library(magrittr) devtools::load_all() for (i in 1:50) { Sys.sleep(3) try({ x <- baixar() d <- ler(x, salvar = TRUE) }) } baixar() %>% decifrar()
for(i in 1:1000) { download() %>% classificar(path = 'data-raw/letras') }
devtools::load_all(".") library(caret) library(dplyr) d <- 'data-raw/letras' %>% dir(full.names = TRUE) %>% arrumar() set.seed(1234567) aff <- d %>% sample_n(4000) d_train <- aff %>% mutate(y = factor(letra)) %>% select(-arq, -group, -letra) m1 <- train(x = dplyr::select(as.data.frame(d_train), -y), y = d_train$y, method = 'rf') saveRDS(m1, 'data-raw/m_rf2.rds') # m2 <- train(x = dplyr::select(as.data.frame(d_train), -y), y = d_train$y, method = 'svmRadial') # saveRDS(m2, 'data-raw/m_svm.rds') # m3 <- train(x = dplyr::select(as.data.frame(d_train), -y), y = d_train$y, method = 'gbm') # saveRDS(m3, 'data-raw/m_gbm.rds') m1 <- readRDS('data-raw/m_rf.rds') # m2 <- readRDS('data-raw/m_svm.rds') # m3 <- readRDS('data-raw/m_gbm.rds') # d_final <- data.frame(pred1 = predict(m1), pred2 = predict(m2)) # m <- train(x = d_final, y = d_train$y, method = 'gbm', tuneLength = 1) # acerto <- d %>% # dplyr::anti_join(aff, c('arq', 'group')) %>% # dplyr::mutate(y = factor(letra)) %>% # dplyr::select(-arq, -group, -letra) %>% { # dd <- . # ddy <- as.data.frame(dplyr::select(dd, -y)) # pred1 <- predict(m1, newdata = ddy) # pred2 <- predict(m2, newdata = ddy) # d_pred <- data.frame(pred1, pred2) # dplyr::mutate(dd, yest = predict(m, newdata = d_pred)) # } %>% # with(table(y, yest)) acerto1 <- d %>% dplyr::anti_join(aff, c('arq', 'group')) %>% dplyr::mutate(y = factor(letra)) %>% dplyr::select(-arq, -group, -letra) %>% { dd <- . ddy <- as.data.frame(dplyr::select(dd, -y)) dplyr::mutate(dd, yest = predict(m1, newdata = ddy)) } %>% with(table(y, yest)) # sum(diag(acerto)) / sum(acerto) sum(diag(acerto1)) / sum(acerto1)
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