这是我过去几天经常遇到的问题。
我拥有以下数据集，用于测试rpart()模型的准确性：
> head(resp41366_observed)
ACTIVITY_X ACTIVITY_Y observed
1: 29 19 Feeding/Moving
2: 34 30 Moving/Feeding
3: 51 47 Moving/Feeding
4: 78 38 Moving/Feeding
5: 18 17 Feeding/Moving
6: 4 0 Feeding/Moving
模型是：
xtree <- rpart(classification ~ ACTIVITY_X + ACTIVITY_Y , data = train, method = "class", parms = list(split = "information"))
为了测试模型的准确性，我使用resp41366来预测resp41366$observed中看到的类，然后将模型预测的类与resp41366$observed类进行比较。
我正在使用的预测变量数据集为pred41361，其格式与resp41366完全相同。这是代码：
#Running the model
> resp41366_pred413561 = predict(xtree,resp41366,type="class")
> resp41366_pred413561<-data.table(resp41366_pred413561)
> names(resp41366_pred413561)="predicted"
#Merging the observed classes with the predicted classes by the model
> test_accuracy<-cbind(resp41366_observed,resp41366_pred413561)
> head(test_accuracy)
observed predicted
1: Feeding/Moving Moving/Feeding
2: Moving/Feeding Moving/Feeding
3: Moving/Feeding Moving/Feeding
4: Moving/Feeding Moving/Feeding
5: Feeding/Moving Feeding/Moving
6: Feeding/Moving Feeding/Moving
#Calculating accuracy
> obs<-as.factor(test_accuracy$observed)
> pred<-as.factor(test_accuracy$predicted)
> mean(obs == pred)
[1] 0.8208556
如果predicted类与observed类非常匹配，这是一个完美的方案。但是，如果重新生成test_accuracy，则可能会发生以下情况（例如）：
#Calculating accuracy
> obs<-as.factor(test_accuracy$observed)
> pred<-as.factor(test_accuracy$predicted)
> mean(obs == pred)
[1] 0.02345
发生这种情况时，我可以通过如下改组predicted类，使它们再次与observed类兼容，将精度恢复到0.8208556：
1）将Feeding/Moving替换为Standing
2）将Moving/Feeding替换为Feeding/Moving
3）将Standing替换为Moving/Feeding
我的问题：是否有一种预定义类的方法，这样我总是得到一致的分类，而无需事后重新安排类？实施predict()时可能会丢失某些东西吗？
我希望有人可以帮助我解决这个问题，因为我需要将此模型用于observed类未知的数据集，并且每次重新生成数据时都需要具有一致的分类。
感谢任何输入！
代码：
#80:20 data split for cross-validation
trainIndex <- sample(1:nrow(tableresults), 0.8 * nrow(tableresults))
train <- tableresults[trainIndex,]
test <- tableresults[-trainIndex,]
#Model implementation
xtree <- rpart(classification ~ ACTIVITY_X + ACTIVITY_Y , data = train, method = "class", parms = list(split = "information"))
数据详细信息：
> dput(tableresults)
structure(list(ACTIVITY_X = c(40L, 60L, 62L, 60L, 66L, 60L, 57L,
54L, 52L, 93L, 80L, 14L, 61L, 51L, 40L, 20L, 21L, 5L, 53L, 48L,
73L, 73L, 21L, 29L, 63L, 59L, 57L, 51L, 53L, 67L, 72L, 74L, 70L,
60L, 74L, 85L, 77L, 68L, 58L, 80L, 34L, 45L, 34L, 60L, 75L, 62L,
66L, 51L, 53L, 48L, 62L, 62L, 57L, 5L, 1L, 12L, 23L, 5L, 4L,
0L, 13L, 45L, 44L, 31L, 68L, 88L, 43L, 70L, 18L, 83L, 71L, 67L,
75L, 74L, 49L, 90L, 44L, 64L, 57L, 22L, 29L, 52L, 37L, 32L, 120L,
45L, 22L, 54L, 30L, 9L, 27L, 14L, 3L, 29L, 12L, 61L, 60L, 29L,
15L, 7L, 6L, 0L, 2L, 0L, 4L, 1L, 7L, 0L, 0L, 0L, 0L, 0L, 1L,
23L, 49L, 46L, 8L, 31L, 45L, 60L, 37L, 61L, 52L, 51L, 38L, 86L,
60L, 41L, 43L, 40L, 42L, 42L, 48L, 64L, 71L, 59L, 0L, 27L, 12L,
3L, 0L, 0L, 8L, 21L, 6L, 2L, 7L, 4L, 3L, 3L, 46L, 46L, 59L, 53L,
37L, 44L, 39L, 49L, 37L, 47L, 17L, 36L, 32L, 33L, 26L, 12L, 8L,
31L, 35L, 27L, 27L, 24L, 17L, 35L, 39L, 28L, 54L, 5L, 0L, 0L,
0L, 0L, 17L, 22L, 25L, 12L, 0L, 5L, 41L, 51L, 66L, 39L, 32L,
53L, 43L, 40L, 44L, 45L, 48L, 51L, 41L, 45L, 39L, 46L, 59L, 31L,
5L, 24L, 18L, 5L, 15L, 13L, 0L, 26L, 0L), ACTIVITY_Y = c(47L,
74L, 63L, 56L, 61L, 53L, 40L, 41L, 49L, 32L, 54L, 13L, 99L, 130L,
38L, 14L, 6L, 5L, 94L, 96L, 38L, 43L, 29L, 47L, 66L, 47L, 38L,
31L, 36L, 35L, 38L, 72L, 54L, 44L, 45L, 51L, 80L, 48L, 39L, 85L,
42L, 39L, 37L, 75L, 36L, 45L, 32L, 35L, 41L, 26L, 99L, 163L,
124L, 0L, 0L, 24L, 37L, 0L, 6L, 0L, 29L, 29L, 26L, 27L, 54L,
147L, 82L, 98L, 12L, 83L, 97L, 104L, 128L, 81L, 42L, 102L, 60L,
79L, 58L, 15L, 14L, 75L, 75L, 40L, 130L, 40L, 13L, 54L, 42L,
7L, 10L, 3L, 0L, 15L, 8L, 75L, 55L, 26L, 18L, 1L, 13L, 0L, 0L,
0L, 1L, 0L, 4L, 0L, 0L, 0L, 0L, 0L, 0L, 17L, 45L, 38L, 10L, 31L,
52L, 36L, 65L, 97L, 45L, 59L, 49L, 92L, 51L, 34L, 21L, 20L, 29L,
28L, 22L, 32L, 30L, 86L, 0L, 15L, 7L, 4L, 0L, 0L, 0L, 11L, 3L,
0L, 1L, 3L, 1L, 0L, 72L, 62L, 98L, 55L, 26L, 39L, 28L, 81L, 20L,
52L, 12L, 48L, 24L, 40L, 30L, 5L, 6L, 40L, 37L, 33L, 26L, 17L,
14L, 39L, 27L, 28L, 67L, 0L, 0L, 0L, 0L, 0L, 10L, 12L, 14L, 7L,
0L, 2L, 39L, 67L, 74L, 28L, 23L, 57L, 34L, 36L, 36L, 37L, 46L,
43L, 73L, 65L, 31L, 64L, 128L, 17L, 3L, 12L, 17L, 0L, 9L, 7L,
0L, 17L, 0L), classification = c("Moving/Feeding", "Standing",
"Standing", "Standing", "Standing", "Standing", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Standing", "Standing", "Feeding/Moving",
"Standing", "Standing", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Standing", "Standing", "Standing", "Standing",
"Moving/Feeding", "Moving/Feeding", "Standing", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Standing", "Standing", "Standing", "Moving/Feeding", "Standing",
"Standing", "Standing", "Standing", "Moving/Feeding", "Standing",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Standing",
"Standing", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Standing", "Standing", "Standing",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Moving/Feeding",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Standing",
"Standing", "Standing", "Standing", "Feeding/Moving", "Standing",
"Standing", "Standing", "Standing", "Standing", "Moving/Feeding",
"Standing", "Moving/Feeding", "Standing", "Standing", "Feeding/Moving",
"Feeding/Moving", "Standing", "Moving/Feeding", "Moving/Feeding",
"Standing", "Moving/Feeding", "Feeding/Moving", "Moving/Feeding",
"Moving/Feeding", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Standing",
"Standing", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Moving/Feeding", "Moving/Feeding",
"Feeding/Moving", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Standing", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Standing", "Standing", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Standing", "Standing", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Standing", "Moving/Feeding", "Standing", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Standing", "Moving/Feeding",
"Moving/Feeding", "Feeding/Moving", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Feeding/Moving", "Feeding/Moving", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Standing", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Moving/Feeding", "Standing", "Standing", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Standing", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving")), class = "data.frame", row.names = c(NA,
-215L))
这里是resp41366_observed。上面使用的resp41366数据集就是resp41366_observed而没有resp41366$observed：
> dput(resp41366_observed)
structure(list(ACTIVITY_X = c(29L, 34L, 51L, 78L, 18L, 4L, 27L,
19L, 23L, 21L, 19L, 52L, 71L, 141L, 103L, 59L, 85L, 129L, 86L,
129L, 82L, 67L, 79L, 49L, 51L, 32L, 27L, 48L, 10L, 2L, 18L, 29L,
39L, 70L, 64L, 88L, 90L, 94L, 69L, 91L, 80L, 81L, 92L, 109L,
96L, 84L, 67L, 89L, 85L, 67L, 79L, 68L, 88L, 72L, 67L, 65L, 71L,
79L, 63L, 64L, 36L, 56L, 61L, 80L, 49L, 45L, 65L, 59L, 58L, 62L,
49L, 58L, 68L, 52L, 78L, 51L, 73L, 75L, 80L, 75L, 89L, 63L, 33L,
41L, 44L, 34L, 43L, 9L, 11L, 21L, 30L, 34L, 41L, 30L, 24L, 31L,
65L, 52L, 21L, 35L, 39L, 35L, 27L, 32L, 38L, 38L, 56L, 65L, 81L,
28L, 38L, 10L, 32L, 45L, 26L, 8L, 0L, 19L, 56L, 7L, 0L, 23L,
13L, 1L, 2L, 29L, 15L, 15L, 1L, 33L, 3L, 45L, 143L, 46L, 78L,
76L, 81L, 44L, 89L, 15L, 45L, 52L, 49L, 64L, 55L, 52L, 72L, 68L,
95L, 66L, 74L, 103L, 49L, 26L, 7L, 29L, 25L, 31L, 7L, 13L, 12L,
3L, 22L, 40L, 40L, 47L, 9L, 29L, 9L, 11L, 14L, 4L, 58L, 53L,
60L, 30L, 40L, 42L, 41L, 48L, 40L, 31L, 51L, 29L, 33L, 76L, 38L,
35L, 27L, 46L, 60L, 54L, 47L, 55L, 35L, 51L, 64L, 63L, 32L, 43L,
52L, 47L, 41L, 64L, 54L, 56L, 66L, 64L, 33L, 26L, 28L, 33L, 45L,
53L, 46L, 37L, 39L, 52L, 31L, 1L, 0L, 56L, 19L, 17L, 21L, 33L,
68L, 61L, 78L, 31L, 0L, 11L, 63L, 62L, 43L, 42L, 154L, 4L, 187L,
43L, 56L, 49L, 62L, 46L, 71L, 56L, 46L, 66L, 14L, 0L, 0L, 7L,
0L, 0L, 0L, 17L, 39L, 23L, 0L, 0L, 0L, 4L, 3L, 9L, 8L, 14L, 7L,
17L, 2L, 6L, 0L, 67L, 49L, 0L, 7L, 2L, 0L, 3L, 0L, 0L, 11L, 0L,
2L, 4L, 10L, 4L, 3L, 55L, 41L, 34L, 43L, 21L, 0L, 6L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 4L, 81L, 63L, 44L, 30L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 27L, 41L, 38L, 26L, 30L, 44L, 57L, 69L, 31L, 54L,
32L, 60L, 37L, 15L, 55L, 49L, 52L, 59L, 49L, 9L, 8L, 12L, 11L,
14L, 20L, 54L, 43L, 64L, 53L, 61L, 108L, 39L, 63L, 54L, 56L,
60L, 46L, 64L, 15L, 3L, 8L, 43L, 90L, 43L, 64L, 38L, 13L, 12L,
46L, 24L, 1L, 30L, 9L, 58L, 105L, 2L, 4L, 2L, 4L, 8L), ACTIVITY_Y = c(19L,
30L, 47L, 38L, 17L, 0L, 20L, 11L, 11L, 8L, 13L, 46L, 105L, 133L,
131L, 64L, 76L, 94L, 58L, 69L, 47L, 57L, 155L, 49L, 72L, 43L,
38L, 53L, 4L, 1L, 12L, 22L, 43L, 91L, 72L, 80L, 74L, 89L, 93L,
115L, 79L, 89L, 85L, 103L, 87L, 84L, 71L, 98L, 83L, 81L, 83L,
74L, 85L, 83L, 58L, 86L, 63L, 55L, 64L, 54L, 34L, 46L, 88L, 71L,
69L, 51L, 58L, 53L, 53L, 67L, 57L, 70L, 71L, 77L, 59L, 74L, 58L,
61L, 93L, 77L, 72L, 73L, 13L, 14L, 24L, 25L, 29L, 4L, 5L, 3L,
13L, 14L, 16L, 10L, 7L, 13L, 54L, 38L, 17L, 19L, 20L, 15L, 10L,
8L, 19L, 15L, 26L, 75L, 62L, 31L, 34L, 9L, 31L, 59L, 27L, 0L,
0L, 3L, 59L, 5L, 0L, 14L, 6L, 0L, 0L, 28L, 7L, 14L, 0L, 24L,
6L, 34L, 168L, 68L, 115L, 103L, 67L, 35L, 122L, 39L, 42L, 42L,
43L, 47L, 44L, 39L, 77L, 43L, 112L, 68L, 59L, 188L, 127L, 25L,
3L, 15L, 15L, 25L, 0L, 6L, 3L, 0L, 30L, 30L, 21L, 38L, 6L, 20L,
17L, 7L, 8L, 5L, 61L, 87L, 44L, 57L, 43L, 73L, 87L, 46L, 97L,
42L, 60L, 29L, 34L, 75L, 43L, 83L, 42L, 86L, 105L, 78L, 72L,
103L, 51L, 88L, 74L, 66L, 42L, 36L, 45L, 77L, 60L, 69L, 49L,
61L, 53L, 44L, 71L, 23L, 28L, 21L, 24L, 61L, 39L, 28L, 29L, 53L,
39L, 0L, 2L, 29L, 25L, 23L, 16L, 43L, 70L, 107L, 149L, 62L, 1L,
5L, 63L, 61L, 32L, 159L, 209L, 1L, 255L, 68L, 68L, 90L, 94L,
64L, 92L, 97L, 75L, 77L, 15L, 0L, 0L, 12L, 0L, 0L, 0L, 33L, 56L,
37L, 0L, 0L, 0L, 1L, 0L, 3L, 6L, 4L, 0L, 11L, 0L, 1L, 0L, 41L,
61L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 5L,
54L, 51L, 26L, 30L, 31L, 0L, 0L, 0L, 5L, 0L, 0L, 0L, 0L, 0L,
16L, 88L, 55L, 42L, 24L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 15L,
30L, 29L, 21L, 26L, 43L, 56L, 40L, 22L, 6L, 19L, 16L, 15L, 13L,
46L, 59L, 88L, 41L, 35L, 9L, 1L, 2L, 5L, 6L, 4L, 40L, 24L, 55L,
41L, 56L, 210L, 26L, 127L, 67L, 65L, 73L, 41L, 47L, 8L, 1L, 10L,
23L, 76L, 36L, 79L, 15L, 0L, 2L, 46L, 39L, 4L, 23L, 2L, 48L,
141L, 1L, 0L, 1L, 1L, 4L), observed = c("Feeding/Moving", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Moving/Feeding", "Standing", "Standing", "Standing",
"Moving/Feeding", "Standing", "Standing", "Standing", "Standing",
"Standing", "Moving/Feeding", "Standing", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Moving/Feeding",
"Standing", "Standing", "Standing", "Standing", "Standing", "Standing",
"Standing", "Standing", "Standing", "Standing", "Standing", "Standing",
"Standing", "Standing", "Standing", "Standing", "Standing", "Standing",
"Standing", "Standing", "Standing", "Moving/Feeding", "Standing",
"Standing", "Standing", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Standing", "Standing", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Standing", "Moving/Feeding",
"Standing", "Moving/Feeding", "Standing", "Standing", "Standing",
"Standing", "Standing", "Standing", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Feeding/Moving", "Moving/Feeding", "Standing",
"Standing", "Moving/Feeding", "Moving/Feeding", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Moving/Feeding", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Moving/Feeding", "Feeding/Moving", "Moving/Feeding",
"Standing", "Moving/Feeding", "Standing", "Standing", "Standing",
"Moving/Feeding", "Standing", "Feeding/Moving", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Standing", "Moving/Feeding", "Standing", "Standing",
"Standing", "Standing", "Standing", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Moving/Feeding", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Moving/Feeding", "Standing", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Standing", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Standing", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Standing",
"Moving/Feeding", "Moving/Feeding", "Standing", "Moving/Feeding",
"Standing", "Standing", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Feeding/Moving", "Moving/Feeding", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Feeding/Moving",
"Feeding/Moving", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Moving/Feeding", "Standing", "Standing", "Standing",
"Moving/Feeding", "Feeding/Moving", "Feeding/Moving", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Standing", "Standing", "Feeding/Moving",
"Standing", "Moving/Feeding", "Moving/Feeding", "Standing", "Standing",
"Moving/Feeding", "Standing", "Standing", "Moving/Feeding", "Standing",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Moving/Feeding",
"Moving/Feeding", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Moving/Feeding", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Standing", "Moving/Feeding",
"Moving/Feeding", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Feeding/Moving",
"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Moving/Feeding",
"Moving/Feeding", "Feeding/Moving", "Moving/Feeding", "Moving/Feeding",
"Moving/Feeding", "Moving/Feeding", "Feeding/Moving", "Moving/Feeding",
"Feeding/Moving", "Moving/Feeding", "Feeding/Moving", "Feeding/Moving",
"Moving/Feeding", "Moving/Feeding", "Standing", "Moving/Feeding",
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"Feeding/Moving", "Feeding/Moving", "Feeding/Moving", "Moving/Feeding",
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