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{
"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
}
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