4
Код:Ошибка при использовании нейронных сетей (CARET пакет)
library(nnet)
library(caret)
#K-folds resampling method for fitting model
ctrl <- trainControl(method = "repeatedcv", number = 10, repeats = 10,
allowParallel = TRUE) #10 separate 10-fold cross-validations
nnetGrid <- expand.grid(decay = seq(0.0002, .0008, length = 4),
size = seq(6, 10, by = 2),
bag = FALSE)
set.seed(100)
nnetFitcv <- train(R ~ .,
data = trainSet,
method = "avNNet",
tuneGrid = nnetGrid,
trControl = ctrl,
preProc = c("center", "scale"),
linout = TRUE,
## Reduce the amount of printed output
trace = FALSE,
## Expand the number of iterations to find
## parameter estimates..
maxit = 2000,
## and the number of parameters used by the model
MaxNWts = 5 * (34 + 1) + 5 + 1)
Ошибка:
Error in train.default(x, y, weights = w, ...) :
final tuning parameters could not be determined
In addition: Warning messages:
1: In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo, :
There were missing values in resampled performance measures.
2: In train.default(x, y, weights = w, ...) :
missing values found in aggregated results
данные:
dput(head(trainSet))
structure(list(fy = c(317.913756282, 365.006253069, 392.548100067,
305.350697829, 404.999341917, 326.558279739), fu = c(538.962896683,
484.423120589, 607.974981919, 566.461909098, 580.287855801, 454.178316794
), E = c(194617.707566, 181322.455065, 206661.286272, 182492.029532,
189867.929239, 181991.379749), eu = c(0.153782620813, 0.208857408687,
0.29933255604, 0.277013319499, 0.251278125174, 0.20012525805),
imp_local = c(1555.3450957, 1595.41614044, 763.56392418,
1716.78277731, 1045.72429616, 802.742305814), imp_global = c(594.038972858,
1359.48216529, 1018.89209367, 850.887850177, 1381.3557372,
1714.66351462), teta1c = c(0.033375064111, 0.021482368218,
0.020905367537, 0.006956337817, 0.034913536977, 0.03009770223
), k1c = c(4000921.55552, 4499908.41979, 9764999.26902, 9273400.46159,
6163057.88855, 12338543.5703), k2_2L = c(98633499.5682, 53562216.5496,
51597126.6866, 79496746.0098, 54060378.6334, 88854286.5457
), k2_3L = c(53752551.0262, 125020222.794, 124021434.482,
125817803.431, 75021821.6702, 35160224.288), k2_4L = c(56725106.5978,
126865701.893, 145764489.664, 64837586.8755, 49128911.0832,
70088564.0166), bmaxc = c(3481281.32908, 4393584.00639, 2614830.02391,
3128593.72039, 3179348.29527, 4274637.35956), dfactorc = c(2.5474729895,
2.94296926288, 2.79505551368, 2.47882735165, 2.46407943564,
1.41121223341), amaxc = c(73832.9746763, 99150.5068997, 77165.4338508,
128546.996471, 53819.0447533, 54870.9707106), teta1s = c(0.015467320192,
0.013675755546, 0.031668366149, 0.028898297322, 0.019211801086,
0.013349768955), k1s = c(5049506.54552, 11250622.6842, 13852560.5089,
18813117.5726, 18362782.7372, 14720875.0829), k2_ab1s = c(276542468.441,
275768806.723, 211613299.608, 264475187.749, 162043062.526,
252936228.465), k2_ab2s = c(108971516.033, 114017918.32,
248886114.151, 213529935.615, 236891513.077, 142986118.909
), k2_ab3s = c(33306211.9166, 28220338.4744, 40462423.2281,
23450400.4429, 46044346.1128, 23695405.2598), bmaxab1 = c(4763935.86742,
4297372.01966, 3752983.00638, 4861240.46459, 4269771.8481,
4162098.23435), bmaxab2 = c(1864128.647, 1789714.6047, 2838412.50704,
2122535.96812, 2512362.60884, 1176995.61871), ab1 = c(66.4926766666,
42.7771212442, 45.4212664748, 50.3764074404, 35.4792060556,
34.1116517971), ab2 = c(21.0285105309, 23.5869838719, 18.8524808986,
10.1121885612, 10.9695055644, 12.1154127169), dfactors = c(2.47803921947,
0.874644748155, 0.749837099991, 1.96711589185, 2.5407774352,
1.28554379333), teta1f = c(0.037308451805, 0.035718600749,
0.012495093438, 0.000815957999, 0.002155991091, 0.02579104469
), k1f = c(14790480.9871, 17223538.1853, 19930679.8931, 3524230.46974,
15721827.0137, 13599317.0371), k2f = c(55614283.976, 54695745.7762,
86690362.7036, 99857853.7312, 63119072.711, 37510791.5472
), bmaxf = c(2094770.19484, 3633133.51482, 1361188.05421,
2001027.51219, 2534273.6726, 3765850.14143), dfactorf = c(0.745459795314,
2.04869176933, 0.853221909609, 1.76652410119, 0.523675021418,
1.0808768613), k2b = c(1956.92858062, 1400.78738327, 1771.23607857,
1104.05501369, 1756.6767193, 1509.9294956), amaxb = c(38588.0915097,
35158.1672213, 25711.062782, 21103.1603387, 27230.6973685,
43720.3558889999), dfactorb = c(0.822346959126, 2.34421354848,
0.79990635332, 2.99070447299, 1.76373031599, 1.38640223249
), roti = c(16.1560390049, 12.7223971386, 6.43238062144,
15.882552267, 16.0836252663, 18.2734832893), rotmaxbp = c(0.235615453341,
0.343204895932, 0.370304533553, 0.488746319999, 0.176135112774,
0.46921999001), R = c(0.022186087, 0.023768855, 0.023911029,
0.023935705, 0.023655335, 0.022402726)), .Names = c("fy",
"fu", "E", "eu", "imp_local", "imp_global", "teta1c", "k1c",
"k2_2L", "k2_3L", "k2_4L", "bmaxc", "dfactorc", "amaxc", "teta1s",
"k1s", "k2_ab1s", "k2_ab2s", "k2_ab3s", "bmaxab1", "bmaxab2",
"ab1", "ab2", "dfactors", "teta1f", "k1f", "k2f", "bmaxf", "dfactorf",
"k2b", "amaxb", "dfactorb", "roti", "rotmaxbp", "R"), row.names = c(7L,
8L, 20L, 23L, 28L, 29L), class = "data.frame")
данных нет равных строк или нулевых значений или NaN. Любая помощь приветствуется.
Как и с другим постом, нам нужны более 6 точек данных, чтобы найти проблему. – topepo
Кроме того, если вы используете $ M $ core с этим, то и «train» и «avNNet» могут работать параллельно. Для некоторых (но не всех) технологий вы можете использовать $ M^2 $ ядра. – topepo
Все данные (включая сборники и тестовые наборы): https://www.wuala.com/jpcgandre/Documents/Data%20SVR/?key=BOD9NTINzRHG Затем я использовал: 'options (цифры = 16) dataA2 <-read. csv ("C: /results/A2.txt", header = TRUE, blank.lines.skip = TRUE, sep = ",") set.seed (1) inTrainSet <- createDataPartition (dataA2 $ R, p = 0.75, list = FALSE) # [[1]] trainSet <- dataA2 [inTrainSet,] testSet <- dataA2 [-inTrainSet,] , чтобы получить поезда и тестовые наборы – jpcgandre