asymptoticComplexityClass: Asymptotic Complexity Classification function

In Anirban166/testComplexity: Asymptotic Complexity Testing Framework

Asymptotic Complexity Classification function

Description

Function to classify the complexity trend between two selected parameters from the data frame provided as input here

Usage

asymptoticComplexityClass(df, output.size, data.size)

Arguments

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.
output.size	A string specifying the column name in the passed data frame to be used as the output size.
data.size	A string specifying the column name in the passed data frame to be used as the data size.

Details

For more information regarding its implementation or functionality/usage, please check https://anirban166.github.io//Generalized-complexity/

Value

A string specifying the resultant complexity class. (Eg: 'Linear', 'Log-linear', 'Quadratic')

Examples

# Avoiding for CRAN since computation time might exceed 5 seconds sometimes:
## Not run: 
# Running the quick sort algorithm with sampling against a set of increasing input data sizes:
sizes = 10^seq(1, 3, by = 0.5)
df <- asymptoticTimings(sort(sample(1:100, data.sizes, replace = TRUE), method = "quick"), sizes)
# Classifying the complexity trend between the data contained in the columns
# 'Timings' and 'Data sizes' from the data frame obtained above:
asymptoticComplexityClass(df, output.size = "Timings", data.size = "Data sizes")
# For quick sort, the log-linear time complexity class is expected.

## End(Not run)

Anirban166/testComplexity documentation built on Sept. 17, 2024, 11:06 a.m.