kdd_coil_test-6.arff

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UCI/numeric/kdd_coil_test-6.arff
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Description

The data contains measurements of river chemical concentrations and algae densities. This data set is from the 1999 Computational Intelligence and Learning (COIL) competition.
File contents

%%%%%%%%%%%%%%%%%%%%
% Data-Description %
%%%%%%%%%%%%%%%%%%%%
%
%                           COIL 1999 Competition Data
% 
% Data Type
% 
%    multivariate
% 
% Abstract
% 
%    This data set is from the 1999 Computational Intelligence and Learning
%    (COIL) competition. The data contains measurements of river chemical
%    concentrations and algae densities.
% 
% Sources
% 
%     Original Owner
% 
% [1]ERUDIT
% European Network for Fuzzy Logic and Uncertainty Modelling
% in Information Technology
% 
%     Donor
% 
% Jens Strackeljan
% Technical University Clausthal
% Institute of Applied Mechanics
% Graupenstr. 3, 38678 Clausthal-Zellerfeld, Germany
% [2]tmjs@itm.tu-clausthal.de
% 
%    Date Donated: September 9, 1999
% 
% Data Characteristics
% 
%    This data comes from a water quality study where samples were taken
%    from sites on different European rivers of a period of approximately
%    one year. These samples were analyzed for various chemical substances
%    including: nitrogen in the form of nitrates, nitrites and ammonia,
%    phosphate, pH, oxygen, chloride. In parallel, algae samples were
%    collected to determine the algae population distributions.
% 
% Other Relevant Information
% 
%    The competition involved the prediction of algal frequency
%    distributions on the basis of the measured concentrations of the
%    chemical substances and the global information concerning the season
%    when the sample was taken, the river size and its flow velocity. The
%    competition [3]instructions contain additional information on the
%    prediction task.
% 
% Data Format
% 
%    There are a total of 340 examples each containing 17 values. The first
%    11 values of each data set are the season, the river size, the fluid
%    velocity and 8 chemical concentrations which should be relevant for
%    the algae population distribution. The last 8 values of each example
%    are the distribution of different kinds of algae. These 8 kinds are
%    only a very small part of the whole community, but for the competition
%    we limited the number to 7. The value 0.0 means that the frequency is
%    very low. The data set also contains some empty fields which are
%    labeled with the string XXXXX.
% 
%    The training data are saved in the file: analysis.data (ASCII format).
% 
%    Table 1: Structure of the file analysis.data
% 
%    A
% 
% 
%    K
% 
%    a
% 
% 
%    g
% 
%    CC[1,1]
% 
% 
%    CC[1,11]
% 
%    AG[1,1]
% 
% 
%    AG[1,7]
% 
%    CC[200,1]
% 
% 
%    CC[200,11]
% 
%    AG[200,1]
% 
% 
%    AG[200,7]
% 
%    Explanation:
%    CC[i,j]: Chemical concentration or river characteristic
%    AG[i,j]: Algal frequency
% 
%    The chemical parameters are labeled as A, ..., K. The columns of the
%    algaes are labeled as a, ..,g.
% 
% Past Usage
% 
%    [4]The Third (1999) International COIL Competition Home Page
%      _________________________________________________________________
% 
% 
%     [5]The UCI KDD Archive
%     [6]Information and Computer Science
%     [7]University of California, Irvine
%     Irvine, CA 92697-3425
% 
%    Last modified: October 13, 1999
% 
% References
% 
%    1. http://www.erudit.de/
%    2. mailto:tmjs@itm.tu-clausthal.de
%    3. file://localhost/research/ml/datasets/uci/raw/data/ucikdd/coil/instructions.txt
%    4. http://www.erudit.de/erudit/activities/ic-99/index.htm
%    5. http://kdd.ics.uci.edu/
%    6. http://www.ics.uci.edu/
%    7. http://www.uci.edu/
%
%%%%%%%%%%%%%%%%%%%%
% Task-Description %
%%%%%%%%%%%%%%%%%%%%
%
% 
% Third International Competition 
% 
% Protecting rivers and streams by monitoring chemical concentrations and 
% algae communities.
% 
% 
% Intelligent Techniques for Monitoring Water Quality using chemical
% indicators and algae population
% 
% Recent years have been characterised by increasing concern at the
% impact man is having on the environment.  
% The impact on the environment of toxic waste, from a wide variety
% of manufacturing processes, is well known. More recently, however,
% it has become clear that the more subtle effects of nutrient level
% and chemical balance changes arising from farming land run-off and
% sewage water treatment also have a serious, but indirect, effect on
% the states of rivers, lakes and even the sea.  In temperate climates
% across the world summers are characterized by numerous reports excessive
% summer algae growth resulting in poor water clarity, mass deaths of
% river fish from reduced oxygen levels and the closure of recreational
% water facilities on account of the toxic effects of this annual algal bloom.
% Reducing the impact of these man-made changes in river nutrient levels
% has stimulated much biological research with the aim of identifying
% the crucial chemical control variables for the biological
% processes.  
% 
% The data used in this problem comes from one such study.
% During the research study water quality samples were
% taken from sites on different European rivers of a period of
% approximately one year.  These samples were analyzed for various
% chemical substances including: nitrogen in the form of nitrates,
% nitrites and ammonia, phosphate, pH, oxygen, chloride.
% In parallel, algae samples were collected to determine the algae population
% distributions. It is well known that the dynamics of the
% algae community is determined by external chemical 
% environment with one or more factors being predominant.
% While the chemical analysis is cheap and easily 
% automated, the biological part involves microscopic examination,
% requires trained manpower and is therefore both 
% expensive and slow. 
% 
% Diatoms like Cymbella are major contributors to primary production
% throughout the world. The diatom reacts with 
% large sensitivity to even small changes in acidity . 
% 
% Over a three and half billion year history algae have evolved and
% adapted as primary plant colonizers of almost 
% every known habitant in terrestrial and aquatic environments.
% They respond very rapidly to man-made environment changes.
% 
% 
% 
% The relationship between the chemical and biological features is
% complex and can be expected to need the application of advanced
% techniques. Typical of such real-life problems, the particular
% data set for the problem contains a mixture of (fuzzy) qualiative
% variables and numerical measurement values, with much of the data
% being incomplete.
% 
% The competition task is the prediction of algal frequency distributions
% on the basis of  the measured concentrations of the chemical
% substances and the global information concerning the season when the sample
% was taken, the river size and its flow velocity. The two last variables
% are given as linguistic variables.
% 
% 340 data sets were taken and each contain 17 values. The
% first 11 values of each data set are the season, the river
% size, the fluid velocity and 8 chemical concentrations which
% should be relevant for the algae population distribution.
% The last 8 values of each data set are the distribution of
% different kinds of algae. These 8 kinds are only a very small
% part of the whole community, but for the competition we limited
% the number to 7. The value 0.0 means that the frequency is very low.
% The data set also contains some empty fields which are labeled
% with the string XXXXX.
% 
% Each participant in the competition receives 200 complete data sets
% (training data) and 140 data sets (evaluation data) containing only
% the 11 values of the river descriptions and the chemical concentrations.
% 
% This training data is to be used in obtainin
% a 'model' providing a prediction of the algal distributions associated
% with the evaluation data.
% 
% 
% 
% The training data are saved in the file:
% 
% analysis.txt (ASCII format).
% 
% Structure of the file analysis.txt
% 
% A                          K              a                   g
% CC1,1   ...                CC1,11         AG1,1    ...        AG1,7
% ....                        ...            ...                 ...
% 
% 
% CC200,1 ...                CC200,11       AG240,1  ...        AG240,7
% 
% 
% Explanation:
% CCi,j:  Chemical concentration    j=1,..11
% AGi,k: Algal frequency            k=1...7
% 
% 
% The chemical parameters are labeled as A, ..., K.
% The columns of the algaes are labeled as a, ..,g.
% 
% 
% Evaluation data are saved in file eval.txt (ASCII format).
% 
% 
% Table 2: Structure of the file eval.* 
% A                               K
% CC1,1     ...                   CC1,11
% 
% .....                            ...
%                                 
% CC140,1   ...                   CC140,11
% 
% _____________________________________________________________
% 
% Objective 
% 
% The objective of the competition is to provide a prediction
% model on basis of the training data. Having obtained this
% prediction model, each participant must provide the solution
% in the form of the results of applying this model to the
% evaluation data. The results obtained in this way should
% correspond to the results of the evaluation data
% (which are known to the organizer). The criteria used to evaluate
% the results is given below.
% All 7 Algae frequency distributions must be determined.
% For this purpose any number of partial models may be developed.
% 
% _____________________________________________________________
% 
% Judgment of the results
% 
% To judge the results, the sum of squared errors will be calculated.
% The following Table describes the results of a particular participant.
% 
% Matrix of results
% a                     g
% 
% Res1,1   ...          Res1,7
% 
% ....                   ...
% 
% Res140,1              Res140,7
% 
% 
% All solutions that lead to a smallest total error will
% be regarded as winner of the contest. 
% 
% 
%
% Information about the dataset
% CLASSTYPE: numeric
% CLASSINDEX: last
%
% ALGAE #: 6/7

@relation coil-test-6

@attribute season {autumn,spring,summer,winter}
@attribute river_size {large_,medium,small_}
@attribute fluid_velocity {high__,low___,medium}
@attribute concentration_1 REAL
@attribute concentration_2 REAL
@attribute concentration_3 REAL
@attribute concentration_4 REAL
@attribute concentration_5 REAL
@attribute concentration_6 REAL
@attribute concentration_7 REAL
@attribute concentration_8 REAL
@attribute algae_6 REAL

@data
summer,small_,medium,7.95000,5.70000,57.33300,2.46000,273.33301,295.66699,380.00000,?,0.00000
winter,small_,medium,7.98000,8.80000,59.33300,7.39200,286.66699,33.33300,138.00000,7.10000,46.40000
summer,small_,medium,8.00000,7.20000,80.00000,1.95700,174.28600,47.85700,113.71400,4.50000,0.00000
spring,small_,high__,8.35000,8.40000,68.00000,3.02600,458.00000,45.20000,111.80000,3.20000,1.00000
spring,small_,medium,8.10000,13.20000,19.00000,0.00000,130.00000,6.00000,40.00000,2.00000,0.00000
summer,small_,medium,8.37000,12.10000,12.85000,0.84000,15.00000,5.00000,10.50700,13.80000,0.00000
spring,small_,high__,7.31000,9.90000,6.00000,1.39500,58.75000,6.00000,16.00000,0.80000,0.00000
autumn,small_,high__,7.91000,11.20000,5.00000,1.38300,6.00000,24.33300,30.00000,32.00000,3.70000
summer,small_,high__,7.99000,10.70000,4.00000,1.36800,117.00000,17.25000,44.75000,0.80000,0.00000
autumn,small_,high__,7.82000,11.50000,8.18000,1.48800,39.00000,16.00000,139.50000,0.40000,1.70000
summer,small_,high__,6.60000,10.80000,4.00000,1.18000,80.00000,2.00000,59.00000,0.60000,0.00000
autumn,small_,high__,6.79000,9.40000,11.42000,1.96600,42.00000,3.00000,15.00000,0.60000,0.00000
summer,small_,high__,6.78000,10.20000,10.70400,1.46000,46.00000,3.00000,13.71400,0.70000,0.00000
summer,small_,high__,7.80000,10.80000,14.56800,1.22800,61.25000,34.50000,62.00000,1.10000,0.00000
spring,small_,high__,8.30000,12.70000,27.00000,4.04000,10.00000,363.00000,482.00000,6.00000,22.80000
autumn,small_,high__,8.20000,11.30000,6.00000,1.56000,10.00000,2.00000,5.00000,?,0.00000
summer,small_,high__,8.20000,10.40000,3.57700,0.78800,10.58300,1.66700,2.08800,0.80000,0.00000
autumn,small_,medium,8.10000,6.40000,21.20000,3.22200,44.00000,54.80000,155.00000,61.52000,0.00000
summer,small_,medium,8.54000,12.83000,22.54500,4.00000,170.50000,68.00000,116.06900,41.60000,2.40000
autumn,small_,medium,8.50000,7.80000,71.00000,11.02000,500.00000,121.00000,?,7.10000,8.30000
spring,small_,medium,7.70000,6.80000,65.00000,1.83300,782.50000,77.25000,340.00000,9.00000,1.00000
autumn,small_,high__,8.40000,10.50000,50.60000,10.49400,334.00000,209.10001,276.66699,20.72000,45.70000
summer,small_,high__,8.50000,11.50000,57.29200,10.52600,312.60001,261.39999,299.39999,23.50000,42.30000
summer,small_,high__,8.10000,12.20000,66.00000,4.08000,10.00000,26.00000,70.00000,1.80000,10.30000
autumn,small_,low___,6.13000,11.23000,8.87000,0.62000,36.00000,3.00000,14.74100,2.10000,0.00000
winter,small_,medium,?,12.10000,18.00000,3.14000,10.00000,21.00000,41.00000,4.80000,14.50000
summer,small_,medium,7.20000,10.40000,18.00000,2.42000,80.00000,11.00000,44.00000,2.50000,3.70000
spring,small_,medium,7.90000,8.60000,27.65000,2.06300,62.50000,7.75000,30.00000,?,0.00000
autumn,small_,medium,7.80000,9.10000,36.12400,5.97400,169.00000,13.09100,71.05700,3.30000,3.00000
summer,small_,high__,7.80000,9.40000,5.71400,0.80700,22.14300,6.00000,18.71400,1.50000,0.00000
autumn,small_,high__,7.80000,11.35000,5.34300,1.36300,19.75000,5.81800,8.84600,1.90000,0.00000
autumn,small_,high__,5.90000,11.90000,?,1.88000,5.00000,1.00000,2.00000,?,0.00000
winter,small_,high__,6.80000,9.10000,?,0.78000,10.00000,1.00000,14.00000,?,0.00000
summer,small_,medium,6.60000,8.80000,?,0.95000,20.00000,1.00000,7.00000,?,0.00000
winter,small_,high__,6.60000,11.80000,?,2.21000,10.00000,1.00000,4.00000,?,0.00000
winter,small_,medium,6.90000,9.20000,?,2.21000,10.00000,2.00000,13.00000,?,3.40000
winter,small_,high__,7.66000,10.80000,4.00000,0.99700,15.00000,1.50000,7.33300,1.00000,0.00000
autumn,small_,high__,7.60000,10.50000,3.05000,1.00200,13.33300,1.66700,10.83300,?,0.00000
winter,medium,medium,8.00000,7.00000,37.09100,2.23700,146.36400,84.09100,172.77800,2.30000,0.00000
spring,medium,medium,8.20000,7.80000,37.62500,1.45300,105.71400,66.71400,143.39999,2.60000,0.00000
autumn,medium,medium,8.20000,10.70000,134.66701,4.50400,617.77802,49.44400,164.77800,19.20000,10.90000
summer,medium,medium,8.00000,8.50000,131.46899,3.45400,792.00000,63.10000,286.60001,8.20000,29.30000
autumn,medium,high__,8.90000,10.50000,34.80000,6.00000,122.55600,41.11100,144.11099,27.03000,17.80000
summer,medium,high__,8.20000,9.20000,30.03700,5.18400,174.80000,86.60000,130.80000,3.45000,2.20000
summer,medium,medium,7.80000,8.80000,29.07800,2.82300,263.55600,27.00000,95.12000,11.50000,1.00000
summer,medium,high__,7.50000,10.80000,10.35700,3.35000,127.66700,22.00000,34.32100,1.20000,0.00000
spring,medium,high__,7.40000,9.00000,13.75000,5.26800,58.75000,56.25000,64.00000,2.50000,27.60000
spring,medium,medium,7.50000,8.90000,55.80000,4.40800,389.00000,127.40000,206.20000,5.00000,50.80000
autumn,medium,medium,9.10000,8.00000,101.20000,4.30600,273.75000,152.87500,290.31299,10.70000,61.10000
autumn,medium,high__,8.90000,8.00000,60.20000,4.03300,306.47101,136.00000,242.94099,18.40000,54.30000
summer,medium,low___,8.50000,10.74000,56.29200,0.69400,264.79999,43.40000,124.94200,30.48000,1.40000
autumn,medium,high__,8.30000,8.60000,75.00000,5.18000,560.00000,30.50000,170.00000,16.70000,70.70000
spring,medium,high__,7.80000,6.30000,136.66701,3.73400,154.44400,35.55600,175.33299,2.70000,3.30000
autumn,medium,medium,7.60000,9.20000,64.77800,6.16400,720.00000,21.77800,242.50000,54.20000,5.10000
summer,medium,medium,7.50000,9.20000,61.55700,7.03500,558.33301,24.50000,257.33301,19.50000,9.50000
autumn,medium,low___,7.50000,8.60000,57.50000,7.36800,577.00000,67.30000,254.44400,22.00000,22.40000
spring,medium,low___,7.70000,4.80000,88.90900,1.71400,669.09100,38.18200,205.18201,2.80000,0.00000
spring,medium,low___,7.90000,7.20000,55.25000,2.23500,89.37500,17.50000,141.50000,17.00000,0.00000
spring,medium,high__,8.06000,2.20000,39.00000,2.08500,773.12500,90.75000,163.25000,26.00000,0.00000
autumn,medium,high__,8.50000,7.50000,9.30000,1.55700,260.00000,9.60000,18.10000,3.90000,8.40000
autumn,medium,medium,8.20000,10.40000,63.30000,0.38900,217.14301,24.33300,114.00000,2.70000,24.60000
winter,medium,medium,8.00000,4.80000,58.76700,0.30800,93.75000,33.37500,110.87500,2.70000,0.00000
autumn,medium,high__,8.70000,10.80000,1.11800,0.53400,26.36400,14.81800,20.90000,1.40000,0.00000
spring,medium,high__,8.40000,11.20000,0.50000,0.32000,10.00000,21.60000,27.60000,0.60000,0.00000
winter,medium,low___,8.50000,8.30000,36.58300,5.63200,440.83301,149.00000,266.36401,19.82700,5.00000
autumn,medium,low___,8.30000,8.80000,64.76800,6.27200,357.16699,219.00000,302.50000,8.26700,2.10000
autumn,medium,medium,8.40000,10.80000,47.30400,7.77300,258.90900,145.09100,223.04401,13.36000,39.80000
autumn,medium,high__,7.90000,11.90000,11.86200,2.20900,128.63600,48.09100,69.07900,2.75500,19.80000
autumn,medium,medium,9.13000,12.00000,30.49600,4.97100,99.60000,64.60000,146.26500,54.13000,20.90000
autumn,medium,high__,7.40000,11.40000,12.03100,1.62100,176.80000,36.30000,58.59900,36.10000,3.10000
summer,medium,medium,8.30000,8.90000,271.50000,6.31500,375.00000,169.00000,313.50000,2.80000,3.80000
winter,medium,medium,8.20000,10.40000,41.00000,5.16000,410.00000,38.00000,61.00000,6.00000,25.70000
summer,medium,medium,8.20000,11.20000,36.00000,4.40000,32.50000,108.00000,155.50000,3.00000,11.60000
spring,medium,low___,8.17000,6.30000,37.30000,0.52700,82.00000,62.00000,133.10001,1.40000,0.00000
autumn,medium,low___,8.33000,10.60000,36.15600,1.13700,119.44400,92.88900,112.85500,10.50000,0.00000
spring,medium,medium,8.50000,6.70000,45.60900,4.41100,160.00000,88.36400,180.36400,32.83300,0.00000
autumn,medium,medium,8.10000,9.10000,47.26700,9.36700,169.09100,75.00000,127.77800,3.66700,37.10000
winter,medium,high__,8.20000,11.90000,12.25000,2.34800,121.87500,14.00000,27.50000,4.60000,16.90000
summer,medium,high__,8.10000,9.40000,11.00000,2.25100,48.75000,17.37500,66.87500,2.50000,8.40000
summer,medium,low___,7.80000,7.90000,87.00000,12.13000,652.50000,93.25000,209.00000,6.00000,0.00000
spring,medium,high__,8.26000,5.00000,44.81800,0.52600,97.27300,105.45500,181.63600,20.60000,0.00000
summer,medium,high__,8.11000,6.60000,49.85700,0.99300,194.28000,77.00000,197.57100,13.00000,0.00000
summer,medium,high__,7.87000,1.80000,49.25000,0.61100,357.12500,128.25000,185.12500,4.50000,0.00000
winter,medium,high__,7.20000,10.10000,49.50000,3.95500,55.00000,18.00000,138.00000,49.00000,4.60000
spring,medium,high__,7.80000,8.30000,51.50000,2.09800,30.20000,24.60000,184.39999,31.30000,0.00000
winter,medium,medium,7.90000,11.30000,82.50000,6.28300,300.00000,12.33300,53.33300,13.70000,12.80000
summer,medium,medium,8.00000,8.80000,176.25000,0.61800,440.00000,16.25000,79.25000,3.50000,0.00000
winter,large_,low___,7.70000,9.30000,66.00000,3.56000,310.00000,37.00000,?,17.35000,0.00000
winter,large_,low___,8.70000,5.40000,48.00000,1.13900,144.28600,36.71400,66.83300,22.01700,0.00000
summer,large_,low___,7.90000,5.30000,48.00000,0.51300,138.33299,61.33300,89.16700,4.00000,0.00000
autumn,large_,low___,8.70000,12.20000,32.23000,1.88700,233.50000,17.50000,66.16700,39.33300,0.00000
winter,large_,low___,8.60000,6.50000,43.00000,0.66800,95.00000,10.50000,74.66700,63.50000,0.00000
spring,large_,high__,7.40000,7.30000,19.00000,4.39000,120.00000,74.85700,166.28600,5.30000,1.70000
summer,large_,high__,7.80000,10.40000,22.50000,4.72000,178.75000,116.50000,201.00000,2.70000,10.40000
winter,large_,low___,8.50000,9.80000,70.25000,1.64400,285.00000,68.71400,132.00000,16.02800,0.00000
spring,large_,low___,7.98000,5.60000,47.06000,3.08800,357.00000,311.39999,342.29999,18.53000,3.10000
summer,large_,low___,7.95000,7.20000,57.28600,3.74600,425.71399,291.14301,330.00000,4.71400,1.70000
spring,large_,medium,7.96000,5.50000,131.36400,3.31300,810.90002,311.45499,349.81799,20.47000,0.00000
winter,large_,medium,8.35000,2.75000,97.73300,3.68100,137.44400,91.00000,155.55600,2.74400,25.20000
summer,large_,medium,8.15000,10.40000,189.56700,5.01100,162.94400,135.77800,219.27800,2.85900,5.80000
winter,large_,high__,8.50000,10.10000,3.00000,0.85100,37.77800,10.77800,23.88900,0.50000,0.00000
winter,large_,medium,8.50000,11.40000,3.00000,0.77400,10.90900,3.72700,8.09100,3.60000,0.00000
spring,large_,medium,8.50000,8.50000,4.02500,0.82500,23.63600,5.58300,31.09100,2.40000,0.00000
autumn,large_,medium,8.40000,11.43000,4.96600,0.96900,24.11100,6.00000,18.16700,2.13300,0.00000
spring,large_,high__,8.20000,9.90000,6.40000,0.55300,21.42900,12.00000,76.28600,1.30000,10.10000
autumn,large_,high__,8.00000,10.98000,9.70000,0.87400,67.70000,26.60000,51.03400,2.20000,4.90000
autumn,large_,low___,8.30000,8.90000,42.05800,5.92200,116.72700,150.58299,220.72301,6.70000,0.00000
spring,large_,low___,8.70000,6.80000,16.88900,2.13900,30.00000,37.11100,85.44400,23.03300,0.00000
winter,large_,medium,8.60000,10.40000,15.18200,2.50200,140.90900,31.90900,77.70000,15.31800,23.30000
summer,large_,medium,8.00000,9.10000,15.37500,2.11800,43.75000,48.87500,86.50000,8.12500,7.70000
summer,large_,medium,8.20000,9.50000,17.87500,2.36300,63.75000,44.00000,77.00000,8.46300,1.50000
spring,large_,medium,8.50000,9.60000,16.54500,3.84900,103.27300,34.27300,63.40000,14.68200,3.70000
spring,large_,medium,8.04000,9.30000,130.26300,3.77600,131.00800,97.50000,152.96600,6.15000,4.10000
autumn,large_,medium,7.95000,9.10000,76.88600,3.46100,93.82700,68.33300,146.04900,3.95000,3.50000
summer,small_,high__,7.25000,9.54000,?,0.64200,85.00000,14.60000,19.45000,0.46000,0.00000
autumn,small_,high__,7.64000,10.30000,34.23500,2.94200,41.43000,17.00000,41.56700,7.43000,11.10000
winter,small_,high__,7.92000,8.50000,10.86700,1.71500,199.54000,3.22200,27.20000,1.90000,0.00000
spring,small_,high__,7.62000,9.40000,11.05500,1.51000,13.56000,4.00000,12.65000,1.45600,1.60000
summer,medium,high__,7.75000,10.70000,15.50000,3.97600,57.64000,10.50000,43.16900,3.12000,10.10000
winter,small_,high__,7.08000,8.40000,9.45000,1.57200,26.54000,4.00000,13.60000,0.67500,0.00000
summer,small_,high__,6.92000,11.10000,9.10000,0.63000,21.00000,5.00000,?,2.46000,1.40000
winter,small_,high__,8.10000,9.80000,14.34000,0.73000,22.50000,23.00000,45.50000,0.85000,0.00000
spring,small_,high__,7.20000,11.30000,8.97000,0.23000,134.50000,13.00000,19.00000,?,0.00000
spring,large_,medium,8.61000,10.10000,3.51800,0.66300,12.22000,3.22200,7.00000,1.30000,0.00000
summer,large_,medium,8.22000,9.50000,2.30000,0.67200,9.87000,4.00000,6.12300,0.80000,0.00000
winter,large_,medium,8.53000,10.50000,3.00000,0.75800,10.35000,4.10000,?,4.00000,0.00000
summer,large_,medium,8.40000,10.00000,3.51000,0.86600,29.65000,5.80000,15.00000,2.86000,0.00000
winter,large_,high__,8.10000,10.90000,9.05600,0.82500,41.00000,20.00000,58.00000,?,0.00000
summer,medium,high__,8.12000,10.20000,7.61300,0.69900,33.56000,28.03400,49.65800,2.20000,5.90000
winter,large_,low___,8.43000,10.80000,35.64200,6.22500,134.00000,103.50000,?,45.37500,4.60000
winter,large_,low___,8.70000,11.70000,21.46560,3.76500,91.45000,38.00000,83.00000,17.00000,2.60000
summer,large_,low___,8.10000,8.20000,26.54000,2.80500,42.75000,48.50000,88.12500,13.98000,0.00000
autumn,large_,low___,8.35000,11.10000,22.56000,3.14000,76.20000,41.00000,98.66500,17.45600,3.10000

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