dataset = grep("normal|happy|sad|sleepy|surprised|wink",
               list.files("data_atividade2/"), value=TRUE)
set.seed(202227)
data <- matrix(ncol = 20691, nrow = 90)
c <- 1
for(i in dataset){#monta matriz do poligono geral de um rosto das pessoas
  path <- paste("data_atividade2", i, sep="/")
  img <- read.pnm(file=path, cellres=1)
  vetor_rosto <- as.numeric(img@grey[40:210, 40:160])
  data[c,] <- vetor_rosto
  c <- c + 1
}
data <- as.data.frame(data)
data$resp <- as.factor(rep(c("happy", "normal", "sad", "sleepy", "surprised","wink"), 15))

id <- c(1:nrow(data))
id_train <- sample(id, 0.8*nrow(data))
train <- data %>% slice(id_train)
test <- data %>% slice(-id_train)

pc <- prcomp(train[-20692], scale=TRUE, retx=FALSE)
train.pc <- as.data.frame(predict(pc, train))
test.pc <- as.data.frame(predict(pc, test))
var.pc.prop <- (pc$sdev^2)/sum(pc$sdev^2)
cum.var <- cumsum(var.pc.prop)
ncomp <- which.max(cum.var > 0.95)

train.pc <- train.pc[1:ncomp]
train.pc$resp <- train$resp
test.pc <- test.pc[1:ncomp]
test.pc$resp <- test$resp
lda.fit <- lda(resp ~ ., data = train.pc)


teste_pred <- predict(lda.fit, test.pc)
tr_pred <- predict(lda.fit, train.pc)
confusionMatrix(teste_pred$class, test.pc$resp)
confusionMatrix(tr_pred$class, train.pc$resp)