modified version in least squares linear regression tutorial

modified variable names in KNeighbors classifer and regressor

Author: param087

Notebooks/Least Squares Linear Regression.ipynb

@@ -99,14 +99,14 @@
      "output_type": "stream",
      "text": [
       "Installing packages:\n",
-      "\t.package(url: \"https://github.com/param087/swiftML\", from: \"0.0.2\")\n",
+      "\t.package(url: \"https://github.com/param087/swiftML\", from: \"0.0.3\")\n",
       "\t\tswiftML\n",
       "With SwiftPM flags: []\n",
       "Working in: /tmp/tmp29sd7bnt/swift-install\n",
       "Fetching https://github.com/param087/swiftML\n",
       "Completed resolution in 2.95s\n",
       "Cloning https://github.com/param087/swiftML\n",
-      "Resolving https://github.com/param087/swiftML at 0.0.2\n",
+      "Resolving https://github.com/param087/swiftML at 0.0.3\n",
       "Compile Swift Module 'swiftML' (16 sources)\n",
       "/tmp/tmp29sd7bnt/swift-install/package/.build/checkouts/swiftML-1801b701/Sources/swiftML/KNeighborsClassifier.swift:106:13: warning: variable 'indices' was written to, but never read\n",
       "        var indices: Tensor<Int32>\n",
@@ -127,7 +127,7 @@
     }
    ],
    "source": [
-    "%install '.package(url: \"https://github.com/param087/swiftML\", from: \"0.0.2\")' swiftML"
+    "%install '.package(url: \"https://github.com/param087/swiftML\", from: \"0.0.3\")' swiftML"
    ]
   },
   {

Sources/swiftML/BernoulliNB.swift

@@ -47,8 +47,6 @@ public class BernoulliNB {
         // Find unique classes in target values.
         (self.classes, self.indices) = Raw.unique(labels.flattened())
 
-        precondition(self.classes.shape[0] == 2, "Labels must have only two classes.")
-
         // Initialize the classLogPrior and featureLogProb based on feature count and sample count.
         var separated = [[Tensor<Float>]]()
         self.classLogPrior = Tensor<Float>(zeros: [self.classes.shape[0]])

Sources/swiftML/KNeighborsClassifier.swift

@@ -100,9 +100,9 @@ public class KNeighborsClassifier {
     /// - Returns: Predicted classification.
     internal func predictSingleSample(_ test: Tensor<Float>) -> Tensor<Int32> {
         var distances = Tensor<Float>(zeros: [self.data.shape[0]])
-        var maxLabel = Tensor<Int32>(zeros: [self.neighborCount])
-        var maxDistances: Tensor<Float>
-        var maxIndex: Tensor<Int32>
+        var minDistanceLabels = Tensor<Int32>(zeros: [self.neighborCount])
+        var minDistances: Tensor<Float>
+        var minDistanceIndex: Tensor<Int32>
         var classes: Tensor<Int32>
         var indices: Tensor<Int32>
 
@@ -112,27 +112,27 @@ public class KNeighborsClassifier {
         }
 
         // Find the top neighbor with minimum distance.
-        (maxDistances, maxIndex) =
+        (minDistances, minDistanceIndex) =
             Raw.topKV2(distances, k: Tensor<Int32>(Int32(data.shape[0])), sorted: true)
-        maxDistances = Raw.reverse(maxDistances, dims: Tensor<Bool>([true]))
-        maxDistances = maxDistances
+        minDistances = Raw.reverse(minDistances, dims: Tensor<Bool>([true]))
+        minDistances = minDistances
             .slice(lowerBounds: Tensor<Int32>([0]),
                 sizes: Tensor<Int32>([Int32(self.neighborCount)]))
 
-        maxIndex = Raw.reverse(maxIndex, dims: Tensor<Bool>([true]))
-        maxIndex = maxIndex
+        minDistanceIndex = Raw.reverse(minDistanceIndex, dims: Tensor<Bool>([true]))
+        minDistanceIndex = minDistanceIndex
             .slice(lowerBounds: Tensor<Int32>([0]),
                 sizes: Tensor<Int32>([Int32(self.neighborCount)]))
 
         for i in 0..<self.neighborCount {
-            maxLabel[i] = self.labels[Int(maxIndex[i].scalarized())]
+            minDistanceLabels[i] = self.labels[Int(minDistanceIndex[i].scalarized())]
         }
 
         // Weights the neighbors based on their weighing method.
         let labelsAndWeightsTensor = computeWeights(
-            distances: maxDistances, labels: Tensor<Float>(maxLabel))
+            distances: minDistances, labels: Tensor<Float>(minDistanceLabels))
 
-        (classes, indices) = Raw.unique(Tensor<Int32>(maxLabel))
+        (classes, indices) = Raw.unique(Tensor<Int32>(minDistanceLabels))
 
         var kClasses = Tensor<Int32>(zeros: [classes.shape[0]])
         var kWeights = Tensor<Float>(zeros: [classes.shape[0]])

Sources/swiftML/KNeighborsRegressor.swift

@@ -97,34 +97,34 @@ public class KNeighborsRegressor {
     /// - Returns: Predicted target value.
     internal func predictSingleSample(_ test: Tensor<Float>) -> Tensor<Float> {
         var distances = Tensor<Float>(zeros: [self.data.shape[0]])
-        var maxLabel = Tensor<Float>(zeros: [self.neighborCount])
-        var maxDistances: Tensor<Float>
-        var maxIndex: Tensor<Int32>
+        var minDistanceLabels = Tensor<Float>(zeros: [self.neighborCount])
+        var minDistances: Tensor<Float>
+        var minDistanceIndex: Tensor<Int32>
 
         // Calculate the distance between test and all data points.
         for i in 0..<self.data.shape[0] {
             distances[i] = minkowskiDistance(self.data[i], test, p: self.p)
         }
 
         // Find the top neighbors with minimum distance.
-        (maxDistances, maxIndex) =
+        (minDistances, minDistanceIndex) =
             Raw.topKV2(distances, k: Tensor<Int32>(Int32(data.shape[0])), sorted: true)
-        maxDistances = Raw.reverse(maxDistances, dims: Tensor<Bool>([true]))
-        maxDistances = maxDistances
+        minDistances = Raw.reverse(minDistances, dims: Tensor<Bool>([true]))
+        minDistances = minDistances
             .slice(lowerBounds: Tensor<Int32>([0]),
                 sizes: Tensor<Int32>([Int32(self.neighborCount)]))
 
-        maxIndex = Raw.reverse(maxIndex, dims: Tensor<Bool>([true]))
-        maxIndex = maxIndex
+        minDistanceIndex = Raw.reverse(minDistanceIndex, dims: Tensor<Bool>([true]))
+        minDistanceIndex = minDistanceIndex
             .slice(lowerBounds: Tensor<Int32>([0]),
                 sizes: Tensor<Int32>([Int32(self.neighborCount)]))
 
         for i in 0..<self.neighborCount {
-            maxLabel[i] = self.labels[Int(maxIndex[i].scalarized())]
+            minDistanceLabels[i] = self.labels[Int(minDistanceIndex[i].scalarized())]
         }
 
         // Average weight based on neighbors weights.
-        let avgWeight = computeWeights(distances: maxDistances, labels: maxLabel)
+        let avgWeight = computeWeights(distances: minDistances, labels: minDistanceLabels)
         return avgWeight
     }