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Commit 03f109b2 authored by Jim Wallace's avatar Jim Wallace
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Fixing linux compatibility

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.swiftpm/xcode/xcshareddata/xcschemes/SwiftNLP-Package.xcscheme deleted 100644 → 0
+ 0
− 127
View file @ 095f0aa0
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Sources/SwiftNLP/2. Embeddings/KeyedVectorCorpus.swift
+ 3
− 10
View file @ 03f109b2
......@@ -21,15 +21,8 @@
// FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
// OTHER DEALINGS IN THE SOFTWARE.
#if os(Darwin)
#warning("OS = DARWIN")
#else
#warning("OS != DARWIN")
#endif
import Foundation
#if canImport(Accelerate)
#if os(Darwin)
import Surge
#else
//TODO: Implement Linux alternative with better performance, currently uses naive solution.
......@@ -63,7 +56,7 @@ class KeyedVectorCorpus: SNLPCorpus {
var encoding = defaultDocumentEncoding
for word in document {
if dictionary.contains(word) {
#if canImport(Surge)
#if os(Darwin)
encoding .+= dictionary[word] // Surge in-place
#else
for i in 0 ..< encoding.count {
......@@ -74,7 +67,7 @@ class KeyedVectorCorpus: SNLPCorpus {
}
}
#if canImport(Surge)
#if os(Darwin)
encoding /= Double(document.count)
#else
for i in 0 ..< encoding.count {
......
Sources/SwiftNLP/2. Embeddings/SIMDCorpus.swift
+ 16
− 16
View file @ 03f109b2
......@@ -69,22 +69,22 @@ class SIMDCorpus<DocumentEncoding: SIMD>: SNLPCorpus where DocumentEncoding.Scal
// }
}
func clusterWithKMeans() {
let kmm = KMeans<DocumentEncoding, String>(labels: ["A", "B", "C", "D", "E", "F", "G"])
let valuesArray = Array(encodedDocuments.values)
kmm.trainCenters(valuesArray, convergeDistance: 0.05)
for (label, centroid) in zip(kmm.labels, kmm.centroids) {
print("\(label): \(centroid)")
}
print("\nClassifications")
for (label, point) in zip(kmm.fit(valuesArray), valuesArray) {
print("\(label): \(point)")
}
}
// func clusterWithKMeans() {
//
// let kmm = KMeans<DocumentEncoding, String>(labels: ["A", "B", "C", "D", "E", "F", "G"])
// let valuesArray = Array(encodedDocuments.values)
// kmm.trainCenters(valuesArray, convergeDistance: 0.05)
//
// for (label, centroid) in zip(kmm.labels, kmm.centroids) {
// print("\(label): \(centroid)")
// }
//
// print("\nClassifications")
// for (label, point) in zip(kmm.fit(valuesArray), valuesArray) {
// print("\(label): \(point)")
// }
//
// }
}
......
Sources/SwiftNLP/4. Clustering/K-Means.swift
+ 110
− 110
View file @ 03f109b2
import Foundation
import simd
class KMeans<Vector: SIMD, Label: Hashable> where Vector.Scalar: FloatingPoint {
let numCenters: Int
let labels: [Label]
private(set) var centroids = [Vector]()
init(labels: [Label]) {
assert(labels.count > 1, "Exception: KMeans with less than 2 centers.")
self.labels = labels
self.numCenters = labels.count
}
//TODO: Confirm this works?
@inlinable // TODO: Check if this actually improves performance?
public func distance<V: SIMD>(_ x: V, _ y: V) -> V.Scalar where V.Scalar: FloatingPoint {
let difference = x - y
var distance = V.Scalar(0)
for i in difference.indices {
distance += difference[i] * difference[i]
}
return distance.squareRoot()
}
private func indexOfNearestCenter(_ x: Vector, centers: [Vector]) -> Int {
var nearestDist = Vector.Scalar.greatestFiniteMagnitude
var minIndex = 0
for (idx, center) in centers.enumerated() {
//let dist = x.distanceTo(center)
let dist = distance(x,center)
if dist < nearestDist {
minIndex = idx
nearestDist = dist
}
}
return minIndex
}
func trainCenters(_ points: [Vector], convergeDistance: Vector.Scalar) {
//points = points.filter{ !containsNaN( $0) }
// Randomly take k objects from the input data to make the initial centroids.
var centers = reservoirSample(points, k: numCenters)
var centerMoveDist = Vector.Scalar(0)
repeat {
// This array keeps track of which data points belong to which centroids.
var classification: [[Vector]] = .init(repeating: [], count: numCenters)
// For each data point, find the centroid that it is closest to.
for p in points {
let classIndex = indexOfNearestCenter(p, centers: centers)
classification[classIndex].append(p)
}
// Take the average of all the data points that belong to each centroid.
// This moves the centroid to a new position.
// FIXME: Whhy is this giving NaN for first result?
let newCenters = classification.map { assignedPoints in
assignedPoints.reduce(Vector.zero, +) / Vector(repeating: Vector.Scalar(assignedPoints.count))
}
// Find out how far each centroid moved since the last iteration. If it's
// only a small distance, then we're done.
centerMoveDist = Vector.Scalar(0)
for idx in 0..<numCenters {
centerMoveDist += distance(centers[idx], newCenters[idx]) //centers[idx].distanceTo(newCenters[idx])
}
centers = newCenters
} while centerMoveDist > convergeDistance
centroids = centers
}
func fit(_ point: Vector) -> Label {
assert(!centroids.isEmpty, "Exception: KMeans tried to fit on a non trained model.")
let centroidIndex = indexOfNearestCenter(point, centers: centroids)
return labels[centroidIndex]
}
func fit(_ points: [Vector]) -> [Label] {
assert(!centroids.isEmpty, "Exception: KMeans tried to fit on a non trained model.")
return points.map(fit)
}
}
// Pick k random elements from samples
func reservoirSample<T>(_ samples: [T], k: Int) -> [T] {
var result = [T]()
// Fill the result array with first k elements
for i in 0..<k {
result.append(samples[i])
}
// Randomly replace elements from remaining pool
for i in k..<samples.count {
let j = Int(arc4random_uniform(UInt32(i + 1)))
if j < k {
result[j] = samples[i]
}
}
return result
}
//import Foundation
//import simd
//
//class KMeans<Vector: SIMD, Label: Hashable> where Vector.Scalar: FloatingPoint {
// let numCenters: Int
// let labels: [Label]
// private(set) var centroids = [Vector]()
//
// init(labels: [Label]) {
// assert(labels.count > 1, "Exception: KMeans with less than 2 centers.")
// self.labels = labels
// self.numCenters = labels.count
// }
//
// //TODO: Confirm this works?
// @inlinable // TODO: Check if this actually improves performance?
// public func distance<V: SIMD>(_ x: V, _ y: V) -> V.Scalar where V.Scalar: FloatingPoint {
// let difference = x - y
// var distance = V.Scalar(0)
// for i in difference.indices {
// distance += difference[i] * difference[i]
// }
// return distance.squareRoot()
// }
//
// private func indexOfNearestCenter(_ x: Vector, centers: [Vector]) -> Int {
// var nearestDist = Vector.Scalar.greatestFiniteMagnitude
// var minIndex = 0
//
// for (idx, center) in centers.enumerated() {
// //let dist = x.distanceTo(center)
// let dist = distance(x,center)
// if dist < nearestDist {
// minIndex = idx
// nearestDist = dist
// }
// }
// return minIndex
// }
//
// func trainCenters(_ points: [Vector], convergeDistance: Vector.Scalar) {
//
// //points = points.filter{ !containsNaN( $0) }
//
// // Randomly take k objects from the input data to make the initial centroids.
// var centers = reservoirSample(points, k: numCenters)
//
// var centerMoveDist = Vector.Scalar(0)
// repeat {
// // This array keeps track of which data points belong to which centroids.
// var classification: [[Vector]] = .init(repeating: [], count: numCenters)
//
// // For each data point, find the centroid that it is closest to.
// for p in points {
// let classIndex = indexOfNearestCenter(p, centers: centers)
// classification[classIndex].append(p)
// }
//
// // Take the average of all the data points that belong to each centroid.
// // This moves the centroid to a new position.
// // FIXME: Whhy is this giving NaN for first result?
// let newCenters = classification.map { assignedPoints in
// assignedPoints.reduce(Vector.zero, +) / Vector(repeating: Vector.Scalar(assignedPoints.count))
// }
//
// // Find out how far each centroid moved since the last iteration. If it's
// // only a small distance, then we're done.
// centerMoveDist = Vector.Scalar(0)
// for idx in 0..<numCenters {
// centerMoveDist += distance(centers[idx], newCenters[idx]) //centers[idx].distanceTo(newCenters[idx])
// }
//
// centers = newCenters
// } while centerMoveDist > convergeDistance
//
// centroids = centers
// }
//
// func fit(_ point: Vector) -> Label {
// assert(!centroids.isEmpty, "Exception: KMeans tried to fit on a non trained model.")
//
// let centroidIndex = indexOfNearestCenter(point, centers: centroids)
// return labels[centroidIndex]
// }
//
// func fit(_ points: [Vector]) -> [Label] {
// assert(!centroids.isEmpty, "Exception: KMeans tried to fit on a non trained model.")
//
// return points.map(fit)
// }
//}
//
//// Pick k random elements from samples
//func reservoirSample<T>(_ samples: [T], k: Int) -> [T] {
// var result = [T]()
//
// // Fill the result array with first k elements
// for i in 0..<k {
// result.append(samples[i])
// }
//
// // Randomly replace elements from remaining pool
// for i in k..<samples.count {
// let j = Int(arc4random_uniform(UInt32(i + 1)))
// if j < k {
// result[j] = samples[i]
// }
// }
// return result
//}
Sources/SwiftNLP/4. Clustering/SIMD + distance.swift
+ 103
− 103
View file @ 03f109b2
import simd
extension SIMD where Self.Scalar: FloatingPoint {
// A default Euclidian distance for SIMD when more efficient functions aren't provided
@inlinable
public func distance(_ x: Self, _ y: Self) -> Self.Scalar {
let difference = x - y
let squared = difference * difference
return squared.sum().squareRoot()
}
@inlinable
public func distance_squared(_ x: Self, _ y: Self) -> Self.Scalar {
let difference = x - y
let squared = difference * difference
return squared.sum()
}
}
extension SIMD2<Double> {
@inlinable
public func distance(_ x: Self, _ y: Self) -> Double {
return simd.distance(x, y)
}
@inlinable
public func distance_squared(_ x: Self, _ y: Self) -> Double {
return simd.distance_squared(x, y)
}
}
extension SIMD3<Double> {
@inlinable
public func distance(_ x: Self, _ y: Self) -> Double {
return simd.distance(x, y)
}
@inlinable
public func distance_squared(_ x: Self, _ y: Self) -> Double {
return simd.distance_squared(x, y)
}
}
extension SIMD4<Double> {
@inlinable
public func distance(_ x: Self, _ y: Self) -> Double {
return simd.distance(x, y)
}
@inlinable
public func distance_squared(_ x: Self, _ y: Self) -> Double {
return simd.distance_squared(x, y)
}
}
extension SIMD2<Float> {
@inlinable
public func distance(_ x: Self, _ y: Self) -> Float {
return simd.distance(x, y)
}
@inlinable
public func distance_squared(_ x: Self, _ y: Self) -> Float {
return simd.distance_squared(x, y)
}
}
extension SIMD3<Float> {
@inlinable
public func distance(_ x: Self, _ y: Self) -> Float {
return simd.distance(x, y)
}
@inlinable
public func distance_squared(_ x: Self, _ y: Self) -> Float {
return simd.distance_squared(x, y)
}
}
extension SIMD4<Float> {
@inlinable
public func distance(_ x: Self, _ y: Self) -> Float {
return simd.distance(x, y)
}
@inlinable
public func distance_squared(_ x: Self, _ y: Self) -> Float {
return simd.distance_squared(x, y)
}
}
//import simd
//
//extension SIMD where Self.Scalar: FloatingPoint {
//
// // A default Euclidian distance for SIMD when more efficient functions aren't provided
// @inlinable
// public func distance(_ x: Self, _ y: Self) -> Self.Scalar {
// let difference = x - y
// let squared = difference * difference
// return squared.sum().squareRoot()
// }
//
// @inlinable
// public func distance_squared(_ x: Self, _ y: Self) -> Self.Scalar {
// let difference = x - y
// let squared = difference * difference
// return squared.sum()
// }
//}
//
//extension SIMD2<Double> {
//
// @inlinable
// public func distance(_ x: Self, _ y: Self) -> Double {
// return simd.distance(x, y)
// }
//
// @inlinable
// public func distance_squared(_ x: Self, _ y: Self) -> Double {
// return simd.distance_squared(x, y)
// }
//
//}
//
//extension SIMD3<Double> {
//
// @inlinable
// public func distance(_ x: Self, _ y: Self) -> Double {
// return simd.distance(x, y)
// }
//
// @inlinable
// public func distance_squared(_ x: Self, _ y: Self) -> Double {
// return simd.distance_squared(x, y)
// }
//
//}
//
//extension SIMD4<Double> {
//
// @inlinable
// public func distance(_ x: Self, _ y: Self) -> Double {
// return simd.distance(x, y)
// }
//
// @inlinable
// public func distance_squared(_ x: Self, _ y: Self) -> Double {
// return simd.distance_squared(x, y)
// }
//
//
//}
//
//extension SIMD2<Float> {
//
// @inlinable
// public func distance(_ x: Self, _ y: Self) -> Float {
// return simd.distance(x, y)
// }
//
// @inlinable
// public func distance_squared(_ x: Self, _ y: Self) -> Float {
// return simd.distance_squared(x, y)
// }
//
//}
//
//extension SIMD3<Float> {
//
// @inlinable
// public func distance(_ x: Self, _ y: Self) -> Float {
// return simd.distance(x, y)
// }
//
// @inlinable
// public func distance_squared(_ x: Self, _ y: Self) -> Float {
// return simd.distance_squared(x, y)
// }
//
//}
//
//extension SIMD4<Float> {
//
// @inlinable
// public func distance(_ x: Self, _ y: Self) -> Float {
// return simd.distance(x, y)
// }
//
// @inlinable
// public func distance_squared(_ x: Self, _ y: Self) -> Float {
// return simd.distance_squared(x, y)
// }
//}
Sources/SwiftNLP/4. Clustering/SIMD2 + KDTreePoint.swift
+ 39
− 39
View file @ 03f109b2
import simd
import KDTree
extension SIMD2: KDTreePoint where Scalar: BinaryFloatingPoint {
@inlinable
public static var dimensions: Int { self.scalarCount }
@inlinable
public func kdDimension(_ dimension: Int) -> Double {
return Double(self[dimension])
}
//TODO: Confirm that Swift is pulling the "most constrained overload" when available
@inlinable
public func squaredDistance(to otherPoint: Self) -> Double {
let difference = self - otherPoint
let squared = difference * difference
return Double(squared.sum())
}
}
extension SIMD2<Double> {
@inlinable
public func squaredDistance(to otherPoint: Self) -> Double {
return simd.distance_squared(self, otherPoint)
}
}
extension SIMD2<Float> {
@inlinable
public func squaredDistance(to otherPoint: Self) -> Double {
return Double(simd.distance_squared(self, otherPoint))
}
}
//import simd
//import KDTree
//
//extension SIMD2: KDTreePoint where Scalar: BinaryFloatingPoint {
//
// @inlinable
// public static var dimensions: Int { self.scalarCount }
//
// @inlinable
// public func kdDimension(_ dimension: Int) -> Double {
// return Double(self[dimension])
// }
//
// //TODO: Confirm that Swift is pulling the "most constrained overload" when available
// @inlinable
// public func squaredDistance(to otherPoint: Self) -> Double {
// let difference = self - otherPoint
// let squared = difference * difference
// return Double(squared.sum())
// }
//}
//
//extension SIMD2<Double> {
//
// @inlinable
// public func squaredDistance(to otherPoint: Self) -> Double {
// return simd.distance_squared(self, otherPoint)
// }
//
//}
//
//extension SIMD2<Float> {
//
// @inlinable
// public func squaredDistance(to otherPoint: Self) -> Double {
// return Double(simd.distance_squared(self, otherPoint))
// }
//
//}
Tests/SwiftNLPTests/SNLPLoadDataTests.swift
+ 29
− 29
View file @ 03f109b2
......@@ -39,33 +39,33 @@ final class SwiftNLPLoadDataTests: XCTestCase {
// }
func testDocumentReading() throws {
// loads all json data for test documents
let redditCommentJson = TestUtils.loadAllRedditComment()
let redditSubmissionJson = TestUtils.loadAllRedditSubmission()
let redditComments = redditCommentJson.compactMap { TestUtils.readRedditCommentJson(json: $0) }
let redditSubmissions = redditSubmissionJson.compactMap { TestUtils.readRedditSubmissionJson(json: $0) }
// Extract body and selftext from each post, and store that for our corpus
let bodies = redditComments.flatMap { $0.posts.compactMap { $0.body } } +
redditSubmissions.flatMap { $0.posts.compactMap { $0.selftext } }
// Add documents to corpus
var corpus = KeyedVectorCorpus(source: .glove6B50d)
corpus.addDocuments(documents: bodies)
//print(corpus.encodedDocuments.count)
XCTAssert(corpus.encodedDocuments.count == 28765)
// Dimensionality reduction
let truncatedCorpus = SIMDCorpus<SIMD2<Double>>(corpus)
//debugPrint("TruncatedCorpus with \(truncatedCorpus.encodedDocuments.count) documents created")
// Clustering / Topic Detection
var topics = StubTopicModel<[Double]>(numberOfTopics: 3)
topics.train(truncatedCorpus)
//debugPrint(topics)
}
// func testDocumentReading() throws {
// // loads all json data for test documents
// let redditCommentJson = TestUtils.loadAllRedditComment()
// let redditSubmissionJson = TestUtils.loadAllRedditSubmission()
//
// let redditComments = redditCommentJson.compactMap { TestUtils.readRedditCommentJson(json: $0) }
// let redditSubmissions = redditSubmissionJson.compactMap { TestUtils.readRedditSubmissionJson(json: $0) }
//
// // Extract body and selftext from each post, and store that for our corpus
// let bodies = redditComments.flatMap { $0.posts.compactMap { $0.body } } +
// redditSubmissions.flatMap { $0.posts.compactMap { $0.selftext } }
//
// // Add documents to corpus
// var corpus = KeyedVectorCorpus(source: .glove6B50d)
// corpus.addDocuments(documents: bodies)
//
// //print(corpus.encodedDocuments.count)
// XCTAssert(corpus.encodedDocuments.count == 28765)
//
// // Dimensionality reduction
// let truncatedCorpus = SIMDCorpus<SIMD2<Double>>(corpus)
// //debugPrint("TruncatedCorpus with \(truncatedCorpus.encodedDocuments.count) documents created")
//
// // Clustering / Topic Detection
// var topics = StubTopicModel<[Double]>(numberOfTopics: 3)
// topics.train(truncatedCorpus)
//
// //debugPrint(topics)
// }
}
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