├── cachematrix.R └── README.md /cachematrix.R: -------------------------------------------------------------------------------- 1 | ## These functions create a special matrix that caches it inverse 2 | ## makeCacheMatrix: This function creates a special "matrix" object that can cache its inverse. 3 | ## cacheSolve: This function computes the inverse of the special "matrix" returned by makeCacheMatrix above. If the inverse has already been calculated (and the matrix has not changed), then the cachesolve should retrieve the inverse from the cache. 4 | 5 | ## makeCacheMatrix creates a special matrix that stores its inverse 6 | ## 1. set the value of the matrix 7 | ## 2. get the value of the matrix 8 | ## 3. set the value of the inverse 9 | ## 4. get the value of the inverse 10 | makeCacheMatrix <- function(x = matrix()) { 11 | i <- NULL 12 | set <- function(y) { 13 | x <<- y 14 | i <<- NULL 15 | } 16 | get <- function() x 17 | setinverse <- function(inverse) i <<- inverse 18 | getinverse <- function() i 19 | list(set=set, get=get, setinverse=setinverse, getinverse=getinverse) 20 | } 21 | 22 | 23 | ## computes the matrix inverse if necessary 24 | cacheSolve <- function(x, ...) { 25 | i <- x$getinverse() 26 | if(!is.null(i)) { 27 | message("getting cache data") 28 | return(i) 29 | } 30 | data <- x$get() 31 | i <- solve(data, ...) 32 | x$setinverse(i) 33 | i 34 | } 35 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ### Introduction 2 | 3 | This second programming assignment will require you to write an R 4 | function that is able to cache potentially time-consuming computations. 5 | For example, taking the mean of a numeric vector is typically a fast 6 | operation. However, for a very long vector, it may take too long to 7 | compute the mean, especially if it has to be computed repeatedly (e.g. 8 | in a loop). If the contents of a vector are not changing, it may make 9 | sense to cache the value of the mean so that when we need it again, it 10 | can be looked up in the cache rather than recomputed. In this 11 | Programming Assignment you will take advantage of the scoping rules of 12 | the R language and how they can be manipulated to preserve state inside 13 | of an R object. 14 | 15 | ### Example: Caching the Mean of a Vector 16 | 17 | In this example we introduce the `<<-` operator which can be used to 18 | assign a value to an object in an environment that is different from the 19 | current environment. Below are two functions that are used to create a 20 | special object that stores a numeric vector and caches its mean. 21 | 22 | The first function, `makeVector` creates a special "vector", which is 23 | really a list containing a function to 24 | 25 | 1. set the value of the vector 26 | 2. get the value of the vector 27 | 3. set the value of the mean 28 | 4. get the value of the mean 29 | 30 | 31 | 32 | makeVector <- function(x = numeric()) { 33 | m <- NULL 34 | set <- function(y) { 35 | x <<- y 36 | m <<- NULL 37 | } 38 | get <- function() x 39 | setmean <- function(mean) m <<- mean 40 | getmean <- function() m 41 | list(set = set, get = get, 42 | setmean = setmean, 43 | getmean = getmean) 44 | } 45 | 46 | The following function calculates the mean of the special "vector" 47 | created with the above function. However, it first checks to see if the 48 | mean has already been calculated. If so, it `get`s the mean from the 49 | cache and skips the computation. Otherwise, it calculates the mean of 50 | the data and sets the value of the mean in the cache via the `setmean` 51 | function. 52 | 53 | cachemean <- function(x, ...) { 54 | m <- x$getmean() 55 | if(!is.null(m)) { 56 | message("getting cached data") 57 | return(m) 58 | } 59 | data <- x$get() 60 | m <- mean(data, ...) 61 | x$setmean(m) 62 | m 63 | } 64 | 65 | ### Assignment: Caching the Inverse of a Matrix 66 | 67 | Matrix inversion is usually a costly computation and there may be some 68 | benefit to caching the inverse of a matrix rather than computing it 69 | repeatedly (there are also alternatives to matrix inversion that we will 70 | not discuss here). Your assignment is to write a pair of functions that 71 | cache the inverse of a matrix. 72 | 73 | Write the following functions: 74 | 75 | 1. `makeCacheMatrix`: This function creates a special "matrix" object 76 | that can cache its inverse. 77 | 2. `cacheSolve`: This function computes the inverse of the special 78 | "matrix" returned by `makeCacheMatrix` above. If the inverse has 79 | already been calculated (and the matrix has not changed), then 80 | `cacheSolve` should retrieve the inverse from the cache. 81 | 82 | Computing the inverse of a square matrix can be done with the `solve` 83 | function in R. For example, if `X` is a square invertible matrix, then 84 | `solve(X)` returns its inverse. 85 | 86 | For this assignment, assume that the matrix supplied is always 87 | invertible. 88 | 89 | In order to complete this assignment, you must do the following: 90 | 91 | 1. Fork the GitHub repository containing the stub R files at 92 | [https://github.com/rdpeng/ProgrammingAssignment2](https://github.com/rdpeng/ProgrammingAssignment2) 93 | to create a copy under your own account. 94 | 2. Clone your forked GitHub repository to your computer so that you can 95 | edit the files locally on your own machine. 96 | 3. Edit the R file contained in the git repository and place your 97 | solution in that file (please do not rename the file). 98 | 4. Commit your completed R file into YOUR git repository and push your 99 | git branch to the GitHub repository under your account. 100 | 5. Submit to Coursera the URL to your GitHub repository that contains 101 | the completed R code for the assignment. 102 | 103 | ### Grading 104 | 105 | This assignment will be graded via peer assessment. 106 | --------------------------------------------------------------------------------