O concurso que elegeu a Miss Distrito Federal 2006 aconteceu no dia 18 de março de 2006 no Teatro Yara Amaral no SESI de Taguatinga, no Distrito Federal. A vencedora foi Ana Cláudia Pimenta, representante de Taguatinga.
Índice
1Resultados
1.1Top 5
2Candidatas
2.1Informações sobre as candidatas
3Fontes
Resultados |
Colocação
Candidata
Região Administrativa
Miss Distrito Federal 2006
Ana Cláudia Pimenta
Taguatinga
2º Lugar
Thayssa Kautchensky
Sudoeste/Octogonal
3º Lugar
Poliana Oliveira
Paranoá
Top 5 |
Candidata
Região Administrativa
Ana Carolina Viana
Guará
Renata Castro
Águas Claras
Candidatas |
Águas Claras - Renata Castro
Brasília - Paola Comin
Brazlândia - Thays Borba
Ceilândia - Débora Rayane
Cruzeiro - Laura Micaela Leite
Guará - Ana Carolina Viana
Lago Norte - Lenisa Braga
Lago Sul - Raquel Cunha
Núcleo Bandeirante - Alline Feitosa
Paranoá - Poliana Oliveira
Riacho Fundo - Ana Paula França
Samambaia - Carla Drumond
Santa Maria - Magda Martins
Sobradinho - Zayne Cristiane de Melo
Sudoeste/Octogonal - Thayssa Natasha Kautchensky
Taguatinga - Ana Cláudia Pimenta
Informações sobre as candidatas |
Ana Cláudia Pimenta disputou o Miss Brasil 2006 e ficou entre as 10 semifinalistas.
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I found a lot of questions abount appendices and ToC. Many users want appendices to be grouped in an Appendix part, however some problems arise with ToC, hyperref, PDF viewer bookmarks, and so on. There are different solutions which require extra packages, command patching and other extra code, however none of them satisfies me. I almost found an easy way to accomplish a good result, where appendices are added to bookmarks in the right way and hyperref links point to the right page. However, the number of the "Appendix" part page is wrong (it's the number of appendix A). Is there any EASY way to fix that? This is a MWE: documentclass{book} usepackage[nottoc,notlot,notlof]{tocbibind} usepackage{hyperref} begin{document} frontmatter tableofcontents mainmatter part{First} chapter{...
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In the sklearn.model_selection.cross_val_predict page it is stated: Generate cross-validated estimates for each input data point. It is not appropriate to pass these predictions into an evaluation metric. Can someone explain what does it mean? If this gives estimate of Y (y prediction) for every Y (true Y), why can't I calculate metrics such as RMSE or coefficient of determination using these results?
python scikit-learn cross-validation
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edited Nov 28 '18 at 17:52
desertnaut
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I have problem with inserting data to MySQL from modal. My modal: <a href="#" class="badge badge-pill badge-success">6 komentarzy</a> <a href="#" class="badge badge-pill badge-danger">brak komentarzy</a> <a data-toggle="modal" href="#add_desk_comm_{$desk_['desk_id']}" data-target="#add_desk_comm_{$desk_['desk_id']}" class="ediiit">(dodaj)</a> <div class="modal fade" id="add_desk_comm_{$desk_['desk_id']}" tabindex="-1" role="dialog" aria-labelledby="edit_printer" aria-hidden="true"> <div class="modal-dialog" role="document"> ...