notebook_6_overview_convergence_time.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:31:36.776017Z",
     "start_time": "2024-07-27T16:31:36.773441Z"
    }
   },
   "source": [
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Matrixkit\n",
    "from matrixkit import preconditioning as precon\n",
    "from matrixkit.core import MatrixData, ValueProperties, BlockProperties\n",
    "from matrixkit.block import supervariable_blocking as sv_blocking\n",
    "from matrixkit.plot import generate_block_matrix as gen_block_mat"
   ],
   "outputs": [],
   "execution_count": 92
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generate Synthetic Data\n",
    "Generate a set of `n` square symmetrical and positive semi matrices of dimensions `MATRIX_DIM` to RAM.\n",
    "\n",
    "## Define Constants\n",
    "The following constants are used throughout the rest of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:31:37.422896Z",
     "start_time": "2024-07-27T16:31:37.419808Z"
    }
   },
   "source": [
    "MATRIX_DIM = 128\n",
    "NUMBER_OF_MATRICES = 100\n",
    "DIAGONAL_BAND_RADIUS = 10\n",
    "RNG_SEED = 42\n",
    "\n",
    "VALUE_COLORBAR = 'rocket'\n",
    "BLOCK_COLORBAR = 'flare'\n",
    "\n",
    "MIN_SV_BLOCK_SIZE = 2  # should not be less than 2\n",
    "MAX_SV_BLOCK_SIZE = 25  # should not be greater than MATRIX_DIM\n",
    "BLOCK_SIZE_LIST = [i for i in range(MIN_SV_BLOCK_SIZE, MAX_SV_BLOCK_SIZE + 1)]\n",
    "NUMBER_OF_DATASETS = MAX_SV_BLOCK_SIZE - MIN_SV_BLOCK_SIZE + 1"
   ],
   "outputs": [],
   "execution_count": 93
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "To test convergence behaviour dependence on block size, several sets with different average block sizes are generated"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:31:39.734119Z",
     "start_time": "2024-07-27T16:31:38.620290Z"
    }
   },
   "source": [
    "# Generate matrices\n",
    "bgr_noise_value_props = ValueProperties(density_range=(0.3, 0.5), value_range=(0.0, 0.5))\n",
    "noise_blk_value_props = ValueProperties(density_range=(0.3, 0.5), value_range=(0.3, 1.0))\n",
    "noise_blk_block_props = BlockProperties(size_range=(3, 32), size_average=10, size_std_dev=0.66, gap_chance=0.5)\n",
    "tdata_blk_value_props = ValueProperties(density_range=(0.5, 0.7), value_range=(0.3, 1.0))\n",
    "tdata_blk_block_props = BlockProperties(size_range=(1, 32), size_average=10, size_std_dev=0.66, gap_chance=0.0)\n",
    "\n",
    "test_data = MatrixData(\n",
    "    dimension=MATRIX_DIM,\n",
    "    band_radius=DIAGONAL_BAND_RADIUS,\n",
    "    sample_size=NUMBER_OF_MATRICES,\n",
    "    background_noise_value_properties=bgr_noise_value_props,\n",
    "    block_noise_value_properties=noise_blk_value_props,\n",
    "    block_noise_block_properties=noise_blk_block_props,\n",
    "    block_data_value_properties=tdata_blk_value_props,\n",
    "    block_data_block_properties=tdata_blk_block_props,\n",
    "    seed=42,\n",
    "    determinant_cutoff=0.0001,\n",
    "    print_debug=False\n",
    ")\n",
    "\n",
    "matrix_shifted = precon.prepare_matrix(test_data.matrices, 'shift')"
   ],
   "outputs": [],
   "execution_count": 94
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": "Predict blocks using supervariable blocking"
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:31:43.148285Z",
     "start_time": "2024-07-27T16:31:40.780817Z"
    }
   },
   "source": [
    "list_of_sv_predictions = []\n",
    "list_of_preconditioner = []\n",
    "\n",
    "print(\"\".join([f\"{i:d}\" for i in test_data.tdata_blk_starts[0]]))\n",
    "for s in BLOCK_SIZE_LIST:\n",
    "    predictions = sv_blocking(input_matrices=test_data.matrices, max_block_size=s)\n",
    "    list_of_sv_predictions.append(predictions)\n",
    "    preconditioner = precon.generate_block_jacobi_preconditioner(sparse_matrices=test_data.matrices, block_start_indicators=predictions, apply_inverse_minmax_norm=True)\n",
    "    list_of_preconditioner.append(preconditioner)\n",
    "    \n",
    "    print(\"\".join([f\"{i:d}\" for i in predictions[0]]))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "execution_count": 95
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:36:01.775931Z",
     "start_time": "2024-07-27T16:35:59.557580Z"
    }
   },
   "cell_type": "code",
   "source": [
    "c_map = 'flare'\n",
    "ctr = 1\n",
    "for i in range(len(BLOCK_SIZE_LIST)):\n",
    "    test_fig = plt.figure(num=1, figsize=(12, 9))\n",
    "    #sp1 = test_fig.add_subplot(24, 4, 2 * ctr - 1)\n",
    "    #sns.heatmap(preconditioner[0], xticklabels=False, yticklabels=False, cmap=c_map, square=True, vmin=-1, vmax=1)\n",
    "    #sp2 = test_fig.add_subplot(24, 4, 2 * ctr)\n",
    "    sp2 = test_fig.add_subplot(4, 6, ctr)\n",
    "    sp2.set_title(f\"BlockSize {BLOCK_SIZE_LIST[i]:2d}\")\n",
    "    sns.heatmap(gen_block_mat(list_of_sv_predictions[i][0]), xticklabels=False, yticklabels=False, cmap=c_map, cbar=False, square=True, vmin=0, vmax=1)\n",
    "    \n",
    "    ctr += 1"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x900 with 24 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 108
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:38:31.879350Z",
     "start_time": "2024-07-27T16:38:29.776475Z"
    }
   },
   "cell_type": "code",
   "source": [
    "c_map = 'icefire'\n",
    "ctr = 1\n",
    "for i in range(len(BLOCK_SIZE_LIST)):\n",
    "    test_fig = plt.figure(num=1, figsize=(12, 9))\n",
    "    sp1 = test_fig.add_subplot(4, 6, ctr)\n",
    "    #sns.heatmap(list_of_preconditioner[i][0], xticklabels=False, yticklabels=False, cmap=c_map, cbar=False, square=True, vmin=-1, vmax=1)\n",
    "    sns.heatmap(list_of_preconditioner[i][0], xticklabels=False, yticklabels=False, cmap=c_map, cbar=False, square=True)\n",
    "    \n",
    "    ctr += 1"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x900 with 24 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 112
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:25:41.895950Z",
     "start_time": "2024-07-27T16:16:16.529918Z"
    }
   },
   "cell_type": "code",
   "source": [
    "list_metadata_shifted = []\n",
    "list_metadata_original = []\n",
    "\n",
    "# Generate solution value vector\n",
    "b = np.ones((NUMBER_OF_MATRICES, MATRIX_DIM))\n",
    "\n",
    "for i in range(NUMBER_OF_DATASETS):\n",
    "    print(f\"Running GMRES for raw test matrix set [{i:02d}] with preconditioner\")\n",
    "    list_metadata_shifted.append(precon.solve_with_gmres_monitored(matrix=matrix_shifted, b_vector=b, preconditioner=list_of_preconditioner[i]))\n",
    "    list_metadata_original.append(precon.solve_with_gmres_monitored(matrix=test_data.matrices, b_vector=b, preconditioner=list_of_preconditioner[i]))"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running GMRES for raw test matrix set [00] with preconditioner\n",
      "(100, 128, 128)\n",
      "\n",
      "With preconditioner:\n",
      "--------------------------------------------------------------------------------\n",
      "  Converged: 3 out of 100\n",
      "  Average iterations: 24833.62\n",
      "--------------------------------------------------------------------------------\n",
      "  iterations: [25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600\n",
      " 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600    15\n",
      " 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600\n",
      " 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600\n",
      " 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600\n",
      " 25600 25600 25600 25600 25600 25600 25600 25600 25600     8 25600 25600\n",
      " 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600\n",
      " 25600   139 25600 25600 25600 25600 25600 25600 25600 25600 25600 25600\n",
      " 25600 25600 25600 25600]\n",
      "(100, 128, 128)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[78], line 10\u001B[0m\n\u001B[0;32m      8\u001B[0m \u001B[38;5;28mprint\u001B[39m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mRunning GMRES for raw test matrix set [\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mi\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m02d\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m] with preconditioner\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m      9\u001B[0m list_metadata_shifted\u001B[38;5;241m.\u001B[39mappend(precon\u001B[38;5;241m.\u001B[39msolve_with_gmres_monitored(matrix\u001B[38;5;241m=\u001B[39mmatrix_shifted, b_vector\u001B[38;5;241m=\u001B[39mb, preconditioner\u001B[38;5;241m=\u001B[39mlist_of_preconditioner[i]))\n\u001B[1;32m---> 10\u001B[0m list_metadata_original\u001B[38;5;241m.\u001B[39mappend(\u001B[43mprecon\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43msolve_with_gmres_monitored\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmatrix\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mtest_data\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmatrices\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mb_vector\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mb\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43mpreconditioner\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mlist_of_preconditioner\u001B[49m\u001B[43m[\u001B[49m\u001B[43mi\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m)\n",
      "File \u001B[1;32mC:\\Projects\\Dev\\opencampus-preconditioner-ai-project\\matrixlib\\preconditioning.py:152\u001B[0m, in \u001B[0;36msolve_with_gmres_monitored\u001B[1;34m(matrix, b_vector, preconditioner, relative_tolerance)\u001B[0m\n\u001B[0;32m    149\u001B[0m     run_matrix \u001B[38;5;241m=\u001B[39m preconditioner[k] \u001B[38;5;241m@\u001B[39m matrix[k]\n\u001B[0;32m    150\u001B[0m     run_b_vector \u001B[38;5;241m=\u001B[39m preconditioner[k] \u001B[38;5;241m@\u001B[39m b_vector[k]\n\u001B[1;32m--> 152\u001B[0m x, info \u001B[38;5;241m=\u001B[39m \u001B[43mgmres\u001B[49m\u001B[43m(\u001B[49m\n\u001B[0;32m    153\u001B[0m \u001B[43m    \u001B[49m\u001B[43mA\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrun_matrix\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    154\u001B[0m \u001B[43m    \u001B[49m\u001B[43mb\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrun_b_vector\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    155\u001B[0m \u001B[43m    \u001B[49m\u001B[43mx0\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mx_vector\u001B[49m\u001B[43m[\u001B[49m\u001B[43mk\u001B[49m\u001B[43m]\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    156\u001B[0m \u001B[43m    \u001B[49m\u001B[43mrtol\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mrelative_tolerance\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    157\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcallback\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[43mcallback\u001B[49m\u001B[43m,\u001B[49m\n\u001B[0;32m    158\u001B[0m \u001B[43m    \u001B[49m\u001B[43mcallback_type\u001B[49m\u001B[38;5;241;43m=\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\u001B[38;5;124;43mpr_norm\u001B[39;49m\u001B[38;5;124;43m'\u001B[39;49m\n\u001B[0;32m    159\u001B[0m \u001B[43m\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    160\u001B[0m x_solutions[k] \u001B[38;5;241m=\u001B[39m x\n\u001B[0;32m    161\u001B[0m info_array[k] \u001B[38;5;241m=\u001B[39m info\n",
      "File \u001B[1;32mC:\\Program Files\\Python312\\Lib\\site-packages\\scipy\\_lib\\deprecation.py:213\u001B[0m, in \u001B[0;36m_deprecate_positional_args.<locals>._inner_deprecate_positional_args.<locals>.inner_f\u001B[1;34m(*args, **kwargs)\u001B[0m\n\u001B[0;32m    211\u001B[0m extra_args \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlen\u001B[39m(args) \u001B[38;5;241m-\u001B[39m \u001B[38;5;28mlen\u001B[39m(all_args)\n\u001B[0;32m    212\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m extra_args \u001B[38;5;241m<\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[1;32m--> 213\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[43mf\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43margs\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[38;5;241;43m*\u001B[39;49m\u001B[43mkwargs\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    215\u001B[0m \u001B[38;5;66;03m# extra_args > 0\u001B[39;00m\n\u001B[0;32m    216\u001B[0m args_msg \u001B[38;5;241m=\u001B[39m [\n\u001B[0;32m    217\u001B[0m     \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mname\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00marg\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    218\u001B[0m     \u001B[38;5;28;01mfor\u001B[39;00m name, arg \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mzip\u001B[39m(kwonly_args[:extra_args], args[\u001B[38;5;241m-\u001B[39mextra_args:])\n\u001B[0;32m    219\u001B[0m ]\n",
      "File \u001B[1;32mC:\\Program Files\\Python312\\Lib\\site-packages\\scipy\\sparse\\linalg\\_isolve\\iterative.py:779\u001B[0m, in \u001B[0;36mgmres\u001B[1;34m(A, b, x0, tol, restart, maxiter, M, callback, restrt, atol, callback_type, rtol)\u001B[0m\n\u001B[0;32m    777\u001B[0m breakdown \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mFalse\u001B[39;00m\n\u001B[0;32m    778\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m col \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mrange\u001B[39m(restart):\n\u001B[1;32m--> 779\u001B[0m     av \u001B[38;5;241m=\u001B[39m \u001B[43mmatvec\u001B[49m\u001B[43m(\u001B[49m\u001B[43mv\u001B[49m\u001B[43m[\u001B[49m\u001B[43mcol\u001B[49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[43m:\u001B[49m\u001B[43m]\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    780\u001B[0m     w \u001B[38;5;241m=\u001B[39m psolve(av)\n\u001B[0;32m    782\u001B[0m     \u001B[38;5;66;03m# Modified Gram-Schmidt\u001B[39;00m\n",
      "File \u001B[1;32mC:\\Program Files\\Python312\\Lib\\site-packages\\scipy\\sparse\\linalg\\_interface.py:236\u001B[0m, in \u001B[0;36mLinearOperator.matvec\u001B[1;34m(self, x)\u001B[0m\n\u001B[0;32m    233\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m x\u001B[38;5;241m.\u001B[39mshape \u001B[38;5;241m!=\u001B[39m (N,) \u001B[38;5;129;01mand\u001B[39;00m x\u001B[38;5;241m.\u001B[39mshape \u001B[38;5;241m!=\u001B[39m (N,\u001B[38;5;241m1\u001B[39m):\n\u001B[0;32m    234\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdimension mismatch\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[1;32m--> 236\u001B[0m y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_matvec\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    238\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(x, np\u001B[38;5;241m.\u001B[39mmatrix):\n\u001B[0;32m    239\u001B[0m     y \u001B[38;5;241m=\u001B[39m asmatrix(y)\n",
      "File \u001B[1;32mC:\\Program Files\\Python312\\Lib\\site-packages\\scipy\\sparse\\linalg\\_interface.py:203\u001B[0m, in \u001B[0;36mLinearOperator._matvec\u001B[1;34m(self, x)\u001B[0m\n\u001B[0;32m    193\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_matvec\u001B[39m(\u001B[38;5;28mself\u001B[39m, x):\n\u001B[0;32m    194\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Default matrix-vector multiplication handler.\u001B[39;00m\n\u001B[0;32m    195\u001B[0m \n\u001B[0;32m    196\u001B[0m \u001B[38;5;124;03m    If self is a linear operator of shape (M, N), then this method will\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    201\u001B[0m \u001B[38;5;124;03m    will define matrix-vector multiplication as well.\u001B[39;00m\n\u001B[0;32m    202\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 203\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mmatmat\u001B[49m\u001B[43m(\u001B[49m\u001B[43mx\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mreshape\u001B[49m\u001B[43m(\u001B[49m\u001B[38;5;241;43m-\u001B[39;49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m,\u001B[49m\u001B[43m \u001B[49m\u001B[38;5;241;43m1\u001B[39;49m\u001B[43m)\u001B[49m\u001B[43m)\u001B[49m\n",
      "File \u001B[1;32mC:\\Program Files\\Python312\\Lib\\site-packages\\scipy\\sparse\\linalg\\_interface.py:340\u001B[0m, in \u001B[0;36mLinearOperator.matmat\u001B[1;34m(self, X)\u001B[0m\n\u001B[0;32m    337\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\u001B[38;5;124mf\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mdimension mismatch: \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m, \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mX\u001B[38;5;241m.\u001B[39mshape\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m    339\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 340\u001B[0m     Y \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43m_matmat\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m    341\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mException\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    342\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m issparse(X) \u001B[38;5;129;01mor\u001B[39;00m is_pydata_spmatrix(X):\n",
      "File \u001B[1;32mC:\\Program Files\\Python312\\Lib\\site-packages\\scipy\\sparse\\linalg\\_interface.py:800\u001B[0m, in \u001B[0;36mMatrixLinearOperator._matmat\u001B[1;34m(self, X)\u001B[0m\n\u001B[0;32m    799\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_matmat\u001B[39m(\u001B[38;5;28mself\u001B[39m, X):\n\u001B[1;32m--> 800\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28;43mself\u001B[39;49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mA\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43mdot\u001B[49m\u001B[43m(\u001B[49m\u001B[43mX\u001B[49m\u001B[43m)\u001B[49m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "execution_count": 78
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:15:23.527451Z",
     "start_time": "2024-07-27T16:15:23.524406Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"Average iterations to solve for each matrix (shifted)\")\n",
    "for i in range(NUMBER_OF_DATASETS):\n",
    "    x, info, iters, residuals = list_metadata_shifted[i]\n",
    "    print(f\"Average for iteration {i + 1:2d}: {sum(iters) / len(iters):7.2f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average iterations to solve for each matrix (shifted)\n",
      "Average for iteration  1:    1.00\n",
      "Average for iteration  2:    1.00\n",
      "Average for iteration  3:    1.00\n",
      "Average for iteration  4:    1.00\n",
      "Average for iteration  5:    1.00\n",
      "Average for iteration  6:    1.00\n",
      "Average for iteration  7:    1.00\n",
      "Average for iteration  8:    1.00\n",
      "Average for iteration  9:    1.00\n",
      "Average for iteration 10:    1.01\n",
      "Average for iteration 11:    1.00\n",
      "Average for iteration 12:    1.01\n",
      "Average for iteration 13:    1.00\n",
      "Average for iteration 14:    1.00\n",
      "Average for iteration 15:    1.00\n",
      "Average for iteration 16:    1.00\n",
      "Average for iteration 17:    1.00\n",
      "Average for iteration 18:    1.00\n",
      "Average for iteration 19:    1.00\n",
      "Average for iteration 20:    1.01\n",
      "Average for iteration 21:    1.01\n",
      "Average for iteration 22:    1.00\n",
      "Average for iteration 23:    1.00\n",
      "Average for iteration 24:    1.01\n"
     ]
    }
   ],
   "execution_count": 75
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-27T16:15:27.793044Z",
     "start_time": "2024-07-27T16:15:27.789537Z"
    }
   },
   "cell_type": "code",
   "source": [
    "print(\"Average iterations to solve for each matrix (original\")\n",
    "for i in range(NUMBER_OF_DATASETS):\n",
    "    x, info, iters, residuals = list_metadata_original[i]\n",
    "    print(f\"Average for iteration {i + 1:2d}: {sum(iters) / len(iters):7.2f}\")"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average iterations to solve for each matrix (original\n",
      "Average for iteration  1:   23.92\n",
      "Average for iteration  2:   22.48\n",
      "Average for iteration  3:   26.83\n",
      "Average for iteration  4:   27.43\n",
      "Average for iteration  5:   26.97\n",
      "Average for iteration  6:  325.47\n",
      "Average for iteration  7:   34.30\n",
      "Average for iteration  8:   41.27\n",
      "Average for iteration  9:   28.87\n",
      "Average for iteration 10:   35.57\n",
      "Average for iteration 11:   26.26\n",
      "Average for iteration 12:   27.28\n",
      "Average for iteration 13:  283.78\n",
      "Average for iteration 14:   55.23\n",
      "Average for iteration 15:   75.81\n",
      "Average for iteration 16:  316.78\n",
      "Average for iteration 17:   57.89\n",
      "Average for iteration 18:   45.07\n",
      "Average for iteration 19:  362.25\n",
      "Average for iteration 20:  299.85\n",
      "Average for iteration 21:  108.37\n",
      "Average for iteration 22:   43.50\n",
      "Average for iteration 23:   40.16\n",
      "Average for iteration 24:  318.36\n"
     ]
    }
   ],
   "execution_count": 76
  }
 ],
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