Enplicaw Documentation
Release 0.1.0
Carlo Oliveira
Contents 1 Enplicaw - Introdução 3 2 Enplicaw - Modulos 5 3 Notas de Lançamento V. 0.1.0 7 3.1 Milestone . . . 7 3.2 Aspectos do Lançamento. . . 7 3.3 Melhoramentos . . . 7 3.4 Consertos . . . 8
3.5 Questões e Problemas Conhecidos . . . 8
3.6 Lançamentos Anteriores e Posteriores . . . 8
4 Enplicaw - Módulos Principais 9 4.1 An Http Server main handler. . . 9
4.2 An implementaion of Weightless Neural Netwok . . . 10
4.3 An implementation of Google Database Store Model . . . 11
5 Enplicaw - Unit Tests 13 5.1 Enplicaw - Teste . . . 13
6 Indices and tables 15
Python Module Index 17
Enplicaw Documentation, Release 0.1.0
Contents:
Enplicaw Documentation, Release 0.1.0
CHAPTER
1
Enplicaw - Introdução
Enplicaw Documentation, Release 0.1.0
CHAPTER
2
Enplicaw - Modulos
Enplicaw é programado emBrython
Funcionalidades Documentadas:
• Modelo do Enplicaw : Entidades BasicasEnplicaw - Módulos Principais • Testes Unitários do Enplicaw :Enplicaw - Unit Tests
Enplicaw Documentation, Release 0.1.0
CHAPTER
3
Notas de Lançamento V. 0.1.0
Enplicaw
3.1 Milestone
Bruce - Protótipo da inteface Gráfica
3.2 Aspectos do Lançamento
3.2.1 Destaques dos Aspectos
Este ambiente inicial permite apenas o teste da interface com o usuário
3.2.2 Aspecto #1
O ambiente apenas apresenta o visual da interface mas não permite nenhuma interação.
3.3 Melhoramentos
Versão para uso do Google Application Engine e PyBuilder.
3.3.1 Melhoramento #1
Foi adicionada uma configuração para uso doGoogle_Cloud. A configuração app.yaml define os aspectos necessários para rodar no servidor do Goolge Application Engine.
3.3.2 Melhoramento #2
Foi adicionada uma configuração para uso do construtor de aplicativosPyBuilder. A configuração build.py define os aspectos necessários para verificar testes, cobertura, cabeçalhos e detalhes para deployment.
Enplicaw Documentation, Release 0.1.0
3.4 Consertos
Nenhum conserto notável.
3.5 Questões e Problemas Conhecidos
A funcionalidade ainda é muito simples, requer melhorias.
Uma nova versão deve suportar o monitoramento da atividade dos alunos.
3.6 Lançamentos Anteriores e Posteriores
Próximo Lançamento: A ser definidoLançamento 0.2.0
CHAPTER
4
Enplicaw - Módulos Principais
4.1 An Http Server main handler
Controller handles routes starting with /main.
class server.controllers.main_controller.Item(label, value) Bases: tuple
label
Alias for field number 0
value
Alias for field number 1
class server.controllers.main_controller.Likert(label, name, value) Bases: tuple
label
Alias for field number 0
name
Alias for field number 1
value
Alias for field number 2
server.controllers.main_controller.endsurvey(*args, **kwargs) Collects the survey data from the form and provides a sumary of collected data. server.controllers.main_controller.home(*args, **kwargs)
Open the survey with surveyor identification.
server.controllers.main_controller.identify(*args, **kwargs) Collects the identification from surveyor an opens the survey form.
server.controllers.main_controller.methodroute(route) server.controllers.main_controller.survey(*args, **kwargs)
Collects the survey data from the form and provides a new form. See also:
Moduleserver.controllers.main_controller
Note: Unidade de Controle Servidor.
Enplicaw Documentation, Release 0.1.0
4.2 An implementaion of Weightless Neural Netwok
Weightless neural network based on WISARD.
class server.models.enplicaw.core.Cortex(retinasize=12, bleach=0, ramorder=2) Neuron cortex with a collection of RAM nodes.An implementaion of Weightless Neural Netwok
Parameters
• retinasize – dimension in pixels of the retina. • bleach – bleaching factor at classify stage. • ramorder – number of bits of the RAM address.
classify(retina)
Classify a given pattern.server.models.enplicaw.core.Cortex Parameters retina – retina pixel vector to be classified.
Returns vector of responding RAM node values.
learn(retina, offset=1)
Learn new patterns.server.models.enplicaw.core.Cortex Parameters
• retina – retina pixel vector to be learned.
• offset – ammount of offset to be added to RAM node at learning.
class server.models.enplicaw.core.Lobe(data, topvalue, factor=1, ramorder=2, en-force_supress=(1, 0), bleach={0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0})
Rede neural sem peso.An implementaion of Weightless Neural Netwok Parameters
• data – input data stream.
• topvalue – the largest value of any data.
• factor – retina reduction factor to speed up processing. • ramorder – number obits adressing the RAM.
• enforce_supress – tuple with one value to offset leraning and a negative to suppress. • bleach – dictionary of bleaches {classkey: bleach_value}
classify()
A simple classification routine.server.models.enplicaw.core.Lobe Returns a colection of tuples of votes given from each discriminator.
classifynlearn()
Classifies a batch of retina data, and attempt to online leanr from the best. server.models.enplicaw.core.Lobe
Returns a colection of tuples of votes given from each discriminator.
learn(retinadata)
Learn patterns from a collection of retinas.server.models.enplicaw.core.Lobe Parameters retinadata – collection of retinas.
Enplicaw Documentation, Release 0.1.0
results()
Compile confidences, hitsample lists.server.models.enplicaw.core.Lobe Returns accuracy performance in hits/total.
classmethod retinify(histogram_data, topvalue, factor=1)
Formats a histogram into a retina format.server.models.enplicaw.core.Lobe Parameters
• histogram_data – input data stream. • topvalue – the largest value of any data.
• factor – retina reduction factor to speed up processing.
Returns the size of the final histogram data and the histogram_data itself.
samplelearn(samplesize)
Extract a sample of retina data for learning.server.models.enplicaw.core.Lobe Parameters samplesize – The amount of retinas to sample.
Returns
server.models.enplicaw.core.main(data, sample=1)
Main task, runs, leaning, classification, and report results.server.models.enplicaw.core.Lobe Parameters
• data – input data stream with all samples formatted as values tuples. • sample – ammount of times that whole cicly will run.
Returns
server.models.enplicaw.core.tupler(x) See also:
Module main
Note: Unidade de Modelo Servidor.
4.3 An implementation of Google Database Store Model
Google HDB storage. See also:
Module main
Note: Unidade de Modelo Servidor.
Enplicaw Documentation, Release 0.1.0
CHAPTER
5
Enplicaw - Unit Tests
5.1 Enplicaw - Teste
Verifica a funcionalidade do cliente web.
class main_tests.EnplicawTest(methodName=’runTest’) Bases: unittest.case.TestCase
setUp()
test_main()
garante que intância de Enplicaw é criada. See also:
ModulesEnplicaw - Módulos Principais
Note: Módulo principal de testes unitários.
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CHAPTER
6
Indices and tables
• genindex • modindex • search
Enplicaw Documentation, Release 0.1.0
Python Module Index
m
main_tests(Web),13s
server.controllers.main_controller (Web),9 server.models.enplicaw.core(Web),10 server.models.ndbstore(Web),11 17Enplicaw Documentation, Release 0.1.0
Index
C
classify() (server.models.enplicaw.core.Cortex method), 10
classify() (server.models.enplicaw.core.Lobe method),10 classifynlearn() (server.models.enplicaw.core.Lobe
method),10
Cortex (class in server.models.enplicaw.core),10
E
endsurvey() (in module server.controllers.main_controller),9
EnplicawTest (class in main_tests),13
H
home() (in module server.controllers.main_controller),9
I
identify() (in module server.controllers.main_controller), 9
Item (class in server.controllers.main_controller),9
L
label (server.controllers.main_controller.Item attribute),9 label (server.controllers.main_controller.Likert attribute),
9
learn() (server.models.enplicaw.core.Cortex method),10 learn() (server.models.enplicaw.core.Lobe method),10 Likert (class in server.controllers.main_controller),9 Lobe (class in server.models.enplicaw.core),10
M
main() (in module server.models.enplicaw.core),11 main_tests (module),13
methodroute() (in module server.controllers.main_controller),9
N
name (server.controllers.main_controller.Likert attribute), 9
R
results() (server.models.enplicaw.core.Lobe method),10 retinify() (server.models.enplicaw.core.Lobe class
method),11
S
samplelearn() (server.models.enplicaw.core.Lobe method),11 server.controllers.main_controller (module),9 server.models.enplicaw.core (module),10 server.models.ndbstore (module),11setUp() (main_tests.EnplicawTest method),13
survey() (in module server.controllers.main_controller),9
T
test_main() (main_tests.EnplicawTest method),13 tupler() (in module server.models.enplicaw.core),11
V
value (server.controllers.main_controller.Item attribute), 9
value (server.controllers.main_controller.Likert attribute), 9
