mirror of
https://github.com/MPCStats/zk-stats-lib.git
synced 2026-01-08 05:04:07 -05:00
396 lines
25 KiB
Plaintext
396 lines
25 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Requirement already satisfied: ezkl==7.0.0 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 1)) (7.0.0)\n",
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"Requirement already satisfied: torch in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 2)) (2.2.0)\n",
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"Requirement already satisfied: requests in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 3)) (2.31.0)\n",
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"Requirement already satisfied: scipy in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 4)) (1.12.0)\n",
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"Requirement already satisfied: numpy in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 5)) (1.26.3)\n",
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"Requirement already satisfied: matplotlib in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 6)) (3.8.2)\n",
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"Requirement already satisfied: statistics in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 7)) (1.0.3.5)\n",
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"Requirement already satisfied: onnx in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from -r ../../requirements.txt (line 8)) (1.15.0)\n",
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"Requirement already satisfied: filelock in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.13.1)\n",
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"Requirement already satisfied: typing-extensions>=4.8.0 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (4.9.0)\n",
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"Requirement already satisfied: sympy in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (1.12)\n",
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"Requirement already satisfied: networkx in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.2.1)\n",
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"Requirement already satisfied: jinja2 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (3.1.3)\n",
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"Requirement already satisfied: fsspec in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from torch->-r ../../requirements.txt (line 2)) (2023.12.2)\n",
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"Requirement already satisfied: charset-normalizer<4,>=2 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from requests->-r ../../requirements.txt (line 3)) (3.3.2)\n",
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"Requirement already satisfied: idna<4,>=2.5 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from requests->-r ../../requirements.txt (line 3)) (3.6)\n",
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"Requirement already satisfied: urllib3<3,>=1.21.1 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from requests->-r ../../requirements.txt (line 3)) (2.2.0)\n",
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"Requirement already satisfied: certifi>=2017.4.17 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from requests->-r ../../requirements.txt (line 3)) (2024.2.2)\n",
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"Requirement already satisfied: contourpy>=1.0.1 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (1.2.0)\n",
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"Requirement already satisfied: cycler>=0.10 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (0.12.1)\n",
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"Requirement already satisfied: fonttools>=4.22.0 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (4.47.2)\n",
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"Requirement already satisfied: kiwisolver>=1.3.1 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (1.4.5)\n",
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"Requirement already satisfied: packaging>=20.0 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (23.2)\n",
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"Requirement already satisfied: pillow>=8 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (10.2.0)\n",
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"Requirement already satisfied: pyparsing>=2.3.1 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (3.1.1)\n",
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"Requirement already satisfied: python-dateutil>=2.7 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from matplotlib->-r ../../requirements.txt (line 6)) (2.8.2)\n",
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"Requirement already satisfied: docutils>=0.3 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from statistics->-r ../../requirements.txt (line 7)) (0.20.1)\n",
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"Requirement already satisfied: protobuf>=3.20.2 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from onnx->-r ../../requirements.txt (line 8)) (4.25.2)\n",
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"Requirement already satisfied: six>=1.5 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib->-r ../../requirements.txt (line 6)) (1.16.0)\n",
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"Requirement already satisfied: MarkupSafe>=2.0 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from jinja2->torch->-r ../../requirements.txt (line 2)) (2.1.4)\n",
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"Requirement already satisfied: mpmath>=0.19 in /Users/jernkun/Library/Caches/pypoetry/virtualenvs/zkstats-OJpceffF-py3.11/lib/python3.11/site-packages (from sympy->torch->-r ../../requirements.txt (line 2)) (1.3.0)\n",
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"\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m23.2.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.0\u001b[0m\n",
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"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"pip install -r ../../requirements.txt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"import ezkl\n",
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"import torch\n",
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"from torch import nn\n",
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"import json\n",
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"import os\n",
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"import time\n",
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"import scipy\n",
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import statistics\n",
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"import math"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"%run -i ../../zkstats/core.py"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"# init path\n",
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"os.makedirs(os.path.dirname('shared/'), exist_ok=True)\n",
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"os.makedirs(os.path.dirname('prover/'), exist_ok=True)\n",
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"verifier_model_path = os.path.join('shared/verifier.onnx')\n",
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"prover_model_path = os.path.join('prover/prover.onnx')\n",
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"verifier_compiled_model_path = os.path.join('shared/verifier.compiled')\n",
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"prover_compiled_model_path = os.path.join('prover/prover.compiled')\n",
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"pk_path = os.path.join('shared/test.pk')\n",
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"vk_path = os.path.join('shared/test.vk')\n",
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"proof_path = os.path.join('shared/test.pf')\n",
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"settings_path = os.path.join('shared/settings.json')\n",
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"srs_path = os.path.join('shared/kzg.srs')\n",
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"witness_path = os.path.join('prover/witness.json')\n",
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"# this is private to prover since it contains actual data\n",
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"sel_data_path = os.path.join('prover/sel_data.json')\n",
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"# this is just dummy random value\n",
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"sel_dummy_data_path = os.path.join('shared/sel_dummy_data.json')"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"======================= ZK-STATS FLOW ======================="
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"def mode_within(data_array, percent):\n",
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" max_sum_freq = 0\n",
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" mode = data_array[0]\n",
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"\n",
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" for check_val in set(data_array):\n",
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" sum_freq = sum(1 for ele in data_array if abs(ele - check_val) <= abs(percent * check_val / 100))\n",
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"\n",
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" if sum_freq > max_sum_freq:\n",
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" mode = check_val\n",
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" max_sum_freq = sum_freq\n",
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"\n",
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" return mode"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"data_path = os.path.join('data.json')\n",
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"dummy_data_path = os.path.join('shared/dummy_data.json')\n",
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"\n",
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"data = json.loads(open(data_path, \"r\").read())['col_name']\n",
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"data_tensor = torch.reshape(torch.tensor(data),(1,-1, 1))\n",
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"\n",
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"create_dummy(data_path, dummy_data_path)\n",
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"dummy_data = json.loads(open(dummy_data_path, \"r\").read())['col_name']\n",
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"dummy_data_tensor = torch.reshape(torch.tensor(dummy_data), (1,-1,1))\n",
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"\n",
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"dummy_theory_output = torch.tensor(mode_within(dummy_data,1))\n",
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"theory_output = torch.tensor(mode_within(data,1))\n",
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"\n",
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"\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"scales = [1]\n",
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"selected_columns = ['col_name']\n",
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"commitment_maps = get_data_commitment_maps(data_path, scales)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"dummy output: tensor(26.2000)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/89/y9dw12v976ngdmqz4l7wbsnr0000gn/T/ipykernel_91066/449633495.py:10: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).\n",
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" result = torch.tensor([torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal for ele in X[0]])\n",
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"/var/folders/89/y9dw12v976ngdmqz4l7wbsnr0000gn/T/ipykernel_91066/449633495.py:10: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
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" result = torch.tensor([torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal for ele in X[0]])\n"
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]
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}
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],
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"source": [
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"print(\"dummy output: \", dummy_theory_output)\n",
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"# Verifier/ data consumer side: send desired calculation\n",
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"class verifier_model(nn.Module):\n",
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" def __init__(self):\n",
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" super(verifier_model, self).__init__()\n",
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" # w represents mean in this case\n",
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" self.w = nn.Parameter(data = dummy_theory_output, requires_grad = False)\n",
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" def forward(self,X):\n",
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" count_equal = torch.sum((torch.abs(X-self.w)<=torch.abs(0.01*self.w)).double())\n",
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" result = torch.tensor([torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal for ele in X[0]])\n",
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" return (torch.sum(result) == X.size()[1], self.w)\n",
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"\n",
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"verifier_define_calculation(dummy_data_path, selected_columns,sel_dummy_data_path,verifier_model, verifier_model_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Theory_output: tensor(77.)\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/89/y9dw12v976ngdmqz4l7wbsnr0000gn/T/ipykernel_91066/1437681360.py:11: TracerWarning: Iterating over a tensor might cause the trace to be incorrect. Passing a tensor of different shape won't change the number of iterations executed (and might lead to errors or silently give incorrect results).\n",
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" result = torch.tensor([torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal for ele in X[0]])\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"==== Generate & Calibrate Setting ====\n",
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"scale: [1]\n",
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"setting: {\"run_args\":{\"tolerance\":{\"val\":0.0,\"scale\":1.0},\"input_scale\":1,\"param_scale\":1,\"scale_rebase_multiplier\":10,\"lookup_range\":[0,0],\"logrows\":14,\"num_inner_cols\":2,\"variables\":[[\"batch_size\",1]],\"input_visibility\":{\"Hashed\":{\"hash_is_public\":true,\"outlets\":[]}},\"output_visibility\":\"Public\",\"param_visibility\":\"Private\"},\"num_rows\":14432,\"total_assignments\":300,\"total_const_size\":0,\"model_instance_shapes\":[[1],[1]],\"model_output_scales\":[0,1],\"model_input_scales\":[1],\"module_sizes\":{\"kzg\":[],\"poseidon\":[14432,[1]],\"elgamal\":[0,[0]]},\"required_lookups\":[],\"check_mode\":\"UNSAFE\",\"version\":\"7.0.0\",\"num_blinding_factors\":null}\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/var/folders/89/y9dw12v976ngdmqz4l7wbsnr0000gn/T/ipykernel_91066/1437681360.py:11: TracerWarning: torch.tensor results are registered as constants in the trace. You can safely ignore this warning if you use this function to create tensors out of constant variables that would be the same every time you call this function. In any other case, this might cause the trace to be incorrect.\n",
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" result = torch.tensor([torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal for ele in X[0]])\n"
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]
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}
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],
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"source": [
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"# prover calculates settings, send to verifier\n",
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"print(\"Theory_output: \", theory_output)\n",
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"class prover_model(nn.Module):\n",
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" def __init__(self):\n",
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" super(prover_model, self).__init__()\n",
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" # w represents mean in this case\n",
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" self.w = nn.Parameter(data = theory_output, requires_grad = False)\n",
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"\n",
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" def forward(self,X):\n",
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" count_equal = torch.sum((torch.abs(X-self.w)<=torch.abs(0.01*self.w)).double())\n",
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" result = torch.tensor([torch.sum((torch.abs(X-ele[0])<=torch.abs(0.01*ele[0])).double())<=count_equal for ele in X[0]])\n",
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" return (torch.sum(result) == X.size()[1], self.w)\n",
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"\n",
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"prover_gen_settings(data_path, selected_columns, sel_data_path, prover_model,prover_model_path, scales, \"resources\", settings_path)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"spawning module 0\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"spawning module 2\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
|
"==== setting up ezkl ====\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"spawning module 0\n",
|
|
"spawning module 2\n",
|
|
"spawning module 0\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"Time setup: 1.646347999572754 seconds\n",
|
|
"=======================================\n",
|
|
"Theory output: tensor(77.)\n",
|
|
"==== Generating Witness ====\n",
|
|
"witness boolean: 1.0\n",
|
|
"witness result 1 : 77.0\n",
|
|
"==== Generating Proof ====\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stderr",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"spawning module 2\n"
|
|
]
|
|
},
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"proof: {'instances': [[[8983262245034475985, 4321099455052339947, 15405541267776549565, 129283864358809647], [12436184717236109307, 3962172157175319849, 7381016538464732718, 1011752739694698287], [2847721487471738066, 3094933423049675290, 17348522929905272778, 2381998173652826967]]], 'proof': '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', 'transcript_type': 'EVM'}\n",
|
|
"Time gen prf: 1.740041971206665 seconds\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"# Here verifier & prover can concurrently call setup since all params are public to get pk.\n",
|
|
"# Here write as verifier function to emphasize that verifier must calculate its own vk to be sure\n",
|
|
"setup(verifier_model_path, verifier_compiled_model_path, settings_path,vk_path, pk_path )\n",
|
|
"\n",
|
|
"print(\"=======================================\")\n",
|
|
"# Prover generates proof\n",
|
|
"print(\"Theory output: \", theory_output)\n",
|
|
"prover_gen_proof(prover_model_path, sel_data_path, witness_path, prover_compiled_model_path, settings_path, proof_path, pk_path)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"77.0"
|
|
]
|
|
},
|
|
"execution_count": 11,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Verifier verifies\n",
|
|
"verifier_verify(proof_path, settings_path, vk_path, selected_columns, commitment_maps)"
|
|
]
|
|
},
|
|
{
|
|
"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"
|
|
},
|
|
"orig_nbformat": 4
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|