diff --git a/.swiftpm/xcode/xcshareddata/xcschemes/SwiftNLP-Package.xcscheme b/.swiftpm/xcode/xcshareddata/xcschemes/SwiftNLP-Package.xcscheme
deleted file mode 100644
index 91190196cbfd66acd172a4b1ba514d4cd45fbcc4..0000000000000000000000000000000000000000
--- a/.swiftpm/xcode/xcshareddata/xcschemes/SwiftNLP-Package.xcscheme
+++ /dev/null
@@ -1,127 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<Scheme
- LastUpgradeVersion = "1500"
- version = "1.7">
- <BuildAction
- parallelizeBuildables = "YES"
- buildImplicitDependencies = "YES">
- <BuildActionEntries>
- <BuildActionEntry
- buildForTesting = "YES"
- buildForRunning = "YES"
- buildForProfiling = "YES"
- buildForArchiving = "YES"
- buildForAnalyzing = "YES">
- <BuildableReference
- BuildableIdentifier = "primary"
- BlueprintIdentifier = "SwiftNLP"
- BuildableName = "SwiftNLP"
- BlueprintName = "SwiftNLP"
- ReferencedContainer = "container:">
- </BuildableReference>
- </BuildActionEntry>
- <BuildActionEntry
- buildForTesting = "YES"
- buildForRunning = "YES"
- buildForProfiling = "YES"
- buildForArchiving = "YES"
- buildForAnalyzing = "YES">
- <BuildableReference
- BuildableIdentifier = "primary"
- BlueprintIdentifier = "Minimal"
- BuildableName = "Minimal"
- BlueprintName = "Minimal"
- ReferencedContainer = "container:">
- </BuildableReference>
- </BuildActionEntry>
- <BuildActionEntry
- buildForTesting = "YES"
- buildForRunning = "YES"
- buildForProfiling = "NO"
- buildForArchiving = "NO"
- buildForAnalyzing = "YES">
- <BuildableReference
- BuildableIdentifier = "primary"
- BlueprintIdentifier = "SwiftNLPTests"
- BuildableName = "SwiftNLPTests"
- BlueprintName = "SwiftNLPTests"
- ReferencedContainer = "container:">
- </BuildableReference>
- </BuildActionEntry>
- </BuildActionEntries>
- </BuildAction>
- <TestAction
- buildConfiguration = "Debug"
- selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
- selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
- shouldUseLaunchSchemeArgsEnv = "YES"
- shouldAutocreateTestPlan = "YES">
- <Testables>
- <TestableReference
- skipped = "NO">
- <BuildableReference
- BuildableIdentifier = "primary"
- BlueprintIdentifier = "SwiftNLPTests"
- BuildableName = "SwiftNLPTests"
- BlueprintName = "SwiftNLPTests"
- ReferencedContainer = "container:">
- </BuildableReference>
- </TestableReference>
- </Testables>
- </TestAction>
- <LaunchAction
- buildConfiguration = "Debug"
- selectedDebuggerIdentifier = "Xcode.DebuggerFoundation.Debugger.LLDB"
- selectedLauncherIdentifier = "Xcode.DebuggerFoundation.Launcher.LLDB"
- launchStyle = "0"
- useCustomWorkingDirectory = "NO"
- ignoresPersistentStateOnLaunch = "NO"
- debugDocumentVersioning = "YES"
- debugServiceExtension = "internal"
- allowLocationSimulation = "YES">
- <MacroExpansion>
- <BuildableReference
- BuildableIdentifier = "primary"
- BlueprintIdentifier = "Minimal"
- BuildableName = "Minimal"
- BlueprintName = "Minimal"
- ReferencedContainer = "container:">
- </BuildableReference>
- </MacroExpansion>
- <EnvironmentVariables>
- <EnvironmentVariable
- key = "REDDIT_CLIENT_SECRET"
- value = "S4P1-g5CMxzTegTwVNn8BSq3_RvrVQ"
- isEnabled = "YES">
- </EnvironmentVariable>
- <EnvironmentVariable
- key = "REDDIT_CLIENT_ID"
- value = "Z19OW-YwogDenXivptNW_Q"
- isEnabled = "YES">
- </EnvironmentVariable>
- </EnvironmentVariables>
- </LaunchAction>
- <ProfileAction
- buildConfiguration = "Release"
- shouldUseLaunchSchemeArgsEnv = "YES"
- savedToolIdentifier = ""
- useCustomWorkingDirectory = "NO"
- debugDocumentVersioning = "YES">
- <MacroExpansion>
- <BuildableReference
- BuildableIdentifier = "primary"
- BlueprintIdentifier = "Minimal"
- BuildableName = "Minimal"
- BlueprintName = "Minimal"
- ReferencedContainer = "container:">
- </BuildableReference>
- </MacroExpansion>
- </ProfileAction>
- <AnalyzeAction
- buildConfiguration = "Debug">
- </AnalyzeAction>
- <ArchiveAction
- buildConfiguration = "Release"
- revealArchiveInOrganizer = "YES">
- </ArchiveAction>
-</Scheme>
diff --git a/Sources/SwiftNLP/2. Embeddings/KeyedVectorCorpus.swift b/Sources/SwiftNLP/2. Embeddings/KeyedVectorCorpus.swift
index f5f9dcb3c5c51356b5f9833bca3b931b2c557aff..d06266f343ec561e76ff880f5df78dbd63d4f5ae 100644
--- a/Sources/SwiftNLP/2. Embeddings/KeyedVectorCorpus.swift
+++ b/Sources/SwiftNLP/2. Embeddings/KeyedVectorCorpus.swift
@@ -21,13 +21,6 @@
// 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)
import Surge
@@ -63,7 +56,7 @@ class KeyedVectorCorpus: SNLPCorpus {
var encoding = defaultDocumentEncoding
for word in document {
if dictionary.contains(word) {
- #if canImport(Surge)
+ #if canImport(Accelerate)
encoding .+= dictionary[word] // Surge in-place
#else
for i in 0 ..< encoding.count {
@@ -74,7 +67,7 @@ class KeyedVectorCorpus: SNLPCorpus {
}
}
- #if canImport(Surge)
+ #if canImport(Accelerate)
encoding /= Double(document.count)
#else
for i in 0 ..< encoding.count {
diff --git a/Sources/SwiftNLP/2. Embeddings/KeyedVectorDictionary.swift b/Sources/SwiftNLP/2. Embeddings/KeyedVectorDictionary.swift
index fcd03d8635f11b7a2c0b943ec57d767af5d38bba..5bf8210863ece2188e7af38869950f806db41452 100644
--- a/Sources/SwiftNLP/2. Embeddings/KeyedVectorDictionary.swift
+++ b/Sources/SwiftNLP/2. Embeddings/KeyedVectorDictionary.swift
@@ -23,7 +23,7 @@
import Foundation
-#if os(Darwin)
+#if canImport(Accelerate)
import SwiftAnnoy
#else
#warning("Need an alternative to SwiftAnnoy for Linux")
diff --git a/Sources/SwiftNLP/2. Embeddings/SIMDCorpus.swift b/Sources/SwiftNLP/2. Embeddings/SIMDCorpus.swift
index 3fc9d5c183814bdc4ab857f6cd94f439e4f34517..771871df4196fce59470bed4495a0936c0ed5e6d 100644
--- a/Sources/SwiftNLP/2. Embeddings/SIMDCorpus.swift
+++ b/Sources/SwiftNLP/2. Embeddings/SIMDCorpus.swift
@@ -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)")
+// }
+//
+// }
}
diff --git a/Sources/SwiftNLP/4. Clustering/K-Means.swift b/Sources/SwiftNLP/4. Clustering/K-Means.swift
index 087ad7676dd52b147b15f39a6da2c76bf8289b93..8180cd970a03bdce865689e6bcd35b8ab944bca7 100644
--- a/Sources/SwiftNLP/4. Clustering/K-Means.swift
+++ b/Sources/SwiftNLP/4. Clustering/K-Means.swift
@@ -1,110 +1,110 @@
-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
+//}
diff --git a/Sources/SwiftNLP/4. Clustering/SIMD + distance.swift b/Sources/SwiftNLP/4. Clustering/SIMD + distance.swift
index a278b9eec7621bd841ca2732e694d19c14b8db8f..5137846388fbb44ca2026f0904db145a9fea6d27 100644
--- a/Sources/SwiftNLP/4. Clustering/SIMD + distance.swift
+++ b/Sources/SwiftNLP/4. Clustering/SIMD + distance.swift
@@ -1,103 +1,103 @@
-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)
+// }
+//}
diff --git a/Sources/SwiftNLP/4. Clustering/SIMD2 + KDTreePoint.swift b/Sources/SwiftNLP/4. Clustering/SIMD2 + KDTreePoint.swift
index 2593f742cd53195d5d62606634893da6ab9f9563..a4d4d6c5d1b2e3b9f8352192753571a05db7e94a 100644
--- a/Sources/SwiftNLP/4. Clustering/SIMD2 + KDTreePoint.swift
+++ b/Sources/SwiftNLP/4. Clustering/SIMD2 + KDTreePoint.swift
@@ -1,39 +1,39 @@
-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))
+// }
+//
+//}
diff --git a/Tests/SwiftNLPTests/SNLPLoadDataTests.swift b/Tests/SwiftNLPTests/SNLPLoadDataTests.swift
index 9e39c1c5b6be02d99c36f3cde415d258fcd3f006..e86742fc502a89ea4648771c2c52857d62473d7e 100644
--- a/Tests/SwiftNLPTests/SNLPLoadDataTests.swift
+++ b/Tests/SwiftNLPTests/SNLPLoadDataTests.swift
@@ -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)
+// }
}