TrueQ[OptionValue[NonNegativeMatrixFactorization, "NonNegative"]];
      normalization = OptionValue[NonNegativeMatrixFactorization, "Normalization"];
      pgoal = OptionValue[NonNegativeMatrixFactorization, PrecisionGoal];
      lbd = OptionValue[NonNegativeMatrixFactorization, "RegularizationParameter"];
      pgoal = OptionValue[NonNegativeMatrixFactorization, PrecisionGoal];
      PRINT = If[TrueQ[OptionValue[NonNegativeMatrixFactorization, "ProfilingPrints"]], Print, None];

      If[! (IntegerQ[k] && k > 0),
        Message[NonNegativeMatrixFactorization::ndim];
        Return[$Failed];
      ];

      If[! (IntegerQ[maxSteps] && maxSteps > 0),
        Message[NonNegativeMatrixFactorization::nmsteps];
        Return[$Failed];
      ];

      If[TrueQ[eps === Automatic], eps = 10^-6.];

      If[! (NumericQ[eps] && eps > 0),
        Message[NonNegativeMatrixFactorization::npreal, "Epsilon"];
        Return[$Failed];
      ];

      If[TrueQ[lbd === Automatic], lbd = 0.01];

      If[! (NumericQ[lbd] && lbd > 0),
        Message[NonNegativeMatrixFactorization::npreal, "RegularizationParameter"];
        Return[$Failed];
      ];

      If[TrueQ[pgoal === Automatic], pgoal = 4];

      If[! (NumericQ[pgoal] && pgoal > 0),
        Message[NonNegativeMatrixFactorization::npreal, "PrecisionGoal"];
        Return[$Failed];
      ];

      {m, n} = Dimensions[V];
      W = SparseArray[RandomReal[{0, 1}, {m, k}]];
      H = SparseArray[ConstantArray[0, {k, n}]];

      normV = Norm[V, "Frobenius"];
      diffNorm = 10 * normV;

      While[nSteps < maxSteps && TrueQ[! NumberQ[pgoal] || NumberQ[pgoal] && (normV > 0) && diffNorm / normV > 10^(-pgoal)],

        nSteps++;

        t = Timing[

          A = Transpose[W].W + lbd * IdentityMatrix[k];
          T = Transpose[W];

          fls = LinearSolve[A];

          H = Table[(b = T.V[[All, i]]; fls[b]), {i, 1, n}];
          H = SparseArray[Transpose[H]];

          If[nonnegQ, H = Clip[H, {0, Max[H]}]];
          W = W * (V.Transpose[H]) / (W.(H.Transpose[H]) + eps);

        ];

        If[NumberQ[pgoal],
          diffNorm = Norm[V - W.H, "Frobenius"];

          If[nSteps < 100 || Mod[nSteps, 100] == 0,
            PRINT["step:", nSteps, ", iteration time:", t, " relative error:", diffNorm / normV]
          ],

          If[nSteps < 100 || Mod[nSteps, 100] == 0,
            PRINT["step:", nSteps, ", iteration time:", t]
          ]

        ];

      ];

      Which[
        MemberQ[{True, Left, Automatic, "Left"}, normalization],
        {W, H} = LeftNormalizeMatrixProduct[W, H],

        MemberQ[{Right, "Right"}, normalization],
        {W, H} = RightNormalizeMatrixProduct[W, H],

        ! MemberQ[{False, None}, normalization],
        Message[NonNegativeMatrixFactorization::nnorm];
      ];

      {W, H}
    ];


(***********************************************************)
(* NonNegativeMatrixFactorizationGlobal                    *)
(***********************************************************)

Clear[NonNegativeMatrixFactorizationGlobal];

SyntaxInformation[NonNegativeMatrixFactorizationGlobal] = { "ArgumentsPattern" -> { _, _, _, OptionsPattern[] } };

NonNegativeMatrixFactorizationGlobal::nmsteps = "The value of the option MaxSteps is expected to be a positive integer";

NonNegativeMatrixFactorizationGlobal::npreal = "The value of the option `1` is expected to be a positive real number or Automatic";

NonNegativeMatrixFactorizationGlobal::nnorm = "The value of the option Normalization is expected to be one of \
Left, Right, True, False, None, or Automatic.";

Options[NonNegativeMatrixFactorizationGlobal] = Options[NonNegativeMatrixFactorization];

SetAttributes[NonNegativeMatrixFactorizationGlobal, HoldAll];

NonNegativeMatrixFactorizationGlobal[V_, W_, H_, opts : OptionsPattern[]] :=
    Block[{t, fls, A, k, T, m, n, b, diffNorm, normV, nSteps = 0,
      nonnegQ, normalization, maxSteps, eps, lbd, pgoal, PRINT},

      eps = OptionValue[NonNegativeMatrixFactorizationGlobal, "Epsilon"];
      maxSteps = OptionValue[NonNegativeMatrixFactorizationGlobal, MaxSteps];
      nonnegQ = TrueQ[OptionValue[NonNegativeMatrixFactorizationGlobal, "NonNegative"]];
      normalization = OptionValue[NonNegativeMatrixFactorizationGlobal, "Normalization"];
      pgoal = OptionValue[NonNegativeMatrixFactorizationGlobal, PrecisionGoal];
      lbd = OptionValue[NonNegativeMatrixFactorizationGlobal, "RegularizationParameter"];
      pgoal = OptionValue[NonNegativeMatrixFactorizationGlobal, PrecisionGoal];
      PRINT = If[TrueQ[OptionValue[NonNegativeMatrixFactorizationGlobal, "ProfilingPrints"]], Print, None];

      If[! (IntegerQ[maxSteps] && maxSteps > 0),
        Message[NonNegativeMatrixFactorizationGlobal::nmsteps];
        Return[$Failed];
      ];

      If[TrueQ[eps === Automatic], eps = 10^-6.];

      If[! (NumericQ[eps] && eps > 0),
        Message[NonNegativeMatrixFactorizationGlobal::npreal, "Epsilon"];
        Return[$Failed];
      ];

      If[TrueQ[lbd === Automatic], lbd = 0.01];

      If[! (NumericQ[lbd] && lbd > 0),
        Message[NonNegativeMatrixFactorizationGlobal::npreal, "RegularizationParameter"];
        Return[$Failed];
      ];

      If[TrueQ[pgoal === Automatic], pgoal = 4];

      If[! (NumericQ[pgoal] && pgoal > 0),
        Message[NonNegativeMatrixFactorizationGlobal::npreal, "PrecisionGoal"];
        Return[$Failed];
      ];


      {m, n} = Dimensions[V];
      k = Dimensions[H][[1]];
      normV = Norm[V, "Frobenius"]; diffNorm = 10 normV;

      While[nSteps < maxSteps && TrueQ[! NumberQ[pgoal] || NumberQ[pgoal] && (normV > 0) && diffNorm / normV > 10^(-pgoal)],

        nSteps++;

        t =
            Timing[
              A = Transpose[W].W + lbd * IdentityMatrix[k];
              T = Transpose[W];
              fls = LinearSolve[A];
              H = Table[(b = T.V[[All, i]]; fls[b]), {i, 1, n}];
              H = SparseArray[Transpose[H]];
              If[nonnegQ,
                H = Clip[H, {0, Max[H]}]
              ];
              W = W * (V.Transpose[H]) / (W.(H.Transpose[H]) + eps);
            ];

        If[NumberQ[pgoal],
          diffNorm = Norm[V - W.H, "Frobenius"];
          If[nSteps < 100 || Mod[nSteps, 100] == 0, PRINT[nSteps, " ", t, " relative error=", diffNorm / normV]],
          If[nSteps < 100 || Mod[nSteps, 100] == 0, PRINT[nSteps, " ", t]]
        ];

      ];

      {W, H}
    ] /; MatrixQ[W] && MatrixQ[H] && Dimensions[W][[2]] == Dimensions[H][[1]];


(***********************************************************)
(* Synonyms                                                *)
(***********************************************************)

Clear[GDCLS];
GDCLS = NonNegativeMatrixFactorization;

Clear[GDCLSGlobal];
GDCLSGlobal = NonNegativeMatrixFactorizationGlobal;


(***********************************************************)
(* Normalize matrices                                      *)
(***********************************************************)

Clear[NormalizeMatrixProduct];
NormalizeMatrixProduct[W_?MatrixQ, H_?MatrixQ] :=
    Block[{d, S, SI},
      d = Table[Norm[W[[All, i]]], {i, Length[W[[1]]]}];
      S = DiagonalMatrix[d];
      SI = DiagonalMatrix[Map[If[# != 0, 1 / #, 0]&, d]];
      {W.(SI), S.H}
    ];

LeftNormalizeMatrixProduct = NormalizeMatrixProduct;

Clear[RightNormalizeMatrixProduct];
RightNormalizeMatrixProduct[W_?MatrixQ, H_?MatrixQ] :=
    Block[{d, S, SI},
      d = Table[Norm[H[[i]]], {i, Length[H]}];
      S = DiagonalMatrix[d];
      SI = DiagonalMatrix[1 / d];
      {W.S, SI.H}
    ];

Clear[BasisVectorInterpretation];
BasisVectorInterpretation[vec_, n_Integer, terms_] :=
    Block[{t},
      (* Applying Abs in order to accommodate the use of this function for SVD bases. *)
      t = Reverse@Ordering[Abs[vec], -n];
      Transpose[{vec[[t]], terms[[t]]}]
    ];


(***********************************************************)
(* Nearest words                                           *)
(***********************************************************)

Clear[NearestWords];
NearestWords[HNF_NearestFunction, word_String, terms : {_String ..},
  stemmingRules_, n_Integer : 20] :=
    Block[{sword, tpos, inds},
      sword = word /. stemmingRules;
      tpos = Position[terms, sword];
      If[Length[tpos] == 0, {},
        inds = HNF[Flatten[tpos][[1]], n];
        terms[[inds]]
      ]
    ];

End[];

EndPackage[]