Merge pull request #1 from raywenderlich/master

merge from orignal

Author: KeithMorning

Big-O Notation.markdown

@@ -132,9 +132,9 @@ Below are some examples for each category of performance:
   The most trivial example of function that takes O(n!) time is given below.
 
   ```swift
-  func nFacFunc(n: Int) {
+  func nFactFunc(n: Int) {
     for i in stride(from: 0, to: n, by: 1) {
-      nFactFunc(n - 1)
+      nFactFunc(n: n - 1)
     }
   }
   ```

Binary Search Tree/README.markdown

@@ -375,7 +375,7 @@ As an exercise, see if you can implement filter and reduce.
 We can make the code more readable by defining some helper functions.
 
 ```swift
-  private func reconnectParentToNode(node: BinarySearchTree?) {
+  private func reconnectParentTo(node: BinarySearchTree?) {
     if let parent = parent {
       if isLeftChild {
         parent.left = node

Binary Search/README.markdown

@@ -4,15 +4,15 @@ Goal: Quickly find an element in an array.
 
 Let's say you have an array of numbers and you want to determine whether a specific number is in that array, and if so, at which index.
 
-In most cases, Swift's `indexOf()` function is good enough for that:
+In most cases, Swift's `Collection.index(of:)` function is good enough for that:
 
 ```swift
 let numbers = [11, 59, 3, 2, 53, 17, 31, 7, 19, 67, 47, 13, 37, 61, 29, 43, 5, 41, 23]
 
-numbers.indexOf(43)  // returns 15
+numbers.index(of: 43)  // returns 15
 ```
 
-The built-in `indexOf()` function performs a [linear search](../Linear%20Search/). In code that looks something like this:
+The built-in `Collection.index(of:)` function performs a [linear search](../Linear%20Search/). In code that looks something like this:
 
 ```swift
 func linearSearch<T: Equatable>(_ a: [T], _ key: T) -> Int? {

Brute-Force String Search/README.markdown

@@ -51,6 +51,6 @@ extension String {
 
 This looks at each character in the source string in turn. If the character equals the first character of the search pattern, then the inner loop checks whether the rest of the pattern matches. If no match is found, the outer loop continues where it left off. This repeats until a complete match is found or the end of the source string is reached.
 
-The brute-force approach works OK, but it's not very efficient (or pretty). It should work fine on small strings, though. For a smarter algorithm that works better with large chunks of text, check out [Boyer-Moore](../Boyer-Moore/) string search.
+The brute-force approach works OK, but it's not very efficient (or pretty). It should work fine on small strings, though. For a smarter algorithm that works better with large chunks of text, check out [Boyer-Moore](../Boyer-Moore-Horspool) string search.
 
 *Written for Swift Algorithm Club by Matthijs Hollemans*

Combinatorics/README.markdown

@@ -99,17 +99,17 @@ Here's a recursive algorithm by Niklaus Wirth:
 
 ```swift
 func permuteWirth<T>(_ a: [T], _ n: Int) {
-  if n == 0 {
-    print(a)   // display the current permutation
-  } else {
-    var a = a
-    permuteWirth(a, n - 1)
-    for i in 0..<n {
-      swap(&a[i], &a[n])
-      permuteWirth(a, n - 1)
-      swap(&a[i], &a[n])
+    if n == 0 {
+        print(a)   // display the current permutation
+    } else {
+        var a = a
+        permuteWirth(a, n - 1)
+        for i in 0..<n {
+            a.swapAt(i, n)
+            permuteWirth(a, n - 1)
+            a.swapAt(i, n)
+        }
     }
-  }
 }
 ```
 

Convex Hull/Convex Hull.xcodeproj/project.pbxproj

@@ -149,7 +149,7 @@
 					};
 					8E6D68B51E59989400161780 = {
 						CreatedOnToolsVersion = 8.2.1;
-						DevelopmentTeam = 7C4LVS3ZVC;
+						DevelopmentTeam = 4SQG5NJNPF;
 						ProvisioningStyle = Automatic;
 					};
 				};
@@ -384,7 +384,7 @@
 			isa = XCBuildConfiguration;
 			buildSettings = {
 				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
-				DEVELOPMENT_TEAM = 7C4LVS3ZVC;
+				DEVELOPMENT_TEAM = 4SQG5NJNPF;
 				INFOPLIST_FILE = "Convex Hull/Info.plist";
 				LD_RUNPATH_SEARCH_PATHS = "$(inherited) @executable_path/Frameworks";
 				PRODUCT_BUNDLE_IDENTIFIER = "workmoose.Convex-Hull";
@@ -397,7 +397,7 @@
 			isa = XCBuildConfiguration;
 			buildSettings = {
 				ASSETCATALOG_COMPILER_APPICON_NAME = AppIcon;
-				DEVELOPMENT_TEAM = 7C4LVS3ZVC;
+				DEVELOPMENT_TEAM = 4SQG5NJNPF;
 				INFOPLIST_FILE = "Convex Hull/Info.plist";
 				LD_RUNPATH_SEARCH_PATHS = "$(inherited) @executable_path/Frameworks";
 				PRODUCT_BUNDLE_IDENTIFIER = "workmoose.Convex-Hull";

Convex Hull/Convex Hull.xcodeproj/project.xcworkspace/xcshareddata/IDEWorkspaceChecks.plist

@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
+<plist version="1.0">
+<dict>
+	<key>IDEDidComputeMac32BitWarning</key>
+	<true/>
+</dict>
+</plist>

Convex Hull/Convex Hull/View.swift

@@ -11,28 +11,20 @@ import UIKit
 class View: UIView {
 
   let MAX_POINTS = 100
-
   var points = [CGPoint]()
-
   var convexHull = [CGPoint]()
 
   override init(frame: CGRect) {
     super.init(frame: frame)
-
-    // last checked with Xcode 9.0b4
-    #if swift(>=4.0)
-      print("Hello, Swift 4!")
-    #endif
-    
-    generatePoints()
+    generateRandomPoints()
     quickHull(points: points)
   }
 
   required init?(coder aDecoder: NSCoder) {
     fatalError("init(coder:) has not been implemented")
   }
 
-  func generatePoints() {
+  func generateRandomPoints() {
     for _ in 0..<MAX_POINTS {
       let offset: CGFloat = 50
       let xrand = CGFloat(arc4random()) / CGFloat(UINT32_MAX) * (self.frame.width - offset) + 0.5 * offset

Convex Hull/README.md

@@ -1,14 +1,23 @@
 # Convex Hull
 
-There are multiple Convex Hull algorithms. This particular implementation uses the Quickhull algorithm.
+Given a group of points on a plane. The Convex Hull algorithm calculates the shape (made up from the points itself) containing all these points. It can also be used on a collection of points of different dimensions. This implementation however covers points on a plane. It essentially calculates the lines between points which together contain all points. In comparing different solutions to this problem we can describe each algorithm in terms of it's big-O time complexity.
 
-Given a group of points on a plane. The Convex Hull algorithm calculates the shape (made up from the points itself) containing all these points. It can also be used on a collection of points of different dimensions. This implementation however covers points on a plane. It essentially calculates the lines between points which together contain all points.
+There are multiple Convex Hull algorithms but this solution is called Quickhull, is comes from the work of both W. Eddy in 1977 and also separately A. Bykat in 1978, this algorithm has an expected time complexity of O(n log n), but it's worst-case time-complexity can be O(n^2) . With average conditions the algorithm has ok efficiency, but it's time-complexity can start to head become more exponential in cases of high symmetry or where there are points lying on the circumference of a circle for example.
 
 ## Quickhull
 
 The quickhull algorithm works as follows:
-The algorithm takes an input of a collection of points. These points should be ordered on their x-coordinate value. We pick the two points A and B with the smallest(A) and the largest(B) x-coordinate. These of course have to be part of the hull. Imagine a line from point A to point B. All points to the right of this line are grouped in an array S1. Imagine now a line from point B to point A. (this is of course the same line as before just with opposite direction) Again all points to the right of this line are grouped in an array, S2 this time.
-We now define the following recursive function:
+
+- The algorithm takes an input of a collection of points. These points should be ordered on their x-coordinate value. 
+- We first find the two points A and B with the minimum(A) and the maximum(B) x-coordinates (as these will obviously be part of the hull). 
+- Use the line formed by the two points to divide the set in two subsets of points, which will be processed recursively.
+- Determine the point, on one side of the line, with the maximum distance from the line. The two points found before along with this one form a triangle.
+- The points lying inside of that triangle cannot be part of the convex hull and can therefore be ignored in the next steps.
+- Repeat the previous two steps on the two lines formed by the triangle (not the initial line).
+- Keep on doing so on until no more points are left, the recursion has come to an end and the points selected constitute the convex hull.
+
+
+Our functioni will have the following defininition:
 
 `findHull(points: [CGPoint], p1: CGPoint, p2: CGPoint)`
 
@@ -18,6 +27,7 @@ findHull(S2, B, A)
 ```
 
 What this function does is the following:
+
 1. If `points` is empty we return as there are no points to the right of our line to add to our hull.
 2. Draw a line from `p1` to `p2`.
 3. Find the point in `points` that is furthest away from this line. (`maxPoint`)

DiningPhilosophers/README.md

@@ -44,7 +44,7 @@ Based on the Chandy and Misra's analysis, a system of preference levels is deriv
 
 # Swift implementation
 
-This Swift 3.0 implementation of the Chandy/Misra solution is based on the GCD and Semaphore tecnique that can be built on both macOS and Linux.
+This Swift 3.0 implementation of the Chandy/Misra solution is based on the GCD and Semaphore technique that can be built on both macOS and Linux.
 
 The code is based on a ForkPair struct used for holding an array of DispatchSemaphore and a Philosopher struct for associate a couple of forks to each Philosopher.