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Commit b48de74

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Small cleanup in chapter 7
1 parent 1ada79c commit b48de74

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  • Classic Computer Science Problems in Swift.playground/Pages/Chapter 7.xcplaygroundpage

1 file changed

+88
-91
lines changed

‎Classic Computer Science Problems in Swift.playground/Pages/Chapter 7.xcplaygroundpage/Contents.swift

Lines changed: 88 additions & 91 deletions
Original file line numberDiff line numberDiff line change
@@ -23,30 +23,6 @@ import Foundation
2323

2424
// MARK: Randomization & Statistical Helpers
2525

26-
struct Random {
27-
private static var seeded = false
28-
29-
// a random Double between *from* and *to*, assumes *from* < *to*
30-
static func double(from: Double, to: Double) -> Double {
31-
if !Random.seeded {
32-
srand48(time(nil))
33-
Random.seeded = true
34-
}
35-
36-
return (drand48() * (to - from)) + from
37-
}
38-
}
39-
40-
/// Create *number* of random Doubles between 0.0 and 1.0
41-
func randomWeights(number: Int) -> [Double] {
42-
return (0..<number).map{ _ in Random.double(from: 0.0, to: 1.0) }
43-
}
44-
45-
/// Create *number* of random Doubles between 0.0 and *limit*
46-
func randomNums(number: Int, limit: Double) -> [Double] {
47-
return (0..<number).map{ _ in Random.double(from: 0.0, to: limit) }
48-
}
49-
5026
// A derivative of the Fisher-Yates algorithm to shuffle an array
5127
extension Array {
5228
public func shuffled() -> Array<Element> {
@@ -62,19 +38,25 @@ extension Array {
6238
}
6339
}
6440

65-
/// assumes all rows are of equal length
66-
/// and divide each column by its max throughout the data set
67-
/// for that column
68-
func normalizeByColumnMax( dataset:inout [[Double]]) {
69-
for colNum in 0..<dataset[0].count {
70-
let column = dataset.map { 0ドル[colNum] }
71-
let maximum = column.max()!
72-
for rowNum in 0..<dataset.count {
73-
dataset[rowNum][colNum] = dataset[rowNum][colNum] / maximum
41+
struct Random {
42+
private static var seeded = false
43+
44+
// a random Double between *from* and *to*, assumes *from* < *to*
45+
static func double(from: Double, to: Double) -> Double {
46+
if !Random.seeded {
47+
srand48(time(nil))
48+
Random.seeded = true
7449
}
50+
51+
return (drand48() * (to - from)) + from
7552
}
7653
}
7754

55+
/// Create *number* of random Doubles between 0.0 and 1.0
56+
func randomWeights(number: Int) -> [Double] {
57+
return (0..<number).map{ _ in Random.double(from: 0.0, to: 1.0) }
58+
}
59+
7860
// MARK: Activation Functions and Their Derivatives
7961

8062
/// the classic sigmoid activation function
@@ -128,8 +110,6 @@ public func sum(x: [Double]) -> Double {
128110
return result
129111
}
130112

131-
132-
133113
/// An individual node in a layer
134114
class Neuron {
135115
var weights: [Double]
@@ -289,63 +269,22 @@ class Network {
289269
}
290270
}
291271

292-
/// Wine Test
272+
/// MARK: Normalization
293273

294-
//var network: Network = Network(layerStructure: [13,7,3], learningRate: 7.0)
295-
//// for training
296-
//var wineParameters: [[Double]] = [[Double]]()
297-
//var wineClassifications: [[Double]] = [[Double]]()
298-
//// for testing/validation
299-
//var wineSamples: [[Double]] = [[Double]]()
300-
//var wineCultivars: [Int] = [Int]()
301-
//
302-
//func parseWineCSV() {
303-
// let myBundle = Bundle.main
304-
// let urlpath = myBundle.path(forResource: "wine", ofType: "csv")
305-
// let url = URL(fileURLWithPath: urlpath!)
306-
// let csv = try! String.init(contentsOf: url)
307-
// let lines = csv.components(separatedBy: "\n")
308-
//
309-
// let shuffledLines = lines.shuffled()
310-
// for line in shuffledLines {
311-
// if line == "" { continue }
312-
// let items = line.components(separatedBy: ",")
313-
// let parameters = items[1...13].map{ Double(0ドル)! }
314-
// wineParameters.append(parameters)
315-
// let species = Int(items[0])!
316-
// if species == 1 {
317-
// wineClassifications.append([1.0, 0.0, 0.0])
318-
// } else if species == 2 {
319-
// wineClassifications.append([0.0, 1.0, 0.0])
320-
// } else {
321-
// wineClassifications.append([0.0, 0.0, 1.0])
322-
// }
323-
// wineCultivars.append(species)
324-
// }
325-
// normalizeByColumnMax(dataset: &wineParameters)
326-
// wineSamples = Array(wineParameters.dropFirst(150))
327-
// wineCultivars = Array(wineCultivars.dropFirst(150))
328-
// wineParameters = Array(wineParameters.dropLast(28))
329-
//}
330-
//
331-
//func interpretOutput(output: [Double]) -> Int {
332-
// if output.max()! == output[0] {
333-
// return 1
334-
// } else if output.max()! == output[1] {
335-
// return 2
336-
// } else {
337-
// return 3
338-
// }
339-
//}
340-
//
341-
//parseWineCSV()
342-
//// train over entire data set 5 times
343-
//for _ in 0..<5 {
344-
// network.train(inputs: wineParameters, expecteds: wineClassifications, printError: false)
345-
//}
346-
//
347-
//let results = network.validate(inputs: wineSamples, expecteds: wineCultivars, interpretOutput: interpretOutput)
348-
//print("\(results.correct) correct of \(results.total) = \(results.percentage * 100)%")
274+
/// assumes all rows are of equal length
275+
/// and divide each column by its max throughout the data set
276+
/// for that column
277+
func normalizeByColumnMax( dataset:inout [[Double]]) {
278+
for colNum in 0..<dataset[0].count {
279+
let column = dataset.map { 0ドル[colNum] }
280+
let maximum = column.max()!
281+
for rowNum in 0..<dataset.count {
282+
dataset[rowNum][colNum] = dataset[rowNum][colNum] / maximum
283+
}
284+
}
285+
}
286+
287+
// MARK: Iris Test
349288

350289
var network: Network = Network(layerStructure: [4,5,3], learningRate: 0.3)
351290
var irisParameters: [[Double]] = [[Double]]()
@@ -403,5 +342,63 @@ let testersCorrects = Array(irisSpecies[140..<150])
403342
let results = network.validate(inputs: testers, expecteds: testersCorrects, interpretOutput: interpretOutput)
404343
print("\(results.correct) correct of \(results.total) = \(results.percentage * 100)%")
405344

345+
/// Wine Test
346+
347+
//var network: Network = Network(layerStructure: [13,7,3], learningRate: 7.0)
348+
//// for training
349+
//var wineParameters: [[Double]] = [[Double]]()
350+
//var wineClassifications: [[Double]] = [[Double]]()
351+
//// for testing/validation
352+
//var wineSamples: [[Double]] = [[Double]]()
353+
//var wineCultivars: [Int] = [Int]()
354+
//
355+
//func parseWineCSV() {
356+
// let myBundle = Bundle.main
357+
// let urlpath = myBundle.path(forResource: "wine", ofType: "csv")
358+
// let url = URL(fileURLWithPath: urlpath!)
359+
// let csv = try! String.init(contentsOf: url)
360+
// let lines = csv.components(separatedBy: "\n")
361+
//
362+
// let shuffledLines = lines.shuffled()
363+
// for line in shuffledLines {
364+
// if line == "" { continue }
365+
// let items = line.components(separatedBy: ",")
366+
// let parameters = items[1...13].map{ Double(0ドル)! }
367+
// wineParameters.append(parameters)
368+
// let species = Int(items[0])!
369+
// if species == 1 {
370+
// wineClassifications.append([1.0, 0.0, 0.0])
371+
// } else if species == 2 {
372+
// wineClassifications.append([0.0, 1.0, 0.0])
373+
// } else {
374+
// wineClassifications.append([0.0, 0.0, 1.0])
375+
// }
376+
// wineCultivars.append(species)
377+
// }
378+
// normalizeByColumnMax(dataset: &wineParameters)
379+
// wineSamples = Array(wineParameters.dropFirst(150))
380+
// wineCultivars = Array(wineCultivars.dropFirst(150))
381+
// wineParameters = Array(wineParameters.dropLast(28))
382+
//}
383+
//
384+
//func interpretOutput(output: [Double]) -> Int {
385+
// if output.max()! == output[0] {
386+
// return 1
387+
// } else if output.max()! == output[1] {
388+
// return 2
389+
// } else {
390+
// return 3
391+
// }
392+
//}
393+
//
394+
//parseWineCSV()
395+
//// train over entire data set 5 times
396+
//for _ in 0..<5 {
397+
// network.train(inputs: wineParameters, expecteds: wineClassifications, printError: false)
398+
//}
399+
//
400+
//let results = network.validate(inputs: wineSamples, expecteds: wineCultivars, interpretOutput: interpretOutput)
401+
//print("\(results.correct) correct of \(results.total) = \(results.percentage * 100)%")
402+
406403
//: [Next](@next)
407404

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