From ffe030b2ce724e7a5d0d6d175cc88d83e0e95b5d Mon Sep 17 00:00:00 2001
From: Iskken <iimanaliev312@gmail.com>
Date: Sun, 12 Jul 2026 23:41:06 +0200
Subject: [PATCH 1/7] Provide minimal knn algorithm version

---
 experiments/knn/Test_utils.ipynb | 1114 ++++++++++++++++++++++++++++++
 glassboxml/models/knn.py         |   19 +
 2 files changed, 1133 insertions(+)
 create mode 100644 experiments/knn/Test_utils.ipynb
 create mode 100644 glassboxml/models/knn.py

diff --git a/experiments/knn/Test_utils.ipynb b/experiments/knn/Test_utils.ipynb
new file mode 100644
index 0000000..05a0e8b
--- /dev/null
+++ b/experiments/knn/Test_utils.ipynb
@@ -0,0 +1,1114 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8cb78c58-1868-4794-aef3-e8f6a68944c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glassboxml.data.generators import generate_classification_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8eb1f929-9680-424e-83a5-eee52144719f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y, w_true, b_true = generate_classification_dataset([1.5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "84421ed5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "       [ 8.31031858e-01],\n",
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+       "       [-5.84874135e-01],\n",
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+       "       [-1.00789295e+00],\n",
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+       "       [-1.89194625e+00],\n",
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+       "       [-2.20967318e-01],\n",
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+       "       [ 2.25559840e-01],\n",
+       "       [ 9.47649610e-01],\n",
+       "       [-1.11110490e+00],\n",
+       "       [-1.17197159e+00],\n",
+       "       [-1.09332386e+00],\n",
+       "       [ 2.88916667e-01],\n",
+       "       [ 1.24494738e+00],\n",
+       "       [-4.31339438e-01],\n",
+       "       [-2.50169984e+00],\n",
+       "       [-1.70399286e+00],\n",
+       "       [-8.33036723e-01],\n",
+       "       [-5.57728887e-01],\n",
+       "       [-4.08388627e-01],\n",
+       "       [ 3.85811230e-02],\n",
+       "       [-3.11746331e-01],\n",
+       "       [ 1.04933961e+00],\n",
+       "       [-6.76017306e-01],\n",
+       "       [-8.62317959e-01],\n",
+       "       [ 4.78875539e-01],\n",
+       "       [-1.53564207e+00],\n",
+       "       [ 3.89691705e-01],\n",
+       "       [ 1.02553184e-01],\n",
+       "       [-1.47442490e-01],\n",
+       "       [ 1.58827611e+00],\n",
+       "       [-6.22207445e-01],\n",
+       "       [ 2.06029817e+00],\n",
+       "       [-2.25431449e-01],\n",
+       "       [-1.27701644e+00],\n",
+       "       [ 6.99195810e-02],\n",
+       "       [-1.07624520e+00],\n",
+       "       [-7.51755848e-01],\n",
+       "       [ 3.97033300e-01],\n",
+       "       [ 5.55582089e-01],\n",
+       "       [-6.22167700e-01],\n",
+       "       [ 9.87405145e-01],\n",
+       "       [ 1.15750776e+00],\n",
+       "       [ 1.43630176e+00],\n",
+       "       [ 5.29413407e-01],\n",
+       "       [ 1.36342871e+00],\n",
+       "       [-1.88079843e+00],\n",
+       "       [-3.17906549e-01],\n",
+       "       [-8.67005256e-01],\n",
+       "       [ 1.19225861e-01],\n",
+       "       [-5.71449219e-01],\n",
+       "       [-1.66157842e-01],\n",
+       "       [ 1.88217488e+00],\n",
+       "       [-1.69719796e-01],\n",
+       "       [ 4.13792317e-01],\n",
+       "       [-2.32269330e-01],\n",
+       "       [ 7.57132880e-02],\n",
+       "       [ 6.01668044e-03],\n",
+       "       [ 4.48324066e-01],\n",
+       "       [ 1.16530754e+00],\n",
+       "       [ 1.64739400e+00],\n",
+       "       [ 3.09620082e-01],\n",
+       "       [ 5.89546886e-01],\n",
+       "       [-1.15086451e+00],\n",
+       "       [-8.78767424e-02],\n",
+       "       [ 9.40289465e-01],\n",
+       "       [ 8.65968638e-01],\n",
+       "       [ 2.11609733e-01],\n",
+       "       [ 8.86393960e-01],\n",
+       "       [ 4.90766709e-01],\n",
+       "       [ 1.20030626e+00],\n",
+       "       [ 2.89359157e-01],\n",
+       "       [-3.55698316e-01],\n",
+       "       [ 3.35841257e-01],\n",
+       "       [-2.93059438e+00],\n",
+       "       [ 3.82885736e-01],\n",
+       "       [-3.64841283e+00],\n",
+       "       [-1.72346341e+00],\n",
+       "       [ 4.51768601e-01],\n",
+       "       [ 4.77529337e-01],\n",
+       "       [-1.16242878e+00],\n",
+       "       [-7.12102042e-01],\n",
+       "       [ 1.37054099e+00],\n",
+       "       [-4.84030132e-01],\n",
+       "       [ 2.24292031e+00],\n",
+       "       [-1.91985318e-03],\n",
+       "       [ 4.08036177e-01],\n",
+       "       [ 1.61687440e+00],\n",
+       "       [ 1.31027116e-01],\n",
+       "       [-1.00234398e+00],\n",
+       "       [-1.09727452e-01],\n",
+       "       [-3.56107362e-02],\n",
+       "       [-1.36474165e+00],\n",
+       "       [-2.55832071e-01],\n",
+       "       [-7.42192094e-01],\n",
+       "       [ 9.24357757e-01],\n",
+       "       [ 3.46118742e-02],\n",
+       "       [-2.82795749e-01],\n",
+       "       [-1.06181931e-01],\n",
+       "       [ 2.23122018e-01],\n",
+       "       [ 6.16814200e-01],\n",
+       "       [-9.99712229e-01],\n",
+       "       [-1.04158763e+00],\n",
+       "       [ 1.10467922e+00],\n",
+       "       [-4.12336872e-01],\n",
+       "       [-1.41684209e+00],\n",
+       "       [ 4.43812709e-01],\n",
+       "       [ 4.63369806e-01],\n",
+       "       [-1.53071526e+00],\n",
+       "       [ 2.29481715e-01],\n",
+       "       [ 7.35583092e-01],\n",
+       "       [ 3.74386452e-01],\n",
+       "       [ 6.31981484e-01],\n",
+       "       [-1.40426910e+00],\n",
+       "       [ 3.31040155e-01],\n",
+       "       [-3.02619759e-01],\n",
+       "       [-4.82790159e-01],\n",
+       "       [ 8.70555609e-01],\n",
+       "       [ 1.47927451e+00],\n",
+       "       [ 1.79437005e+00],\n",
+       "       [ 1.31480787e+00],\n",
+       "       [-1.09734182e-01],\n",
+       "       [ 3.52720163e-01],\n",
+       "       [ 7.66822872e-01],\n",
+       "       [ 1.21177949e-01],\n",
+       "       [ 1.30764188e-01],\n",
+       "       [ 8.23753132e-01]])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "17a579fe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "e8213628",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x770115fb6510>]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(X, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b249fd51",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.11.7.final.0)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/glassboxml/models/knn.py b/glassboxml/models/knn.py
new file mode 100644
index 0000000..34da20e
--- /dev/null
+++ b/glassboxml/models/knn.py
@@ -0,0 +1,19 @@
+import numpy as np
+from collections import Counter
+
+class KNN:
+    def __init__(self, k=5):
+        self.k = k
+
+    def fit(self, X, y):
+        self.X_train = np.array(X)
+        self.y_train = np.array(y)
+
+    def predict(self, X):
+        return np.array([self._predict_sample(x) for x in np.array(X)])
+
+    def _predict_sample(self, x):
+        distances = np.linalg.norm(self.X_train - x, axis=1)
+        k_indices = np.argsort(distances)[:self.k]
+        k_labels = self.y_train[k_indices]
+        return Counter(k_labels).most_common(1)[0][0]

From cfeed5b274d7d0cc5d489e5f9970ff842db530c2 Mon Sep 17 00:00:00 2001
From: Iskken <iimanaliev312@gmail.com>
Date: Mon, 13 Jul 2026 06:27:06 +0200
Subject: [PATCH 2/7] Rework basic knn implementation

---
 experiments/knn/Test_utils.ipynb  | 1114 -----------------------------
 experiments/knn/scratch.ipynb     |  172 +++++
 glassboxml/distances/distances.py |    8 +
 glassboxml/models/knn.py          |   16 +-
 4 files changed, 191 insertions(+), 1119 deletions(-)
 delete mode 100644 experiments/knn/Test_utils.ipynb
 create mode 100644 experiments/knn/scratch.ipynb
 create mode 100644 glassboxml/distances/distances.py

diff --git a/experiments/knn/Test_utils.ipynb b/experiments/knn/Test_utils.ipynb
deleted file mode 100644
index 05a0e8b..0000000
--- a/experiments/knn/Test_utils.ipynb
+++ /dev/null
@@ -1,1114 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8cb78c58-1868-4794-aef3-e8f6a68944c0",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from glassboxml.data.generators import generate_classification_dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "id": "8eb1f929-9680-424e-83a5-eee52144719f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X, y, w_true, b_true = generate_classification_dataset([1.5])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "id": "84421ed5",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[ 3.04717080e-01],\n",
-       "       [-1.03998411e+00],\n",
-       "       [ 7.50451196e-01],\n",
-       "       [ 9.40564716e-01],\n",
-       "       [-1.95103519e+00],\n",
-       "       [-1.30217951e+00],\n",
-       "       [ 1.27840403e-01],\n",
-       "       [-3.16242592e-01],\n",
-       "       [-1.68011575e-02],\n",
-       "       [-8.53043928e-01],\n",
-       "       [ 8.79397975e-01],\n",
-       "       [ 7.77791935e-01],\n",
-       "       [ 6.60306976e-02],\n",
-       "       [ 1.12724121e+00],\n",
-       "       [ 4.67509342e-01],\n",
-       "       [-8.59292463e-01],\n",
-       "       [ 3.68750784e-01],\n",
-       "       [-9.58882601e-01],\n",
-       "       [ 8.78450301e-01],\n",
-       "       [-4.99259110e-02],\n",
-       "       [-1.84862364e-01],\n",
-       "       [-6.80929544e-01],\n",
-       "       [ 1.22254134e+00],\n",
-       "       [-1.54529482e-01],\n",
-       "       [-4.28327822e-01],\n",
-       "       [-3.52133550e-01],\n",
-       "       [ 5.32309186e-01],\n",
-       "       [ 3.65444064e-01],\n",
-       "       [ 4.12732612e-01],\n",
-       "       [ 4.30821003e-01],\n",
-       "       [ 2.14164760e+00],\n",
-       "       [-4.06415016e-01],\n",
-       "       [-5.12242729e-01],\n",
-       "       [-8.13772728e-01],\n",
-       "       [ 6.15979423e-01],\n",
-       "       [ 1.12897229e+00],\n",
-       "       [-1.13947458e-01],\n",
-       "       [-8.40156477e-01],\n",
-       "       [-8.24481216e-01],\n",
-       "       [ 6.50592788e-01],\n",
-       "       [ 7.43254171e-01],\n",
-       "       [ 5.43154268e-01],\n",
-       "       [-6.65509707e-01],\n",
-       "       [ 2.32161323e-01],\n",
-       "       [ 1.16685809e-01],\n",
-       "       [ 2.18688597e-01],\n",
-       "       [ 8.71428778e-01],\n",
-       "       [ 2.23595549e-01],\n",
-       "       [ 6.78913563e-01],\n",
-       "       [ 6.75790695e-02],\n",
-       "       [ 2.89119399e-01],\n",
-       "       [ 6.31288226e-01],\n",
-       "       [-1.45715582e+00],\n",
-       "       [-3.19671216e-01],\n",
-       "       [-4.70372654e-01],\n",
-       "       [-6.38877848e-01],\n",
-       "       [-2.75142251e-01],\n",
-       "       [ 1.49494131e+00],\n",
-       "       [-8.65831116e-01],\n",
-       "       [ 9.68278355e-01],\n",
-       "       [-1.68286977e+00],\n",
-       "       [-3.34885030e-01],\n",
-       "       [ 1.62753065e-01],\n",
-       "       [ 5.86222331e-01],\n",
-       "       [ 7.11226580e-01],\n",
-       "       [ 7.93347235e-01],\n",
-       "       [-3.48725072e-01],\n",
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-       "       [-1.72346341e+00],\n",
-       "       [ 4.51768601e-01],\n",
-       "       [ 4.77529337e-01],\n",
-       "       [-1.16242878e+00],\n",
-       "       [-7.12102042e-01],\n",
-       "       [ 1.37054099e+00],\n",
-       "       [-4.84030132e-01],\n",
-       "       [ 2.24292031e+00],\n",
-       "       [-1.91985318e-03],\n",
-       "       [ 4.08036177e-01],\n",
-       "       [ 1.61687440e+00],\n",
-       "       [ 1.31027116e-01],\n",
-       "       [-1.00234398e+00],\n",
-       "       [-1.09727452e-01],\n",
-       "       [-3.56107362e-02],\n",
-       "       [-1.36474165e+00],\n",
-       "       [-2.55832071e-01],\n",
-       "       [-7.42192094e-01],\n",
-       "       [ 9.24357757e-01],\n",
-       "       [ 3.46118742e-02],\n",
-       "       [-2.82795749e-01],\n",
-       "       [-1.06181931e-01],\n",
-       "       [ 2.23122018e-01],\n",
-       "       [ 6.16814200e-01],\n",
-       "       [-9.99712229e-01],\n",
-       "       [-1.04158763e+00],\n",
-       "       [ 1.10467922e+00],\n",
-       "       [-4.12336872e-01],\n",
-       "       [-1.41684209e+00],\n",
-       "       [ 4.43812709e-01],\n",
-       "       [ 4.63369806e-01],\n",
-       "       [-1.53071526e+00],\n",
-       "       [ 2.29481715e-01],\n",
-       "       [ 7.35583092e-01],\n",
-       "       [ 3.74386452e-01],\n",
-       "       [ 6.31981484e-01],\n",
-       "       [-1.40426910e+00],\n",
-       "       [ 3.31040155e-01],\n",
-       "       [-3.02619759e-01],\n",
-       "       [-4.82790159e-01],\n",
-       "       [ 8.70555609e-01],\n",
-       "       [ 1.47927451e+00],\n",
-       "       [ 1.79437005e+00],\n",
-       "       [ 1.31480787e+00],\n",
-       "       [-1.09734182e-01],\n",
-       "       [ 3.52720163e-01],\n",
-       "       [ 7.66822872e-01],\n",
-       "       [ 1.21177949e-01],\n",
-       "       [ 1.30764188e-01],\n",
-       "       [ 8.23753132e-01]])"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "17a579fe",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "e8213628",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x770115fb6510>]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.plot(X, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b249fd51",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": ".venv (3.11.7.final.0)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/experiments/knn/scratch.ipynb b/experiments/knn/scratch.ipynb
new file mode 100644
index 0000000..b3d78b4
--- /dev/null
+++ b/experiments/knn/scratch.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8cb78c58-1868-4794-aef3-e8f6a68944c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glassboxml.data.generators import generate_classification_dataset"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "8eb1f929-9680-424e-83a5-eee52144719f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X, y, w_true, b_true = generate_classification_dataset([1.5])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "330bc2ee",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from glassboxml.models.knn import KNN\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "\n",
+    "X_train, X_test, y_train, y_test = train_test_split(\n",
+    "    X, y, test_size=0.33, random_state=42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "1b0358e9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = KNN(5)\n",
+    "model.fit(X_train, y_train)\n",
+    "y_pred = model.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "b811db49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(0.996969696969697)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from glassboxml.metrics.classification import accuracy\n",
+    "\n",
+    "accuracy(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "88c483ad",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0,\n",
+       "       1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0,\n",
+       "       1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
+       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
+       "       1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,\n",
+       "       1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
+       "       1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1,\n",
+       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
+       "       0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
+       "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
+       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
+       "       1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
+       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n",
+       "       0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
+       "       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0])"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "18c7f19d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n",
+       "       0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
+       "       0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,\n",
+       "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
+       "       0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,\n",
+       "       0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
+       "       0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,\n",
+       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1,\n",
+       "       1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,\n",
+       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
+       "       0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
+       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
+       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
+       "       0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ba8799d1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.11.7.final.0)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/glassboxml/distances/distances.py b/glassboxml/distances/distances.py
new file mode 100644
index 0000000..6756553
--- /dev/null
+++ b/glassboxml/distances/distances.py
@@ -0,0 +1,8 @@
+import numpy as np
+
+def calculate_euclidean(point1, point2):
+    if (len(point1) != len(point2)):
+        raise Exception("Dimensions of points are not equal!")
+    n_features = len(point1)
+
+    return np.sqrt(np.sum(np.square([point1[i] - point2[i] for i in range(len(point1))])))
\ No newline at end of file
diff --git a/glassboxml/models/knn.py b/glassboxml/models/knn.py
index 34da20e..a748027 100644
--- a/glassboxml/models/knn.py
+++ b/glassboxml/models/knn.py
@@ -1,5 +1,5 @@
 import numpy as np
-from collections import Counter
+from glassboxml.distances.distances import calculate_euclidean
 
 class KNN:
     def __init__(self, k=5):
@@ -13,7 +13,13 @@ def predict(self, X):
         return np.array([self._predict_sample(x) for x in np.array(X)])
 
     def _predict_sample(self, x):
-        distances = np.linalg.norm(self.X_train - x, axis=1)
-        k_indices = np.argsort(distances)[:self.k]
-        k_labels = self.y_train[k_indices]
-        return Counter(k_labels).most_common(1)[0][0]
+        distances = []
+        for i in range(len(self.X_train)):
+            distances.append((calculate_euclidean(self.X_train[i], x), i))
+
+        k_closest_points = sorted(distances)[:self.k]
+        if np.sum([1 if self.y_train[i] == 1 else 0 for _, i in k_closest_points]) > (self.k / 2):
+            result = 1
+        else:
+            result = 0
+        return result
\ No newline at end of file

From 7d5aa328d9c257973e03bca68a8d1ac23e996627 Mon Sep 17 00:00:00 2001
From: Iskken <iimanaliev312@gmail.com>
Date: Mon, 13 Jul 2026 20:46:33 +0200
Subject: [PATCH 3/7] Optimize KNN's prediction code

---
 experiments/knn/scratch.ipynb     | 68 +++++++++++--------------------
 glassboxml/distances/distances.py |  3 +-
 glassboxml/models/knn.py          | 16 ++++----
 3 files changed, 33 insertions(+), 54 deletions(-)

diff --git a/experiments/knn/scratch.ipynb b/experiments/knn/scratch.ipynb
index b3d78b4..d15a512 100644
--- a/experiments/knn/scratch.ipynb
+++ b/experiments/knn/scratch.ipynb
@@ -17,7 +17,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "X, y, w_true, b_true = generate_classification_dataset([1.5])"
+    "X, y, w_true, b_true = generate_classification_dataset([1.5, 2.0, 0.4, 0.1])"
    ]
   },
   {
@@ -55,7 +55,7 @@
     {
      "data": {
       "text/plain": [
-       "np.float64(0.996969696969697)"
+       "np.float64(0.9545454545454546)"
       ]
      },
      "execution_count": 5,
@@ -72,62 +72,39 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "88c483ad",
+   "id": "b4a63140",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "array([0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0,\n",
-       "       1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0,\n",
-       "       1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1,\n",
-       "       0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0,\n",
-       "       1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1,\n",
-       "       1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
-       "       1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1,\n",
-       "       1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0,\n",
-       "       0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
-       "       1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
-       "       0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
-       "       1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1,\n",
-       "       1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1,\n",
-       "       0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
-       "       1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 0])"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "predictions on training data: [0 0 0 1 1 1 2 2 2]\n",
+      "expected:                     [0 0 0 1 1 1 2 2 2]\n"
+     ]
     }
    ],
    "source": [
-    "y_test"
+    "import numpy as np\n",
+    "X = np.array([[0.0],[0.1],[0.2], [5.0],[5.1],[5.2], [10.0],[10.1],[10.2]])\n",
+    "y = np.array([0,0,0, 1,1,1, 2,2,2])\n",
+    "\n",
+    "model = KNN(k=3)\n",
+    "model.fit(X, y)\n",
+    "print('predictions on training data:', model.predict(X))\n",
+    "print('expected:                    ', y)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "18c7f19d",
+   "id": "9df85a4e",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "array([1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1,\n",
-       "       0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1,\n",
-       "       0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0,\n",
-       "       1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1,\n",
-       "       0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0,\n",
-       "       0, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
-       "       0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0,\n",
-       "       0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1,\n",
-       "       1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
-       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1,\n",
-       "       1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1,\n",
-       "       0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0,\n",
-       "       0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0,\n",
-       "       1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0,\n",
-       "       0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1])"
+       "array([1.73205081, 6.4807407 , 3.74165739])"
       ]
      },
      "execution_count": 7,
@@ -136,13 +113,16 @@
     }
    ],
    "source": [
-    "y_pred"
+    "X_sample = np.array([[1,1,1], [1,4,5], [3,2,1]])\n",
+    "x = np.array([0,0,0])\n",
+    "\n",
+    "np.linalg.norm(X_sample - x, axis=1)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "ba8799d1",
+   "id": "ddf12c44",
    "metadata": {},
    "outputs": [],
    "source": []
diff --git a/glassboxml/distances/distances.py b/glassboxml/distances/distances.py
index 6756553..f31a422 100644
--- a/glassboxml/distances/distances.py
+++ b/glassboxml/distances/distances.py
@@ -3,6 +3,5 @@
 def calculate_euclidean(point1, point2):
     if (len(point1) != len(point2)):
         raise Exception("Dimensions of points are not equal!")
-    n_features = len(point1)
-
+    
     return np.sqrt(np.sum(np.square([point1[i] - point2[i] for i in range(len(point1))])))
\ No newline at end of file
diff --git a/glassboxml/models/knn.py b/glassboxml/models/knn.py
index a748027..f844fca 100644
--- a/glassboxml/models/knn.py
+++ b/glassboxml/models/knn.py
@@ -1,4 +1,5 @@
 import numpy as np
+from collections import Counter
 from glassboxml.distances.distances import calculate_euclidean
 
 class KNN:
@@ -13,13 +14,12 @@ def predict(self, X):
         return np.array([self._predict_sample(x) for x in np.array(X)])
 
     def _predict_sample(self, x):
-        distances = []
-        for i in range(len(self.X_train)):
-            distances.append((calculate_euclidean(self.X_train[i], x), i))
+        #Use vectorized calculations for calculating distances
+        distances = np.linalg.norm(self.X_train - x, axis=1)
 
-        k_closest_points = sorted(distances)[:self.k]
-        if np.sum([1 if self.y_train[i] == 1 else 0 for _, i in k_closest_points]) > (self.k / 2):
-            result = 1
-        else:
-            result = 0
+        #Use argpartition to obtain k closest neighbours
+        partitioned_indices = np.argpartition(distances, self.k) #Put k closest elements at the front of the index list
+        k_closest_indices = partitioned_indices[:self.k]
+
+        result = Counter(self.y_train[k_closest_indices]).most_common(1)[0][0]
         return result
\ No newline at end of file

From 28caf1eaee0539124b00ff8fe71a5d9c5d248ce8 Mon Sep 17 00:00:00 2001
From: Iskken <iimanaliev312@gmail.com>
Date: Tue, 14 Jul 2026 02:59:34 +0200
Subject: [PATCH 4/7] Partition KNN into KNNClassifier and KNNRegressor

---
 glassboxml/models/knn.py | 21 ++++++++++++++++-----
 1 file changed, 16 insertions(+), 5 deletions(-)

diff --git a/glassboxml/models/knn.py b/glassboxml/models/knn.py
index f844fca..e324971 100644
--- a/glassboxml/models/knn.py
+++ b/glassboxml/models/knn.py
@@ -1,8 +1,7 @@
 import numpy as np
 from collections import Counter
-from glassboxml.distances.distances import calculate_euclidean
 
-class KNN:
+class _KNNBase:
     def __init__(self, k=5):
         self.k = k
 
@@ -13,7 +12,7 @@ def fit(self, X, y):
     def predict(self, X):
         return np.array([self._predict_sample(x) for x in np.array(X)])
 
-    def _predict_sample(self, x):
+    def _k_nearest_labels(self, x):
         #Use vectorized calculations for calculating distances
         distances = np.linalg.norm(self.X_train - x, axis=1)
 
@@ -21,5 +20,17 @@ def _predict_sample(self, x):
         partitioned_indices = np.argpartition(distances, self.k) #Put k closest elements at the front of the index list
         k_closest_indices = partitioned_indices[:self.k]
 
-        result = Counter(self.y_train[k_closest_indices]).most_common(1)[0][0]
-        return result
\ No newline at end of file
+        return self.y_train[k_closest_indices]
+
+    def _predict_sample(self, x):
+        raise NotImplementedError
+
+class KNNClassifier(_KNNBase):
+    def _predict_sample(self, x):
+        k_labels = self._k_nearest_labels(x)
+        return Counter(k_labels).most_common(1)[0][0]
+
+class KNNRegressor(_KNNBase):
+    def _predict_sample(self, x):
+        k_labels = self._k_nearest_labels(x)
+        return np.mean(k_labels)
\ No newline at end of file

From 081338853fce0dfd10f602f7457120bedf92733c Mon Sep 17 00:00:00 2001
From: Iskken <iimanaliev312@gmail.com>
Date: Tue, 14 Jul 2026 20:35:45 +0200
Subject: [PATCH 5/7] Code tests for knn

---
 .vscode/settings.json         |   7 ++
 experiments/knn/scratch.ipynb |  42 +++++++++++-
 glassboxml/models/knn.py      |   7 ++
 tests/models/test_knn.py      | 118 ++++++++++++++++++++++++++++++++++
 4 files changed, 171 insertions(+), 3 deletions(-)
 create mode 100644 .vscode/settings.json
 create mode 100644 tests/models/test_knn.py

diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..3e99ede
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "."
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}
\ No newline at end of file
diff --git a/experiments/knn/scratch.ipynb b/experiments/knn/scratch.ipynb
index d15a512..aaa4250 100644
--- a/experiments/knn/scratch.ipynb
+++ b/experiments/knn/scratch.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 8,
    "id": "8cb78c58-1868-4794-aef3-e8f6a68944c0",
    "metadata": {},
    "outputs": [],
@@ -121,16 +121,52 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "ddf12c44",
    "metadata": {},
+   "outputs": [
+    {
+     "ename": "AxisError",
+     "evalue": "axis 1 is out of bounds for array of dimension 1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+      "\u001b[31mAxisError\u001b[39m                                 Traceback (most recent call last)",
+      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 10\u001b[39m\n\u001b[32m      7\u001b[39m model = KNNClassifier(k = \u001b[32m3\u001b[39m)\n\u001b[32m      8\u001b[39m model.fit(X,y)\n\u001b[32m---> \u001b[39m\u001b[32m10\u001b[39m y_pred = \u001b[43mmodel\u001b[49m\u001b[43m.\u001b[49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/GlassBoxML/glassboxml/models/knn.py:13\u001b[39m, in \u001b[36m_KNNBase.predict\u001b[39m\u001b[34m(self, X)\u001b[39m\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m np.array(\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_predict_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m)\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/GlassBoxML/glassboxml/models/knn.py:13\u001b[39m, in \u001b[36m<listcomp>\u001b[39m\u001b[34m(.0)\u001b[39m\n\u001b[32m     12\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mpredict\u001b[39m(\u001b[38;5;28mself\u001b[39m, X):\n\u001b[32m---> \u001b[39m\u001b[32m13\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m np.array([\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_predict_sample\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m np.array(X)])\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/GlassBoxML/glassboxml/models/knn.py:37\u001b[39m, in \u001b[36mKNNClassifier._predict_sample\u001b[39m\u001b[34m(self, x)\u001b[39m\n\u001b[32m     36\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_predict_sample\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[32m---> \u001b[39m\u001b[32m37\u001b[39m     k_labels = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_k_nearest_labels\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m     38\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m Counter(k_labels).most_common(\u001b[32m1\u001b[39m)[\u001b[32m0\u001b[39m][\u001b[32m0\u001b[39m]\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/GlassBoxML/glassboxml/models/knn.py:17\u001b[39m, in \u001b[36m_KNNBase._k_nearest_labels\u001b[39m\u001b[34m(self, x)\u001b[39m\n\u001b[32m     15\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34m_k_nearest_labels\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[32m     16\u001b[39m     \u001b[38;5;66;03m#Use vectorized calculations for calculating distances\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m17\u001b[39m     distances = \u001b[43mnp\u001b[49m\u001b[43m.\u001b[49m\u001b[43mlinalg\u001b[49m\u001b[43m.\u001b[49m\u001b[43mnorm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mX_train\u001b[49m\u001b[43m \u001b[49m\u001b[43m-\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[32;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[32m     19\u001b[39m     \u001b[38;5;66;03m# argpartition(distances, k) needs an extra element past the k we\u001b[39;00m\n\u001b[32m     20\u001b[39m     \u001b[38;5;66;03m# keep to anchor the partition, so it only works for k < n_samples.\u001b[39;00m\n\u001b[32m     21\u001b[39m     \u001b[38;5;66;03m# If k covers the whole training set (or more), there's nothing to\u001b[39;00m\n\u001b[32m     22\u001b[39m     \u001b[38;5;66;03m# partition - every point is a \"k nearest\" neighbour.\u001b[39;00m\n\u001b[32m     23\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m.k >= \u001b[38;5;28mlen\u001b[39m(distances):\n",
+      "\u001b[36mFile \u001b[39m\u001b[32m~/GlassBoxML/.venv/lib/python3.11/site-packages/numpy/linalg/_linalg.py:2804\u001b[39m, in \u001b[36mnorm\u001b[39m\u001b[34m(x, ord, axis, keepdims)\u001b[39m\n\u001b[32m   2801\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mord\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mord\u001b[39m == \u001b[32m2\u001b[39m:\n\u001b[32m   2802\u001b[39m     \u001b[38;5;66;03m# special case for speedup\u001b[39;00m\n\u001b[32m   2803\u001b[39m     s = (x.conj() * x).real\n\u001b[32m-> \u001b[39m\u001b[32m2804\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m sqrt(\u001b[43madd\u001b[49m\u001b[43m.\u001b[49m\u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[43m=\u001b[49m\u001b[43maxis\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m=\u001b[49m\u001b[43mkeepdims\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m   2805\u001b[39m \u001b[38;5;66;03m# None of the str-type keywords for ord ('fro', 'nuc')\u001b[39;00m\n\u001b[32m   2806\u001b[39m \u001b[38;5;66;03m# are valid for vectors\u001b[39;00m\n\u001b[32m   2807\u001b[39m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mord\u001b[39m, \u001b[38;5;28mstr\u001b[39m):\n",
+      "\u001b[31mAxisError\u001b[39m: axis 1 is out of bounds for array of dimension 1"
+     ]
+    }
+   ],
+   "source": [
+    "from glassboxml.models.knn import KNNClassifier\n",
+    "import numpy as np\n",
+    "\n",
+    "X = np.array([0.0, 0.1, 0.2,  5.1, 5.2, 5.5,  10.245, 11, 12.5])\n",
+    "y = np.array([0, 0, 0,  1, 1, 1,  2, 2, 2])\n",
+    "\n",
+    "model = KNNClassifier(k = 3)\n",
+    "model.fit(X,y)\n",
+    "\n",
+    "y_pred = model.predict(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4150c622",
+   "metadata": {},
    "outputs": [],
    "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv (3.11.7.final.0)",
+   "display_name": ".venv (3.11.7)",
    "language": "python",
    "name": "python3"
   },
diff --git a/glassboxml/models/knn.py b/glassboxml/models/knn.py
index e324971..d2f3609 100644
--- a/glassboxml/models/knn.py
+++ b/glassboxml/models/knn.py
@@ -16,6 +16,13 @@ def _k_nearest_labels(self, x):
         #Use vectorized calculations for calculating distances
         distances = np.linalg.norm(self.X_train - x, axis=1)
 
+        # argpartition(distances, k) needs an extra element past the k we
+        # keep to anchor the partition, so it only works for k < n_samples.
+        # If k covers the whole training set (or more), there's nothing to
+        # partition - every point is a "k nearest" neighbour.
+        if self.k >= len(distances):
+            return self.y_train
+
         #Use argpartition to obtain k closest neighbours
         partitioned_indices = np.argpartition(distances, self.k) #Put k closest elements at the front of the index list
         k_closest_indices = partitioned_indices[:self.k]
diff --git a/tests/models/test_knn.py b/tests/models/test_knn.py
new file mode 100644
index 0000000..6501901
--- /dev/null
+++ b/tests/models/test_knn.py
@@ -0,0 +1,118 @@
+import numpy as np
+from glassboxml.models.knn import KNNClassifier, KNNRegressor
+
+# Test KNNClassifier
+def test_multi_class_classification():
+    X = np.array([[0.0], [0.1], [0.2],  [5.1], [5.2], [5.5],  [10.245], [11], [12.5]])
+    y = np.array([0, 0, 0,  1, 1, 1,  2, 2, 2])
+
+    model = KNNClassifier(k = 3)
+    model.fit(X,y)
+
+    y_pred = model.predict(X)
+
+    assert np.array_equal(y, y_pred)
+
+def test_KNNClassifier_k_equal_1():
+    X = np.array([[0.0], [0.1],  [0.456]])
+    y = np.array([0, 1, 2])
+    X_test = np.array([[0.01], [0.245], [0.5555]])
+
+    model = KNNClassifier(k = 1)
+    model.fit(X,y)
+
+    y_pred = model.predict(X_test)
+
+    assert np.array_equal(y, y_pred)
+
+def test_majority_vote_sanity():
+    X = np.array([[0.3], [0.4], [0.5]
+                  , [0.6], [0.7], [0.8], [0.9], [0.10]])
+    y = np.array([0, 1, 1,
+                  1, 1, 0, 0, 0])
+    x_test = [0.51]
+    
+    model = KNNClassifier(k = 5)
+    model.fit(X,y)
+
+    y_pred = model.predict(x_test)
+
+    assert y_pred == [1]
+
+def test_equal_class_labels():
+    X = [[1], [2], [3], [4]]
+    y = [0, 0, 1, 1]
+
+    x_test = [2.1]
+    
+    model = KNNClassifier(k = 4)
+    model.fit(X,y)
+
+    y_pred = model.predict(x_test)
+
+    assert y_pred == [0]
+
+def test_KNNClassifier_prediction_accuracy():
+    from glassboxml.data.generators import generate_classification_dataset
+    from sklearn.model_selection import train_test_split
+    from glassboxml.metrics.classification import accuracy
+
+    X, y, w_true, b_true = generate_classification_dataset([1.555, 0.245, 6.777, 0.123])
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
+
+    model = KNNClassifier()
+    model.fit(X_train, y_train)
+
+    y_pred = model.predict(X_test)
+
+    assert accuracy(y_test, y_pred) >= 0.9
+
+def test_k_equal_n_samples_collapses_to_global_majority():
+    # 5 points clustered near 0 labeled class 0, 2 points clustered near 10 labeled class 1
+    X = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [10.0], [10.1]])
+    y = np.array([0, 0, 0, 0, 0, 1, 1])
+
+    query_near_class_1 = np.array([[10.05]])
+
+    # With k=1, the nearest neighbour is a class-1 point, so k changes the prediction
+    model_k1 = KNNClassifier(k = 1)
+    model_k1.fit(X, y)
+    assert model_k1.predict(query_near_class_1) == [1]
+
+    # With k=n_samples, every training point is used regardless of query location,
+    # so the prediction must collapse to the global majority class (0) everywhere,
+    # even for a query sitting right next to the minority cluster
+    model_k_all = KNNClassifier(k = len(X))
+    model_k_all.fit(X, y)
+
+    query_near_class_0 = np.array([[0.05]])
+    assert model_k_all.predict(query_near_class_1) == [0]
+    assert model_k_all.predict(query_near_class_0) == [0]
+
+# Test KNNRegressor
+def test_exact_mean():
+    X = np.array([[1], [2], [3], [4], [5]])
+    y = np.array([1.5, 0.5, 2.9, 3.4, 5.25])
+
+    x_test = np.array([[1.6], [3.1], [4.1]])
+
+    y_expected = np.array([np.mean([1.5,0.5,2.9]), np.mean([0.5, 2.9, 3.4]), np.mean([2.9, 3.4, 5.25])])
+
+    model = KNNRegressor(k = 3)
+    model.fit(X,y)
+    y_pred = model.predict(x_test)
+
+    assert np.array_equal(y_expected, y_pred)
+
+def test_KNNRegressor_k_equal_1():
+    X = np.array([[0.0], [0.1],  [0.456]])
+    y = np.array([0, 1, 2])
+    X_test = np.array([[0.01], [0.245], [0.5555]])
+
+    model = KNNRegressor(k = 1)
+    model.fit(X,y)
+
+    y_pred = model.predict(X_test)
+
+    assert np.array_equal(y, y_pred)
\ No newline at end of file

From 5ffb6f0fe8d0287e53b293b1737a5bcade87bf36 Mon Sep 17 00:00:00 2001
From: Iskken <iimanaliev312@gmail.com>
Date: Tue, 14 Jul 2026 22:30:20 +0200
Subject: [PATCH 6/7] Fix CI: add scikit-learn as a test extra instead of
 relying on local install

---
 .github/workflows/tests.yml | 3 +--
 Jenkinsfile                 | 3 +--
 pyproject.toml              | 3 +++
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml
index 77a5694..3fd684c 100644
--- a/.github/workflows/tests.yml
+++ b/.github/workflows/tests.yml
@@ -21,8 +21,7 @@ jobs:
 
       - name: Install dependencies
         run: |
-          pip install -e .
-          pip install pytest
+          pip install -e ".[test]"
 
       - name: Run tests
         run: pytest
diff --git a/Jenkinsfile b/Jenkinsfile
index 4083153..d3d4b36 100644
--- a/Jenkinsfile
+++ b/Jenkinsfile
@@ -17,8 +17,7 @@ pipeline {
                 sh '''
                     python3 -m venv .venv
                     .venv/bin/pip install --upgrade pip
-                    .venv/bin/pip install -e .
-                    .venv/bin/pip install pytest
+                    .venv/bin/pip install -e ".[test]"
                 '''
             }
         }
diff --git a/pyproject.toml b/pyproject.toml
index 8667383..65b0fde 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,6 +6,9 @@ dependencies = [
     "matplotlib"
 ]
 
+[project.optional-dependencies]
+test = ["pytest", "scikit-learn"]
+
 [build-system]
 requires = ["setuptools"]
 build-backend = "setuptools.build_meta"

From 0d13707a0713338fa700ca4f511d7eeff24fc62a Mon Sep 17 00:00:00 2001
From: Iskken <iimanaliev312@gmail.com>
Date: Wed, 15 Jul 2026 03:33:10 +0200
Subject: [PATCH 7/7] Update: KNN for WebApp, ReadMe

---
 README.md                |   5 +++++
 docs/generate_plots.py   |  29 +++++++++++++++++++++++++++++
 docs/images/knn.png      | Bin 0 -> 127357 bytes
 webapp/app.py            |  29 +++++++++++++++++++++++++++++
 webapp/static/index.html |   6 ++++++
 5 files changed, 69 insertions(+)
 create mode 100644 docs/images/knn.png

diff --git a/README.md b/README.md
index 6ce3da9..898d3f7 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,11 @@ Gini-impurity splitting, recursive binary partitioning, configurable max depth.
 
 ![Decision tree decision regions](docs/images/decision_tree.png)
 
+### K-Nearest Neighbors
+Instance-based classification (and regression) via majority vote / mean over the `k` closest training points, vectorized distance computation.
+
+![KNN decision regions](docs/images/knn.png)
+
 Plots were generated with `python docs/generate_plots.py` — rerun it after changing a model to refresh them.
 
 ## Project layout
diff --git a/docs/generate_plots.py b/docs/generate_plots.py
index 9a1ecd7..f8d3cdc 100644
--- a/docs/generate_plots.py
+++ b/docs/generate_plots.py
@@ -9,6 +9,7 @@
     generate_regression_dataset,
 )
 from glassboxml.models.decision_tree import DecisionTree
+from glassboxml.models.knn import KNNClassifier
 from glassboxml.models.linear_regression import LinearRegression
 from glassboxml.models.logistic_regression import LogisticRegression
 
@@ -87,8 +88,36 @@ def plot_decision_tree():
     plt.close()
 
 
+def plot_knn():
+    X, y, w_true, b_true = generate_classification_dataset(
+        w_true=[1.5, -2.0], b_true=0.5, n_samples=300, noise_std=0.5,
+    )
+    model = KNNClassifier(k=5)
+    model.fit(X, y)
+
+    x0_min, x0_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5
+    x1_min, x1_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5
+    xx0, xx1 = np.meshgrid(
+        np.linspace(x0_min, x0_max, 200),
+        np.linspace(x1_min, x1_max, 200),
+    )
+    grid = np.column_stack([xx0.ravel(), xx1.ravel()])
+    zz = model.predict(grid).reshape(xx0.shape)
+
+    plt.figure(figsize=(6, 4.5))
+    plt.contourf(xx0, xx1, zz, alpha=0.3, cmap="coolwarm")
+    plt.scatter(X[:, 0], X[:, 1], c=y, cmap="coolwarm", alpha=0.8, edgecolors="k", linewidths=0.3)
+    plt.title(f"K-Nearest Neighbors (k={model.k})")
+    plt.xlabel("x1")
+    plt.ylabel("x2")
+    plt.tight_layout()
+    plt.savefig(f"{OUT_DIR}/knn.png", dpi=150)
+    plt.close()
+
+
 if __name__ == "__main__":
     plot_linear_regression()
     plot_logistic_regression()
     plot_decision_tree()
+    plot_knn()
     print(f"Saved plots to {OUT_DIR}/")
diff --git a/docs/images/knn.png b/docs/images/knn.png
new file mode 100644
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diff --git a/webapp/app.py b/webapp/app.py
index 53ea99b..9d2eda7 100644
--- a/webapp/app.py
+++ b/webapp/app.py
@@ -15,6 +15,7 @@
 from glassboxml.models.decision_tree import DecisionTree
 from glassboxml.models.linear_regression import LinearRegression
 from glassboxml.models.logistic_regression import LogisticRegression
+from glassboxml.models.knn import KNNRegressor, KNNClassifier
 
 STATIC = Path(__file__).parent / "static"
 
@@ -29,6 +30,7 @@ class RunRequest(BaseModel):
     epochs: int = 500
     learning_rate: float = 0.1
     max_depth: int = 3
+    k: int = 5
 
 
 @app.get("/")
@@ -44,6 +46,8 @@ def run(req: RunRequest):
         return _logistic_regression(req)
     if req.algorithm == "decision_tree":
         return _decision_tree(req)
+    if req.algorithm == "knn":
+        return _knn(req)
     return {"error": f"Unknown algorithm: {req.algorithm}"}
 
 
@@ -131,3 +135,28 @@ def _decision_tree(req: RunRequest):
             "max_depth": req.max_depth,
         },
     }
+
+def _knn(req: RunRequest):
+    X, y, _, _ = generate_classification_dataset(
+        w_true=[1.5, -2.0], b_true=0.5,
+        n_samples=req.n_samples, noise_std=req.noise_std,
+    )
+
+    model = KNNClassifier(k = req.k)
+    model.fit(X,y)
+
+    y_pred = model.predict(X)
+    accuracy = float(accuracy_metric(y, y_pred))
+
+    return {
+        "scatter": {
+            "x1": X[:, 0].tolist(),
+            "x2": X[:, 1].tolist(),
+            "labels": y.tolist(),
+            "predicted": y_pred.tolist(),
+        },
+        "metrics": {
+            "accuracy": f"{round(accuracy * 100, 1)}%",
+            "k": req.k,
+        },
+    }
\ No newline at end of file
diff --git a/webapp/static/index.html b/webapp/static/index.html
index 52aa96a..d1acfcd 100644
--- a/webapp/static/index.html
+++ b/webapp/static/index.html
@@ -95,6 +95,7 @@ <h1>GlassBoxML — Interactive Demo</h1>
       <option value="linear_regression">Linear Regression (1D, gradient descent)</option>
       <option value="logistic_regression">Logistic Regression (2D, gradient descent)</option>
       <option value="decision_tree">Decision Tree (2D, Gini impurity)</option>
+      <option value="knn">K-Nearest Neighbours (2D, majority vote)</option>
     </select>
 
     <div class="params-grid" id="params"></div>
@@ -128,6 +129,11 @@ <h1>GlassBoxML — Interactive Demo</h1>
       { id: "noise_std", label: "Noise std", value: 0.3, min: 0,  max: 3,   step: 0.1 },
       { id: "max_depth", label: "Max depth", value: 3,   min: 1,  max: 10,  step: 1  },
     ],
+    knn: [
+      { id: "n_samples", label: "Samples",   value: 200, min: 20, max: 500, step: 10 },
+      { id: "noise_std", label: "Noise std", value: 0.3, min: 0,  max: 3,   step: 0.1 },
+      { id: "k", label: "k (neighbors)", value: 5, min: 1, max: 25, step: 1 },
+    ],
   };
 
   let chart = null;