Merge pull request #12 from Anirban166/Generalizedcomplexity

Generalizedcomplexity

R/asymptoticComplexityClass.R

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+#' Function to classify the complexity trend between two selected parameters from the data frame provided as input here
+#'
+#' @title Asymptotic Complexity Classification function
+#'
+#' @param model.df A data frame composing for two columns at the least, where one should be the contain the output-parameter sizes and one should contain the data sizes.
+#'
+#' @param output.size A string specifying the column name in the passed data frame to be used as the output size.
+#'
+#' @param data.size A string specifying the column name in the passed data frame to be used as the data size.
+#'
+#' @return A string specifying the resultant complexity class. (Eg: 'Linear', 'Log-linear', 'Quadratic')
+#'
+#' @export
+
+asymptoticComplexityClass = function(df, output.size, data.size)
+{
+  if(class(df) == "data.frame" & output.size %in% colnames(df) & data.size %in% colnames(df))
+  {
+    d <- data.frame('output' = df[[output.size]], 'size' = df[[data.size]])
+    asymptoticComplexityClassifier(d)
+  }
+  else stop("Input parameter must be a data frame containing the two specified columns passed as parameters")
+}
+

R/asymptoticComplexityClassifier.R

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+#' Function to classify the complexity trend between the two parameters as provided by asymptoticComplexityClass
+#'
+#' @title Asymptotic Complexity Classifier function
+#'
+#' @param model.df A data frame returned by asymptoticComplexityClass.
+#'
+#' @return A string specifying the resultant complexity class. (Eg: 'Linear', 'Log-linear', 'Quadratic')
+#'
+#' @export
+#' @importFrom boot cv.glm
+#' @importFrom stats fitted
+
+asymptoticComplexityClassifier = function(df)
+{
+  if(class(df) == "data.frame" & 'output' %in% colnames(df) & 'size' %in% colnames(df))
+  {
+    constant   <- glm(output~1,                 data = df); df['constant'] = fitted(constant)
+    linear     <- glm(output~size,              data = df); df['linear'] = fitted(linear)
+    squareroot <- glm(output~sqrt(size),        data = df); df['squareroot'] = fitted(squareroot)
+    log        <- glm(output~log(size),         data = df); df['log'] = fitted(log)
+    log.linear <- glm(output~size*log(size),    data = df); df['log-linear'] = fitted(log.linear)
+    quadratic  <- glm(output~I(size^2),         data = df); df['quadratic'] = fitted(quadratic)
+    cubic      <- glm(output~I(size^3),         data = df); df['cubic'] = fitted(cubic)
+
+    model.list <- list('constant'   = constant,
+                       'linear'     = linear,
+                       'squareroot' = squareroot,
+                       'log'        = log,
+                       'log-linear' = log.linear,
+                       'quadratic'  = quadratic,
+                       'cubic'      = cubic)
+
+    cross.validated.errors <- lapply(model.list, function(x) cv.glm(df, x)$delta[2])
+    best.model <- names(which.min(cross.validated.errors))
+    print(best.model)
+  }
+  else stop("Input parameter must be a data frame containing the two specified columns passed as parameters")
+}