LilRhino (version 1.2.2)

Feed_Reduction: A Function for converting data into approximations of probability space.

Description

It takes the number of unique labels in the training data and tries to predict a one vs all binary neural network for each unique label. The output is an approximation of the probability that each individual input does not not match the label. Travis Barton (2018) http://wbbpredictions.com/wp-content/uploads/2018/12/Redditbot_Paper.pdf

Usage

Feed_Reduction(X, Y, X_test, val_split = .1,
               nodes = NULL, epochs = 15,
               batch_size = 30, verbose = 0)

Arguments

X

Training data

Y

Training labels

X_test

Testing data

val_split

The validation split for the keras, binary, neural networks

nodes

The number nodes for the hidden layers, default is 1/4 of the length of the training data.

epochs

The number of epochs for the fitting of the networks

batch_size

The batch size for the networks

verbose

Weither or not you want details about the run as its happening. 0 = silent, 1 = progress bar, 2 = one line per epoch.

Value

Train

The training data in the new probability space

Test

The testing data in the new probability space

Details

This is a new technique for dimensionality reduction of my own creation. Data is converted to the same number of dimensions as there are unique labels. Each dimension is an approximation of the probability that the data point is inside the a unique label. The return value is a list the training and test data with their dimensionality reduced.

References

Check out http://wbbpredictions.com/wp-content/uploads/2018/12/Redditbot_Paper.pdf for details on the proccess

See Also

Binary_Network

Examples

# NOT RUN {
if(8 * .Machine$sizeof.pointer == 64){
#Feed Network Testing
library(keras)

  install_keras()
  dat <- keras::dataset_mnist()
  X_train = array_reshape(dat$train$x/255, c(nrow(dat$train$x/255), 784))
  y_train = dat$train$y
  X_test = array_reshape(dat$test$x/255, c(nrow(dat$test$x/255), 784))
  y_test = dat$test$y

  Reduced_Data2 = Feed_Reduction(X_train, y_train, X_test,
                                val_split = .3, nodes = 350,
                                30, 50, verbose = 1)

  library(e1071)
  names(Reduced_Data2$test) = names(Reduced_Data2$train)
  newdat = as.data.frame(cbind(rbind(Reduced_Data2$train, Reduced_Data2$test), c(y_train, y_test)))
  colnames(newdat) = c(paste("V", c(1:11), sep = ""))
  mod = svm(V11~., data = newdat, subset = c(1:60000),
           kernel = 'linear', cost = 1, type = 'C-classification')
  preds = predict(mod, newdat[60001:70000,-11])
  sum(preds == y_test)/10000

}
# }