%
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
%
% Weight treated as the class attribute. Identifier deleted.
%
% As used by Kilpatrick, D. & Cameron-Jones, M. (1998). Numeric prediction
% using instance-based learning with encoding length selection. In Progress
% in Connectionist-Based Information Systems. Singapore: Springer-Verlag.
%
% !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
%
% NAME:  fishcatch
% TYPE:  Sample 
% SIZE:  159 observations, 8 variables
% 
% DESCRIPTIVE ABSTRACT:
% 
% 159 fishes of 7 species are caught and measured. Altogether there are
% 8 variables.  All the fishes are caught from the same lake
% (Laengelmavesi) near Tampere in Finland.
% 
% SOURCES:
% Brofeldt, Pekka: Bidrag till kaennedom on fiskbestondet i vaera
%         sjoear. Laengelmaevesi. T.H.Jaervi: Finlands Fiskeriet  Band 4,
%         Meddelanden utgivna av fiskerifoereningen i Finland.
%         Helsingfors 1917
% 
% VARIABLE DESCRIPTIONS:
% 
% 1  Obs       Observation number ranges from 1 to 159
% 2  Species   (Numeric)
%         Code Finnish  Swedish    English        Latin      
%          1   Lahna    Braxen     Bream          Abramis brama
%          2   Siika    Iiden      Whitewish      Leusiscus idus
%          3   Saerki   Moerten    Roach          Leuciscus rutilus
%          4   Parkki   Bjoerknan  ?              Abramis bjrkna
%          5   Norssi   Norssen    Smelt          Osmerus eperlanus
%          6   Hauki    Jaedda     Pike           Esox lucius
%          7   Ahven    Abborre    Perch          Perca fluviatilis
% 
% 3  Weight      Weight of the fish (in grams)
% 4  Length1     Length from the nose to the beginning of the tail (in cm)
% 5  Length2     Length from the nose to the notch of the tail (in cm)
% 6  Length3     Length from the nose to the end of the tail (in cm)
% 7  Height%     Maximal height as % of Length3
% 8  Width%      Maximal width as % of Length3
% 9  Sex         1 = male 0 = female
% 
% 
% 
%           ___/////___                  _
%          /           \    ___          |
%        /\             \_ /  /          H
%      <   )            __)  \           |
%        \/_\\_________/   \__\          _
% 
%      |------- L1 -------|
%      |------- L2 ----------|
%      |------- L3 ------------|
% 
% 
% Values are aligned and delimited by blanks.
% Missing values are denoted with NA.
% There is one data line for each case.
% 
% SPECIAL NOTES:
% I have usually calculated
%            Height =  Height%*Length3/100
%            Widht  =  Widht%*Length3/100
% 
% 
% PEDAGOGICAL NOTES:
% I have mainly used only  Species=7 (Perch) and here is some of the
% models and test, we have used
% 
%       Weight=a+b*(Length3*Height*Width)+epsilon
%          Ho: a=0;
%          Heteroscedastic case. Question: What is proper weighting, 
%          if you use Length3 as a weighting variable.
% 
%       Log(Weight)=a+b1*Length3+epsilon
% 
%       Weight^(1/3)=a+b1*Length3+epsilon
%       (Given by Box-Cox-transformation)
%          Ho: a=0;
% 
%       Log(Weight)=a+b1*Length3+b2*Height+b3*Width+epsilon
%          Ho: b1+b2+b3=3;  
%          i.e. dimension of the fish = 3
% 
%       Weight^(1/3)=a+b1*Length3+b2*Height+b3*Width+epsilon
%       (Given by Box-Cox-transformation)
%          Ho: a=0;
% 
%       Weight=a*Length3^b1*Height^b2*Width^b3+epsilon
%          Nonlinear, heteroscedastic case.
%          What is proper weighting?
% 
%       Is obs 143
% 
%       143  7  840.0 32.5  35.0  37.3  30.8  20.9  0
% 
%       an outlier? It had in its stomach 6 roach.
% 
% 
% 
% REFERENCES:
% Brofeldt, Pekka: Bidrag till kaennedom on fiskbestondet i vaara
%         sjoear. Laengelmaevesi. T.H.Jaervi: Finlands Fiskeriet  Band 4,
%         Meddelanden utgivna av fiskerifoereningen i Finland.
%         Helsingfors 1917
% 
% 
% SUBMITTED BY:
% Juha Puranen
% Departement of statistics
% PL33 (Aleksanterinkatu 7)
% 000014 University of Helsinki
% Finland
% e-mail: jpuranen@noppa.helsinki.fi
% 

@relation 'fishcatch'
@attribute 'Species' { 1, 2, 3, 4, 5, 6, 7}
@attribute 'Length1' real
@attribute 'Length2' real
@attribute 'Length3' real
@attribute 'Height' real
@attribute 'Width' real
@attribute 'Sex' { 1, 0}
@attribute 'class' real
@data
1,23.2,25.4,30,38.4,13.4,?,242
1,24,26.3,31.2,40,13.8,?,290
1,23.9,26.5,31.1,39.8,15.1,?,340
1,26.3,29,33.5,38,13.3,?,363
1,26.5,29,34,36.6,15.1,?,430
1,26.8,29.7,34.7,39.2,14.2,?,450
1,26.8,29.7,34.5,41.1,15.3,?,500
1,27.6,30,35,36.2,13.4,?,390
1,27.6,30,35.1,39.9,13.8,?,450
1,28.5,30.7,36.2,39.3,13.7,?,500
1,28.4,31,36.2,39.4,14.1,?,475
1,28.7,31,36.2,39.7,13.3,?,500
1,29.1,31.5,36.4,37.8,12,?,500
1,29.4,32,37.2,40.2,13.9,1,600
1,29.4,32,37.2,41.5,15,?,600
1,30.4,33,38.3,38.8,13.8,1,700
1,30.4,33,38.5,38.8,13.5,?,700
1,30.9,33.5,38.6,40.5,13.3,?,610
1,31,33.5,38.7,37.4,14.8,?,650
1,31.3,34,39.5,38.3,14.1,1,575
1,31.4,34,39.2,40.8,13.7,?,685
1,31.5,34.5,39.7,39.1,13.3,?,620
1,31.8,35,40.6,38.1,15.1,?,680
1,31.9,35,40.5,40.1,13.8,?,700
1,31.8,35,40.9,40,14.8,1,725
1,32,35,40.6,40.3,15,?,720
1,32.7,36,41.5,39.8,14.1,?,714
1,32.8,36,41.6,40.6,14.9,?,850
1,33.5,37,42.6,44.5,15.5,0,1000
1,35,38.5,44.1,40.9,14.3,0,920
1,35,38.5,44,41.1,14.3,?,955
1,36.2,39.5,45.3,41.4,14.9,1,925
1,37.4,41,45.9,40.6,14.7,0,975
1,38,41,46.5,37.9,13.7,?,950
2,23.6,26,28.7,29.2,14.8,?,270
2,24.1,26.5,29.3,27.8,14.5,?,270
2,25.6,28,30.8,28.5,15.2,?,306
2,28.5,31,34,31.6,19.3,?,540
2,33.7,36.4,39.6,29.7,16.6,0,800
2,37.3,40,43.5,28.4,15,?,1000
3,12.9,14.1,16.2,25.6,14,?,40
3,16.5,18.2,20.3,26.1,13.9,?,69
3,17.5,18.8,21.2,26.3,13.7,?,78
3,18.2,19.8,22.2,25.3,14.3,?,87
3,18.6,20,22.2,28,16.1,?,120
3,19,20.5,22.8,28.4,14.7,?,0
3,19.1,20.8,23.1,26.7,14.7,0,110
3,19.4,21,23.7,25.8,13.9,0,120
3,20.4,22,24.7,23.5,15.2,0,150
3,20.5,22,24.3,27.3,14.6,0,145
3,20.5,22.5,25.3,27.8,15.1,0,160
3,21,22.5,25,26.2,13.3,?,140
3,21.1,22.5,25,25.6,15.2,0,160
3,22,24,27.2,27.7,14.1,?,169
3,22,23.4,26.7,25.9,13.6,?,161
3,22.1,23.5,26.8,27.6,15.4,0,200
3,23.6,25.2,27.9,25.4,14,?,180
3,24,26,29.2,30.4,15.4,?,290
3,25,27,30.6,28,15.6,0,272
3,29.5,31.7,35,27.1,15.3,?,390
4,13.5,14.7,16.5,41.5,14.1,?,55
4,14.3,15.5,17.4,37.8,13.3,1,60
4,16.3,17.7,19.8,37.4,13.5,1,90
4,17.5,19,21.3,39.4,13.7,1,120
4,18.4,20,22.4,39.7,14.7,?,150
4,19,20.7,23.2,36.8,14.2,?,140
4,19,20.7,23.2,40.5,14.7,0,170
4,19.8,21.5,24.1,40.4,13.1,0,145
4,21.2,23,25.8,40.1,14.2,?,200
4,23,25,28,39.6,14.8,0,273
4,24,26,29,39.2,14.6,0,300
5,9.3,9.8,10.8,16.1,9.7,1,6.7
5,10,10.5,11.6,17,10,0,7.5
5,10.1,10.6,11.6,14.9,9.9,1,7
5,10.4,11,12,18.3,11.5,0,9.7
5,10.7,11.2,12.4,16.8,10.3,1,9.8
5,10.8,11.3,12.6,15.7,10.2,1,8.7
5,11.3,11.8,13.1,16.9,9.8,1,10
5,11.3,11.8,13.1,16.9,8.9,0,9.9
5,11.4,12,13.2,16.7,8.7,0,9.8
5,11.5,12.2,13.4,15.6,10.4,0,12.2
5,11.7,12.4,13.5,18,9.4,0,13.4
5,12.1,13,13.8,16.5,9.1,0,12.2
5,13.2,14.3,15.2,18.9,13.6,0,19.7
5,13.8,15,16.2,18.1,11.6,0,19.9
6,30,32.3,34.8,16,9.7,?,200
6,31.7,34,37.8,15.1,11,0,300
6,32.7,35,38.8,15.3,11.3,?,300
6,34.8,37.3,39.8,15.8,10.1,?,300
6,35.5,38,40.5,18,11.3,?,430
6,36,38.5,41,15.6,9.7,1,345
6,40,42.5,45.5,16,9.5,?,456
6,40,42.5,45.5,15,9.8,?,510
6,40.1,43,45.8,17,11.2,?,540
6,42,45,48,14.5,10.2,?,500
6,43.2,46,48.7,16,10,0,567
6,44.8,48,51.2,15,10.5,0,770
6,48.3,51.7,55.1,16.2,11.2,?,950
6,52,56,59.7,17.9,11.7,?,1250
6,56,60,64,15,9.6,?,1600
6,56,60,64,15,9.6,0,1550
6,59,63.4,68,15.9,11,0,1650
7,7.5,8.4,8.8,24,16,?,5.9
7,12.5,13.7,14.7,24,13.6,?,32
7,13.8,15,16,23.9,15.2,?,40
7,15,16.2,17.2,26.7,15.3,?,51.5
7,15.7,17.4,18.5,24.8,15.9,?,70
7,16.2,18,19.2,27.2,17.3,?,100
7,16.8,18.7,19.4,26.8,16.1,?,78
7,17.2,19,20.2,27.9,15.1,?,80
7,17.8,19.6,20.8,24.7,14.6,?,85
7,18.2,20,21,24.2,13.2,?,85
7,19,21,22.5,25.3,15.8,?,110
7,19,21,22.5,26.3,14.7,?,115
7,19,21,22.5,25.3,16.3,1,125
7,19.3,21.3,22.8,28,15.5,0,130
7,20,22,23.5,26,14.5,0,120
7,20,22,23.5,24,15,?,120
7,20,22,23.5,26,15,?,130
7,20,22,23.5,25,15,?,135
7,20,22,23.5,23.5,17,0,110
7,20.5,22.5,24,24.4,15.1,0,130
7,20.5,22.5,24,28.3,15.1,0,150
7,20.7,22.7,24.2,24.6,15,?,145
7,21,23,24.5,21.3,14.8,?,150
7,21.5,23.5,25,25.1,14.9,?,170
7,22,24,25.5,28.6,14.6,?,225
7,22,24,25.5,25,15,?,145
7,22.6,24.6,26.2,25.7,15.9,?,188
7,23,25,26.5,24.3,13.9,0,180
7,23.5,25.6,27,24.3,15.7,?,197
7,25,26.5,28,25.6,14.8,?,218
7,25.2,27.3,28.7,29,17.9,0,300
7,25.4,27.5,28.9,24.8,15,0,260
7,25.4,27.5,28.9,24.4,15,?,265
7,25.4,27.5,28.9,25.2,15.8,0,250
7,25.9,28,29.4,26.6,14.3,?,250
7,26.9,28.7,30.1,25.2,15.4,0,300
7,27.8,30,31.6,24.1,15.1,0,320
7,30.5,32.8,34,29.5,17.7,?,514
7,32,34.5,36.5,28.1,17.5,?,556
7,32.5,35,37.3,30.8,20.9,0,840
7,34,36.5,39,27.9,17.6,0,685
7,34,36,38.3,27.7,17.6,0,700
7,34.5,37,39.4,27.5,15.9,0,700
7,34.6,37,39.3,26.9,16.2,0,690
7,36.5,39,41.4,26.9,18.1,0,900
7,36.5,39,41.4,26.9,14.5,?,650
7,36.6,39,41.3,30.1,17.8,?,820
7,36.9,40,42.3,28.2,16.8,0,850
7,37,40,42.5,27.6,17,0,900
7,37,40,42.4,29.2,17.6,0,1015
7,37.1,40,42.5,26.2,15.6,0,820
7,39,42,44.6,28.7,15.4,0,1100
7,39.8,43,45.2,26.4,16.1,0,1000
7,40.1,43,45.5,27.5,16.3,0,1100
7,40.2,43.5,46,27.4,17.7,1,1000
7,41.1,44,46.6,26.8,16.3,0,1000