Write the following functions in R. Each function, takes arguments named acutals and predictions.

• Both actuals and predictions should each be vectors of 1s and 0s.
• Actuals represents the actual values for a dataset. Predictions represents the predicted values that are output from a model. # The following function calculates the numbers of True Positives TP <- function(actuals, prediction) # The following function calculates the numbers of False Positives FP <- function(actuals, prediction) # The following function calculates the numbers of True Negatives TN <- function(actuals, prediction) # The following function calculates the numbers of False Negatives FN <- function(actuals, prediction) # The following function calculates the precision of the predictions precision <- function (actuals, predictions) # The following function calculates the recall of the predictions recall <- function (actuals, predictions) # fallOut <- function(acutals, predictions) # The following calculates the f-beta-measure of the precision and the recall. # When beta is 1 (i.e. the default), the function calculates the f1measure. fMeasure <- function(acutals, predictions, )
• # Average Precision # use this formula: # where k is the rank in the sequence of retrieved documents, n is the number of retrieved documents, # P(k) is the precision at cut-off k in the list, # and rel(k) is an indicator function equaling 1 if the item at rank k is a relevant document, zero otherwise. avgPrecision <- function(actual, predictions)

precisionAtK <- function (actuals, prediction, (prediction))

RPrecision <- function (actuals, prediction, (prediction))

# Each item in the listOfQueryResults contains a vector of actuals and a vector of predictions. meanAvgPrecision <- function(listOfQueryResults)