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

In Travis-Barton/LilRhino: For Implementation of Feed Reduction, Learning Examples, NLP and Code Management

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.

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.

Value

Train	The training data in the new probability space
Test	The testing data in the new probability space

Author(s)

Travis Barton.

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

}

## End(Not run)

Travis-Barton/LilRhino documentation built on May 3, 2022, 3:36 a.m.