Commit Inicial

This commit is contained in:
2020-11-20 11:23:40 +01:00
commit 5611e5bc01
2914 changed files with 2625178 additions and 0 deletions

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17 18 4 69 75 15 55 30 54 66 57 34 11 51 84 64 19 0 77 67 60 61 13 27 58 70 12 47 49 74 32 80 82 38 45 29 53 42 37 14 9 5 36 43 7
31 16 59 85 56 20 68 71 62 48 35 44 87 39 33 10 76 52 28 40 26 83 3 2 50 24 73 88 79 25 22 23 46 6 41 8 63 72 65 1 21 78 81 86 89

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48 19 35 54 44 16 32 60 73 62 67 28 61 57 55 2 10 12 25 20 83 71 52 3 40 69 68 13 49 11 84 50 31 51 22 34 87 70 74 77 75 79 85 58 76 47 26 80 82 46 9 7 86 41 43 42 6 37 8 45 21 89 29 53 36 1 5 14
30 4 33 59 27 56 64 39 24 88 23 0 66 18 15 17 72 78 63 38 81 65
68 83 31 69 15 74 20 59 76 56 44 60 18 23 79 16 33 87 58 48 25 26 28 35 73 80 3 19 27 84 55 24 62 64 4 2 32 12 54 51 39 22 30 40 77 70 17 75 46 82 65 37 21 63 38 42 5 36 72 45 29 9 41 78 7 81 8 14
61 85 13 88 66 50 49 57 47 10 71 52 11 67 34 0 1 43 6 53 86 89
85 79 2 3 49 55 71 73 39 12 30 58 23 22 13 77 50 75 16 48 4 51 66 44 27 35 76 70 10 0 31 25 59 28 67 32 87 62 80 83 61 56 54 26 33 52 24 40 46 82 21 38 1 72 5 29 8 43 7 36 86 53 89 41 6 42 45 63
20 68 64 57 17 74 88 11 84 19 34 69 15 47 60 18 9 65 78 81 14 37
0 66 68 23 60 28 44 20 64 88 61 35 32 25 47 49 67 39 17 77 83 80 50 70 26 74 62 57 52 75 15 24 85 40 48 56 31 27 73 54 79 58 4 2 10 84 33 59 46 82 6 86 53 89 7 14 21 43 1 78 63 8 38 72 42 5 29 9
55 19 12 18 76 69 3 87 30 51 16 34 22 13 71 11 37 65 41 36 45 81

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printf('lendo problema %s ...\n', problema);
n_entradas= 8; n_clases= 3; n_fich= 1; fich{1}= 'post-operative.data'; n_patrons(1)= 90;
n_max= max(n_patrons);
x = zeros(n_fich, n_max, n_entradas); cl= zeros(n_fich, n_max);
n_patrons_total = sum(n_patrons); n_iter=0;
for i_fich=1:n_fich
f=fopen(fich{i_fich}, 'r');
if -1==f
error('erro en fopen abrindo %s\n', fich{i_fich});
end
for i=1:n_patrons(i_fich)
fprintf(2,'%5.1f%%\r', 100*n_iter++/n_patrons_total);
for j = 1:n_entradas
t = fscanf(f,'%s',1);
if t ~= '?'
if j==1 || j==2
val={'low','mid','high'};
elseif j==3
val={'excellent','good'};
elseif j==4
val={'high','mid'};
elseif j==5 || j==6 || j==7
val={'mod-stable','stable','unstable'};
else
x(i_fich,i,j)=str2double(t);
end
n=length(val);
for k=1:n
if strcmp(t, val{k})
x(i_fich,i,j)=k; break
end
end
else
x(i_fich,i,j) = 0;
end
end
t = fscanf(f,'%s',1); % lectura da clase
if t=='A'
cl(i_fich,i)=0;
elseif t=='I'
cl(i_fich,i)=1;
elseif t=='S'
cl(i_fich,i)=2;
else
error('clase %i descoñecida', t)
end
end
fclose(f);
end

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@relation post-operative
@attribute 'f1' real
@attribute 'f2' real
@attribute 'f3' real
@attribute 'f4' real
@attribute 'f5' real
@attribute 'f6' real
@attribute 'f7' real
@attribute 'f8' real
@attribute 'clase' {0,1,2}
@data
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% Rows Columns
3 3
% Matrix elements
0.0 1.0 1.0
1.0 0.0 1.0
1.0 1.0 0.0

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mid low excellent mid stable stable stable 15 A
mid high excellent high stable stable stable 10 S
high low excellent high stable stable mod-stable 10 A
mid low good high stable unstable mod-stable 15 A
mid mid excellent high stable stable stable 10 A
high low good mid stable stable unstable 15 S
mid low excellent high stable stable mod-stable 05 S
high mid excellent mid unstable unstable stable 10 S
mid high good mid stable stable stable 10 S
mid low excellent mid unstable stable mod-stable 10 S
mid mid good mid stable stable stable 15 A
mid low good high stable stable mod-stable 10 A
high high excellent high unstable stable unstable 15 A
mid high good mid unstable stable mod-stable 10 A
mid low good high unstable unstable stable 15 S
high high excellent high unstable stable unstable 10 A
low high good high unstable stable mod-stable 15 A
mid low good high unstable stable stable 10 A
mid high good mid unstable stable unstable 15 A
mid mid good mid stable stable stable 10 A
low high good mid unstable stable stable 15 A
low mid excellent high unstable stable unstable 10 S
mid mid good mid unstable stable unstable 15 A
mid mid good mid unstable stable stable 10 A
high high good mid stable stable mod-stable 10 A
low mid good mid unstable stable stable 10 A
high mid good low stable stable mod-stable 10 A
low mid excellent high stable stable mod-stable 10 A
mid mid excellent mid stable stable unstable 15 A
mid mid good mid unstable stable unstable 10 S
mid mid good high unstable stable stable 10 A
low low good mid unstable stable unstable 10 A
mid mid excellent high unstable stable mod-stable 10 A
mid low good mid stable stable stable 10 A
low mid excellent high stable stable mod-stable 10 A
mid mid good mid stable stable stable 10 A
low mid excellent mid stable stable stable 10 S
low low good mid unstable stable unstable 10 S
low low good mid stable stable stable 07 S
mid mid good high unstable stable mod-stable 10 A
low low good mid unstable stable stable 10 A
low mid good mid stable stable stable 15 S
high high good high unstable stable stable 15 S
mid mid good mid stable stable stable 10 S
low low excellent mid stable stable stable 10 A
low mid good mid unstable stable stable 10 S
low mid good high unstable stable stable ? I
mid mid excellent mid unstable stable stable 10 A
high high excellent high stable stable unstable ? A
mid high good low unstable stable stable 10 A
mid high good mid unstable mod-stable mod-stable 10 A
low high excellent mid unstable stable stable 10 A
mid low excellent high unstable stable unstable 10 A
mid mid good mid unstable stable mod-stable 10 S
high high excellent mid unstable stable mod-stable 10 A
mid mid good mid unstable stable stable 15 A
high mid good high stable stable unstable 15 A
mid low good high unstable stable mod-stable 10 A
low low good high stable stable stable 10 A
mid high good mid stable stable mod-stable 10 A
mid high good mid unstable stable unstable 10 A
mid low excellent high stable stable stable 10 A
mid mid good mid stable stable unstable 10 A
mid low excellent mid stable stable unstable 10 S
high mid excellent mid unstable unstable unstable 10 A
mid mid good high stable stable stable 10 S
mid low excellent mid unstable stable stable 10 A
mid mid excellent mid unstable stable stable 10 A
mid mid excellent high stable stable stable 10 A
mid mid excellent low stable stable stable 10 A
mid low excellent mid unstable unstable unstable ? A
low low excellent mid stable stable stable 10 A
mid mid excellent mid stable stable mod-stable 10 S
mid mid excellent high stable stable stable 10 A
mid low excellent high stable stable mod-stable 10 A
low mid good mid stable stable unstable 10 A
mid mid excellent mid stable stable mod-stable 10 A
mid mid excellent mid stable stable unstable 10 A
mid mid excellent mid unstable unstable stable 10 S
mid mid good high stable stable stable 10 A
mid mid excellent mid stable stable stable 15 A
mid mid excellent mid stable stable stable 10 S
mid low good mid stable stable unstable 10 I
high mid excellent mid unstable stable unstable 05 A
mid mid excellent mid stable stable unstable 10 A
mid mid excellent mid unstable stable stable 10 A
mid mid excellent mid unstable stable stable 15 S
mid mid good mid unstable stable stable 15 A
mid mid excellent mid unstable stable stable 10 A
mid mid good mid unstable stable stable 15 S

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1. Title: Postoperative Patient Data
2. Source Information:
-- Creators: Sharon Summers, School of Nursing, University of Kansas
Medical Center, Kansas City, KS 66160
Linda Woolery, School of Nursing, University of Missouri,
Columbia, MO 65211
-- Donor: Jerzy W. Grzymala-Busse (jerzy@cs.ukans.edu) (913)864-4488
-- Date: June 1993
3. Past Usage:
1. A. Budihardjo, J. Grzymala-Busse, L. Woolery (1991). Program LERS_LB 2.5
as a tool for knowledge acquisition in nursing, Proceedings of the 4th
Int. Conference on Industrial & Engineering Applications of AI & Expert
Systems, pp. 735-740.
2. L. Woolery, J. Grzymala-Busse, S. Summers, A. Budihardjo (1991). The use
of machine learning program LERS_LB 2.5 in knowledge acquisition for
expert system development in nursing. Computers in Nursing 9, pp. 227-234.
4. Relevant Information:
The classification task of this database is to determine where
patients in a postoperative recovery area should be sent to next.
Because hypothermia is a significant concern after surgery
(Woolery, L. et. al. 1991), the attributes correspond roughly to body
temperature measurements.
Results:
-- LERS (LEM2): 48% accuracy
5. Number of Instances: 90
6. Number of Attributes: 9 including the decision (class attribute)
7. Attribute Information:
1. L-CORE (patient's internal temperature in C):
high (> 37), mid (>= 36 and <= 37), low (< 36)
2. L-SURF (patient's surface temperature in C):
high (> 36.5), mid (>= 36.5 and <= 35), low (< 35)
3. L-O2 (oxygen saturation in %):
excellent (>= 98), good (>= 90 and < 98),
fair (>= 80 and < 90), poor (< 80)
4. L-BP (last measurement of blood pressure):
high (> 130/90), mid (<= 130/90 and >= 90/70), low (< 90/70)
5. SURF-STBL (stability of patient's surface temperature):
stable, mod-stable, unstable
6. CORE-STBL (stability of patient's core temperature)
stable, mod-stable, unstable
7. BP-STBL (stability of patient's blood pressure)
stable, mod-stable, unstable
8. COMFORT (patient's perceived comfort at discharge, measured as
an integer between 0 and 20)
9. decision ADM-DECS (discharge decision):
I (patient sent to Intensive Care Unit),
S (patient prepared to go home),
A (patient sent to general hospital floor)
8. Missing Attribute Values:
Attribute 8 has 3 missing values
9. Class Distribution:
I (2)
S (24)
A (64)

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n_entradas= 8
n_clases= 3
n_arquivos= 1
fich1= post-operative_R.dat
n_patrons1= 90
n_patrons_entrena= 45
n_patrons_valida= 45
n_conxuntos= 1

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f1 f2 f3 f4 f5 f6 f7 f8 clase
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2 0.111882 1.59868 -1.03965 -1.07744 -0.994429 -0.202146 -0.031621 -0.192238 2
3 1.79011 -1.33767 -1.03965 -1.07744 -0.994429 -0.202146 -1.45457 -0.192238 0
4 0.111882 -1.33767 0.951172 -1.07744 -0.994429 3.43649 -1.45457 1.47136 0
5 0.111882 0.130505 -1.03965 -1.07744 -0.994429 -0.202146 -0.031621 -0.192238 0
6 1.79011 -1.33767 0.951172 0.718291 -0.994429 -0.202146 1.39132 1.47136 2
7 0.111882 -1.33767 -1.03965 -1.07744 -0.994429 -0.202146 -1.45457 -1.85584 2
8 1.79011 0.130505 -1.03965 0.718291 0.994429 3.43649 -0.031621 -0.192238 2
9 0.111882 1.59868 0.951172 0.718291 -0.994429 -0.202146 -0.031621 -0.192238 2
10 0.111882 -1.33767 -1.03965 0.718291 0.994429 -0.202146 -1.45457 -0.192238 2
11 0.111882 0.130505 0.951172 0.718291 -0.994429 -0.202146 -0.031621 1.47136 0
12 0.111882 -1.33767 0.951172 -1.07744 -0.994429 -0.202146 -1.45457 -0.192238 0
13 1.79011 1.59868 -1.03965 -1.07744 0.994429 -0.202146 1.39132 1.47136 0
14 0.111882 1.59868 0.951172 0.718291 0.994429 -0.202146 -1.45457 -0.192238 0
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16 1.79011 1.59868 -1.03965 -1.07744 0.994429 -0.202146 1.39132 -0.192238 0
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90 0.111882 0.130505 0.951172 0.718291 0.994429 -0.202146 -0.031621 1.47136 2