- 1. 1 Jubatus2013 218 Jubatus Team
2. l (Yuya Unno)l Twitter: @unnonounol Preferred Infrastructurel
l l 2 3. Jubatusl l l OKl 3 4. l l Jubatusl 4 5. l l Jubatusl 5 6.
JubatusOSSl NTT SIC*Preferred Infrastructurel 201110OSS
http://jubat.us/ 6* NTT SIC: NTT 7. l l 7 8. 1l xyl l or
(classifier) or 8 9. l l Jubatusl 9 10. Jubatusl jubaclassiferl -f
$ jubaclassifiercant start standalone mode withoutconfigpath
specifiedusage: jubaclassifier [options] ...options:... []$
jubaclassifier -f
/opt/jubatus/share/jubatus/example/config/classifier/pa1.json10 11.
l l 11 12. JubatusJubatus (jubaclassifier) Jubatus l Jubatusl l
C++/Ruby/Python/Java12 13.
https://github.com/jubatus/jubatus-examplel jubaclassifierl $ cd
jubatus-example/gender/python$ ./gender.pyfemale 0.473417669535male
0.388551652431 female 2.79595327377male -2.3630161285413 14.
1.8T0.3 3.2(+ 1.1l 14 15. 2.5 T0.8 2.8(+0.5l l 15 16. l
sample.py#()client = jubatus.Classifier(host, port)train_data = [
... ]client.train(name, train_data)test_data = [ ... ]results =
client.classify(name, test_data)#() 16 17. Jubatusl l #()client =
jubatus.Classifier(host, port)train_data = [ ...
]client.train(name, train_data)test_data = [ ... ]results =
client.classify(name, test_data)#() 17 18. (train)l l client =
jubatus.Classifier(host, port)train_data = [(male, datum([(hair,
short), ...),...]client.train(name, train_data)test_data = [ ...
]results = client.classify(name, test_data) 18 19. (classify)l l
client = jubatus.Classifier(host, port)train_data = [ ...
]client.train(name, train_data)test_data = [datum([(hair, short),
... ),...]results = client.classify(name, test_data) 19 20. datuml
l l hairshorttopT shirtheight1.81datum([(hair, short), (top, T
shirt),],[(height, 1.81)])20 21. l l client =
jubatus.Classifier(host, port)train_data = [(male, datum([(hair,
short), ...),...# ]client.train(name, train_data)test_data = [ ...
]results = client.classify(name, test_data) 21 22. l l client =
jubatus.Classifier(host, port)train_data = [(male (adult),
datum([(hair,short), ...),...]client.train(name,
train_data)test_data = [ ... ]results = client.classify(name,
test_data) 22 23. l l Jubatusl 23 24. {"converter" :
{...},"parameter" : {"regularization_weight" : 1.0},"method" :
"PA1"}24 25. {"converter" : { ... },"parameter" : { ... },"method"
: AROW"}l method l PA1 AROW l 25 26. {"converter" : { ...
},"parameter" : {"regularization_weight" : 10.0},"method" : "PA1"}l
parameter l l 26 27. {"converter" : {...},parameter : { ...
},"method" : "PA1"}l converter l 27 28. (0, 1, 0, 2.5, -1, ) /SVM,
LogReg, (1, 0.5, 0.1, -2, 3, ) PA, CW, ALOW, Nave Bayes (0, 1, 0,
1.5, 2, ) CNB, DT, RF, ANN, K-means, Spectral Clustering, MMC, LSI,
LDA, GM, HMM, MRF, CRF, 28 29. l l l l l l 29 30. l l 2 1 1 IT 1
110.7 150 300 31. {"hair": "short","top": "T shirt","bottom":
"jeans","height": 1.70}hair=short 1.0top=T shirt1.0bottom=jeans
1.0height 1.7031 32. { "hair": "short", l "top": "T shirt",
"bottom": "jeans", "height": 1.70 }l 1.0 hair=short 1.0 top=T
shirt1.0 bottom=jeans 1.0 height 1.70 32 33. string_rules...
"string_rules" : [ { "key" : "*, "type" : "str", "sample_weight" :
"bin, "global_weight" : "bin" } ],...l key: * l type: str l
sample_weight, global_weight: 1.0 33 34. {"hair": "short",l "top":
"T shirt", "bottom": "jeans","height": 1.70}hair=short 1.0top=T
shirt1.0bottom=jeans 1.0height 1.70 34 35. num_rules... num_rules"
: [ { "key" : "*, "type" : num } ],...l key: * l type: num 35 36.
1.01.0 2.0 1.0 1.0l 36 37. ... "string_rules" : [ { "key" : "*,
"type" : space", "sample_weight" : "bin, "global_weight" : "bin" }
],...l typespacel 37 38. l l http://jubat.us/ja/l l l
http://groups.google.com/group/jubatusl l
https://github.com/jubatus/jubatus38 39. l jubatus-examplel l l
http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasetsl news20l
Enjoy! 39
