quinta-feira, 29 de outubro de 2020

Weka - Redes Neurais - Exemplo Iris

 Weka - Redes Neurais - Exemplo Iris

Fonte Internet: 

Robson Campos de Lima
21:33


Flores Iris Lindíssimas Classif. - Fisher 1936 Multiv. - Gabriel 2019 Intel. Artificial









Leitura do Arquivo - para Download
Padrão ARFF, do Weka:

Arquivo_Iris_Weka











Weka - Estrutura do Arquivo ARFF - Iris










Colorindo o Arquivo
















Arquivo Inteiro - Iris


% 1. Title: Iris Plants Database


% 2. Sources:

%      (a) Creator: R.A. Fisher
%      (b) Donor: Michael Marshall (MARSHALL%PLU@io.arc.nasa.gov)
%      (c) Date: July, 1988
% 3. Past Usage:
%    - Publications: too many to mention!!!  Here are a few.
%    1. Fisher,R.A. "The use of multiple measurements in taxonomic problems"
%       Annual Eugenics, 7, Part II, 179-188 (1936); also in "Contributions
%       to Mathematical Statistics" (John Wiley, NY, 1950).
%    2. Duda,R.O., & Hart,P.E. (1973) Pattern Classification and Scene Analysis.
%       (Q327.D83) John Wiley & Sons.  ISBN 0-471-22361-1.  See page 218.
%    3. Dasarathy, B.V. (1980) "Nosing Around the Neighborhood: A New System
%       Structure and Classification Rule for Recognition in Partially Exposed
%       Environments".  IEEE Transactions on Pattern Analysis and Machine
%       Intelligence, Vol. PAMI-2, No. 1, 67-71.
%       -- Results:
%          -- very low misclassification rates (0% for the setosa class)
%    4. Gates, G.W. (1972) "The Reduced Nearest Neighbor Rule".  IEEE 
%       Transactions on Information Theory, May 1972, 431-433.
%       -- Results:
%          -- very low misclassification rates again
%    5. See also: 1988 MLC Proceedings, 54-64.  Cheeseman et al's AUTOCLASS II
%       conceptual clustering system finds 3 classes in the data.
% 4. Relevant Information:
%    --- This is perhaps the best known database to be found in the pattern
%        recognition literature.  Fisher's paper is a classic in the field
%        and is referenced frequently to this day.  (See Duda & Hart, for
%        example.)  The data set contains 3 classes of 50 instances each,
%        where each class refers to a type of iris plant.  One class is
%        linearly separable from the other 2; the latter are NOT linearly
%        separable from each other.
%    --- Predicted attribute: class of iris plant.
%    --- This is an exceedingly simple domain.
% 5. Number of Instances: 150 (50 in each of three classes)
% 6. Number of Attributes: 4 numeric, predictive attributes and the class
% 7. Attribute Information:
%    1. sepal length in cm
%    2. sepal width in cm
%    3. petal length in cm
%    4. petal width in cm
%    5. class: 
%       -- Iris Setosa
%       -- Iris Versicolour
%       -- Iris Virginica
% 8. Missing Attribute Values: None
% Summary Statistics:
%                Min  Max   Mean    SD   Class Correlation
%    sepal length: 4.3  7.9   5.84  0.83    0.7826   
%     sepal width: 2.0  4.4   3.05  0.43   -0.4194
%    petal length: 1.0  6.9   3.76  1.76    0.9490  (high!)

%     petal width: 0.1  2.5   1.20  0.76    0.9565  (high!)
% 9. Class Distribution: 33.3% for each of 3 classes.


@RELATION iris



@ATTRIBUTE sepallength REAL
@ATTRIBUTE sepalwidth REAL
@ATTRIBUTE petallength       REAL
@ATTRIBUTE petalwidth  REAL

@ATTRIBUTE class       {Iris-setosa,Iris-versicolor,Iris-virginica}

@DATA
5.1,3.5,1.4,0.2,Iris-setosa
4.9,3.0,1.4,0.2,Iris-setosa
4.7,3.2,1.3,0.2,Iris-setosa
4.6,3.1,1.5,0.2,Iris-setosa
5.0,3.6,1.4,0.2,Iris-setosa
5.4,3.9,1.7,0.4,Iris-setosa
4.6,3.4,1.4,0.3,Iris-setosa
5.0,3.4,1.5,0.2,Iris-setosa
4.4,2.9,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.4,3.7,1.5,0.2,Iris-setosa
4.8,3.4,1.6,0.2,Iris-setosa
4.8,3.0,1.4,0.1,Iris-setosa
4.3,3.0,1.1,0.1,Iris-setosa
5.8,4.0,1.2,0.2,Iris-setosa
5.7,4.4,1.5,0.4,Iris-setosa
5.4,3.9,1.3,0.4,Iris-setosa
5.1,3.5,1.4,0.3,Iris-setosa
5.7,3.8,1.7,0.3,Iris-setosa
5.1,3.8,1.5,0.3,Iris-setosa
5.4,3.4,1.7,0.2,Iris-setosa
5.1,3.7,1.5,0.4,Iris-setosa
4.6,3.6,1.0,0.2,Iris-setosa
5.1,3.3,1.7,0.5,Iris-setosa
4.8,3.4,1.9,0.2,Iris-setosa
5.0,3.0,1.6,0.2,Iris-setosa
5.0,3.4,1.6,0.4,Iris-setosa
5.2,3.5,1.5,0.2,Iris-setosa
5.2,3.4,1.4,0.2,Iris-setosa
4.7,3.2,1.6,0.2,Iris-setosa
4.8,3.1,1.6,0.2,Iris-setosa
5.4,3.4,1.5,0.4,Iris-setosa
5.2,4.1,1.5,0.1,Iris-setosa
5.5,4.2,1.4,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
5.0,3.2,1.2,0.2,Iris-setosa
5.5,3.5,1.3,0.2,Iris-setosa
4.9,3.1,1.5,0.1,Iris-setosa
4.4,3.0,1.3,0.2,Iris-setosa
5.1,3.4,1.5,0.2,Iris-setosa
5.0,3.5,1.3,0.3,Iris-setosa
4.5,2.3,1.3,0.3,Iris-setosa
4.4,3.2,1.3,0.2,Iris-setosa
5.0,3.5,1.6,0.6,Iris-setosa
5.1,3.8,1.9,0.4,Iris-setosa
4.8,3.0,1.4,0.3,Iris-setosa
5.1,3.8,1.6,0.2,Iris-setosa
4.6,3.2,1.4,0.2,Iris-setosa
5.3,3.7,1.5,0.2,Iris-setosa
5.0,3.3,1.4,0.2,Iris-setosa
7.0,3.2,4.7,1.4,Iris-versicolor
6.4,3.2,4.5,1.5,Iris-versicolor
6.9,3.1,4.9,1.5,Iris-versicolor
5.5,2.3,4.0,1.3,Iris-versicolor
6.5,2.8,4.6,1.5,Iris-versicolor
5.7,2.8,4.5,1.3,Iris-versicolor
6.3,3.3,4.7,1.6,Iris-versicolor
4.9,2.4,3.3,1.0,Iris-versicolor
6.6,2.9,4.6,1.3,Iris-versicolor
5.2,2.7,3.9,1.4,Iris-versicolor
5.0,2.0,3.5,1.0,Iris-versicolor
5.9,3.0,4.2,1.5,Iris-versicolor
6.0,2.2,4.0,1.0,Iris-versicolor
6.1,2.9,4.7,1.4,Iris-versicolor
5.6,2.9,3.6,1.3,Iris-versicolor
6.7,3.1,4.4,1.4,Iris-versicolor
5.6,3.0,4.5,1.5,Iris-versicolor
5.8,2.7,4.1,1.0,Iris-versicolor
6.2,2.2,4.5,1.5,Iris-versicolor
5.6,2.5,3.9,1.1,Iris-versicolor
5.9,3.2,4.8,1.8,Iris-versicolor
6.1,2.8,4.0,1.3,Iris-versicolor
6.3,2.5,4.9,1.5,Iris-versicolor
6.1,2.8,4.7,1.2,Iris-versicolor
6.4,2.9,4.3,1.3,Iris-versicolor
6.6,3.0,4.4,1.4,Iris-versicolor
6.8,2.8,4.8,1.4,Iris-versicolor
6.7,3.0,5.0,1.7,Iris-versicolor
6.0,2.9,4.5,1.5,Iris-versicolor
5.7,2.6,3.5,1.0,Iris-versicolor
5.5,2.4,3.8,1.1,Iris-versicolor
5.5,2.4,3.7,1.0,Iris-versicolor
5.8,2.7,3.9,1.2,Iris-versicolor
6.0,2.7,5.1,1.6,Iris-versicolor
5.4,3.0,4.5,1.5,Iris-versicolor
6.0,3.4,4.5,1.6,Iris-versicolor
6.7,3.1,4.7,1.5,Iris-versicolor
6.3,2.3,4.4,1.3,Iris-versicolor
5.6,3.0,4.1,1.3,Iris-versicolor
5.5,2.5,4.0,1.3,Iris-versicolor
5.5,2.6,4.4,1.2,Iris-versicolor
6.1,3.0,4.6,1.4,Iris-versicolor
5.8,2.6,4.0,1.2,Iris-versicolor
5.0,2.3,3.3,1.0,Iris-versicolor
5.6,2.7,4.2,1.3,Iris-versicolor
5.7,3.0,4.2,1.2,Iris-versicolor
5.7,2.9,4.2,1.3,Iris-versicolor
6.2,2.9,4.3,1.3,Iris-versicolor
5.1,2.5,3.0,1.1,Iris-versicolor
5.7,2.8,4.1,1.3,Iris-versicolor
6.3,3.3,6.0,2.5,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
7.1,3.0,5.9,2.1,Iris-virginica
6.3,2.9,5.6,1.8,Iris-virginica
6.5,3.0,5.8,2.2,Iris-virginica
7.6,3.0,6.6,2.1,Iris-virginica
4.9,2.5,4.5,1.7,Iris-virginica
7.3,2.9,6.3,1.8,Iris-virginica
6.7,2.5,5.8,1.8,Iris-virginica
7.2,3.6,6.1,2.5,Iris-virginica
6.5,3.2,5.1,2.0,Iris-virginica
6.4,2.7,5.3,1.9,Iris-virginica
6.8,3.0,5.5,2.1,Iris-virginica
5.7,2.5,5.0,2.0,Iris-virginica
5.8,2.8,5.1,2.4,Iris-virginica
6.4,3.2,5.3,2.3,Iris-virginica
6.5,3.0,5.5,1.8,Iris-virginica
7.7,3.8,6.7,2.2,Iris-virginica
7.7,2.6,6.9,2.3,Iris-virginica
6.0,2.2,5.0,1.5,Iris-virginica
6.9,3.2,5.7,2.3,Iris-virginica
5.6,2.8,4.9,2.0,Iris-virginica
7.7,2.8,6.7,2.0,Iris-virginica
6.3,2.7,4.9,1.8,Iris-virginica
6.7,3.3,5.7,2.1,Iris-virginica
7.2,3.2,6.0,1.8,Iris-virginica
6.2,2.8,4.8,1.8,Iris-virginica
6.1,3.0,4.9,1.8,Iris-virginica
6.4,2.8,5.6,2.1,Iris-virginica
7.2,3.0,5.8,1.6,Iris-virginica
7.4,2.8,6.1,1.9,Iris-virginica
7.9,3.8,6.4,2.0,Iris-virginica
6.4,2.8,5.6,2.2,Iris-virginica
6.3,2.8,5.1,1.5,Iris-virginica
6.1,2.6,5.6,1.4,Iris-virginica
7.7,3.0,6.1,2.3,Iris-virginica
6.3,3.4,5.6,2.4,Iris-virginica
6.4,3.1,5.5,1.8,Iris-virginica
6.0,3.0,4.8,1.8,Iris-virginica
6.9,3.1,5.4,2.1,Iris-virginica
6.7,3.1,5.6,2.4,Iris-virginica
6.9,3.1,5.1,2.3,Iris-virginica
5.8,2.7,5.1,1.9,Iris-virginica
6.8,3.2,5.9,2.3,Iris-virginica
6.7,3.3,5.7,2.5,Iris-virginica
6.7,3.0,5.2,2.3,Iris-virginica
6.3,2.5,5.0,1.9,Iris-virginica
6.5,3.0,5.2,2.0,Iris-virginica
6.2,3.4,5.4,2.3,Iris-virginica
5.9,3.0,5.1,1.8,Iris-virginica
%
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Analise - Sequencia











































segunda-feira, 26 de outubro de 2020

Aula 26/10/2020

 Link: https://meet.google.com/fxi-dxeg-kdp

Videoaulas:

https://youtu.be/G8saNTISBfE

https://youtu.be/psfLKRk_0SQ


Pauta:

- Falar sobre o curso que começa 31/10/2020 - Sábado 15 horas ate as 18 horas

  Podem convidar 3 amigos

- Assuntos 4.0 e 5.0

- Machine Learning Supervisionado para Previsão - Regressão - Exemplo Biodiversidade Animal

https://saudebusiness.com/voce-informa/industria-5-0-a-reconciliacao-entre-o-homem-e-a-maquina/#:~:text=Atualmente%2C%20os%20rob%C3%B4s%20j%C3%A1%20s%C3%A3o,e%20a%20consist%C3%AAncia%20dos%20rob%C3%B4s.

Machine Learning para Predição - Regressão Exemplo de Biodiversidade Animal

 Videoaula:

 

https://youtu.be/Thsnpu1cxr8



Machine Learning para Predição - Regressão

Exemplo de Biodiversidade Animal

















Área de Cultivo de Grãos 

Soja - Milho - Algodão - Trigo - Aveia



 





















Exemplo de Biomonitor




Área de Cultivo de Grãos 

Soja - Milho - Algodão - Trigo - Aveia







Dados para Rodar no Weka
Arquivo

Autor: Gabriel Sarriés

DBO – Demanda Bioquímica de Oxigênio

ICobV – Índice de Cobertura Vegetal

ICArb – Índice de Cobertura Arbórea

IBCont – Bioindicador de Contaminação (agrotóxicos)

Dis_Pl – Distancia do plantio de grãos.


DBO

ICobV

ICArb

Bcont

Dis_Pl

IBD_A

1,604

89

60

11

9

90

0,385

90

61

10

8,9

91

0,216

91

62

9

9,1

92

0,303

90

59

10

8,8

89

1,961

20

12

81

0,2

20

0,782

21

14

79

0,3

22

0,57

22

15

78

0,25

23

2,187

22

12

77

0,2

24

0,764

59

35

41

6

60

0,273

60

32

40

6,5

61

1,883

64

33

38

5,8

63

0,581

62

32

37

5,6

62

0,18

79

50

21

8,2

80

0,007

80

49

20

7,8

79

2,028

80

48

18

8,2

81

2,431

79

47

21

7,7

78




Arquivo para Weka (.arff)

@RELATION biodiv_Animal

@ATTRIBUTE dbo REAL

@ATTRIBUTE  ICob_V REAL

@ATTRIBUTE ICArb REAL

@ATTRIBUTE BCont REAL

@ATTRIBUTE Dist_Pla REAL

@ATTRIBUTE Ibd_A REAL

@DATA

6.416555198,60,99,99,5,90
1.538176272,61,98,99,6,91
...