1 files changed, 25 insertions(+), 0 deletions(-)

M 2predict.r

@@ 41,3 41,28 @@ train_and_test(df, 'pca_24')
 df2 <- read.xlsx("./data/主成分数据(1).xlsx", cols = seq(3, 25), sheet = "主成分数据表1")
 
 train_and_test(df2, 'pca_k')
+
+# draw plot with net
+# c("S-ZORB.PC_3301.DACA",
+#   "S-ZORB.FC_1102.PV",
+#   "S-ZORB.TC_1607.DACA",
+#   "S-ZORB.AT-0004.DACA.PV",
+#   "S-ZORB.FT_3702.DACA",
+#   "饱和烃",
+#   "S-ZORB.AT-0003.DACA.PV",
+#   "S-ZORB.TXE_2203A.DACA",
+#   "S-ZORB.FT_2502.DACA",
+#   "S-ZORB.AT_6201.DACA",
+#   "S-ZORB.PC_3101.DACA",
+#   "S-ZORB.FT_1001.PV",
+#   "S-ZORB.AT-0001.DACA.PV",
+#   "S-ZORB.AT-0009.DACA.PV",
+#   "S-ZORB.TXE_2202A.DACA",
+#   "S-ZORB.PDT_3601.DACA",
+#   "S-ZORB.FC_2801.PV") -> feature
+# df[, feature] -> df_
+#
+# colnames(df_) <- paste("D", seq(1,17), sep='')
+# df_[, "RONL"] <- processed["RON损失（不是变量）"]
+# net_ <- neuralnet(RONL ~ ., df_, hidden = 10, rep = 100, linear.output = T)
+# plot(net_)