playground_pck.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNh/tSHZg8kb4S4nrjcWrTi",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pckuo/dale-regression/blob/main/playground_pck.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install qpsolvers[open_source_solvers]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TTrEJDd1P-pT",
        "outputId": "eec2f8f5-e271-4a4b-ce12-d0d99ec441cc"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: qpsolvers[open_source_solvers] in /usr/local/lib/python3.10/dist-packages (4.3.3)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (1.26.4)\n",
            "Requirement already satisfied: scipy>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (1.13.1)\n",
            "Requirement already satisfied: daqp>=0.5.1 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (0.5.1.post1)\n",
            "Requirement already satisfied: highspy>=1.1.2.dev3 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (1.7.2)\n",
            "Requirement already satisfied: qpalm>=1.2.1 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (1.2.3)\n",
            "Requirement already satisfied: cvxopt>=1.2.6 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (1.3.2)\n",
            "Requirement already satisfied: clarabel>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (0.9.0)\n",
            "Requirement already satisfied: osqp>=0.6.2 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (0.6.7.post0)\n",
            "Requirement already satisfied: quadprog>=0.1.11 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (0.1.12)\n",
            "Requirement already satisfied: piqp>=0.2.2 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (0.4.2)\n",
            "Requirement already satisfied: ecos>=2.0.8 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (2.0.14)\n",
            "Requirement already satisfied: proxsuite>=0.2.9 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (0.6.6)\n",
            "Requirement already satisfied: scs>=3.2.0 in /usr/local/lib/python3.10/dist-packages (from qpsolvers[open_source_solvers]) (3.2.6)\n",
            "Requirement already satisfied: qdldl in /usr/local/lib/python3.10/dist-packages (from osqp>=0.6.2->qpsolvers[open_source_solvers]) (0.1.7.post4)\n",
            "Requirement already satisfied: cmeel in /usr/local/lib/python3.10/dist-packages (from proxsuite>=0.2.9->qpsolvers[open_source_solvers]) (0.53.3)\n",
            "Requirement already satisfied: tomli<3.0.0,>=2.0.1 in /usr/local/lib/python3.10/dist-packages (from cmeel->proxsuite>=0.2.9->qpsolvers[open_source_solvers]) (2.0.1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "gpCTyKNHO17Y"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "# from sklearn.linear_model import LinearRegression\n",
        "from qpsolvers import solve_ls\n",
        "import time\n",
        "import pickle\n",
        "# import jax.numpy as jnp\n",
        "# from jax import grad, random, lax"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Train a Linear Perceptron with Quadratic Programming"
      ],
      "metadata": {
        "id": "RD7laQdbPDVQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "SEED = 42\n",
        "\n",
        "# simulation\n",
        "# N = 10000         # n_neuorns\n",
        "# alpha = 1         # proportional limit\n",
        "noise_coeff = 0.01  # noise_level\n",
        "n_runs = 5         # n_runs per N\n",
        "\n",
        "alphas = np.arange(0.1, 2.025, 0.025)\n",
        "# Ns = np.logspace(2,4, num=4)\n",
        "Ns = np.array([2000])\n",
        "gammas = np.arange(0, 1.1, 0.1)"
      ],
      "metadata": {
        "id": "63QjtJsVPAPB"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# set seed\n",
        "np.random.seed(SEED)\n",
        "\n",
        "results_dict = {}\n",
        "\n",
        "for gamma in gammas:\n",
        "  for N in Ns:\n",
        "    for alpha in alphas:\n",
        "      print(f'alpha {alpha}, N {N}, gamma {gamma}')\n",
        "      P = int(alpha * N)\n",
        "\n",
        "      # save results\n",
        "      results_dict_alpha_N_gamma = {\n",
        "          'w_student': [],\n",
        "          'mean_error_w': [],\n",
        "          'mse_w': [],\n",
        "          'mean_w_teacher_sq': [],\n",
        "          'mean_w_student_sq': [],\n",
        "          'mean_scalar_product_w_teacher_student': []\n",
        "      }\n",
        "\n",
        "      start = time.time()\n",
        "      for run_id in range(n_runs):\n",
        "        # teacher weights\n",
        "        w_teacher = np.random.normal(size=int(N))\n",
        "\n",
        "        # data\n",
        "        X = np.random.normal(size=(P, int(N)))\n",
        "        eps = noise_coeff * np.random.normal(size=P)\n",
        "        y = 1/np.sqrt(N) * X @ w_teacher + eps\n",
        "\n",
        "        # solve with sklearn linear regression\n",
        "        # reg_nnls = LinearRegression(positive=True)\n",
        "        # w_student_sk = reg_nnls.fit(X, y).coef_\n",
        "\n",
        "        # solve with quadratic programming\n",
        "        constrained_dim = int(gamma*N)\n",
        "        unconstrained_dim = N - constrained_dim\n",
        "        I = -1*np.eye(constrained_dim)\n",
        "        G = np.block([\n",
        "            [I, np.zeros((constrained_dim, unconstrained_dim))],\n",
        "            [np.zeros((unconstrained_dim, constrained_dim)), np.zeros((unconstrained_dim, unconstrained_dim))]\n",
        "        ])\n",
        "        w_student = solve_ls(\n",
        "          R=1/np.sqrt(N)*X,\n",
        "          s=y,\n",
        "          G=G,\n",
        "          h=np.zeros(int(N)),\n",
        "          solver=\"osqp\"\n",
        "        )\n",
        "        # solver: osqp: 6min/ 9G for N=1e4\n",
        "        # solver: quadprog: >15min/ 6G for N=1e4\n",
        "        # solver: cvxopt: >11min/ 6G for N=1e4\n",
        "\n",
        "        # compute errors\n",
        "        # mean_error_w_sk = np.sum((w_student_sk - w_teacher))/ N\n",
        "        mean_error_w = np.sum((w_student - w_teacher))/ N\n",
        "        mse_w = np.sum((w_student - w_teacher)**2)/ N\n",
        "        mean_w_teacher_sq = np.sum(w_teacher**2)/ N\n",
        "        mean_w_student_sq = np.sum(w_student**2)/ N\n",
        "        mean_scalar_product_w_teacher_student = np.sum(w_student * w_teacher)/ N\n",
        "\n",
        "        # save results\n",
        "        results_dict_alpha_N_gamma['w_student'].append(w_student)\n",
        "        results_dict_alpha_N_gamma['mean_error_w'].append(mean_error_w)\n",
        "        results_dict_alpha_N_gamma['mse_w'].append(mse_w)\n",
        "        results_dict_alpha_N_gamma['mean_w_teacher_sq'].append(mean_w_teacher_sq)\n",
        "        results_dict_alpha_N_gamma['mean_w_student_sq'].append(mean_w_student_sq)\n",
        "        results_dict_alpha_N_gamma['mean_scalar_product_w_teacher_student'].append(mean_scalar_product_w_teacher_student)\n",
        "\n",
        "      end = time.time()\n",
        "      print(f' elapsed time: {end - start}')\n",
        "      print(f' mse_w: {results_dict_alpha_N_gamma[\"mse_w\"]}')\n",
        "\n",
        "      for k, v in results_dict_alpha_N_gamma.items():\n",
        "        results_dict_alpha_N_gamma[k] = np.array(v)\n",
        "\n",
        "      results_dict[(alpha, int(N), gamma)] = results_dict_alpha_N_gamma\n",
        "\n",
        "\n",
        "with open('./result_dict.pkl', 'wb') as f:\n",
        "  pickle.dump(results_dict, f)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LE6nTb0JQbYA",
        "outputId": "74dea0ac-2d10-49b8-d5e1-9237a0144287"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "alpha 1.0, N 2000, gamma 0.0\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/qpsolvers/conversions/ensure_sparse_matrices.py:24: UserWarning: Converted P to scipy.sparse.csc.csc_matrix\n",
            "For best performance, build P as a scipy.sparse.csc_matrix rather than as a numpy.ndarray\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/qpsolvers/conversions/ensure_sparse_matrices.py:24: UserWarning: Converted G to scipy.sparse.csc.csc_matrix\n",
            "For best performance, build G as a scipy.sparse.csc_matrix rather than as a numpy.ndarray\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " elapsed time: 17.755202531814575\n",
            " mse_w: [0.16160015505297506, 0.11134149218677769, 0.35396039880284275]\n",
            "alpha 0.975, N 2000, gamma 0.0\n",
            " elapsed time: 10.070661306381226\n",
            " mse_w: [0.035438423876083834, 0.027231893904956877, 0.029107087378956686]\n",
            "alpha 1.025, N 2000, gamma 0.0\n",
            " elapsed time: 10.897682905197144\n",
            " mse_w: [0.005086712439093129, 0.0039340086567214, 0.003613903431081841]\n",
            "alpha 0.95, N 2000, gamma 0.0\n",
            " elapsed time: 10.59195590019226\n",
            " mse_w: [0.0546075523556838, 0.05187629061849455, 0.045292731047014215]\n",
            "alpha 1.05, N 2000, gamma 0.0\n",
            " elapsed time: 10.591421365737915\n",
            " mse_w: [0.0018435952066186184, 0.002274821510009419, 0.002770787560550188]\n",
            "alpha 0.6, N 2000, gamma 0.0\n",
            " elapsed time: 9.95019817352295\n",
            " mse_w: [0.38820089347911996, 0.3887884268327668, 0.4075679327743784]\n",
            "alpha 0.7, N 2000, gamma 0.0\n",
            " elapsed time: 9.759997844696045\n",
            " mse_w: [0.2947371086678511, 0.27957953912321193, 0.29600394737463154]\n",
            "alpha 0.8, N 2000, gamma 0.0\n",
            " elapsed time: 11.630239725112915\n",
            " mse_w: [0.18484508498016458, 0.20301262706705056, 0.1752136130404267]\n",
            "alpha 0.9, N 2000, gamma 0.0\n",
            " elapsed time: 10.566637516021729\n",
            " mse_w: [0.09135075784729463, 0.09535299100664746, 0.1038754750677464]\n",
            "alpha 1.1, N 2000, gamma 0.0\n",
            " elapsed time: 10.571019887924194\n",
            " mse_w: [0.000923723148412885, 0.0008360989640383987, 0.0011197488951571412]\n",
            "alpha 1.2, N 2000, gamma 0.0\n",
            " elapsed time: 10.409212827682495\n",
            " mse_w: [0.0005020436254547507, 0.0004848656051089678, 0.00041535542434435415]\n",
            "alpha 1.3, N 2000, gamma 0.0\n",
            " elapsed time: 10.200984716415405\n",
            " mse_w: [0.00032896650139680204, 0.0003620258027775285, 0.00033645944342583934]\n",
            "alpha 1.4, N 2000, gamma 0.0\n",
            " elapsed time: 11.094719648361206\n",
            " mse_w: [0.000252980462404507, 0.00024076327314969217, 0.00024260256888268553]\n",
            "alpha 1.0, N 2000, gamma 0.1\n",
            " elapsed time: 15.00120735168457\n",
            " mse_w: [0.9820149660576906, 0.929793310901352, 0.8953489665180192]\n",
            "alpha 0.975, N 2000, gamma 0.1\n",
            " elapsed time: 17.8638756275177\n",
            " mse_w: [1.2961059343707584, 1.5892952796039725, 1.9804958758625526]\n",
            "alpha 1.025, N 2000, gamma 0.1\n",
            " elapsed time: 12.606612920761108\n",
            " mse_w: [0.7773661994977158, 0.7513307850881374, 0.6270056516446978]\n",
            "alpha 0.95, N 2000, gamma 0.1\n",
            " elapsed time: 21.041261434555054\n",
            " mse_w: [2.7286471289355334, 3.937099194604065, 2.2066341955192574]\n",
            "alpha 1.05, N 2000, gamma 0.1\n",
            " elapsed time: 11.918833255767822\n",
            " mse_w: [0.47359131793212944, 0.45908531795283747, 0.5029149440904945]\n",
            "alpha 0.6, N 2000, gamma 0.1\n",
            " elapsed time: 10.248414278030396\n",
            " mse_w: [0.5506259341707112, 0.606818426983981, 0.6242716983843225]\n",
            "alpha 0.7, N 2000, gamma 0.1\n",
            " elapsed time: 10.28740930557251\n",
            " mse_w: [0.6267123848978999, 0.7003079272670418, 0.6643160117604466]\n",
            "alpha 0.8, N 2000, gamma 0.1\n",
            " elapsed time: 10.168762922286987\n",
            " mse_w: [0.858410252598482, 0.7983843507649336, 0.7524370069315665]\n",
            "alpha 0.9, N 2000, gamma 0.1\n",
            " elapsed time: 9.8221914768219\n",
            " mse_w: [289.909543912576, 92.05219464737345, 85.91281628834803]\n",
            "alpha 1.1, N 2000, gamma 0.1\n",
            " elapsed time: 11.977070808410645\n",
            " mse_w: [0.3877112855116469, 0.3089441696178038, 0.3087583603448945]\n",
            "alpha 1.2, N 2000, gamma 0.1\n",
            " elapsed time: 11.622030973434448\n",
            " mse_w: [0.30554196750433754, 0.2286774916131946, 0.1882519006862328]\n",
            "alpha 1.3, N 2000, gamma 0.1\n",
            " elapsed time: 11.636639833450317\n",
            " mse_w: [0.18987206385337146, 0.24253909811277563, 0.18499325363564448]\n",
            "alpha 1.4, N 2000, gamma 0.1\n",
            " elapsed time: 11.29136347770691\n",
            " mse_w: [0.16081289034710863, 0.17705770373516924, 0.15149904254700788]\n",
            "alpha 1.0, N 2000, gamma 0.2\n",
            " elapsed time: 12.680929899215698\n",
            " mse_w: [0.9625469796089604, 0.9146099777316898, 1.0389211865823642]\n",
            "alpha 0.975, N 2000, gamma 0.2\n",
            " elapsed time: 13.21843957901001\n",
            " mse_w: [1.2579628757276147, 1.122308675359844, 1.332712004844248]\n",
            "alpha 1.025, N 2000, gamma 0.2\n",
            " elapsed time: 11.67345404624939\n",
            " mse_w: [0.7172677158304848, 0.9576711687182868, 0.7778077972281433]\n",
            "alpha 0.95, N 2000, gamma 0.2\n",
            " elapsed time: 17.02226185798645\n",
            " mse_w: [1.8051530582227344, 1.5884987340757357, 1.7702221015892128]\n",
            "alpha 1.05, N 2000, gamma 0.2\n",
            " elapsed time: 11.749374628067017\n",
            " mse_w: [0.6399507563354527, 0.5657305041280692, 0.6067727526882373]\n",
            "alpha 0.6, N 2000, gamma 0.2\n",
            " elapsed time: 10.308383226394653\n",
            " mse_w: [0.9732246373481367, 1.006927701560643, 1.0383737411821443]\n",
            "alpha 0.7, N 2000, gamma 0.2\n",
            " elapsed time: 9.94836950302124\n",
            " mse_w: [1.6974895098299299, 1.8947494824371107, 1.491515015095937]\n",
            "alpha 0.8, N 2000, gamma 0.2\n",
            " elapsed time: 9.855590343475342\n",
            " mse_w: [453.52642126630343, 476.08478231142135, 972.9405878040558]\n",
            "alpha 0.9, N 2000, gamma 0.2\n",
            " elapsed time: 23.21021032333374\n",
            " mse_w: [4.638349610020578, 5.7197441997012906, 4.846094007764301]\n",
            "alpha 1.1, N 2000, gamma 0.2\n",
            " elapsed time: 11.843311309814453\n",
            " mse_w: [0.5840537798507409, 0.5135784505692472, 0.5039660661057974]\n",
            "alpha 1.2, N 2000, gamma 0.2\n",
            " elapsed time: 11.99799394607544\n",
            " mse_w: [0.3953109583590293, 0.416173808312927, 0.39464622605966454]\n",
            "alpha 1.3, N 2000, gamma 0.2\n",
            " elapsed time: 11.622941970825195\n",
            " mse_w: [0.23903118200415513, 0.3222696027356101, 0.3592361684979378]\n",
            "alpha 1.4, N 2000, gamma 0.2\n",
            " elapsed time: 11.886881113052368\n",
            " mse_w: [0.2986387210117955, 0.3207178607627179, 0.24863972235432374]\n",
            "alpha 1.0, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 11.68044924736023\n",
            " mse_w: [1.0021691258606298, 1.099150450153309, 1.1976029855155959]\n",
            "alpha 0.975, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 12.150001525878906\n",
            " mse_w: [1.09746195657874, 0.9908089361540906, 1.0943811460210535]\n",
            "alpha 1.025, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 11.316009283065796\n",
            " mse_w: [0.8945199788785728, 0.9577897360405293, 0.9618191435915296]\n",
            "alpha 0.95, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 12.439753770828247\n",
            " mse_w: [1.3514864706847862, 1.5338067914825981, 1.8615979158109968]\n",
            "alpha 1.05, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 12.049592733383179\n",
            " mse_w: [0.8399533357851826, 0.7262079583679462, 0.7458696953880944]\n",
            "alpha 0.6, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 10.346291780471802\n",
            " mse_w: [2.074939763645304, 2.4448004817234987, 2.0151714839405206]\n",
            "alpha 0.7, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 10.164758682250977\n",
            " mse_w: [258.2126143251237, 144.69050199556264, 1630.4469537834746]\n",
            "alpha 0.8, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 14.581913232803345\n",
            " mse_w: [3.729507204604543, 4.557890916696371, 3.8872936417420525]\n",
            "alpha 0.9, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 16.437225580215454\n",
            " mse_w: [1.7510737388905742, 2.3814483948655845, 2.4640935493457023]\n",
            "alpha 1.1, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 11.64950680732727\n",
            " mse_w: [0.5810574415345114, 0.7143648500828904, 0.6499366514764485]\n",
            "alpha 1.2, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 11.685872077941895\n",
            " mse_w: [0.5174285749005907, 0.44563795219976554, 0.48882681123519817]\n",
            "alpha 1.3, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 11.69623351097107\n",
            " mse_w: [0.4293502797801323, 0.4209689527062583, 0.46828934502892194]\n",
            "alpha 1.4, N 2000, gamma 0.30000000000000004\n",
            " elapsed time: 11.752634763717651\n",
            " mse_w: [0.37774353861480975, 0.44555036219789734, 0.4038350257988434]\n",
            "alpha 1.0, N 2000, gamma 0.4\n",
            " elapsed time: 11.894514083862305\n",
            " mse_w: [1.0827135696951495, 0.8328942686006264, 0.9009575409379785]\n",
            "alpha 0.975, N 2000, gamma 0.4\n",
            " elapsed time: 12.428447246551514\n",
            " mse_w: [1.001116601284932, 1.1302816959966657, 1.0070655487689277]\n",
            "alpha 1.025, N 2000, gamma 0.4\n",
            " elapsed time: 11.40082836151123\n",
            " mse_w: [1.0191083131472192, 0.940582544650629, 0.948430138226044]\n",
            "alpha 0.95, N 2000, gamma 0.4\n",
            " elapsed time: 12.85070538520813\n",
            " mse_w: [1.082760373170215, 1.2750504015112023, 1.1074039027070086]\n",
            "alpha 1.05, N 2000, gamma 0.4\n",
            " elapsed time: 10.957431316375732\n",
            " mse_w: [0.9290409445530452, 0.945240186465843, 0.7267030342852299]\n",
            "alpha 0.6, N 2000, gamma 0.4\n",
            " elapsed time: 10.263118028640747\n",
            " mse_w: [227.6695760525483, 833.2392804346864, 273.98518119510055]\n",
            "alpha 0.7, N 2000, gamma 0.4\n",
            " elapsed time: 12.346835851669312\n",
            " mse_w: [3.5628561533661998, 4.242631385241495, 3.4139440966154075]\n",
            "alpha 0.8, N 2000, gamma 0.4\n",
            " elapsed time: 25.057569980621338\n",
            " mse_w: [9.564990857413468, 6.913676327983093, 7.908367185342144]\n",
            "alpha 0.9, N 2000, gamma 0.4\n",
            " elapsed time: 13.121400594711304\n",
            " mse_w: [1.5507149757883407, 1.8240516746069946, 1.7640933986694567]\n",
            "alpha 1.1, N 2000, gamma 0.4\n",
            " elapsed time: 11.708059787750244\n",
            " mse_w: [0.735003448594724, 0.7844130871177415, 0.6538955883745008]\n",
            "alpha 1.2, N 2000, gamma 0.4\n",
            " elapsed time: 11.706197023391724\n",
            " mse_w: [0.591210334315618, 0.6036081822626475, 0.5404050864153768]\n",
            "alpha 1.3, N 2000, gamma 0.4\n",
            " elapsed time: 11.482497930526733\n",
            " mse_w: [0.5378615234069976, 0.5067252345990839, 0.4921862638129943]\n",
            "alpha 1.4, N 2000, gamma 0.4\n",
            " elapsed time: 11.521196842193604\n",
            " mse_w: [0.4237621115223281, 0.5355864787237609, 0.4386052851708274]\n",
            "alpha 1.0, N 2000, gamma 0.5\n",
            " elapsed time: 10.701403141021729\n",
            " mse_w: [0.9117557592872938, 1.1827877459850924, 1.0532942111136079]\n",
            "alpha 0.975, N 2000, gamma 0.5\n",
            " elapsed time: 11.59623122215271\n",
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            "alpha 1.025, N 2000, gamma 0.5\n",
            " elapsed time: 11.431400537490845\n",
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            "alpha 0.95, N 2000, gamma 0.5\n",
            " elapsed time: 11.749059200286865\n",
            " mse_w: [1.0006288801262202, 1.3021997270084165, 1.108331818446229]\n",
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            " elapsed time: 11.40537428855896\n",
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            "alpha 0.6, N 2000, gamma 0.5\n",
            " elapsed time: 11.269415140151978\n",
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            "alpha 0.7, N 2000, gamma 0.5\n",
            " elapsed time: 16.07023572921753\n",
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            "alpha 0.8, N 2000, gamma 0.5\n",
            " elapsed time: 20.57515525817871\n",
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            "alpha 0.9, N 2000, gamma 0.5\n",
            " elapsed time: 11.920406579971313\n",
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            " elapsed time: 10.653645038604736\n",
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            "alpha 1.2, N 2000, gamma 0.5\n",
            " elapsed time: 11.498509168624878\n",
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            "alpha 1.3, N 2000, gamma 0.5\n",
            " elapsed time: 11.368194818496704\n",
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            "alpha 1.4, N 2000, gamma 0.5\n",
            " elapsed time: 11.613818168640137\n",
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            "alpha 1.0, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 11.55365514755249\n",
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            " elapsed time: 11.010619163513184\n",
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            " elapsed time: 10.89878249168396\n",
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            " elapsed time: 10.397143125534058\n",
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            " elapsed time: 11.145684719085693\n",
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            "alpha 0.6, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 11.915159940719604\n",
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            "alpha 0.7, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 25.175041913986206\n",
            " mse_w: [11.256686089820036, 5.7986420525396865, 9.81476895518883]\n",
            "alpha 0.8, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 13.465211153030396\n",
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            "alpha 0.9, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 12.015424251556396\n",
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            "alpha 1.1, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 11.110154390335083\n",
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            "alpha 1.2, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 11.357263803482056\n",
            " mse_w: [0.6821178983367866, 0.6955381056959552, 0.7748028406785958]\n",
            "alpha 1.3, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 11.52696704864502\n",
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            "alpha 1.4, N 2000, gamma 0.6000000000000001\n",
            " elapsed time: 10.281221389770508\n",
            " mse_w: [0.690428572756073, 0.548979440862883, 0.5222620640202557]\n",
            "alpha 1.0, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 11.619396209716797\n",
            " mse_w: [1.0604401821003608, 1.0079614401906511, 0.9499665731078545]\n",
            "alpha 0.975, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 11.734238624572754\n",
            " mse_w: [1.004208504662551, 1.0515574995308012, 1.038479041148343]\n",
            "alpha 1.025, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 11.14205026626587\n",
            " mse_w: [0.9953118297050858, 1.015895353225377, 0.9260698033036264]\n",
            "alpha 0.95, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 11.378895282745361\n",
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            "alpha 1.05, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 11.506864070892334\n",
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            "alpha 0.6, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 19.52912402153015\n",
            " mse_w: [5.013838635993777, 4.5616018009003945, 5.086491880685285]\n",
            "alpha 0.7, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 16.935280084609985\n",
            " mse_w: [3.4783934125158007, 3.566533929601, 4.0637165952258325]\n",
            "alpha 0.8, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 13.242572546005249\n",
            " mse_w: [1.9787184179335182, 1.9780179611501902, 1.594901377258193]\n",
            "alpha 0.9, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 10.739619255065918\n",
            " mse_w: [1.2562426391111938, 1.1035095849501682, 1.3630132326728694]\n",
            "alpha 1.1, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 10.96237325668335\n",
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            "alpha 1.2, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 11.69869327545166\n",
            " mse_w: [0.7682805496600318, 0.843105412080834, 0.678939361984426]\n",
            "alpha 1.3, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 11.45904278755188\n",
            " mse_w: [0.6571972988744054, 0.7374932330050697, 0.6539337431383905]\n",
            "alpha 1.4, N 2000, gamma 0.7000000000000001\n",
            " elapsed time: 11.708088636398315\n",
            " mse_w: [0.7371503038855733, 0.6351407838938306, 0.6678636652173584]\n",
            "alpha 1.0, N 2000, gamma 0.8\n",
            " elapsed time: 11.543318271636963\n",
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            " elapsed time: 11.377537488937378\n",
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            " elapsed time: 10.168732166290283\n",
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            " elapsed time: 11.089762687683105\n",
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            "alpha 0.6, N 2000, gamma 0.8\n",
            " elapsed time: 22.661192893981934\n",
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            " elapsed time: 14.071614742279053\n",
            " mse_w: [2.351406393393263, 2.9362152144662814, 2.5672916716315566]\n",
            "alpha 0.8, N 2000, gamma 0.8\n",
            " elapsed time: 12.763365268707275\n",
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            "alpha 0.9, N 2000, gamma 0.8\n",
            " elapsed time: 11.583764553070068\n",
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            " elapsed time: 11.251449823379517\n",
            " mse_w: [0.9148174206311417, 0.880659286145016, 0.8369364494997692]\n",
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            " elapsed time: 11.560390710830688\n",
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            " elapsed time: 10.286594152450562\n",
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            "alpha 1.4, N 2000, gamma 0.8\n",
            " elapsed time: 11.041555643081665\n",
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            "alpha 1.0, N 2000, gamma 0.9\n",
            " elapsed time: 11.599904298782349\n",
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            "alpha 0.975, N 2000, gamma 0.9\n",
            " elapsed time: 11.11064338684082\n",
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            " elapsed time: 10.900361061096191\n",
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            " elapsed time: 11.09045696258545\n",
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            " elapsed time: 10.308439254760742\n",
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            " elapsed time: 17.72972536087036\n",
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            " elapsed time: 11.93243145942688\n",
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            " elapsed time: 11.41804051399231\n",
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            " elapsed time: 10.429429769515991\n",
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            " elapsed time: 11.098302125930786\n",
            " mse_w: [0.9153074613761653, 0.9097503574417076, 0.9949890713219859]\n",
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            " elapsed time: 11.05086898803711\n",
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            " elapsed time: 11.182143211364746\n",
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            " elapsed time: 10.199908018112183\n",
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            " elapsed time: 9.108647108078003\n",
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            " elapsed time: 9.954662084579468\n",
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            " elapsed time: 10.140484809875488\n",
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            " elapsed time: 9.978995084762573\n",
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            " elapsed time: 9.50332522392273\n",
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            " elapsed time: 10.472997188568115\n",
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            "alpha 0.9, N 2000, gamma 1.0\n",
            " elapsed time: 10.281566858291626\n",
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            "alpha 1.1, N 2000, gamma 1.0\n",
            " elapsed time: 10.734200239181519\n",
            " mse_w: [0.830138268495372, 0.9603013481101714, 0.945171830681769]\n",
            "alpha 1.2, N 2000, gamma 1.0\n",
            " elapsed time: 9.585215330123901\n",
            " mse_w: [0.8423462432654449, 0.9353095430887731, 0.8954091927910653]\n",
            "alpha 1.3, N 2000, gamma 1.0\n",
            " elapsed time: 10.755309581756592\n",
            " mse_w: [0.8559359333129821, 0.8374284048515497, 0.8258405790442385]\n",
            "alpha 1.4, N 2000, gamma 1.0\n",
            " elapsed time: 10.763110160827637\n",
            " mse_w: [0.8226063560947519, 0.734994214761128, 0.8426629945748849]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib as mpl\n",
        "\n",
        "metric = 'mse_w'\n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize=(6,4), dpi=200)\n",
        "for gamma_id, gamma in enumerate(gammas):\n",
        "  mean_metric = []\n",
        "  for alpha in np.sort(alphas):\n",
        "    mean_metric.append(np.mean(results_dict[(alpha, int(N), gamma)][metric]))\n",
        "\n",
        "  scatter = ax.plot(\n",
        "      np.sort(alphas),\n",
        "      mean_metric,\n",
        "      label=f'$\\gamma={gamma:.1f}$',\n",
        "      c=mpl.colormaps['coolwarm'](gamma/np.max(gammas))\n",
        "  )\n",
        "ax.set_xlabel('alpha=P/N')\n",
        "ax.set_ylabel('generalization error')\n",
        "ax.set_yscale('log')\n",
        "ax.legend()\n",
        "\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 776
        },
        "id": "i0xpO_xC-kAM",
        "outputId": "4186b9af-2464-4d16-943f-fef26d1caf58"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x796d61d82920>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gamma = 0.2\n",
        "N_cutoff = int(N * gamma)\n",
        "\n",
        "for alpha in [0.6, 0.8, 0.9, 1.0]:\n",
        "  # plt.hist(results_dict[(alpha, int(N), gamma)]['w_student'][0][:N_cutoff], bins=np.linspace(-1, 8, 20), histtype='step', label=f'{alpha}')\n",
        "  plt.hist(abs(results_dict[(alpha, int(N), gamma)]['w_student'][0][:N_cutoff]), bins=np.logspace(-10, 1, 100), histtype='step', label=f'{alpha}', cumulative=True)\n",
        "  # plt.hist(results_dict[(alpha, int(N), gamma)]['w_student'][0][N_cutoff:], histtype='step')\n",
        "plt.xscale('log')\n",
        "plt.legend()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "PnhJyRvEtHYo",
        "outputId": "507cc900-65ea-4100-f4ee-5891739093de"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x796d5611b220>"
            ]
          },
          "metadata": {},
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "metric = 'mse_w'\n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize=(6,4), dpi=200)\n",
        "gamma = 1\n",
        "for alpha in alphas:\n",
        "  scatter = ax.scatter(\n",
        "      [alpha]*n_runs,\n",
        "      results_dict[(alpha, int(N), gamma)][metric],\n",
        "      c=mpl.colormaps['coolwarm'](gamma/np.max(gammas)),\n",
        "      s=4\n",
        "  )\n",
        "ax.set_xlabel('alpha=P/N')\n",
        "ax.set_ylabel('generalization error')\n",
        "# ax.set_yscale('log')\n",
        "ax.legend()\n",
        "fig.colorbar(scatter)\n",
        "\n",
        ""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 859
        },
        "id": "YRqrELh5pnao",
        "outputId": "86a81abf-1af1-4ad1-e027-b7669de6d281"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-14-e3207b7fe02e>:6: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n",
            "  scatter = ax.scatter(\n",
            "WARNING:matplotlib.legend:No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x796d642a7130>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MSE\n",
        "print(f'mean_error_w: {mean_error_w}')\n",
        "print(f'mse_w: {mse_w}')\n",
        "# print(f'mean_w_teacher_sq: {mean_w_teacher_sq}')\n",
        "# print(f'mean_w_student_sq: {mean_w_student_sq}')\n",
        "# print(f'mean_scalar_product_w_teacher_student: {mean_scalar_product_w_teacher_student}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rnz0dK_6T00a",
        "outputId": "7839ecf8-0046-4c58-87d5-ffd53a9efbdd"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean_error_w: 0.5818401061397249\n",
            "mse_w: 1.0168924646503388\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(w_student - w_teacher)"
      ],
      "metadata": {
        "id": "SgDubtx1S6C7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alpha = 1.25\n",
        "for N in Ns:\n",
        "  plt.hist(\n",
        "      results_dict[(alpha, int(N))]['w_student'][0],\n",
        "      bins=np.linspace(-2,5,20),\n",
        "      density=True,\n",
        "      histtype='step',\n",
        "      label=f'{N}'\n",
        "  )\n",
        "plt.legend()"
      ],
      "metadata": {
        "id": "S0vIBrRgh8nd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('./result_dict_alpha_gamma.pkl', 'wb') as f:\n",
        "  pickle.dump(results_dict, f)"
      ],
      "metadata": {
        "id": "4ASlwKNdJ8Xr"
      },
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.download( \"./result_dict_alpha_gamma.pkl\" )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "rP4-5sXML5Tb",
        "outputId": "b24587a0-39bd-43af-9158-e700a57d2d56"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_d9f4f430-3077-4726-aa2e-e4b49122a7d1\", \"result_dict_alpha_gamma.pkl\", 6915185)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "fJDZT3MOMIJ2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}