{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def checkPalindrome(number):\n", " number = str(number)\n", " l = 0\n", " r = len(number)-1\n", " while l < r:\n", " if number[l] != number[r]:\n", " return False\n", " l, r = l+1, r-1\n", " return True\n", "\n", "def generate_non_palindromic_numbers(count):\n", " palindromic_numbers = []\n", " i = 0\n", " while count > 0:\n", " binary_str = bin(i)[2:] # Convert the index to binary\n", " # palindromic_number = '1' + binary_str + binary_str[::-1][1:] + '1' # Create a palindromic number\n", " palindromic_number = binary_str\n", " palindromic_number = palindromic_number.zfill(10)\n", " if not checkPalindrome(palindromic_number):\n", " palindromic_numbers.append(str(palindromic_number)) # Ensure it's 10 digits long\n", " count -= 1\n", " i += 1\n", " return palindromic_numbers\n", "\n", "def generate_palindromic_numbers(count):\n", " palindromic_numbers = []\n", " i = 0\n", " while count > 0:\n", " binary_str = bin(i)[2:] # Convert the index to binary\n", " # palindromic_number = '1' + binary_str + binary_str[::-1][1:] + '1' # Create a palindromic number\n", " palindromic_number = binary_str\n", " palindromic_number = palindromic_number.zfill(10)\n", " if len(palindromic_number) > 10:\n", " print('all 10 digits palindromic numbers exhausted at', len(palindromic_numbers))\n", " return palindromic_numbers\n", " if checkPalindrome(palindromic_number):\n", " palindromic_numbers.append(str(palindromic_number)) # Ensure it's 10 digits long\n", " count -= 1\n", " i += 1\n", " return palindromic_numbers\n" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "all 10 digits palindromic numbers exhausted at 8\n" ] } ], "source": [ "data = generate_non_palindromic_numbers(512) + generate_palindromic_numbers(512)*16" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "label = []\n", "for number in data:\n", " if checkPalindrome(number):\n", " label.append(1)\n", " else:\n", " label.append(0)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.DataFrame({'number':data, 'label':label})" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import pickle\n", "with open('data6dig.pckl', 'wb') as file:\n", " pickle.dump(df, file)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "df.to_csv('data.csv')" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(512, 512)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "label.count(0), label.count(1)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.11.4" } }, "nbformat": 4, "nbformat_minor": 2 }