From 16b723cb22f7482fed92c06a72397a727fefba4a Mon Sep 17 00:00:00 2001 From: Girraj Jangid <34280160+Girrajjangid@users.noreply.github.com> Date: Wed, 28 Sep 2022 14:17:36 +0530 Subject: [PATCH] Revert "Added new example." --- K-Nearest Neighbors/KNN_on_random_data.ipynb | 342 ------------------- 1 file changed, 342 deletions(-) delete mode 100644 K-Nearest Neighbors/KNN_on_random_data.ipynb diff --git a/K-Nearest Neighbors/KNN_on_random_data.ipynb b/K-Nearest Neighbors/KNN_on_random_data.ipynb deleted file mode 100644 index 28db7c6..0000000 --- a/K-Nearest Neighbors/KNN_on_random_data.ipynb +++ /dev/null @@ -1,342 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "KNN.ipynb", - "provenance": [], - "collapsed_sections": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - } - }, - "cells": [ - { - "cell_type": "code", - "metadata": { - "id": "kP3XNAqKw-e7", - "colab_type": "code", - "colab": {} - }, - "source": [ - "#import required libraries\n", - "\n", - "from sklearn.datasets import make_blobs\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "fOx5_nFJxDuZ", - "colab_type": "code", - "colab": {} - }, - "source": [ - "X,Y = make_blobs(centers=2,cluster_std = 1.0, center_box=(-10.0,10.0),random_state = 4)" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "Vh9y1A9KzLbt", - "colab_type": "code", - "outputId": "312ea98d-228a-482d-bf82-bf6c16774b6f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 67 - } - }, - "source": [ - "print(Y)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[0 1 1 1 0 0 0 0 0 1 0 1 0 0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 0\n", - " 1 1 0 1 0 1 0 1 0 0 0 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 0\n", - " 0 0 0 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 1 0 0 1 1 0]\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "Xq1UOaAQxQ7e", - "colab_type": "code", - "outputId": "80fd28a6-fb00-440f-f86f-61a8325732bc", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 269 - } - }, - "source": [ - "#take any arbitrary test point\n", - "\n", - "test_point = np.array([10,2])\n", - "plt.scatter(test_point[0],test_point[1],color='red')\n", - "plt.scatter(X[:,0],X[:,1],c=Y)\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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BUvvyzrUr1jHz059o06GUe15ewvvL5jLxt5+58NYlvD3mrVofxzDSLalVMaq6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\npyeXPnRu7T9EFlm1ZA03DL6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- "text/plain": [ - "
" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "4ia6OjPmySij", - "colab_type": "code", - "colab": {} - }, - "source": [ - "def dist(a1,a2):\n", - " return np.sum((a1-a2)**2)**.5\n", - "\n", - "def KNN(X,Y,test_point,k=5):\n", - " m = X.shape[0]\n", - " all_dist = []\n", - " for i in range(m):\n", - " d = dist(test_point,X[i])\n", - " all_dist.append((d,Y[i]))\n", - " \n", - " all_dist.sort()\n", - " a = np.array(all_dist[:k])[:,1]\n", - " labels,freq = np.unique(a,return_counts=True)\n", - " print(a)\n", - " idx = np.argmax(freq)\n", - " out = int(labels[idx])\n", - " return out" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "xGUPcp4t1X24", - "colab_type": "code", - "outputId": "d258ed51-7d6c-46c8-c374-f74a81f33907", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 50 - } - }, - "source": [ - "#call KNN function here\n", - "\n", - "KNN(X,Y,test_point)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "[0. 0. 0. 0. 1.]\n" - ], - "name": "stdout" - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "0" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 24 - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "bQubfows2z1Z", - "colab_type": "code", - "colab": {} - }, - "source": [ - "#import MNIST dataset\n", - "\n", - "from keras.datasets import mnist" - ], - "execution_count": 0, - "outputs": [] - }, - { - "cell_type": "code", - "metadata": { - "id": "8xTjWJDh4i_b", - "colab_type": "code", - "outputId": "ed4dd5d4-a5f6-4fb4-f555-477129c8c71b", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34 - } - }, - "source": [ - "(X,Y),(Xt,Yt) = mnist.load_data()\n", - "print(X.shape,Y.shape,Xt.shape,Yt.shape)" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "stream", - "text": [ - "(60000, 28, 28) (60000,) (10000, 28, 28) (10000,)\n" - ], - "name": "stdout" - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "x3_LIeEn46mp", - "colab_type": "code", - "outputId": "fd4c2561-5ba4-4bd9-eee9-962cd9e131ed", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 269 - } - }, - "source": [ - "plt.imshow(X[0],cmap='gray')\n", - "plt.show()" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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" - ] - }, - "metadata": { - "tags": [] - } - } - ] - }, - { - "cell_type": "code", - "metadata": { - "id": "rqBkWCHh5EMs", - "colab_type": "code", - "outputId": "22762a4c-dfcf-4ce7-fa65-c93f9009135f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 286 - } - }, - "source": [ - "test_point = Xt[0]\n", - "plt.imshow(test_point,cmap='gray')" - ], - "execution_count": 0, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": { - "tags": [] - }, - "execution_count": 32 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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