Table of Contents

Assign the same matrix to all elements of a struct array

• Use the deal function and []:
• [allGrad(:).Vb] = deal(df_Vb);

Multiply a 3d matrix with a vector to get a 2d matrix

• % size(A) = [ 22    40    10]
  % size(v) = [ 1    10]
  H = bsxfun(@times,A,reshape(v,[1 1 length(v)]));
  H = sum(H ,3);
  % size(H) = [22 40]

Concatenate a cell array of matrices to one 3d matrix

• mat3d = cat(3,manyMatrices{:});

Find a Row in Another Matrix

• % check if queryRow is also a row in matrixToSearchIn
  [exists location] = ismember(queryRow,matrixToSearchIn,'rows')
• bsxfun with eq, would work too

Preallocate a Cell Array of Full Matrices and Sparse Matrices

• User some values insides the zeros function that resembles the size of your matrices
• bigCellArray = cell(largeNumber,1);
  bigCellArray(:) = {zeros(1,80)};
• For sparse matrices, we need to approximate how much data they are likely to store
• oneMat = spalloc(numRows,numCols,40*numRows); % we have around at most numUsedCols = 40, each has numRows entries
  allMats = repmat({oneMat},totalNum,1);

Set the value at many (row,column) index pairs in a matrix

• A(sub2ind(size(A),rowIndices,columnIndices))=1;

Create new matrix B where each column in A is repeated k times:

• B=A(:,ceil([1:size(A,2)*k]/k));

Delete All-Zero Columns or Rows

• If you want to delete all rows that contain only zeros:
  A=A(any(A,2),:)
• This sets the matrix to all those rows we want, we can also explicitly delete them:
  A(~any(A,2),:) = [];
  
• If you want to delete all columns that contain only zeros:
  A=A(:,any(A))

Find n smallest values in a matrix and return their indices

• or just sort the entire matrix and get indices of sorted matrix elements
  [val pos ] = sort(costMatrix(:));
      [x,y] = ind2sub(size(costMatrix),pos);

Normalize rows of matrix to sum to 1

• and do not produce an Na N?, if the sum is 0:
  E = spdiags(spfun(@(x) 1./x,sum(E,2)),0,size(E,1),size(E,1))*E;

Normalize each row or column to length one

• % each row with diag:
  E = E*diag(spfun(@(x) 1./x,sqrt(sum(E.^2,1))),0);
  % or each column with bsxfun
  E= bsxfun(@times,1./sqrt(sum(E.^2)),E);

To free forgotten file handle locks

• fclose('all')

Read in text file

• each line in one char cell
  t = textread('plant.train','%s','delimiter', '\n');

Plot figure to a printable pdf of correct size

• set dimensions by hand to fit your figure
• set(gcf,'paperunits','centimeters')
  set(gcf,'papersize',[18,6]) % Desired outer dimensionsof figure
  set(gcf,'paperposition',[0,0,18,6]) % Place plot on figure
  print -dpdf myfigure.pdf

cell2str

• function s = cell2str(cellstr,delimeter)
  % Richard _at_ Socher - org
  % concatenates a cell array of strings into one long string
  
  if nargin==1
      delimeter = ','
  end
  
  s = [];
  for i=1:length(cellstr),
          s = [s, cellstr{i}];
          if i~=length(cellstr),
                  s = [s, delimeter];
          end
  end

Speed

• never grow matrices inside a loop, always pre-allocate (even if you don't know how large it will be) with an upper bound on what you need and just delete zero or unused entries at the end
• Never change a lot of rows in a matrix, instead change columns, since matlab is column major.

Frequently compare times of different functions

• Use it to compare two possible ways to do the same thing
  tic; for i=1:10000;inv(rand(3,3));end;toc
  

Simple Parallelization

• if you want to use 4 cores easily:
  matlabpool open 4
  parfor i = 1:length(parameter)
      runComplicatedFunction(parameter);
  end

Good Optimization Package

• http://www.cs.ubc.ca/~schmidtm/Software/minFunc.html - has L-BFGS etc.

General Matlab Speed-up Packages

• http://research.microsoft.com/en-us/um/people/minka/software/lightspeed/

Plot a tree with labels on each node

• Abhishek Sharma modified the below file for prettier printing, see his files: Attach:myTreeplot.txt, Attach:makeTreeFromParsed.txt (change to .m after download)
• function myTreeplot(p,labels)
  %TREEPLOT Plot picture of tree.
  %   TREEPLOT(p) plots a picture of a tree given a row vector of
  %   parent pointers, with p(i) == 0 for a root and labels on each node.
  %
  %   Example:
  %      myTreeplot([2 4 2 0 6 4 6],{'i' 'like' 'labels' 'on' 'pretty' 'trees' '.'})
  %   returns a binary tree with labels.
  %
  %   Copyright 1984-2004 The MathWorks, Inc.
  %   $Revision: 5.12.4.2 $  $Date: 2004/06/25 18:52:28 $
  %   Modified by Richard @ Socher . org to display text labels
  
  [x,y,h]=treelayout(p,1:length(p)); % RS: The second argument (1:length(p)) ensures that the leaf nodes are printed in their original order in the parent vector
  f = find(p~=0);
  pp = p(f);
  X = [x(f); x(pp); repmat(NaN,size(f))];
  Y = [y(f); y(pp); repmat(NaN,size(f))];
  X = X(:);
  Y = Y(:);
  
  n = length(p);
  if n < 500,
      plot (x, y, 'ro', X, Y, 'r-');
  else
      plot (X, Y, 'r-');
  end;
  xlabel(['height = ' int2str(h)]);
  axis([0 1 0 1]);
  
  for l=1:length(labels)
      text(x(l),y(l),labels{l},'Interpreter','none')
  end

Convenient File Reading

• Just reads a file into an array of cells, each cell holds one line
• function fileLines = readTextFile(fileName)
  
  fid = fopen(fileName, 'r');
  fileLines = textscan(fid, '%s', 'delimiter', '\n', 'bufsize', 99900000);
  fclose(fid);
  fileLines = fileLines{1};
  
  % % % %Example usage:
  % fileName = '../data/someFile.csv'
  % fileLines = readTextFile(fileName);
  % for li = 1:length(fileLines)
  %     [~, ~, ~, ~, ~, ~, splitLine] = regexp(fileLines{li}, '\t');
  %     pols = regexp(fileLines{li}, '\[.(\d)\]', 'tokens');
  % %...
  % end

Read in Sparse Index:Value matrix

• function sparseMat = readSparseFeatures(filename)
  % reads in a sparse index:value feature matrix
  % format: first line: number of colums,
  % remaning lines are index:value pairs
  % assumes that feature indices (columns) start at 0 (will add 1)
  % by richard@socher.org
  fid = fopen(filename, 'r');
  fileLines = textscan(fid, '%s', 'delimiter', '\n', 'bufsize', 99900000);
  fclose(fid);
  fileLines = fileLines{1};
  
  numCols= str2double(fileLines{1});
  fileLines = fileLines(2:end);
  numRows = length(fileLines);
  
  % not pretty to keep adding these indices/values
  % but it's still fast for most files,
  % could pre-allocate these and then cut off afterwards
  allRows  = [];
  allCols = [];
  allVals = [];
  for li = 1:length(fileLines)
      indexValuePairs = textscan(fileLines{li}, '%f:%f');
      allRows = [allRows; repmat(li,length(indexValuePairs{1}),1)];
      allCols = [allCols ;indexValuePairs{1}+1];
      allVals = [allVals ;indexValuePairs{2}];
  end
  
  sparseMat = sparse(allRows,allCols,allVals,numRows,numCols);

Convenient Text File Writing of Matrices and String Cells

• function writeTextFile(fileName,fileInput,opt)
  % writeTextFile(fileName,fileInput,options)
  % Prints a matrix to text file, a cell array of strings, or a cell array of cells of strings
  % opt.writeFlag =
  %       'a'     open or create file for writing; append data to end of file
  %       'r+'    open (do not create) file for reading and writing
  %       'w+'    open or create file for reading and writing; discard
  %               existing contents
  %       'a+'    (default) open or create file for reading and writing; append data
  %               to end of file
  %       'W'     open file for writing without automatic flushing
  %       'A'     open file for appending without automatic flushing
  % opt.separator =
  %       '\n' (default) or ';' or ' ' etc.
  % richard _at_ socher .org
  
  if ~exist('opt','var') || (exist('opt','var') && ~isfield(opt,'writeFlag'))
      opt.writeFlag='a+';
  end
  
  if ~exist('opt','var') || (exist('opt','var') && ~isfield(opt,'separator'))
      opt.separator='\n';
  end
  if isnumeric(fileInput)
      % Write the matrix to file with separated
      fid = fopen(fileName, opt.writeFlag);
      fprintf(fid, [repmat(['%g' opt.separator], 1, size(fileInput,2)-1) '%g\n'],fileInput);
      fclose(fid);
  
  elseif iscell(fileInput{1})
      fid = fopen(fileName, opt.writeFlag);
      for s = 1:length(fileInput)
          for w = 1:length(fileInput{s})
              if ischar(fileInput{s})
                  fprintf(fid, ['%s' opt.separator], fileInput{s}{w});
              else
                  fprintf(fid, ['%s' opt.separator], num2str(fileInput{s}{w}));
              end
          end
          fprintf(fid, '\n');
      end
      fclose(fid);
  else
  
      fid = fopen(fileName, opt.writeFlag);
      for s = 1:length(fileInput)
          if ischar(fileInput{s})
              fprintf(fid, ['%s' opt.separator], fileInput{s});
          else
              fprintf(fid, ['%s' opt.separator], num2str(fileInput{s}));
          end
      end
      if ~strcmp(opt.separator,'\n')
          fprintf(fid, '\n');
      end
      fclose(fid);
  end

Extract Sentences from a Wall Street Journal tree file

• % reads in WSJ file in which each tree is on one line
  % splits it at spaces,
  % matches all words that end with some parentheses
  % richard @ socher .org
  
  inFile = '../data/trees/wsj/finalTestTrees.txt'
  outFile = [inFile '_justSentences.txt']
  
  lines = readTextFile(inFile);
  
  fid = fopen(outFile, 'w');
  
  for l = 1:length(lines)
      [~, ~, ~, ~, ~, ~, splitStrings] = regexp(lines{l}, ' ');
      thisSent = [];
      for w = 1:length(splitStrings)
          s = regexp(splitStrings{w},'([^ \t\r\n\f\v\)]+)\)+$','tokens');
          if ~isempty(s)
              thisSent = [thisSent s{1}{1} ' '];
          end
      end
      fprintf(fid, '%s\n', thisSent);
      fprintf(1,'.')
  end
  fprintf(1,'\n')
  fclose(fid);

Find out how many matlab licenses are being used.

• lmstat -a

Saving Only a Matrix to PNG File

• The commented out print command uses the current figure which needs a GUI. imwrite can save the matrix without a desktop.
• %print('-dpng',[filename '.png'])
     imwrite(I,[filename  '.png'])

Saving an image from imshow without a white/grey background

• imshow(img)
   set(gca, 'position', [0 0 1 1], 'visible', 'off')   
   print('img.png', '-dpng')
   

Fast Word to Number Mapping with Java Map Containers

• Uses code above to read a text file with just the words and then each word's line number is the value for that word in a Java Container (its index).
• Then reads in another file of sentences of words and maps the words to numbers.
• words = readTextFile('wordsOfVocab.txt');
  wordMap = containers.Map(words,1:length(words));
  % for unknown words, the slow way: unkNum = find(strcmp(words,'UNK'));
  unkNum = wordMap('UNK')
  
  % load some file
  % fileLine contains words separated by spaces
  
  [~, ~, ~, ~, ~, ~, splitLine] = regexp(fileLine, ' ');
  sent = zeros(1,length(splitLine));
  for w = 1:length(splitLine)
      if wordMap.isKey(splitLine{w})
          sent(w) = wordMap(splitLine{w});
      else
          sent(w) = unkNum;
      end
  end

Find all dependencies of a function

• [list, ~, ~] = depfun('yourFunctionName')
  % filter out matlab built-in functions
  for i=1:length(list)
     if isempty(strfind(list{i},'R2010a'))
        disp(list{i})     
     end
  end

Visualizing a Bivariate Gaussian Distribution

• You can define your covariance matrix sigma and see how it changes the shape of the Normal distribution
  %% Bivariate Gaussian in 2D
  [X1,X2] = meshgrid(-2:.01:2,-2:.01:2);
  mu=[0 0];
  Sigma=[1 0;0 1];
  P=mvnpdf([X1(:),X2(:)],mu,Sigma);
  P=reshape(P,size(X1));
  pcolor(X1,X2,P);
  shading interp
  
  %% Bivariate Gaussian in 3D
  h=surfc(X1,X2,P);
  set(h, 'linestyle', 'none');
  alpha(.5);

Sort a cell array based on the length of each cell

• If you want the shortest entries of allSNum to come first
  [~, Index] = sort(cellfun('length', allSNum), 'ascend');
  allSNum = allSNum(Index);

Element-wise max of all elements in a matrix

• max(1,W) =
  W(ones(size(W))<W)=1;

Subassign a smaller matrix into a larger matrix at a range of indices

Compute the Probability of a Point being an outlier

• Based on paper by Kriegel et al. Lo OP?: Local Outlier Probabilities:

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