Selasa, 12 Juli 2011

Logika Fuzzy - Pengantar

  1. Sejarah Logika Fuzzy

Logika Fuzzy pertama kali diperkenalkan oleh Prof. Lotfi Zadeh pada tahun 1965. Dia adalah orang Iran yang menjadi guru besar di University of California at Berkeley dalam papernya yang berjudul “Fuzzy Set”. Dalam paper tersebut dia mempaparkan ide dasar fuzzy set yang meliputi inclusion, union, intersection, complement, relation dan convexity. Lotfi Zadeh mengatakan penerapan integrasi Logika Fuzzy kedalam sistem informasi dan rekayasa proses akan menghasilkan sistem kontrol, alat-alat rumah tangga, dan sistem pengambil keputusan yang lebih fleksibel, mantap, dan canggih dibandingkan dengan sistem konvensional.

  1. Pengertian Logika Fuzzy

Logika Fuzzy merupakan perkembangan dari logika boolean yang hanya mengenal nilai 0 atau 1, benar atau salah, hitam atau putih. Logika Fuzzy memiliki karakteristik dan keunggulan dalam menangani permasalahan yang bersifat ketidakpastian dan kebenaran parsial. Logika Fuzzy memungkinkan nilai keanggotaan antara 0 dan 1, tingkat keabuan dan juga hitam dan putih, dan dalam bentuk linguistik, konsep tidak pasti seperti "sedikit", "lumayan", dan "sangat". Hal ini sangat berpengaruh dalam penyelesaian masalah di dunia nyata yang biasanya tidak bisa dilihat sebagai hitam atau putih. Kenyataannya terdapat banyak hal yang bernilai abu-abu dan jika diperhatikan akan membantu kita untuk membuat keputusan yang secara intuitif tampak lebih adil.

Gambar 1. Perbedaan temperatur dalam Logika Fuzzy

  1. Beberapa Hal yang Perlu diketahui dalam Logika Fuzzy

Ada beberapa hal yang perlu diketahui dalam sistem fuzzy, yaitu:

a. Variabel Fuzzy

Variabel fuzzy merupakan variabel yang akan dibahas dalam suatu sistem fuzzy. Contoh: umur, permintaan, persediaan, produksi, dsb.

b. Himpunan Fuzzy

Merupakan suatu kelompok yang mewakili suatu kondisi atau keadaan tertentu dalam suatu sistem fuzzy. Contoh: Variabel suhu, terbagi menjadi 3 himpunan fuzzy, yaitu: DINGIN, SEJUK, NORMAL, HANGAT, dan PANAS.

Himpunan fuzzy memiliki 2 atribut , yaitu:

1. Linguistik, yaitu penamaan kelompok yang mewakili suatu keadaan atau kondisi tertentu dengan menggunakan bahasa alami, seperti: DINGIN, SEJUK, NORMAL, HANGAT, dan PANAS.

2. Numeris, yaitu suatu nilai (angka) yang menunjukkan ukuran dari suatu variabel, seperti: 25, 40, 35, 50, dsb.

c. Semesta Pembicaraan

Semesta pembicaraan adalah keseluruhan nilai yang diperbolehkan untuk

dioperasikan dalam suatu variabel fuzzy. Semesta pembicaraan merupakan

himpunan bilangan real yang senantiasa naik (bertambah) secara monoton dari kiri

ke kanan. Nilai semesta pembicaraan dapat berupa bilangan positif maupun

negatif. Adakalanya nilai semesta pembicaraan ini tidak dibatasi batas atasnya.

Contoh: Semesta pembicaraan untuk variabel suhu : [0, 40].

d. Domain

Domain himpunan fuzzy adalah keseluruhan nilai yang diijinkan dalam semesta

pembicaraan dan boleh dioperasikan dalam suatu himpunan fuzzy. Seperti halnya

semesta pembicaraan, domain merupakan himpunan bilangan real yang senantiasa

naik (bertambah) secara monoton dari kiri ke kanan. Nilai domain dapat berupa

bilangan positif maupun negatif.

  1. Sistem Inferensi Fuzzy (Fuzzy Inference System/FIS)

Sistem Inferensi Fuzzy (Fuzzy Inference System/FIS) disebut juga fuzzy inference engine adalah sistem yang dapat melakukan penalaran dengan prinsip serupa seperti manusia melakukan penalaran dengan nalurinya. Terdapat beberapa jenis FIS yang dikenal yaitu Mamdani, Sugeno dan Tsukamoto.

REFERENSI

Anonim1. http://id.wikipedia.org. Logika Fuzzy. Tanggal Akses : 23 Februari 2011

Anonim2. http://repository.usu.ac.id. Sistem Pendukung Keputusan Menggunakan

Sistem Fuzzy. Tanggal Akses : 23 Februari 2011

Anonim3. http://www.ittelkom.ac.id. Sistem Fuzzy. Tanggal Akses : 23 Februari 2011

Kewarganegaraan

Judul : Kewarganegaraan dan Hak dan Kewajiban Warga Negara dan Negara

Oleh : Kelompok 6

Tanggal : Jum’at, 13 Mei 2011


abstrak

Warga negara Indonesia ialah orang-orang bangsa Indonesia asli dan orang-orang bangsa lain yang disahkan dengan undang-undang sebagai warga negara. Kewarganegaraan artinya keanggotaan yang menunjukkan hubungan atau ikatan antara negara dengan warga negara.

Hak warga negara Indonesia antara lain:

1. Hak atas pekerjaan dan penghidupan yang layak.

2. Hak untuk hidup dan mempertahankan kehidupan.

3. Hak untuk membentuk keluarga dan melanjutkan keturunan melalui perkawinan yang sah.

4. Hak atas kelangsungan hidup.

5. Hak untuk mengembangkan diri dan melalui pemenuhan kebutuhan dasarnya dan berhak mendapat pendidikan, ilmu pengetahuan dan teknologi, seni dan budaya demi meningkatkan kualitas hidupnya demi kesejahteraan hidup manusia.

6. Hak untuk mempunyai hak milik pribadi.

Kewajiban warga negara Indonesia antara lain :

1. Wajib menaati hukum dan pemerintahan.

2. Wajib ikut serta dalam upaya pembelaan negara.

3. Wajib menghormati hak asasi manusia orang lain.

4. Wajib tunduk kepada pembatasan yang ditetapkan dengan undang-undang.

5. Wajib ikut serta dalam usaha pertahanan dan keamanan negara.

Hak dari negara yaitu negara mempunyai hak untuk merdeka dan diakui oleh negara lain. Kemudian hak yurisdikasi teritorial dan hak membela diri atau hak mempertahankan diri. Sedangkan kewajiban dari negara yaitu tidak mengambil jalan kekerasan dalam menyelesaikan urusan negara, kemudian negara juga mempunyai kewajiban untuk melaksanakan kewajiban traktat dengan iktikad.

Sebagai warga negara yang baik, sepatutnya kita menyelelaraskan antara hak dan kewajiban. Tidak hanya menuntut hak saja, tetapi juga harus melakukan sesuatu yang juga menjadi kewajiban kita. Tetapi tidak dipungkiri bahwa kita memang menuntut hak yang seharusnya kita dapatkan dari negara, misalnya hak untuk bersekolah, hak dilindungi, dan hak sama dimata hukum. Selain itu, diharapkan kita bisa turut berparisipasi dalam membantu negara guna mendapatkan haknya, yaitu dengan memberikan prestasi, sehingga negara kita bisa lebih diakui oleh negara lain.

Jurnal - Application of SOM neural network in clustering.

Application of SOM neural network in clustering.

Abstract:

The Self-Organizing Map (SOM) is an unsupervised neural network algorithm that projects high-dimensional data onto a two-dimensional map. The projection preserves the topology of the data so that similar data items will be mapped to nearby locations on the map. One of the SOM neural network's applications is clustering of animals due their features. In this paper we produce an experiment to analyze the SOM in clustering different species of animals.

Keywords: SOM Neural Network; Feature; Clustering; Animal

Article Type:

Clinical report

Subject:

Neural circuitry (Research)
Computational neuroscience (Research)

Authors:

Behbahani, Soroor
Nasrabadi, Ali Moti

Pub Date:

12/01/2009

Publication:

Name: Journal of Biomedical Science and Engineering (JBiSE) Publisher: Scientific Research Publishing, Inc. Audience: Academic Format: Magazine/Journal Subject: Science and technology Copyright: COPYRIGHT 2009 Scientific Research Publishing, Inc. ISSN: 1937-6871

Issue:

Date: Dec, 2009 Source Volume: 2 Source Issue: 8

Topic:

Event Code: 310 Science & research

Geographic:

Geographic Scope: Iran Geographic Code: 7IRAN Iran

Accession Number:

228716899

Full Text:

1. INTRODUCTION

The Self-Organizing Map (SOM) is a fairly well-known neural network and indeed one of the most popular unsupervised learning algorithms. Since its invention by Finnish Professor Teuvo Kohonen in the early 1980s, more than 4000 research articles have been published on the algorithm, its visualization and applications. The maps comprehensively visualize natural groupings and relationships in the data and have been successfully applied in a broad spectrum of research areas ranging from speech recognition to financial analysis. The Self-organizing Map performs a non-linear projection of multidimensional data onto a two-dimensional display. The mapping is topology-preserving, meaning that the more alike two data samples are in the input space, the closer they will appear together on the final map. The SOM belongs to the class of Neural Network algorithms. This is a group of algorithms based on analogies to the neural structures of the brain. The SOM in particular was inspired by an interesting phenomenon: as physicians have discovered, some areas of brain tissue can be ordered according to an input signal. Basically, the SOM is a computer program simulating this biological ordering process. Applied to electronic datasets, the algorithm is capable of producing a map that shows similar input data items appearing close to each other. There are numerous applications involving the SOM algorithm but the most widespread use is the identification and visualization of natural groupings in the data. The process of finding similar items is generally referred to as clustering. Compared to the k-means clustering algorithm, the SOM exemplifies a robust and structured self-organizing neural networks are based on the principle of transforming a set of p-variate observations into a spatial representation of smaller dimensionality, which may allow a more effective visualization of correlations in the original data [4].

2. SELF-ORGANIZING MAP

The Self-Organizing Map belongs to the class of unsupervised and competitive learning algorithms. It is a sheet-like neural network, with nodes arranged as a regular, usually two-dimensional grid. As explained in the previous section on Neural Networks, we usually think of the node connections as being associated with a vector of weights. In the case of Self-Organizing Maps, it is easier to think of each node as being directly associated with a weight vector.

The items in the input data set are assumed to be in a vector format. If n is the dimension of the input space, then every node on the map grid holds an n-dimensional vector of weights:

mi = [mi1, mi2, mi3, . , min] (1)

The basic principle of the Self-Organizing Map is to adjust these weight vectors until the map represents a picture of the input data set. Since the number of map nodes is significantly smaller than the number of items in the dataset, it is needless to say that it is impossible to represent every input item from the data space on the map. Rather, the objective is to achieve a configuration in which the distribution of the data is reflected and the most important metric relationships are preserved. In particular, we are interested in obtaining a correlation between the similarity of items in the dataset and the distance of their most alike representatives on the map. In other words, items that are similar in the input space should map to nearby nodes on the grid [4].

2.1. Image's Characteristics

To represent the 3D image of 12 lead ECG, three axes for time, temporal and spatial are needed witch temporal axis represented the time domain of the cardiac signal and the spatial axis represented the locations of the limb and thoracic leads. The data axis is represented two extracted features of cardiac signal contains amplitude and wavelet coefficients. 6 leads are used to represent the image obtained by thoracic leads and 6 leads of 12 are used to represent the image obtained by limb leads.

In order to determine the information between consecutive leads in the spatial axis, an interpolation technique was used to witch could cause to homogeneousness of the image.

3. AN EXAMPLE OF SOM NEURAL NETWORK APPLICATION

More researches are performing in the field of SOM neural network applications in last two decades. One of the most important and famous examples of this application is clustering of animals due their features.

General features are using in this example based on the Kohonen animal data base (Table 1).

But the fact is that, these features are not sufficient for different species of animals.

In previous experiments, it had been assumed that there were only one species for each animal, whereas there may be exist more than 10 species for each special animal. So, for analyzing the ability of SOM neural network we perform a new experiment and assumed more than one species for them and increase the number of features to invent better separability. These features consist of geographical dispersion, nourishing and habitat, etc (Table 2).

The SOM size in this research is 7*7 and the initial weights are selected randomly.

Although there are 3 animals that are not settle in right location in SOM map, and selected wrong neurons, the results shows that extracted features could well separate the different species of animals. This result shows that the selected features for these 3 animals have not sufficient ability to separate them. This problem could be solved by adding extra features or choosing the features with more precise. One of the most important points in neural networks is the method of features extraction, but increasing the number of features could not always be the best solution for approving the results, because sometimes increasing the features lead to derangement in network. Another reason of bad result in neural networks relates to number of inputs. Increasing the number of inputs (animal species) leads to spreading the SOM size and could decrease the ability of it, because there would be more correlation between inputs, so the statistic of error will be increased.

4. RESULTS

Choosing suitable features for separating animal's species lead to good results of SOM neural network .There were some similarity between some of the animal's feature in Kohonen data base. For example the features of Goose and Owl, as well as, horse and zebra are exactly the same. And this similarity leads to wrong results in clustering of these animals. Although there are some errors, in this new experiment, these errors occurred between different species of one animal not between different animals. So the more similarity between animal's species, the more errors will occur.

[FIGURE 1 OMITTED]

5. CONCLUSIONS

SOM is a highly useful multivariate visualization method that allows the multidimensional data to be displayed as a 2-dimensional map. This is the main advantage of SOM. The map units clustering makes it easy to observe similarities in the data. Through our experiment, we demonstrated that the possibility of quick observation of relationship between component (feature) and the class as well as the relationship among different component (feature) of the dataset from the visualization of a dataset. SOM is also capable of handling several types of classification problems while providing a useful, interactive, and intelligible summary of the data.

However, SOM also has some disadvantages. For example, adjacent map units point to adjacent input data vector, so sometimes distortions are possible because high dimensional topography can not always be represented in 2D. To avoid such phenomenon, training rate and the neighborhood radius should not be reduced too quickly.

Hence, SOM usually need many iterations of training. And SOM also does not provide an estimation of such map distortion. Alternatives to the SOM have been developed in order to overcome the theoretical problems and to enable probabilistic analysis.

Current research showed a simple application of SOM neural network in clustering. This method can be used in many applications that need classification and one of them could be disease clustering. As seen in current research, we used fuzzy method to determine the features of each animal.

Similar to this research, we can determine the features of diseases. This method could help the physician in their diagnosis. We can use the sign of diseases as the input of SOM neural network. As some classes of disease have similar symptoms the SOM neural network can show a limitation of neighbor diseases that have such symptoms, so the physician can focus on them to diagnose the patient's disease with more accuracy. Fuzzy features can increase the ability of SOM neural network if they choose carefully with more accuracy and of course it need some trail and error methods to find a rule to relate a membership function to each disease and its symptoms.

doi: 10.4236/jbise.2009.28093

Received 11 June 2009; revised 29 June 2009; accepted 27 July 2009.

REFERENCES

[1] A. Forti, (2006) Growing hierarchical tree SOM: An unsupervised neural network with dynamic topology, , Gian Luca Foresti, Neural Networks, 19, 1568-1580.

[2] S. Haykin, (1999) Neural networks a comprehensive foundation (2nd ed.), Prentice Hall.

[3] R. G. Adams, K. Butchart and N. Davey, (1999) Hierarchical classification with a competitive evolutionary neural tree, Neural Networks, 12, 541-551.

[4] J. Li, Information visualization of self organizing maps. doi: 10.4236/jbise.2009.28094

Soroor Behbahani (1), Ali Moti Nasrabadi (2)

(1) Biomedical Engineering Department, Science and Research Branch, Islamic Azad University, Tehran, Iran;

(2) Biomedical Engineering Department, Faculty of Engineering, Shahed University, Tehran, Iran.

Email: soroor behbahani@yahoo.com; a m nasrabadi@yahoo.com

Table 1. The animal data set.

Animal Dove Hen Duck Goose

is

Small 1 1 1 1

Medium 0 0 0 0

Big 0 0 0 0

has

Two kgs 1 1 1 1

Four legs 0 0 0 0

Hair 0 0 0 0

Hooves 0 0 0 0

Mane 0 0 0 0

Feathers 1 1 1 1

likes to

Hunt 0 0 0 0

Run 0 0 0 0

Fly 1 0 0 1

Swim 0 0 1 1

Animal Owl Hawk Eagle Fox

is

Small 1 1 0 0

Medium 0 0 1 1

Big 0 0 0 0

has

Two kgs 1 1 1 0

Four legs 0 0 0 1

Hair 0 0 0 1

Hooves 0 0 0 0

Mane 0 0 0 0

Feathers 1 1 1 0

likes to

Hunt 1 1 1 1

Run 0 0 0 0

Fly 1 1 1 0

Swim 0 0 0 0

Animal Dog Wolf Cat Tiger

is

Small 0 0 1 0

Medium 1 1 0 0

Big 0 0 0 1

has

Two kgs 0 0 0 0

Four legs 1 1 1 1

Hair 1 1 1 1

Hooves 0 0 0 0

Mane 0 0 0 0

Feathers 0 0 0 0

likes to

Hunt 0 1 1 1

Run 1 1 0 1

Fly 0 0 0 0

Swim 0 0 0 0

Animal Lion Horse Zebra Cow

is

Small 0 0 0 0

Medium 0 0 0 0

Big 1 1 1 1

has

Two kgs 0 0 0 0

Four legs 1 1 1 1

Hair 1 1 1 1

Hooves 0 1 1 1

Mane 1 1 1 1

Feathers 0 0 0 0

likes to

Hunt 1 0 0 0

Run 1 1 1 0

Fly 0 0 0 0

Swim 0 0 0 0

Table 2. Increasing number of features and animals species.

Eagle Brownfish

Redfox Afghanfox Owl Owl

Small 0 0 0 0

Medium 0.4 0.5 0.1 0.43

Big 0 0 0 0

2 leg 0 0 1 1

4 leg 1 1 0 0

Hair 1 1 0 0

Hoove 0 0 0 0

Mane 0 0 0 0

Feathers 0 0 1 1

Hunt 1 1 0.95 1

Run 1 1 0 0

Fly 0 0 1 1

Swim 0 0 0 0

Asia 1 1 1 1

Africa 1 0 0.4 0

Us 1 0 0.4 1

Europe 0 1 0 0

Mountainous 1 0 1 0

Plain 1 1 0 1

River 0 0 1 1

Jungle 1 1 0 0

Domestic 0 0 0 0

Carnivorous 1 1 1 1

Herbivorous 0 0 0 0

Frugivorous 1 0 0 0

Egg 0 0 1 1

Milk 1 1 0 0

Colour variation 0.5 0.45 0.2 0.25

Longeared Shorteared Barn Saker

Owl Owl Owl Falcon

Small 0 0.875 0.475 0

Medium 0.21 0 0 0.083

Big 0 0 0 0

2 leg 1 1 1 1

4 leg 0 0 0 0

Hair 0 0 0 0

Hoove 0 0 0 0

Mane 0 0 0 0

Feathers 1 1 1 1

Hunt 1 1 1 1

Run 0 0 0 0

Fly 1 1 1 0.8

Swim 0 0 0 0

Asia 1 1 1 1

Africa 0 0 0.7 0.86

Us 1 0 1 0

Europe 0 1 0 1

Mountainous 0 0 0.47 0.5

Plain 0 0 0 1

River 0 1 0 0

Jungle 1 0 0 0

Domestic 0 0 1 0

Carnivorous 1 1 1 1

Herbivorous 0 0 0 0

Frugivorous 0 0 0 0

Egg 1 1 1 1

Milk 0 0 0 0

Colour variation 0.2 0.13 0.3 0.24

Lanner Peregrine Osprey Booted

Falcon Falcon Eagle Eagle

Small 0 0.95 0 0

Medium 0.33 0.13 0.08 0.08

Big 0 0 0 0

2 leg 1 1 1 1

4 leg 0 0 0 0

Hair 0 0 0 0

Hoove 0 0 0 0

Mane 0 0 0 0.066

Feathers 1 1 1 1

Hunt 0.9 0.91 1 1

Run 0 0 0 0

Fly 1 1 1 1

Swim 0 0 0 0

Asia 1 0.98 1 1

Africa 0 1 0 0.243

Us 0.5 1 0.5 0.7

Europe 0.95 0 0 1

Mountainous 0.27 1 1 0

Plain 1 0 0 0

River 0 0.96 0 0

Jungle 0.032 0 0 1

Domestic 0 0 0 0

Carnivorous 1 1 1 1

Herbivorous 0 0 0 0

Frugivorous 0 0 0 0

Egg 1 1 1 1

Milk 0 0 0 0

Colour variation 0.54 0.35 0.24 0.4

Lesser

Bonelli Spotted spotted Imperial

Eagle Eagle Eagle Eagle

Small 0 0 0 0

Medium 0.41 0.52 0.58 0.61

Big 0 0 0 0

2 leg 1 1 1 1

4 leg 0 0 0 0

Hair 0 0 0 0

Hoove 0 0 0 0

Mane 0 0 0 0

Feathers 1 1 1 1

Hunt 1 1 1 0.99

Run 0 0 0 0

Fly 1 1 0.95 1

Swim 0 0 0 0

Asia 1 0.91 1 0

Africa 0 1 0 1

Us 0.61 0 0.43 1

Europe 1 1 0.4 0

Mountainous 1 0.5 0 0.5

Plain 0.5 0 0.35 1

River 0 1 0 1

Jungle 0 1 0.9 0

Domestic 0 0 0 0

Carnivorous 0.95 0.89 1 1

Herbivorous 0 0 0 0

Frugivorous 0 0 0 0

Egg 0.92 1 1 1

Milk 0 0 0 0

Colour variation 0.33 0.21 0.4 0.16

Red Withe

Golden breasted Greylag fronted

Eagle Goose Goose Goose

Small 0 0 0 0

Medium 0.71 0.23 0.68 0.31

Big 0 0 0 0

2 leg 1 1 1 1

4 leg 0 0 0 0

Hair 0 0 0 0

Hoove 0 0 0 0

Mane 0 0 0 0

Feathers 1 1 1 0.898

Hunt 1 0 0 0

Run 0 0 0.0363 0

Fly 1 1 1 1

Swim 0 1 0.99 0.85

Asia 0.87 1 0 0

Africa 1 0 0.9 0

Us 0 0 1 0

Europe 1 0.441 0 0

Mountainous 1 0 0.85 1

Plain 0 1 0 0.2

River 0 1 1 1

Jungle 0.03 0 0 0

Domestic 0 0 1 0.5

Carnivorous 1 0 0 0

Herbivorous 0 0 0 0

Frugivorous 0 0.98 1 1

Egg 1 1 1 1

Milk 0 0 0 0.021

Colour variation 0.13 0.5 0.1 0.15

Santhebert Pointer Wood Stock

Dog Dog Pigeon Dove

Small 0 0 1 0.8

Medium 0.76 0.31 0.211 0.02

Big 0 0 0 0

2 leg 0 0 1 1

4 leg 1 1 0 0

Hair 1 1 0 0

Hoove 0 0 0 0

Mane 0 0 0 0

Feathers 0 0 1 1

Hunt 0 0 0 0

Run 1 0.89 0 0

Fly 0 0 1 1

Swim 0 0.88 1 0

Asia 0 0 1 0.91

Africa 1 0.66 1 0.5

Us 0 1 0 1

Europe 0.515 0.79 1 1

Mountainous 0 0 0.2 0

Plain 0 0 1 1

River 0.9 0.78 0 1

Jungle 0 0.023 1 0

Domestic 0 0 1 1

Carnivorous 0.858 1 0 0

Herbivorous 0.02 0 0 0

Frugivorous 0.04 0.1 1 1

Egg 0 0 0 1

Milk 0.88 0 1 0

Colour variation 0.12 0.54 0.67 0.2

Rock Collared

Dove Dave Wolf Kaiot

Small 0.8 0.675 0 0

Medium 0 0 0.48 0.825

Big 0 0 0 0

2 leg 1 1 0 0

4 leg 0 0 1 1

Hair 0 0 1 1

Hoove 0 0 0 0

Mane 0 0 0 0

Feathers 1 1 0 0

Hunt 0 0 1 1

Run 0 0 0.88 0.88

Fly 0.965 1 0 0

Swim 0 0 0 0

Asia 0.87 0.92 1 1

Africa 0.11 0.5 0 0

Us 1 0.19 0.17 1

Europe 0 1 1 0

Mountainous 0.95 0.21 0 0.4

Plain 0 0 1 1

River 1 0.2 0.11 0

Jungle 0 0 1 0

Domestic 1 0.95 0.032 0

Carnivorous 0 0 1 1

Herbivorous 0 0.1 0 0

Frugivorous 1 1 0 0

Egg 1 1 1 0

Milk 0 0 0 1

Colour variation 0.3 0.47 0.31 0.21

Iranian

Tiger Lion Horse Zebra

Small 0 0 0 0

Medium 0 0 0.85 0.62

Big 0.88 1 1 0

2 leg 0 0 0 0

4 leg 1 1 1 1

Hair 1 1 1 1

Hoove 0 0 1 1

Mane 0 1 1 1

Feathers 0 0 0 0

Hunt 1 0.89 0 0

Run 1 1 0.84 0.87

Fly 0 0 0 0

Swim 0 0 0 0

Asia 1 1 1 1

Africa 1 0 0.416 0

Us 0.9 0.3 0 0.91

Europe 1 0.78 0.445 0

Mountainous 1 0.95 0.985 0

Plain 0 1 0.95 0.35

River 0 0 0 0.019

Jungle 1 1 0.5 1

Domestic 0 0 1 0

Carnivorous 1 1 0 0

Herbivorous 0 0 1 1

Frugivorous 0.21 0.0033 0 0.1

Egg 0 0 0 0

Milk 1 1 1 1

Colour variation 0.3 0 0.92 0.54

Asian

Marbled Chinchila goloden

Zebra Cat Cat Cat

Small 0 0 0 0

Medium 0.967 0.45 0.16 0.55

Big 0 0 0 0.01

2 leg 0 0 0 0

4 leg 1 1 1 1

Hair 1 0 0 1

Hoove 1 0 0 0

Mane 1 0 0 0

Feathers 0 0 0 0

Hunt 0 1 0 1

Run 1 0.99 0.88 1

Fly 0 0 0.96 0

Swim 0 1 1 0

Asia 0.78 0 0 1

Africa 0 1 1 0

Us 1 0 0 0.33

Europe 0.93 0.35 0.032 0

Mountainous 0 1 1 0

Plain 0.64 0.033 0 1

River 0 0 1 1

Jungle 1 0 0 1

Domestic 0 0.91 1 1

Carnivorous 0 1 1 1

Herbivorous 0.8 0 0 0

Frugivorous 0 0.2 0 0

Egg 0 0 0 0

Milk 1 1 1 1

Colour variation 0.75 0.21 0.3 0.4

Caucasian

Blackear black Mallard Gadwell

Cat Grouse Duck Duck

Small 0.818 1 0 0

Medium 0.8 0.03 0.28 0.16

Big 0 0 0 0

2 leg 0 1 1 1

4 leg 1 0 0 0

Hair 1 0 0 0

Hoove 0 0 0 0

Mane 0 0 0 0

Feathers 0 1 1 1

Hunt 1 0 0 0

Run 1 0 0 0

Fly 0 1 1 1

Swim 0 0 0 0

Asia 1 1 1 0.91

Africa 1 0 0.8 0.4

Us 0 0.5 1 0

Europe 1 0 0 1

Mountainous 1 0.99 1 0.45

Plain 0 0.5 0 0

River 0 0 1 0.3

Jungle 1 0.5 0 0

Domestic 0 0.95 0 0.5

Carnivorous 1 0 0 0

Herbivorous 0 0 0 0

Frugivorous 0 1 1 1

Egg 0 1 1 1

Milk 1 0 0 0

Colour variation 0.7 0.1 0.21 0.3

Wigeon Panital Garganey

Duck Duck Duck

Small 0 0 0

Medium 0.083 0.25 0.191

Big 0 0 0

2 leg 1 1 0

4 leg 0 0 1

Hair 0 0 0

Hoove 0 0 0

Mane 0 0 0

Feathers 1 1 0

Hunt 0 0 1

Run 0 0 0

Fly 0.95 1 0

Swim 0 0 0

Asia 1 1 1

Africa 0 1 1

Us 1 0 0

Europe 0 0 1

Mountainous 0 0 0

Plain 1 0 0

River 1 1 1

Jungle 0 0 1

Domestic 0 0.75 0

Carnivorous 0 0 0

Herbivorous 0 0 0

Frugivorous 1 1 1

Egg 1 1 1

Milk 0 0 0

Colour variation 0.24 0.5 0.4

Marbledteal Black Grizli

Duck Bear Bear

Small 0.95 0 0

Medium 0 0 0

Big 0 0.01 0.28

2 leg 1 0 0

4 leg 0 1 1

Hair 0 1 1

Hoove 0 0 0

Mane 0 0 0

Feathers 1 0 0

Hunt 0 1 1

Run 0 1 1

Fly 0.95 0 0

Swim 0 0 0

Asia 1 0 1

Africa 0.033 0 0

Us 0 1 1

Europe 1 0 1

Mountainous 0 0 0

Plain 0.5 0 1

River 1 1 1

Jungle 1 1 1

Domestic 0 0 0

Carnivorous 0 1 1

Herbivorous 0 0 1

Frugivorous 1 0 1

Egg 1 1 0

Milk 0 0 1

Colour variation 0.3 0.4 0.13

Panda Orangotan Shampain

Bear Monky Monky

Small 0 0 0

Medium 0 0 0.65

Big 0.17 0.45 0.23

2 leg 0 1 1

4 leg 1 0 0

Hair 1 1 1

Hoove 0 0 0

Mane 0 0 0

Feathers 0 0 0

Hunt 1 1 1

Run 1 0 0

Fly 0 0 0

Swim 0 0 0

Asia 0.85 0.95 0

Africa 0 1 0.89

Us 0 0 0.045

Europe 1 1 0

Mountainous 1 0.21 0

Plain 0 0 0.9

River 0 0.89 0

Jungle 1 1 0.8

Domestic 0 0 0

Carnivorous 1 0 0.14

Herbivorous 1 1 0

Frugivorous 0 1 1

Egg 0 0 0

Milk 1 0.87 1

Colour variation 0.36 0.04 0.351

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