CODEWITHSUNDEEP
rneural-networkdata-miningnnet
Francesco
Francesco

Reputation: 69

How use Rminer and nnet

I'm new programmer in R and i'm writing my thesis for training a neural network. First i use rminer for datamining and after nnet for training. Now i don't know which function use for divide data-set in training set and validation set, therefore k-fold cross validation, and after use nnet for each of this. sorry for my english. Thanks in advance

Upvotes: 0

Views: 1134

Answers (2)

nick
nick

Reputation: 1160

It's maybe too late, but I found this Q while I was looking for an answer to my Q. You can use something like this

    # Splitting in training, Cross-Validation and test datasets
        #The entire dataset has 100% of the observations. The training dataset will have 60%, the Cross-Validation (CV) will have 20% and the testing dataset will have 20%.                                                                                                                                
        train_ind <- sample(seq_len(nrow(DF.mergedPredModels)), size = floor(0.6 * nrow(DF.mergedPredModels)))
        trainDF.mergedPredModels <- DF.mergedPredModels[train_ind, ]

        # The CV and testing datasets' observations will be built from the observations from the initial dataset excepting the ones from the training dataset
        # Cross-Validation dataset
        # The CV's number of observations can be changed simply by changing "0.5" to a fraction of your choice but the CV and testing dataset's fractions must add up to 1.
        cvDF.mergedPredModels <- DF.mergedPredModels[-train_ind, ][sample(seq_len(nrow(DF.mergedPredModels[-train_ind, ])), size = floor(0.5 * nrow(DF.mergedPredModels[-train_ind, ]))),]

        # Testing dataset
        testDF.mergedPredModels <- DF.mergedPredModels[-train_ind, ][-sample(seq_len(nrow(DF.mergedPredModels[-train_ind, ])), size = floor(0.5 * nrow(DF.mergedPredModels[-train_ind, ]))),]

        #temporal data and other will be added after the predictions are made because I don't need the models to be built on the dates. Additionally, you can add these columns to the training, CV and testing datasets and plot the real values of your predicted parameter and the respective predicitons over your time variables (half-hour, hour, day, week, month, quarter, season, year, etc.).
        # aa = Explicitly specify the columns to be used in the temporal datasets
        aa <- c("date", "period", "publish_date", "quarter", "month", "Season")
        temporaltrainDF.mergedPredModels <- trainDF.mergedPredModels[, c(aa)]
        temporalcvDF.mergedPredModels <- cvDF.mergedPredModels[, c(aa)]
        temporaltestDF.mergedPredModels <- testDF.mergedPredModels[, c(aa)]

        # bb = Explicitly specify the columns to be used in the training, CV and testing datasets
        bb <- c("quarter", "month", "Season", "period", "temp.mean", "wind_speed.mean", "solar_radiation", "realValue")
        trainDF.mergedPredModels.Orig <- trainDF.mergedPredModels[, c(bb)]
        trainDF.mergedPredModels <- trainDF.mergedPredModels[, c(bb)]
        smalltrainDF.mergedPredModels.Orig <- trainDF.mergedPredModels.Orig[1:10,] #see if the models converge without errors
        cvDF.mergedPredModels <- cvDF.mergedPredModels[, c(bb)]
        testDF.mergedPredModels <- testDF.mergedPredModels[, c(bb)]
# /Splitting in training, Cross-Validation and test datasets

Upvotes: 0

asb
asb

Reputation: 4432

Here is a way to get help on a new topic / package in R when you don't know how to go about it:

library(help=package.name)

This will give you an overview of all the functions and data sets defined in the language with a brief title of each. After you have identified the functions that you need, you can consult the documentation of the functions of interest like so:

?function.name

In the documentation, also pay attention to the See Also section which typically lists functions that are useful in conjunction with the function being considered. Also, work the examples. You can also use

example(function.name)

for a demonstration of the function's use and common idioms using it.

Lastly, if you are lucky, the package author may have written a vignette for the package. You can search for all vignettes in a package like this:

vignette(package="package.name")

Hopefully, this will get you started with the rminer and nnet packages.

Upvotes: 1

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