Changeset 137


Ignore:
Timestamp:
12/18/19 15:46:35 (5 years ago)
Author:
bduin
Message:
 
Location:
prdatasets
Files:
4 added
2 deleted
46 edited

Legend:

Unmodified
Added
Removed

  • prdatasets

    • Property svn:ignore

      •  

        old new  
        33*.data
        44*.asv
         5data

  • prdatasets/arrhythmia.m

    r132 r137  
    1 %ARRHYTHMIA 420 objects with 279 features in 12 classes
     1%ARRHYTHMIA 452 objects with 279 features in 13 classes
    22%
    3 %        X = ARRHYTHMIA(CLASS,VAL)
     3%        X = ARRHYTHMIA(VAL)
    44%
    55% The aim is to distinguish between the presence and absence of cardiac
    6 % arrhythmia and to classify it in one of the 16 groups. Class 01 refers
    7 % to 'normal' ECG classes 02 to 15 refers to different classes of
    8 % arrhythmia and class 16 refers to the rest of unclassified ones.
    9 %
    10 %       X = ARRHYTHMIA(VAL);
     6% arrhythmia and to classify it in one of the 13 groups. Class 1 refers
     7% to 'normal' ECG classes 2 to 13 refer to different classes of
     8% arrhythmia.
    119%
    1210% By default features with missing values are removed. When something else
    1311% is desired, use one of the options in MISVAL for VAL.
    1412%
    15 % SEE ALSO <a href="http://prtools.tudelft.nl/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
     13% SEE ALSO <a href="http://37steps.com/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
    1614% PRTOOLS, DATASETS, MISVAL
    1715
    18 % Copyright: R.P.W. Duin
     16% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1917
    20 function x = arrhythmia(val)
    21 if nargin < 1,  val = 'f-remove'; end
    22 a = pr_getdata('http://prtools.tudelft.nl/prdatasets/arrhythmia.dat',1);
     18function a = arrhythmia(val)
    2319
    24 
    25 user.desc=['The Arrhymthmia database from UCI. The aim is to ' ...
     20if nargin < 1,   val = 'f-remove'; end
     21a = pr_loadmatfile;
     22if isempty(a)
     23  opt.delimeter = ',';
     24  opt.labfeat   = 280;
     25  fl = {'age' 'sex' 'height' 'weight' 'QRS duration' 'P-R interval' ...
     26    'Q-T interval' 'T interval' 'P interval' 'QRS' 'T' 'P' 'QRST' ...
     27    'J' 'heartrate'};
     28  fl1 = {'Q wave width' 'R wave width' 'S wave width' 'R'' wave width' ...
     29    'S'' wave width' 'number of intrinsic deflections' ...
     30    'ragged R wave' 'diphasic derivation of R wave' ...
     31    'ragged P wave' 'diphasic derivation of P wave' ...
     32    'ragged T wave' 'diphasic derivation of T wave'};
     33  fl = [fl strcat('DI-',fl1) strcat('DII-',fl1) strcat('DIII-',fl1) ...
     34    strcat('AVR-',fl1) strcat('AVL-',fl1) strcat('AVF-',fl1) ...
     35    strcat('V1-',fl1) strcat('V2-',fl1) strcat('V3-',fl1) ...
     36    strcat('V4-',fl1) strcat('V5-',fl1) strcat('V6-',fl1)];
     37  fl2 = {'JJ wave ampl' 'Q wave ampl' 'R wave ampl' ...
     38    'S wave ampl' 'R'' wave ampl' 'S'' wave ampl' 'P wave ampl' ...
     39    'T wave ampl' 'QRSA sum' 'QRSTA'};
     40  fl = [fl strcat('DI-',fl2) strcat('DII-',fl2) strcat('DIII-',fl2) ...
     41    strcat('AVR-',fl2) strcat('AVL-',fl2) strcat('AVF-',fl2) ...
     42    strcat('V1-',fl2) strcat('V2-',fl2) strcat('V3-',fl2) ...
     43    strcat('V4-',fl2) strcat('V5-',fl2) strcat('V6-',fl2)];
     44  opt.featnames = fl;
     45  opt.dsetname = 'Arrhythmia normal';
     46%   opt.classnames = {'benign' 'malignant'};
     47  opt.desc=['The Arrhymthmia database from UCI. The aim is to ' ...
    2648           'distinguish between the presence and absence of cardiac ' ...
    2749           'arrhythmia and to classify it in one of the 16 groups.'];
    28 user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/arrhythmia';
    29 
    30 fl = {'age' 'sex' 'height' 'weight' 'QRS duration' 'P-R interval' ...
    31 'Q-T interval' 'T interval' 'P interval' 'QRS' 'T' 'P' 'QRST' ...
    32 'J' 'heartrate'};
    33 fl1 = {'Q wave width' 'R wave width' 'S wave width' 'R'' wave width' ...
    34 'S'' wave width' 'number of intrinsic deflections' ...
    35 'ragged R wave' 'diphasic derivation of R wave' ...
    36 'ragged P wave' 'diphasic derivation of P wave' ...
    37 'ragged T wave' 'diphasic derivation of T wave'};
    38 fl = [fl strcat('DI-',fl1) strcat('DII-',fl1) strcat('DIII-',fl1) ...
    39 strcat('AVR-',fl1) strcat('AVL-',fl1) strcat('AVF-',fl1) ...
    40 strcat('V1-',fl1) strcat('V2-',fl1) strcat('V3-',fl1) ...
    41 strcat('V4-',fl1) strcat('V5-',fl1) strcat('V6-',fl1)];
    42 fl2 = {'JJ wave ampl' 'Q wave ampl' 'R wave ampl' ...
    43 'S wave ampl' 'R'' wave ampl' 'S'' wave ampl' 'P wave ampl' ...
    44 'T wave ampl' 'QRSA sum' 'QRSTA'};
    45 fl = [fl strcat('DI-',fl2) strcat('DII-',fl2) strcat('DIII-',fl2) ...
    46 strcat('AVR-',fl2) strcat('AVL-',fl2) strcat('AVF-',fl2) ...
    47 strcat('V1-',fl2) strcat('V2-',fl2) strcat('V3-',fl2) ...
    48 strcat('V4-',fl2) strcat('V5-',fl2) strcat('V6-',fl2)];
    49 
    50 x = pr_dataset(a(:,1:(end-1)),a(:,end));
    51 x = setfeatlab(x,fl);
    52 x = setname(x,'Arrhythmia normal');
    53 x = setuser(x,user);
    54 x = misval(x,val);
     50  opt.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/arrhythmia'; 
     51  a = pr_download('http://prtools.tudelft.nl/prdatasets/arrhythmia.dat',[],opt);
     52end
     53a = misval(a,val);
    5554
    5655return

  • prdatasets/auto_mpg.m

    r132 r137  
    77%
    88% There are two classes: 229 objects < 25 mpg and 169 objects > 25 mpg.
    9 % For some reasons one of the original features is missing (horsepower).
     9% For some reason one of the original features is missing (horsepower).
    1010%
    1111% See also DATASETS, PRDATASETS
    1212
    13 % Copyright: R.P.W. Duin
     13% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1414% Faculty EWI, Delft University of Technology
    1515% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1818
    1919function a = auto_mpg
    20 a = pr_getdata('http://prtools.tudelft.nl/prdatasets/auto_mpg.mat',1);
    21 %a = setname(a,'Car-miles-per-gallon');
     20
     21a = pr_getdata;
    2222a = setlablist(a,char('< 25 mpg ','> 25 mpg'));
    2323a = setfeatlab(a,char(...
    … …  
    2828        'model year: multi-valued discrete', ...
    2929        'origin: multi-valued discrete'));
     30a = setname(a,'Auto MPG');

  • prdatasets/biomed.m

    r132 r137  
    1515% Just the original features 3, 5, 6, 7 and 8 are used.
    1616%
    17 % SEE ALSO <a href="http://prtools.tudelft.nl/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
     17% SEE ALSO <a href="http://37steps.com/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
    1818% PRTOOLS, DATASETS, MISVAL
    1919
    20 % Copyright: R.P.W. Duin
     20% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    2121
    22 function x = biomed(val)
     22function a = biomed(val)
    2323
    2424if nargin < 1, val = 'remove'; end
    25 a = pr_getdata('http://prtools.tudelft.nl/prdatasets/biomed.dat',1);
    2625
    27 user.desc = 'The purpose of the analysis is to develop a screening procedure to detect carriers and to describe its effectiveness. ';
    28 user.link = 'http://lib.stat.cmu.edu/datasets/';
    29 cl = {'carrier' 'normal'};
    30 fl = {'age' 'measurement 1' 'measurement 2' 'measurement 3' 'measurement 4'};
    31 
    32 x = pr_dataset(a(:,[3 5 6 7 8]),cl(a(:,end)));
    33 x = setfeatlab(x,fl);
    34 x = setname(x,'Biomed');
    35 x = setuser(x,user);
    36 x = misval(x,val);
     26a = pr_loadmatfile;
     27if isempty(a)
     28  opt.labfeat   = 9;
     29  opt.feats     = [3,5,6,7,8];
     30  opt.featnames = {'age' 'measurement 1' 'measurement 2' 'measurement 3' 'measurement 4'};
     31  opt.classnames = {'carrier' 'normal'};
     32  opt.desc = 'The purpose of the analysis is to develop a screening procedure to detect carriers and to describe its effectiveness. ';
     33  opt.link = 'http://lib.stat.cmu.edu/datasets/';
     34  a = pr_download('http://prtools.tudelft.nl/prdatasets/biomed.dat',[],opt);
     35end
     36a = misval(a,val);
    3737
    3838return

  • prdatasets/breast.m

    r132 r137  
    1313%
    1414% By default objects with missing values are removed. When something else
    15 % is desired, use one of the options in MISVAL for Val.
     15% is desired, use one of the options in MISVAL for VAL.
    1616%
    17 % SEE ALSO <a href="http://prtools.tudelft.nl/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
     17% SEE ALSO <a href="http://37steps.com/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
    1818% PRTOOLS, DATASETS, MISVAL
    1919
    20 % Copyright: R.P.W. Duin
     20% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    2121
    22 function x = breast(val)
     22function a = breast(val)
    2323
    2424if nargin < 1, val = 'remove'; end
    25 %prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/breastorg.dat');
    2625
    27 a = pr_getdata('http://prtools.tudelft.nl/prdatasets/breastorg.dat',1);
    28 
    29 user.desc='The original database of the Wisconsin Breast Cancer Databases from UCI, containing 699 instances, collected between 1989 and 1991. ';
    30 user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/breast-cancer-wisconsin/';
    31 cl = {'benign' 'malignant'};
    32 fl = {'Clump Thickness' 'Uniformity of Cell Size' ...
    33 'Uniformity of Cell Shape' 'Marginal Adhesion' ...
    34 'Single Epithelial Cell Size' 'Bare Nuclei' 'Bland Chromatin' ...
    35 'Normal Nucleoli' 'Mitoses'};
    36 
    37 %a = load('breastorg.dat'); % Octave cannot find it
    38 %a = load(fullfile(fileparts(which(mfilename)),'breastorg.dat'));
    39 J = find(a==-1);
    40 a(J) = NaN;
    41 nlab = a(:,end)/2;   % the labels for the classes are (2,4), very strange
    42 x = pr_dataset(a(:,2:(end-1)), cl(nlab) );
    43 x = setfeatlab(x,fl);
    44 x = setname(x,'Breast Wisconsin');
    45 x = misval(x,val);
    46 x = setuser(x,user);
     26a = pr_loadmatfile;
     27if isempty(a)
     28  opt.delimeter = ',';
     29  opt.labfeat   = 11;
     30  opt.featnames = {'Clump Thickness' 'Uniformity of Cell Size' ...
     31    'Uniformity of Cell Shape' 'Marginal Adhesion' ...
     32    'Single Epithelial Cell Size' 'Bare Nuclei' 'Bland Chromatin' ...
     33    'Normal Nucleoli' 'Mitoses'};
     34  opt.feats = [2:10];
     35  opt.misvalue = -1;
     36  opt.classnames = {'benign' 'malignant'};
     37  opt.desc='The original database of the Wisconsin Breast Cancer Databases from UCI, containing 699 instances, collected between 1989 and 1991. ';
     38  opt.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/breast-cancer-wisconsin/';
     39  opt.dsetname = 'Breast Wisconsin';
     40  a = pr_download('http://prtools.tudelft.nl/prdatasets/breastorg.dat',[],opt);
     41end
     42a = misval(a,val);
    4743
    4844return

  • prdatasets/car.m

    r127 r137  
    88% 1(1), pp. 145-157, 1990.). All 6 features are nominal.
    99% The instances completely cover the attribute space.
     10%
     11% SEE ALSO <a href="http://37steps.com/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
     12% PRTOOLS, DATASETS
    1013
    11 function x = car
     14% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1215
    13 prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/car.data');
    14 % for some mysterious reason it appeared to be necessary on my Windows 7
    15 % system to repeat this command
    16 prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/car.data');
    17 user.desc = 'Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.).';
    18 fl = {'buying price' 'maintenance price' 'nr of doors' 'nr of persons' 'luggage boot' 'safety'};
    19  
    20 datfile = fullfile(fileparts(which(mfilename)),'car.data');
    21 x = pr_readdataset(datfile,0,',',[],'ccccccc',7);
    22 x = setfeatlab(x,fl);
    23 x = setuser(x,user);
    24 x = setname(x,'Car');
     16function a = car
     17
     18a = pr_loadmatfile;
     19if isempty(a)
     20  opt.delimeter = ',';
     21  opt.format = 'ccccccc';
     22  opt.labfeat = 7;
     23  opt.desc = 'Car Evaluation Database was derived from a simple hierarchical decision model originally developed for the demonstration of DEX (M. Bohanec, V. Rajkovic: Expert system for decision making. Sistemica 1(1), pp. 145-157, 1990.).';
     24  opt.featnames = {'buying price' 'maintenance price' 'nr of doors' 'nr of persons' 'luggage boot' 'safety'};
     25  opt.desc = 'The purpose of the analysis is to develop a screening procedure to detect carriers and to describe its effectiveness. ';
     26  opt.link = 'http://lib.stat.cmu.edu/datasets/';
     27  opt.dsetname = 'Car';
     28  a = pr_download('http://prtools.tudelft.nl/prdatasets/car.data',[],opt);
     29end
    2530
    2631return

  • prdatasets/cbands.m

    r132 r137  
    99% See also DATASETS, PRDATASETS
    1010
    11 % Copyright: R.P.W. Duin
     11% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1212% Faculty EWI, Delft University of Technology
    1313% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1515function a = cbands
    1616
    17 prdatasets(mfilename,1);
    18 a = pr_dataset('cbands');
     17a = pr_getdata;
    1918a = setname(a,'Chromosome Bands');

  • prdatasets/chromo.m

    r132 r137  
    66% thresholded chromosomes. This technique is outdated.
    77
    8 % Copyright: R.P.W. Duin
     8% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    99% Faculty EWI, Delft University of Technology
    1010% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1212function a = chromo
    1313
    14 prdatasets(mfilename,1);
    15 datfile = fullfile(fileparts(which(mfilename)),'chromo');
    16 a = pr_dataset(datfile);
     14a = pr_getdata;
    1715a = setname(a,'Chromosome Features');

  • prdatasets/diabetes.m

    r127 r137  
    88% //www.ics.uci.edu/~mlearn/MLRepository.html
    99
    10 function x = diabetes
    11 
    12 prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/diabetes.dat');
    13 user.desc='The Pima Indians Diabetes Database from UCI.';
    14 user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/pima-indians-diabetes/';
    15 cl = {'present' 'absent'};
    16 fl = {'NumPregnancies' 'plasmaGlucose' 'diastolicBloodPr' ...
    17 'tricepsSkinfold' '2hrSerumInsulin' 'BodyMassIndex' ...
    18 'DiabetesPedigreeFn' 'Age'};
    19 
    20 a = load('diabetes.dat');
    21 x = pr_dataset(a(:,1:(end-1)),cl(a(:,end)+1));
    22 x = setfeatlab(x,fl);
    23 x = setname(x,'Diabetes');
    24 x = setuser(x,user);
     10function a = diabetes
     11 
     12a = pr_loadmatfile;
     13if isempty(a)
     14  opt.delimeter = ' ';
     15  opt.labfeat   = 9;
     16  opt.featnames = {'NumPregnancies' 'plasmaGlucose' 'diastolicBloodPr' ...
     17        'tricepsSkinfold' '2hrSerumInsulin' 'BodyMassIndex' ...
     18        'DiabetesPedigreeFn' 'Age'};
     19  opt.classnames = {'present' 'absent'};
     20  opt.link  = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/pima-indians-diabetes/';
     21  opt.desc  = 'The Pima Indians Diabetes Database from UCI.';
     22  a = pr_download('http://prtools.tudelft.nl/prdatasets/diabetes.dat',[],opt);
     23end
    2524
    2625return

  • prdatasets/ecoli.m

    r127 r137  
    77% This is the 'Ecoli'  dataset of the UCI Machine Learning Repository,
    88% //www.ics.uci.edu/~mlearn/MLRepository.html
    9 function x = ecoli
    109
    11 prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/ecoli.dat');
    12 user.desc='The Ecoli database from UCI. Goal is to Predict the localization site of protein in a cell, by Kenta Nakai Institue of Molecular and Cellular Biology Osaka, University.';
    13 user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/ecoli/';
    14 cl = {'cytoplasm' 'inner membrane without signal sequence' ...
    15 'periplasm' 'inner membrane, uncleavable signal sequence' ...
    16 'outer membrane' 'outer membrane lipoprotein' ...
    17 'inner membrane lipoprotein' 'inner membrane, cleavable signal sequence'};
    18 fl = {'mcg' 'gvh' 'lip' 'chg' 'aac' 'alm1' 'alm2'};
     10function a = ecoli
    1911
    20 a = load('ecoli.dat');
    21 x = pr_dataset(a(:,1:(end-1)),cl(a(:,end)));
    22 x = setfeatlab(x,fl);
    23 x = setname(x,'Ecoli');
    24 x = setuser(x,user);
     12a = pr_loadmatfile;
     13if isempty(a)
     14  opt.labfeat   = 8;
     15  opt.featnames = {'mcg' 'gvh' 'lip' 'chg' 'aac' 'alm1' 'alm2'};
     16  opt.classnames = {'cytoplasm' 'inner membrane without signal sequence' ...
     17    'periplasm' 'inner membrane, uncleavable signal sequence' ...
     18    'outer membrane' 'outer membrane lipoprotein' ...
     19    'inner membrane lipoprotein' 'inner membrane, cleavable signal sequence'};
     20  opt.desc='The Ecoli database from UCI. Goal is to Predict the localization site of protein in a cell, by Kenta Nakai Institue of Molecular and Cellular Biology Osaka, University.';
     21  opt.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/ecoli/';
     22  opt.dsetname = 'Ecoli';
     23  a = pr_download('http://prtools.tudelft.nl/prdatasets/ecoli.dat',[],opt);
     24end
    2525
    2626return

  • prdatasets/emim.m

    r132 r137  
    1616% See also DATASETS, PRDATASETS
    1717
    18 % Copyright: R.P.W. Duin
     18% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1919% Faculty EWI, Delft University of Technology
    2020% P.O. Box 5038, 2600 GA Delft, The Netherlands
    … …  
    2828end
    2929
    30 name = ['emim' num2str(n)];
    31 prdatasets(mfilename,1,['http://prtools.tudelft.nl/prdatasets/' name '.mat']);
    32 a = pr_dataset(name);
     30name = ['emim' num2str(n) '.mat'];
     31a = pr_getdata(['http://prtools.tudelft.nl/prdatasets/' name],[],name);
    3332a = setname(a,['EM' num2str(n) ' Image Delft']);
    3433

  • prdatasets/glass.m

    r132 r137  
    1616% See also DATASETS, PRDATASETS
    1717
    18 % Copyright: R.P.W. Duin
     18% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1919% Faculty EWI, Delft University of Technology
    2020% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    2222function a = glass
    2323
    24 prdatasets(mfilename,1);
    25 a = pr_dataset('glass');
     24a = pr_getdata('http://prtools.tudelft.nl/prdatasets/glass.mat',1);
    2625a = setname(a,'Glass Identification Dataset');
    2726a = setlablist(a,char('BuildingFloat','BuildingNonFloat','Car','Other'));

  • prdatasets/heart.m

    r132 r137  
    1111% is desired, use one of the options in MISVAL.
    1212%
    13 % SEE ALSO <a href="http://prtools.tudelft.nl/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
     13% SEE ALSO <a href="http://37steps.com/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
    1414% PRTOOLS, DATASETS, MISVAL
    1515
    16 % Copyright: R.P.W. Duin
     16% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1717
    18 function x = heart(val)
     18function a = heart(val)
    1919
    20 prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/heart.dat');
    2120if nargin<1
    2221        val = 'remove';
    2322end
    24 
    25 user.desc='The Cleveland database from the Heart Disease Databases from UCI.  The class disease-presence is used as target class. ';
    26 user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/heart-cleveland/';
    27 cl = {'absent' 'present'};
    28 fl = {'age' 'sex' 'cp' 'trestbps' 'chol' 'fbs' 'restecg' ...
    29         'thalach' 'exang' 'oldpeak' 'slope' 'ca' 'thal'};
    30 fd = {[0 inf; 0 inf] [0 1] [1 2 3 4] [0 inf; 0 inf] [0 inf; 0 inf] ...
     23a = pr_loadmatfile;
     24if isempty(a)
     25  opt.labfeat   = 14;
     26  opt.featnames = {'age' 'sex' 'cp' 'trestbps' 'chol' 'fbs' 'restecg' ...
     27      'thalach' 'exang' 'oldpeak' 'slope' 'ca' 'thal'};
     28  opt.classnames = {'absent' 'present'  'present'  'present'  'present'};
     29        opt.desc='The Cleveland database from the Heart Disease Databases from UCI.  The class disease-presence is used as target class. ';
     30        opt.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/heart-cleveland/';
     31  opt.dsetname = 'Heart Cleveland';
     32  a = pr_download('http://prtools.tudelft.nl/prdatasets/heart.dat',[],opt);
     33  nlab = getnlab(a);
     34  nlab(nlab>1) = 2;
     35  a = setnlab(a,nlab);
     36  a = remclass(a);
     37  fd = {[0 inf; 0 inf] [0 1] [1 2 3 4] [0 inf; 0 inf] [0 inf; 0 inf] ...
    3138      [0 1] [0 1 2] [0 inf; 0 inf] [0 1] [0 inf; 0 inf] [1 2 3] [-1 0 1 2 3] [3 6 7]};
    32 
    33 a = load('heart.dat');
    34 lab = (a(:,end)>0);
    35 x = pr_dataset(a(:,1:(end-1)),cl(lab+1));
    36 x = setfeatlab(x,fl);
    37 x = setname(x,'Heart Cleveland');
    38 x = setfeatdom(x,fd);
    39 [x,msg] = misval(x,val);
    40 user.desc = [user.desc msg];
    41 x = setuser(x,user);
     39  x = setfeatdom(a,fd);
     40  pr_savematfile(a);
     41end
     42[a,msg] = misval(a,val);
    4243
    4344return

  • prdatasets/hepatitis.m

    r132 r137  
    11%HEPATITIS 155 objects with 19 features in 2 classes
    22%
    3 %             X = HEPATITIS
     3%             A = HEPATITIS
    44%
    55% The hepatitis database from UCI to predict if the patient will live or
    66% die.
    77%
    8 %             X = HEPATITIS(VAL)
     8%             A = HEPATITIS(VAL)
    99%
    1010% By default objects with missing values are removed. When something else
    1111% is desired, use one of the options in MISVAL.
    1212%
    13 % SEE ALSO <a href="http://prtools.tudelft.nl/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
     13% SEE ALSO <a href="http://37steps.com/prtools">PRTools Guide</a>, <a href="http://archive.ics.uci.edu/ml/">UCI Website</a>
    1414% PRTOOLS, DATASETS, MISVAL
    1515
    16 % Copyright: R.P.W. Duin
     16% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1717
    18 function x = hepatitis(val);
     18function a = hepatitis(val)
    1919
    20 prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/hepatitis.dat');
    2120if nargin<1
    22         val = 'remove';
     21  val = 'remove';
    2322end
    2423
    25 user.desc = 'The hepatitis database from UCI to predict if the patient will live or die.';
    26 user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/hepatitis/';
    27 cl = {'die' 'live'};
    28 fl = {'age' 'sex' 'steroid' 'antivirals' 'fatigue' 'malaise' ...
    29 'anorexia' 'liver big' 'liver firm' 'spleen palpable' 'spiders' ...
    30 'ascites' 'varices' 'bilirubin' 'alk phosphate' 'sgot' 'albumin' ...
    31 'protime' 'histology'};
    32 
    33 a = load('hepatitis.dat');
    34 x = pr_dataset(a(:,2:end),cl(a(:,1)));
    35 x = setfeatlab(x,fl);
    36 x = setname(x,'Hepatitis');
    37 [x,msg] = misval(x,val);
    38 user.desc = [user.desc msg];
    39 x = setuser(x,user);
     24a = pr_loadmatfile;
     25if isempty(a)
     26  opt.delimeter = ' ';
     27  opt.labfeat   = 1;
     28  opt.featnames = {'age' 'sex' 'steroid' 'antivirals' 'fatigue' 'malaise' ...
     29    'anorexia' 'liver big' 'liver firm' 'spleen palpable' 'spiders' ...
     30    'ascites' 'varices' 'bilirubin' 'alk phosphate' 'sgot' 'albumin' ...
     31    'protime' 'histology'};
     32  opt.classnames = {'die' 'live'};
     33  opt.desc = 'The hepatitis database from UCI to predict if the patient will live or die.';
     34  user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/hepatitis/';
     35  opt.dsetname = 'Hepatitis Data Set';
     36  a = pr_download('http://prtools.tudelft.nl/prdatasets/hepatitis.dat',[],opt);
     37end
     38a = misval(a,val);

  • prdatasets/imox.m

    r132 r137  
    1010% See also DATASETS, PRDATASETS, X80
    1111
    12 % Copyright: R.P.W. Duin
     12% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1313% Faculty EWI, Delft University of Technology
    1414% P.O. Box 5031, 2600 GA Delft, The Netherlands
    1515
    16 function a = imox;
     16function a = imox
    1717
    18 prdatasets(mfilename,1);
    19 a = pr_dataset('imox');
     18a = pr_getdata;
    2019a = setname(a,'IMOX Dataset');
    2120a = setlablist(a,char('I','M','O','X'));

  • prdatasets/ionosphere.m

    r132 r137  
    1111% See also DATASETS, PRDATASETS
    1212
    13 % Copyright: R.P.W. Duin
     13% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1414% Faculty EWI, Delft University of Technology
    1515% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1717function a = ionosphere
    1818
    19 prdatasets(mfilename,1);
    20 % the stats toolbox has a ionosphere.mat file too, avoid it
    21 pp = fileparts(which(mfilename));
    22 a = pr_dataset(fullfile(pp,'ionosphere'));
     19a = pr_getdata;
    2320a = setname(a,'Ionosphere Dataset');
    2421a = setlablist(a,{'good','bad'});

  • prdatasets/iris.m

    r132 r137  
    55% Iris plants dataset by R.A.Fisher, 150 objects, 4 features, 3
    66% classes.
    7 function x = iris;
    87
    9 prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/iris.dat');
    10 user.desc = 'Iris Plant database from UCI. A classic dataset in the pattern recognition literature.  The original dataset is a multiclass classification problem, introduced by R.A. Fisher, The use of multiple measurements in taxonomic problems. Ann Eugenics, 7:179--188, 1936.';
    11 user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/iris/';
    12 cl = {'Iris-setosa' 'Iris-versicolor' 'Iris-virginica'};
    13 fl = {'sepal length' 'sepal width' 'petal length' 'petal width'};
     8function a = iris
    149
    15 % the fuzzy toolbox has a iris.dat file too, avoid it
    16 pp = fileparts(which(mfilename));
    17 a = load(fullfile(pp,'iris.dat'));
    18 x = pr_dataset(a(:,1:(end-1)), cl(a(:,end)));
    19 x = setfeatlab(x,fl);
    20 x = setuser(x,user);
    21 x = setname(x,'Iris');
     10a = pr_loadmatfile;
     11if isempty(a)
     12  opt.labfeat    = 5;
     13  opt.featnames  = {'sepal length' 'sepal width' 'petal length' 'petal width'};
     14  opt.classnames = {'Iris-setosa' 'Iris-versicolor' 'Iris-virginica'};
     15  opt.desc       = 'Iris Plant database from UCI. A classic dataset in the pattern recognition literature.  The original dataset is a multiclass classification problem, introduced by R.A. Fisher, The use of multiple measurements in taxonomic problems. Ann Eugenics, 7:179--188, 1936.';
     16  opt.link       = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/iris/';
     17  opt.dsetname   = 'Iris';
     18  a = pr_download('http://prtools.tudelft.nl/prdatasets/iris.dat',[],opt);
     19end
    2220
    2321return

  • prdatasets/kimia.m

    r132 r137  
    1111% See also DATASETS, PRDATASETS, PRDATAFILES, PRDATAFILES/KIMIA_IMAGES
    1212
    13 % Copyright: R.P.W. Duin
     13% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1414% Faculty EWI, Delft University of Technology
    1515% P.O. Box 5031, 2600 GA Delft, The Netherlands
    1616
    17 function a = kimia;
     17function a = kimia
    1818
    19 prdatasets(mfilename,1);
     19a = pr_getdata;
    2020labs = {'bone' 'glass' 'heart' 'misk' 'bird' 'brick' 'camel' ...
    2121         'car' 'children' 'van' 'elephant' 'head' 'fork' 'fountain' ...
    2222         'hammer' 'key' 'ray' 'turtle'};
    23 a = pr_dataset('kimia');
    2423a = setlablist(a,labs);
    2524a = setname(a,'Kimia Dataset');

  • prdatasets/lena.m

    r132 r137  
    88% See also DATASETS, PRDATASETS
    99
    10 % Copyright: R.P.W. Duin
     10% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1111% Faculty EWI, Delft University of Technology
    1212% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1414function a = lena
    1515
    16 prdatasets(mfilename,2);
    17 s = warning;
    18 a = pr_dataset('lena');
     16a = pr_getdata;
    1917a = setfeatlab(a,['red  ';'green';'blue ']);
    2018a = setname(a,'Lena');

  • prdatasets/liver.m

    r132 r137  
    99% See also DATASETS, PRDATASETS
    1010
    11 % Copyright: R.P.W. Duin
     11% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1212% Faculty EWI, Delft University of Technology
    1313% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1515function a = liver
    1616
    17 prdatasets(mfilename,1);
    18 a = pr_dataset('liver');
     17a = pr_getdata;
    1918a = setname(a,'Liver disorder dataset');
    20 
    2119a = setlablist(a,char('healthy','disorder'));
    2220a = setfeatlab(a,char(...

  • prdatasets/malaysia.m

    r132 r137  
    88% See also DATASETS, PRDATASETS
    99
    10 % Copyright: R.P.W. Duin
     10% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1111% Faculty EWI, Delft University of Technology
    1212% P.O. Box 5038, 2600 GA Delft, The Netherlands
    1313
    14 function a = malaysia;
     14function a = malaysia
    1515
    16 prdatasets(mfilename,1);
    17 a = pr_dataset('malaysia');
     16a = pr_getdata;
    1817a = setname(a,'Malaysia Data');

  • prdatasets/mfeat.m

    r132 r137  
    2424% MFEAT_PIX, MFEAT_ZER
    2525
    26 % Copyright: R.P.W. Duin
     26% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    2727% Faculty EWI, Delft University of Technology
    2828% P.O. Box 5031, 2600 GA Delft, The Netherlands

  • prdatasets/mfeat_all.m

    r132 r137  
    1919% See also DATASETS, PRDATASETS,
    2020
    21 % Copyright: R.P.W. Duin
     21% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    2222% Faculty EWI, Delft University of Technology
    2323% P.O. Box 5031, 2600 GA Delft, The Netherlands

  • prdatasets/mfeat_fac.m

    r132 r137  
    88% See also DATASETS, PRDATASETS, MFEAT
    99
    10 % Copyright: R.P.W. Duin
     10% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1111% Faculty EWI, Delft University of Technology
    1212% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1414function a = mfeat_fac
    1515
    16 prdatasets(mfilename,1);
    17 a = pr_dataset('mfeat_fac');
     16a = pr_getdata;
    1817a = setname(a,'MFEAT Face Features');

  • prdatasets/mfeat_fou.m

    r132 r137  
    77% See also DATASETS, PRDATASETS, MFEAT
    88
    9 % Copyright: R.P.W. Duin
     9% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1010% Faculty EWI, Delft University of Technology
    1111% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1313function a = mfeat_fou
    1414
    15 prdatasets(mfilename,1);
    16 a = pr_dataset('mfeat_fou');
     15a = pr_getdata;
    1716a = setname(a,'MFEAT Fourier Features');

  • prdatasets/mfeat_kar.m

    r132 r137  
    77% See also DATASETS, PRDATASETS, MFEAT
    88
    9 % Copyright: R.P.W. Duin
     9% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1010% Faculty EWI, Delft University of Technology
    1111% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1313function a = mfeat_kar
    1414
    15 prdatasets(mfilename,1);
    16 a = pr_dataset('mfeat_kar');
     15a = pr_getdata;
    1716a = setname(a,'MFEAT KL Features');

  • prdatasets/mfeat_mor.m

    r132 r137  
    77% See also DATASETS, PRDATASETS, MFEAT
    88
    9 % Copyright: R.P.W. Duin
     9% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1010% Faculty EWI, Delft University of Technology
    1111% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1313function a = mfeat_mor
    1414
    15 prdatasets(mfilename,1);
    16 a = pr_dataset('mfeat_mor');
     15a = pr_getdata;
    1716a = setname(a,'MFEAT Morphological Features');

  • prdatasets/mfeat_pix.m

    r132 r137  
    88% See also DATASETS, PRDATASETS, MFEAT
    99
    10 % Copyright: R.P.W. Duin
     10% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1111% Faculty EWI, Delft University of Technology
    1212% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1414function a = mfeat_pix
    1515
    16 prdatasets(mfilename,1);
    17 a = pr_dataset('mfeat_pix');
     16a = pr_getdata;
    1817a = setname(a,'MFEAT Pixel Features');

  • prdatasets/mfeat_zer.m

    r132 r137  
    77% See also DATASETS, PRDATASETS, MFEAT
    88
    9 % Copyright: R.P.W. Duin
     9% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1010% Faculty EWI, Delft University of Technology
    1111% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1313function a = mfeat_zer
    1414
    15 prdatasets(mfilename,1);
    16 a = pr_dataset('mfeat_zer');
     15a = pr_getdata;
    1716a = setname(a,'MFEAT Zernike Moments');

  • prdatasets/mnist8.m

    r132 r137  
    1313% See also DATASETS, PRDATASETS
    1414
    15 % Copyright: R.P.W. Duin
     15% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1616% Faculty EWI, Delft University of Technology
    1717% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1919function [a,b] = mnist8
    2020
    21 prdatasets(mfilename,1);
    22 a = pr_dataset('mnist8');
     21a = pr_getdata;
    2322if nargout == 2
    2423  b = a(60001:end,:);

  • prdatasets/nist16.m

    r132 r137  
    88% See also DATASETS, PRDATASETS
    99
    10 % Copyright: R.P.W. Duin
     10% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1111% Faculty EWI, Delft University of Technology
    1212% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1414function a = nist16
    1515
    16 prdatasets(mfilename,1);
    17 a = pr_dataset('nist16');
     16a = pr_getdata;
    1817a = setname(a,'NIST16 Normalised Digits');
    1918

  • prdatasets/nist32.m

    r132 r137  
    77% See also DATASETS, PRDATASETS, NIST16
    88
    9 % Copyright: R.P.W. Duin
     9% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1010% Faculty EWI, Delft University of Technology
    1111% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1313
    1414function a = nist32(N)
     15
    1516if nargin < 1, N = [0:9]; end
    16 a = pr_getdata('http://prtools.tudelft.nl/prdatasets/nist32.mat',1);
    17 
    18 % prdatasets(mfilename,1);
    19 % a = pr_dataset('nist32');
     17a = pr_getdata;
    2018a = setname(a,'NIST32 Binary Digits');
    2119a = selclass(a,N+1);

  • prdatasets/pr_dataset.m

    r132 r137  
    99  [varargout{:}] = prdataset(varargin{:});
    1010end
     11
     12end

  • prdatasets/pr_download.m

    r136 r137  
    9393%% find directory to be used
    9494if isempty(datname)
    95   datname = callername;
     95  datname = pr_callername;
    9696  dirname = fullfile(fileparts(which(datname)),'data');
    9797else
    … …  
    212212  a = setname(a,opt.dsetname);
    213213else
    214   a = setname(a,callername);
     214  a = setname(a,pr_callername);
    215215end
    216216
    … …  
    224224
    225225
    226 function ask_download(urlname,size)
     226function ask_download(urlname,datsize)
    227227%% user controlled downloading
    228228  global ASK
    229   if isempty(ASK)
    230     ASK = true;
    231   end
    232  
    233   if ASK
    234     if ~isempty(size) && size ~= 0
    235       siz = ['(' num2str(size) ' MB)'];
     229 
     230  if ASK && ~isempty(datsize) % ask only if datsize has been set
     231    if datsize ~= 0
     232      siz = ['(' num2str(datsize) ' MB)'];
    236233    else
    237234      siz = '';
    … …  
    297294return
    298295
    299 function name = callername
    300 %%
    301 [ss,~] = dbstack;
    302 if length(ss) < 3
    303         name = [];
    304 else
    305         name = ss(3).name;
    306 end
    307 
    308296function [a,new] = pr_download_old(url,varargin)
    309297%% This is the old version of pr_download, to be called from the old
    … …  
    448436disp('Parsing ...')
    449437c = mytextscan(fid,strrep(form,'n','s'),del,nhead);
    450 a = cell2dset(c,form,misval);
     438a = pr_cell2dset(c,form,misval);
    451439
    452440if ~nosave % don't save if not needed (e.g. called by pr_download_uci)

  • prdatasets/pr_getdata.m

    r132 r137  
    1010% If given, SIZE (in MByte) is displayed in the request.
    1111%
    12 % If available, the dataset DSET stored in FIELD is returned in OUT.
    13 % In case of a datafile DFILE, it is created, if necessary, with given TYPE
    14 % and returned in OUT. In case the download was successful but no dataset
    15 % or datfile could be created (e.g. because of empty TYPE) OUT is empty,
    16 % otherwise an error is generated.
     12% A mat-file returns a structure with fields pointing to stored variables.
     13% If available, the dataset DSET stored in FIELD (default 1) is returned in
     14% OUT. In case of a datafile DFILE, it is created, if necessary, with given
     15% TYPE and returned in OUT. In case the download was successful but no
     16% dataset or datfile could be created (e.g. because of empty TYPE) OUT is
     17% empty, otherwise an error is generated.
    1718%
    1819% This is a low-level routine, typically used in COMMAND and not called
    … …  
    2021% is constucted and returned to the user.
    2122%
    22 % SEE ALSO DATASETS, DATAFILES
     23% SEE ALSO (<a href="http://37steps.com/prhtml/37tools.html">37tools Contents</a>)
     24% DATASETS, DATAFILES
    2325
    24 % Copyright: R.P.W. Duin
     26% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    2527
    2628function out = pr_getdata(varargin)
    … …  
    3335%  dset  : becomes full path and name of dataset
    3436%  ddir  : becomes full path of dataset
    35 name = callername;
     37name = pr_callername;
    3638argin = setdefaults(varargin,[],[],[],[],true);
    3739[url,size,dset,field,ask] = deal(argin{:});
     40
     41if isempty(url)
     42  url = ['http://prtools.tudelft.nl/prdatasets/' name '.mat'];
     43end
    3844[dummy,uname,ext] = fileparts(url);
    3945
    … …  
    4147  ddir = pwd;
    4248else
    43   ddir = fileparts(which(name));
     49  ddir = fullfile(fileparts(which(name)),'data');
    4450end
    4551
    … …  
    117123  try
    118124    s = prload(dsetmat);
    119   catch
     125  catch ME
    120126    out = 'error';
    121127    return
    … …  
    143149end
    144150
    145 function name = callername
    146 [ss,dummy] = dbstack;
    147 if length(ss) < 3
    148         name = [];
    149 else
    150         name = ss(3).name;
    151 end
    152151
    153152

  • prdatasets/pr_readdataset.m

    r136 r137  
    1111%               default is determined from first line, e.g. 'nncc' for two
    1212%               numeric and two categorical features, see SETFEATDOM and
    13 %               CELL2DSET
     13%               PR_CELL2DSET
    1414%  NLAB       - feature to be interpreted as class label, default [].
    1515%
    … …  
    1818%
    1919%SEE ALSO
    20 %DATASETS, SETFEATDOM, CELL2DSET
     20%DATASETS, SETFEATDOM, PR_CELL2DSET
    2121
    2222% Copyright: R.P.W. Duin
    … …  
    3939  end
    4040  c = mytextscan(fid,strrep(form,'n','s'),del,nhead);
    41   a = cell2dset(c,form,misval);
     41  a = pr_cell2dset(c,form,misval);
    4242  if ~isempty(flab)
    4343    a = feat2lab(a,flab);

  • prdatasets/ringnorm.m

    r132 r137  
    1313% See also DATASETS, PRDATASETS, TWONORM
    1414
    15 % Copyright: R.P.W. Duin
     15% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1616% Faculty EWI, Delft University of Technology
    1717% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1919function a = ringnorm
    2020
    21 prdatasets(mfilename,1);
    22 a = pr_dataset('ringnorm');
     21a = pr_getdata;
    2322a = setname(a,'Ringnorm Data');
    2423

  • prdatasets/satellite.m

    r132 r137  
    1111% See also DATASETS, PRDATASETS, SOYBEAN1
    1212
    13 % Copyright: R.P.W. Duin
     13% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1414% Faculty EWI, Delft University of Technology
    1515% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1717function a = satellite
    1818
    19 prdatasets(mfilename,1);
    20 a = pr_dataset('satellite');
     19a = pr_getdata;
    2120a = setname(a,'Satellite dataset');

  • prdatasets/sonar.m

    r127 r137  
    88%metal cylinder and those bounced off a roughly cylindrical rock.
    99
    10 function x = sonar
     10function a = sonar
    1111
    12 prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/sonar.dat');
    13 user.desc = 'The Sonar dataset from the undocumented databases from UCI. The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.';
    14 user.link = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/undocumented/connectionist-bench/sonar';
    15 cl = {'mines' 'rocks'};
    16 a = load('sonar.dat');
    17 
    18 x = pr_dataset(a(:,1:(end-1)),cl(a(:,end)));
    19 x = setname(x,'Sonar');
    20 x = setuser(x,user);
    21 
     12a = pr_loadmatfile;
     13if isempty(a)
     14  opt.labfeat    = 61;
     15  opt.classnames = {'mines' 'rocks'};
     16  opt.desc       = 'The Sonar dataset from the undocumented databases from UCI. The task is to train a network to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock.';
     17  opt.link       = 'ftp://ftp.ics.uci.edu/pub/machine-learning-databases/undocumented/connectionist-bench/sonar';
     18  a = pr_download('http://prtools.tudelft.nl/prdatasets/sonar.dat',[],opt);
     19end
    2220
    2321return

  • prdatasets/soybean1.m

    r132 r137  
    88% See also DATASETS, PRDATASETS, SOYBEAN2
    99
    10 % Copyright: R.P.W. Duin
     10% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1111% Faculty EWI, Delft University of Technology
    1212% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1414function a = soybean1
    1515
    16 prdatasets(mfilename,1);
    17 a = pr_dataset('soybean1');
     16a = pr_getdata;
    1817a = setname(a,'Large soybean dataset');
    1918a = setlablist(a,char(...

  • prdatasets/soybean2.m

    r132 r137  
    88% See also DATASETS, PRDATASETS, SOYBEAN1
    99
    10 % Copyright: R.P.W. Duin
     10% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1111% Faculty EWI, Delft University of Technology
    1212% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1414function a = soybean2
    1515
    16 prdatasets(mfilename,1);
    17 a = pr_dataset('soybean2');
     16a = pr_getdata;
    1817a = setname(a,'Small soybean dataset');
    1918a = setlablist(a,char(...

  • prdatasets/texturel.m

    r132 r137  
    1515% See also DATASETS, PRDATASETS, TEXTURET
    1616
    17 % Copyright: R.P.W. Duin
     17% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1818% Faculty EWI, Delft University of Technology
    1919% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    2121function a = texturel
    2222
    23 prdatasets(mfilename,4);
    24 a = pr_dataset('texturel');
     23a = pr_getdata;
    2524a = setname(a,'Texture Learning Set');

  • prdatasets/texturet.m

    r132 r137  
    1515% See also DATASETS, PRDATASETS, TEXTUREL
    1616
    17 % Copyright: R.P.W. Duin
     17% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1818% Faculty EWI, Delft University of Technology
    1919% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    2121function a = texturet
    2222
    23 prdatasets(mfilename,4);
    24 a = pr_dataset('texturet');
     23a = pr_getdata;
    2524a = setname(a,'Texture Test Set');

  • prdatasets/twonorm.m

    r132 r137  
    1313% See also DATASETS, PRDATASETS, RINGNORM
    1414
    15 % Copyright: R.P.W. Duin
     15% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1616% Faculty EWI, Delft University of Technology
    1717% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1919function a = twonorm
    2020
    21 prdatasets(mfilename,1);
    22 a = pr_dataset('twonorm');
     21a = pr_getdata;
    2322a = setname(a,'Twonorm Data');
    2423

  • prdatasets/wine.m

    r132 r137  
    11%WINE Wine recognition dataset 178 objects with 13 features in 3 classes
    22%
    3 %       A = WINE
     3%  A = WINE
    44%
    55% Load the dataset in A. This dataset is taken from the UCI
    … …  
    88% See also DATASETS, PRDATASETS
    99
    10 % Copyright: R.P.W. Duin
     10% Copyright: R.P.W. Duin, r.p.w.duin@37steps.com
    1111% Faculty EWI, Delft University of Technology
    1212% P.O. Box 5031, 2600 GA Delft, The Netherlands
    … …  
    1414function a = wine
    1515
    16 url = 'http://prtools.tudelft.nl/prdatasets/wine.dat';
    17 %prdatasets(mfilename,1,'http://prtools.tudelft.nl/prdatasets/wine.dat');
    18 user.desc = 'These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars.  The analysis determined the quantities of 13 constituents found in each of the three types of wines.';
    19 user.link = '';
    20 opt.user = user;
    21 opt.labfeat = 1;
    22 opt.featnames = char(...
     16a = pr_loadmatfile;
     17if isempty(a)
     18  opt.delimeter = ',';
     19  opt.desc      = 'These data are the results of a chemical analysis of wines grown in the same region in Italy but derived from three different cultivars.  The analysis determined the quantities of 13 constituents found in each of the three types of wines.';
     20  opt.labfeat   = 1;
     21  opt.featnames = char(...
    2322        'alcohol', ...
    2423        'malic acid', ...
    … …  
    3433        'OD280/OD315 of diluted wines', ...       
    3534        'proline');
    36 a = pr_download(url,[],opt);
    37 
    38 % %a = load('wine.dat');
    39 % %a = pr_dataset(a(:,2:end),a(:,1));
    40 % a = feat2lab(a,1);
    41 % a = setname(a,'Wine recognition data');
    42 % a = setlablist(a,char('cultivar 1','cultivar 2','cultivar 3'));
    43 % a = setfeatlab(a,char(...
    44 %         'alcohol', ...
    45 %         'malic acid', ...
    46 %         'ash', ...
    47 %       'alcalinity of ash', ...
    48 %       'magnesium', ...
    49 %       'total phenols', ...
    50 %       'flavanoids', ...
    51 %       'nonflavanoid phenols', ...   
    52 %       'proanthocyanine', ...   
    53 %       'color intensity', ...       
    54 %       'hue', ...   
    55 %       'OD280/OD315 of diluted wines', ...       
    56 %         'proline'));
    57 % a = setuser(a,user);
    58 
     35  opt.classnames = {'cultivar 1','cultivar 2','cultivar 3'};
     36  opt.dsetname   = 'Wine recognition data';
     37  a = pr_download('http://prtools.tudelft.nl/prdatasets/wine.dat',[],opt);
     38end

  • prdatasets/x80.m

    r132 r137  
    2222% P.O. Box 5031, 2600 GA Delft, The Netherlands
    2323
    24 function a = x80;
     24function a = x80
    2525
    26 prdatasets('80x',1);
    27 a = pr_dataset('80x');
     26a = pr_getdata('http://prtools.tudelft.nl/prdatasets/80x.mat',1);
    2827a = setname(a,'80X Dataset');
    2928a = setlablist(a,char('8','0','X'));
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