包含列表的归一化矩阵

Question

当前，我具有以下数据帧（前30列来自dput()：]

structure(list(PacketTime = c(0.0636830000000002, 0.0691829999999989, 
0.0639040000000008, 0.0636270000000003, 0.0656370000000024, 0.064778000000004, 
0.0616950000000003, 0.0666280000000015, 0.0630829999999989, 0.0665130000000005, 
0.0621160000000032, 0.0654010000000014, 0.0652889999999928, 0.0640989999999988, 
0.0621339999999861, 0.0645319999999998, 0.065757000000005, 0.0624459999999942, 
0.061782000000008, 0.0626439999999917, 0.0648419999999987, 0.0664910000000134, 
0.0644649999999984, 0.0654030000000034, 0.0657139999999998, 0.0642799999999966, 
0.069137000000012, 0.0631520000000023, 0.0634139999999945, 0.0615009999999927
), FrameLen = list(c(304L, 276L, 276L), c(304L, 276L, 276L), 
    c(304L, 276L, 276L), c(304L, 276L, 276L), c(304L, 276L, 276L
    ), c(304L, 276L, 276L), c(304L, 276L, 276L), c(304L, 276L, 
    276L, 276L, 276L), c(304L, 276L, 276L), c(304L, 276L, 276L, 
    276L, 276L), c(304L, 276L, 276L), c(304L, 276L, 276L), c(304L, 
    276L, 276L), c(304L, 276L, 276L), c(304L, 276L, 276L), c(304L, 
    276L, 276L), c(304L, 276L, 276L, 276L, 276L), c(304L, 276L, 
    276L), c(304L, 276L, 276L), c(304L, 276L, 276L), c(304L, 
    276L, 276L, 276L, 276L), c(304L, 276L, 276L), c(304L, 276L, 
    276L), c(304L, 276L, 276L, 276L), c(304L, 276L, 276L, 276L, 
    276L), c(304L, 276L, 276L), c(304L, 276L, 276L), c(304L, 
    276L, 276L), c(304L, 276L, 276L), c(304L, 276L, 276L)), IPLen = list(
    c(300L, 272L, 272L), c(300L, 272L, 272L), c(300L, 272L, 272L
    ), c(300L, 272L, 272L), c(300L, 272L, 272L), c(300L, 272L, 
    272L), c(300L, 272L, 272L), c(300L, 272L, 272L, 272L, 272L
    ), c(300L, 272L, 272L), c(300L, 272L, 272L, 272L, 272L), 
    c(300L, 272L, 272L), c(300L, 272L, 272L), c(300L, 272L, 272L
    ), c(300L, 272L, 272L), c(300L, 272L, 272L), c(300L, 272L, 
    272L), c(300L, 272L, 272L, 272L, 272L), c(300L, 272L, 272L
    ), c(300L, 272L, 272L), c(300L, 272L, 272L), c(300L, 272L, 
    272L, 272L, 272L), c(300L, 272L, 272L), c(300L, 272L, 272L
    ), c(300L, 272L, 272L, 272L), c(300L, 272L, 272L, 272L, 272L
    ), c(300L, 272L, 272L), c(300L, 272L, 272L), c(300L, 272L, 
    272L), c(300L, 272L, 272L), c(300L, 272L, 272L)), Movement = c(0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0)), row.names = c(NA, -30L), class = c("tbl_df", 
"tbl", "data.frame"))

从这里，我可以使用keras包使用以下命令将数据帧（在变量packets中）放入矩阵中：

packets.m <- as.matrix(packets)

但是，当我尝试将其传递给模型（不进行归一化）或在传递前进行归一化时，收到以下错误：

py_call_impl（可调用，dots $ args，dots $ keywords）错误：矩阵类型不能转换为python（只能转换整数，数字，复数，逻辑和字符矩阵

因此，如何有效地规范包含列表的两列FrameLen和IPLen，以便可以使用keras包将其准确地用于深度学习模型？

EDIT：完整的dput()可以在此处找到，用于数据包数据帧https://pastebin.com/cXKdSB2y

Answer 1

取决于您如何训练这些数据

library(tidyverse)

多个实例

df %>% 
  unnest()

多项功能

df %>% 
  mutate(position = map(FrameLen,seq_along),id = row_number) %>%
  unnest() %>% 
  pivot_wider(names_from = position,values_from = c(FrameLen,IPLen))