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codes!
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proto.m  //(in progress, very messy) prototype 

svmResults //classification based on SVM using vlfeat, adapted from a vlfeat example

results.m // does classification using logistic regression

getDataMat.m // takes optional arg and returns data_x matrix of HOG-ified samples, 
             // data_y matrix of corresponding classes

preprocess.m // writes new preprocessed images for all images in a folder

removeborder.m //removes border 

segmeter2.m //bounding box stuff, returns Character struct of bounding box locations

extract.m //extracts character from bounding box from segmenter2, turns into matrix

parse.py //parses a matrix into LaTeX, outputs to .tex file

getClass.m //returns the class of a sample given a filename

makeHoldout.py //takes random subset of data for holdout data

synth.m // (in progress) synthetic data creation through linear transformations?

HOG.m // Histogram of Oriented Gradients code

lrCostFunction.m // Computes cost and gradient for logistic regression with regularization

oneVsAll.m  //trains multiple logistic regression classifiers and returns all
             //the classifiers in a matrix all_theta, where the i-th row of
             //all_theta corresponds to the classifier for label i.
             // uses fmincg

fmincg.m //crazy minimization function for oneVsAll.m


predictOneVsAll.m // Predicts whether the label is 0 or 1 using learned logistic 
                  // regression parameters all_theta from ex3.m


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IMAGES!: 
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data/     

      Caltech-101/  //folders for each class's files
           
      old trains/   //old svm stuff
______________________________________________________________________________
//folder of formulas to test on

fakeFormula/ 

______________________________________________________________________________
//folder of processed data

dataset_proc/ 

            oren_#.jpg //pre-processed file created by preprocess, 
                       //used in getDataMat.m

______________________________________________________________________________
//folder of raw data

dataset_raw/     

              ds_#.jpg // original scans of handwritten dataset
              sample_10x10grid.png //grid used to make dataset

______________________________________________________________________________
//folder of output of extracted

extracted/     

              #.jpg 

        __________________________
        formula1/
                extracted formula test samples

        __________________________
        formula1Filtered/
                extracted formula test samples,pruned to be only correct ones


______________________________________________________________________________
//folder of logic images

logic/

        images of logic symbols
        __________________________
        formula/
                some formula test samples

______________________________________________________________________________
//folder of misc images

misc/      

     155pipeline.xml  //pipeline diagram
                      //made with http://www.diagram.ly/

     155pipeline.png  //pipeline diagram at current phase

     accuracies.txt  // file with accuracies for different lambda values

______________________________________________________________________________
//folder of plots made during project

plots/    

     lambdaVSacc#.jpg  //plot of lambda parameter in results.m
                       //         vs mean Cross-validation accuracy

______________________________________________________________________________
//folder for localization/bounding box output pics

segmenter_output/        
    
                InftyBOX#  //infty dataset example with bounding boxes from 
                           //     segmenter2
 

______________________________________________________________________________
//folder for testing formula data 

fakeFormula/

            /funct#


  
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.mat files!
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hog_theta_cvf2.mat //theta matrix of best accuracy cross-validation fold (fold 2)
                   //used HOG data

mistakes.mat //confusion matrix of mistakes made by classifier,
            // true value is rows, false is cols. 
            // z-axis is different cross-validation folds


data_x.mat //saved current versions of the data_x produced by getDataMat.m 
           // (HOG features)

data_y.mat //saved current versions of the data_y produced by getDataMat.m

plain_pixels_data_x //saved version of data_x that wasn't passed through any feature
                    //extractors. size = 1000x24963

parse.mat   //toy matrix for testing parse.py

//used to generate plots
lambda_test_acc_x.mat //vector of lambda values for regularized logistic regression
                      //    tested in results.m

lambda_test_acc_y.mat //vector of Cross validation mean accuracy for regularized 
                      //    logistic regression tested in results.m

all_theta_toy.mat // this is a saved version of theta for the classifier

logic_theta5class.mat //theta for 5 classes:
                      //forAll,exist,x,y,R

logic_x             // HOG data for logic symbols
logic_y             // class values for logic symbols


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need to sort after changing a lot of folders!
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//output of parse.py
test.tex

//made from test.tex
test.pdf
test1.pdf