Comparative Analysis between BFGS & Adam.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "32b92576",
      "metadata": {
        "id": "32b92576"
      },
      "source": [
        "# Task 1:\n",
        "## Implement the Adam (Batch Version) algorithm to train a multivariable linear regression model.\n",
        "\n",
        "\n",
        "### Please fulfil the following requirements:\n",
        "<br>\n",
        "<b>\n",
        "    \n",
        "    1. Check your implementation on the data in the attached file.\n",
        "    2. You must obtain r2-score > 0.9.\n",
        "    3. You must plot the following curves.\n",
        "        - Loss vs. iterations.\n",
        "        - Loss vs. each parameter (i.e. loss vs theta 0, loss vs. theta 1 .... etc.).\n",
        "    4. Choose hyper parameters to obtain the required accuracy.\n",
        "    5. The implementation must be vectoried and calculations must be performed as matrix vector \n",
        "    multiplications.\n",
        "    6. Apply the stop conditions (gradient < 0.001 and cost convergence check).                                            \n",
        "</b>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [10.3, 8.3]"
      ],
      "metadata": {
        "id": "6X9-VaTF5LrN"
      },
      "id": "6X9-VaTF5LrN",
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('/content/MultiVarLR.csv')"
      ],
      "metadata": {
        "id": "isl7plTXbIAO"
      },
      "id": "isl7plTXbIAO",
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xs = df.iloc[:, :-1].to_numpy()\n",
        "b = np.ones((24,1))\n",
        "\n",
        "Xs = np.hstack((b,Xs))\n",
        "\n",
        "y = df.iloc[:, -1].to_numpy().reshape(24,1)"
      ],
      "metadata": {
        "id": "Ri3O2OmmdgjE"
      },
      "id": "Ri3O2OmmdgjE",
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def adam(features, target, lr, beta_1, beta_2, epsilon=1E-5, max_iter=500):\n",
        "    \n",
        "    thetas_upd, loss, preds = ([] for i in range(3))\n",
        "    \n",
        "    n_samp, n_feat = features.shape\n",
        "    \n",
        "    thetas = np.zeros((n_feat, 1))\n",
        "    mom_theta, rms_theta = np.zeros((n_feat, 1)), np.zeros((n_feat, 1))\n",
        "\n",
        "    print(thetas_upd)\n",
        "    \n",
        "    for i in range(max_iter):\n",
        "        if i > 2 and abs(loss[-2] - loss[-1]) < 0.0001:\n",
        "            break\n",
        "\n",
        "        print('\\n')\n",
        "        print('*' * 18 + ' Iteration ' + str(i) + ' ' + '*' * 20)\n",
        "        print('\\n')\n",
        "\n",
        "        h_x = features @ thetas\n",
        "\n",
        "        preds.append(h_x)\n",
        "\n",
        "        err = h_x - target\n",
        "        cost = (err.T@err) / (2*n_samp)\n",
        "\n",
        "        loss.append(cost[0])\n",
        "\n",
        "        print('\\nError Vector: \\n{}\\n\\n j = {}\\n'.format(err.T, cost))\n",
        "\n",
        "        grad = (features.T @ err) / n_samp\n",
        "\n",
        "        mom_theta = (beta_1 * mom_theta) + ((1 - beta_1) * grad)\n",
        "        rms_theta = (beta_2 * rms_theta) + ((1 - beta_2) * grad ** 2)\n",
        "\n",
        "        mom_corr = mom_theta / (1 - (beta_1) ** (i + 1))\n",
        "        rms_corr = rms_theta / (1 - (beta_2) ** (i + 1))\n",
        "\n",
        "        grad_norm = np.linalg.norm(grad)\n",
        "\n",
        "        print('\\nGradient Vector :', grad)\n",
        "        print('\\nGradient Vector Norm:', grad_norm)\n",
        "\n",
        "        if grad_norm <= 0.0001:\n",
        "            break\n",
        "\n",
        "        print('m_0_0:', mom_theta)\n",
        "        print('\\n')\n",
        "        print('m_0_0_corr:', mom_corr)\n",
        "\n",
        "        print('v_0_0:', rms_theta)\n",
        "        print('\\n')\n",
        "        print('v_0_0_corr:', rms_corr)\n",
        "\n",
        "        thetas -= (lr * mom_corr) / (np.sqrt(rms_corr) + epsilon)\n",
        "\n",
        "        print('\\ntheta_new : \\n', thetas)\n",
        "\n",
        "        if(i==0):\n",
        "          for theta_val in thetas:\n",
        "            thetas_upd.append([theta_val[0]])\n",
        "        \n",
        "        else:\n",
        "          for idx, value in enumerate(thetas):\n",
        "            thetas_upd[idx] = np.append(thetas_upd[idx], value)\n",
        "\n",
        "    return thetas_upd, thetas, loss, preds"
      ],
      "metadata": {
        "id": "PbxcGK075zIV"
      },
      "id": "PbxcGK075zIV",
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "thetas_upd, opt_thetas, loss, preds = adam(Xs, y, 0.5, beta_1=0.8, beta_2=0.99, epsilon=1E-4, max_iter=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5siBUWSDfRj",
        "outputId": "9d7ae1d5-556b-41b7-f156-114f46a631a0"
      },
      "id": "d5siBUWSDfRj",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "\n",
            "\n",
            "****************** Iteration 282 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.22318527  1.47784451  2.842882   -1.83534666  4.293111    1.35407773\n",
            "  -1.44758321 -0.63031998  0.24127596  2.90862324  1.77348926  2.27952237\n",
            "   1.19702965  3.31913703 -3.43919105 -2.26358078 -2.01859647  0.2538393\n",
            "  -0.2863565  -0.97394334 -0.54528264 -7.59583409  2.38816705 -1.21393611]]\n",
            "\n",
            " j = [[3.18840327]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07732679]\n",
            " [ 0.68427675]\n",
            " [-0.08448222]\n",
            " [-0.67717557]]\n",
            "\n",
            "Gradient Vector Norm: 0.969493732851901\n",
            "m_0_0: [[ 0.07736874]\n",
            " [ 0.72346437]\n",
            " [-0.0731701 ]\n",
            " [-0.7277224 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07736874]\n",
            " [ 0.72346437]\n",
            " [-0.0731701 ]\n",
            " [-0.7277224 ]]\n",
            "v_0_0: [[3.67520016e+01]\n",
            " [2.41157991e+05]\n",
            " [2.42260348e+05]\n",
            " [2.55390663e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.90222444e+01]\n",
            " [2.56054790e+05]\n",
            " [2.57225243e+05]\n",
            " [2.71166643e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.01363888]\n",
            " [ 0.40307746]\n",
            " [ 0.52512046]\n",
            " [ 1.08701404]]\n",
            "\n",
            "\n",
            "****************** Iteration 283 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.22452879  1.4774806   2.84500646 -1.84005105  4.29077995  1.35770106\n",
            "  -1.44141004 -0.63011979  0.24514833  2.90916306  1.77365229  2.27728632\n",
            "   1.19114223  3.32267211 -3.4383662  -2.27260773 -2.01121741  0.25146742\n",
            "  -0.28851769 -0.9696639  -0.54484992 -7.59240533  2.38324312 -1.21566604]]\n",
            "\n",
            " j = [[3.18696343]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.0773058 ]\n",
            " [ 0.67488006]\n",
            " [-0.08680257]\n",
            " [-0.66543701]]\n",
            "\n",
            "Gradient Vector Norm: 0.9548719212186983\n",
            "m_0_0: [[ 0.07735615]\n",
            " [ 0.71374751]\n",
            " [-0.07589659]\n",
            " [-0.71526533]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07735615]\n",
            " [ 0.71374751]\n",
            " [-0.07589659]\n",
            " [-0.71526533]]\n",
            "v_0_0: [[3.63845413e+01]\n",
            " [2.38746415e+05]\n",
            " [2.39837745e+05]\n",
            " [2.52836761e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.86082364e+01]\n",
            " [2.53337755e+05]\n",
            " [2.54495783e+05]\n",
            " [2.68289253e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.01986358]\n",
            " [ 0.40236843]\n",
            " [ 0.52519568]\n",
            " [ 1.0877045 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 284 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.22586129  1.47713852  2.84713229 -1.84473834  4.28841197  1.36128487\n",
            "  -1.43531933 -0.62994649  0.24893595  2.90968415  1.77377869  2.27509946\n",
            "   1.18532525  3.32616212 -3.43755212 -2.28154407 -2.00389111  0.24914777\n",
            "  -0.29063814 -0.96541889 -0.54443238 -7.58900215  2.378398   -1.21736861]]\n",
            "\n",
            " j = [[3.18554502]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07728276]\n",
            " [ 0.66556418]\n",
            " [-0.08902796]\n",
            " [-0.6538706 ]]\n",
            "\n",
            "Gradient Vector Norm: 0.9404366234355759\n",
            "m_0_0: [[ 0.07734147]\n",
            " [ 0.70411084]\n",
            " [-0.07852286]\n",
            " [-0.70298638]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07734147]\n",
            " [ 0.70411084]\n",
            " [-0.07852286]\n",
            " [-0.70298638]]\n",
            "v_0_0: [[3.60207557e+01]\n",
            " [2.36358956e+05]\n",
            " [2.37439367e+05]\n",
            " [2.50308398e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.81988716e+01]\n",
            " [2.50651193e+05]\n",
            " [2.51796936e+05]\n",
            " [2.65444135e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.02612035]\n",
            " [ 0.40166524]\n",
            " [ 0.52527392]\n",
            " [ 1.08838673]]\n",
            "\n",
            "\n",
            "****************** Iteration 285 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.22718262  1.47681802  2.8492594  -1.84940835  4.28600758  1.36482917\n",
            "  -1.42931098 -0.62979951  0.25263986  2.91018657  1.77386893  2.27296137\n",
            "   1.17957814  3.32960722 -3.43674851 -2.29039034 -1.9966176   0.24687979\n",
            "  -0.29271842 -0.96120824 -0.54402971 -7.58562459  2.37363074 -1.21904385]]\n",
            "\n",
            " j = [[3.18414753]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07725767]\n",
            " [ 0.65632775]\n",
            " [-0.09116118]\n",
            " [-0.64247627]]\n",
            "\n",
            "Gradient Vector Norm: 0.9261862609315956\n",
            "m_0_0: [[ 0.07732471]\n",
            " [ 0.69455423]\n",
            " [-0.08105053]\n",
            " [-0.69088436]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07732471]\n",
            " [ 0.69455423]\n",
            " [-0.08105053]\n",
            " [-0.69088436]]\n",
            "v_0_0: [[3.56606078e+01]\n",
            " [2.33995370e+05]\n",
            " [2.35064974e+05]\n",
            " [2.47805318e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.77940927e+01]\n",
            " [2.47994728e+05]\n",
            " [2.49128323e+05]\n",
            " [2.62630890e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.03240917]\n",
            " [ 0.40096788]\n",
            " [ 0.52535511]\n",
            " [ 1.08906079]]\n",
            "\n",
            "\n",
            "****************** Iteration 286 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.22849255  1.47651889  2.85138778 -1.8540609   4.28356733  1.36833405\n",
            "  -1.42338483 -0.62967823  0.25626113  2.91067042  1.77392353  2.27087165\n",
            "   1.17390034  3.33300761 -3.43595506 -2.29914707 -1.98939687  0.24466294\n",
            "  -0.29475902 -0.95703183 -0.54364156 -7.58227266  2.36894046 -1.22069176]]\n",
            "\n",
            " j = [[3.18277048]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07723057]\n",
            " [ 0.64717289]\n",
            " [-0.09320156]\n",
            " [-0.63125039]]\n",
            "\n",
            "Gradient Vector Norm: 0.9121189016441307\n",
            "m_0_0: [[ 0.07730588]\n",
            " [ 0.68507796]\n",
            " [-0.08348073]\n",
            " [-0.67895757]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07730588]\n",
            " [ 0.68507796]\n",
            " [-0.08348073]\n",
            " [-0.67895757]]\n",
            "v_0_0: [[3.53040614e+01]\n",
            " [2.31655421e+05]\n",
            " [2.32714324e+05]\n",
            " [2.45327269e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.73938432e+01]\n",
            " [2.45367987e+05]\n",
            " [2.46489571e+05]\n",
            " [2.59849124e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.03873002]\n",
            " [ 0.40027637]\n",
            " [ 0.52543919]\n",
            " [ 1.08972676]]\n",
            "\n",
            "\n",
            "****************** Iteration 287 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.22979095  1.47624087  2.85351733 -1.85869584  4.28109172  1.37179951\n",
            "  -1.4175408  -0.6295821   0.25980078  2.91113572  1.77394292  2.26882983\n",
            "   1.16829124  3.33636345 -3.43517148 -2.30781486 -1.98222896  0.24249665\n",
            "  -0.29676052 -0.95288962 -0.54326767 -7.57894644  2.36432618 -1.2223124 ]]\n",
            "\n",
            " j = [[3.18141339]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07720144]\n",
            " [ 0.63809615]\n",
            " [-0.09515399]\n",
            " [-0.62019498]]\n",
            "\n",
            "Gradient Vector Norm: 0.8982331841306144\n",
            "m_0_0: [[ 0.07728499]\n",
            " [ 0.6756816 ]\n",
            " [-0.08581538]\n",
            " [-0.66720505]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07728499]\n",
            " [ 0.6756816 ]\n",
            " [-0.08581538]\n",
            " [-0.66720505]]\n",
            "v_0_0: [[3.49510803e+01]\n",
            " [2.29338871e+05]\n",
            " [2.30387181e+05]\n",
            " [2.42874000e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.69980673e+01]\n",
            " [2.42770607e+05]\n",
            " [2.43880314e+05]\n",
            " [2.57098450e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.04508286]\n",
            " [ 0.3995907 ]\n",
            " [ 0.52552607]\n",
            " [ 1.09038469]]\n",
            "\n",
            "\n",
            "****************** Iteration 288 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.23107756  1.4759838   2.85564808 -1.86331295  4.27858133  1.37522567\n",
            "  -1.41177869 -0.62951046  0.26325994  2.91158262  1.77392765  2.26683557\n",
            "   1.16275031  3.33967499 -3.43439742 -2.31639419 -1.97511378  0.24038042\n",
            "  -0.29872341 -0.94878144 -0.54290763 -7.57564589  2.35978706 -1.22390573]]\n",
            "\n",
            " j = [[3.18007579]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07717035]\n",
            " [ 0.6291031 ]\n",
            " [-0.09701429]\n",
            " [-0.60930282]]\n",
            "\n",
            "Gradient Vector Norm: 0.8845268052175864\n",
            "m_0_0: [[ 0.07726206]\n",
            " [ 0.6663659 ]\n",
            " [-0.08805517]\n",
            " [-0.6556246 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07726206]\n",
            " [ 0.6663659 ]\n",
            " [-0.08805517]\n",
            " [-0.6556246 ]]\n",
            "v_0_0: [[3.46016291e+01]\n",
            " [2.27045486e+05]\n",
            " [2.28083309e+05]\n",
            " [2.40445263e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.66067102e+01]\n",
            " [2.40202225e+05]\n",
            " [2.41300188e+05]\n",
            " [2.54378487e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.05146769]\n",
            " [ 0.39891088]\n",
            " [ 0.5256157 ]\n",
            " [ 1.09103465]]\n",
            "\n",
            "\n",
            "****************** Iteration 289 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.23235232  1.47574733  2.85777985 -1.86791215  4.2760366   1.37861248\n",
            "  -1.40609842 -0.62946282  0.26663957  2.91201109  1.77387811  2.26488829\n",
            "   1.15727686  3.3429423  -3.43363268 -2.32488576 -1.96805144  0.23831357\n",
            "  -0.30064833 -0.9447073  -0.54256125 -7.57237115  2.35532203 -1.22547189]]\n",
            "\n",
            " j = [[3.17875722]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07713719]\n",
            " [ 0.62018432]\n",
            " [-0.09879333]\n",
            " [-0.59858206]]\n",
            "\n",
            "Gradient Vector Norm: 0.8709990480309228\n",
            "m_0_0: [[ 0.07723709]\n",
            " [ 0.65712958]\n",
            " [-0.0902028 ]\n",
            " [-0.6442161 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07723709]\n",
            " [ 0.65712958]\n",
            " [-0.0902028 ]\n",
            " [-0.6442161 ]]\n",
            "v_0_0: [[3.42556723e+01]\n",
            " [2.24775035e+05]\n",
            " [2.25802476e+05]\n",
            " [2.38040814e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.62197176e+01]\n",
            " [2.37662487e+05]\n",
            " [2.38748837e+05]\n",
            " [2.51688859e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.05788445]\n",
            " [ 0.39823691]\n",
            " [ 0.525708  ]\n",
            " [ 1.0916767 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 290 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.23361483  1.47553146  2.85991282 -1.87249312  4.27345819  1.38196018\n",
            "  -1.40049971 -0.6294384   0.26994091  2.91242137  1.77379495  2.2629878\n",
            "   1.15187047  3.34616578 -3.43287673 -2.33328995 -1.9610417   0.23629576\n",
            "  -0.30253563 -0.94066691 -0.542228   -7.56912204  2.35093037 -1.2270107 ]]\n",
            "\n",
            " j = [[3.17745723]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07710218]\n",
            " [ 0.61135575]\n",
            " [-0.10047655]\n",
            " [-0.58801476]]\n",
            "\n",
            "Gradient Vector Norm: 0.857646481299629\n",
            "m_0_0: [[ 0.07721011]\n",
            " [ 0.64797481]\n",
            " [-0.09225755]\n",
            " [-0.63297583]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07721011]\n",
            " [ 0.64797481]\n",
            " [-0.09225755]\n",
            " [-0.63297583]]\n",
            "v_0_0: [[3.39131750e+01]\n",
            " [2.22527288e+05]\n",
            " [2.23544452e+05]\n",
            " [2.35660410e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.58370361e+01]\n",
            " [2.35151043e+05]\n",
            " [2.36225909e+05]\n",
            " [2.49029193e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.06433313]\n",
            " [ 0.39756879]\n",
            " [ 0.52580291]\n",
            " [ 1.09231091]]\n",
            "\n",
            "\n",
            "****************** Iteration 291 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.23486532  1.47533556  2.86204652 -1.87705599  4.27084642  1.38526852\n",
            "  -1.39498263 -0.62943697  0.2731647   2.91281324  1.77367837  2.26113326\n",
            "   1.14653024  3.3493453  -3.43212968 -2.34160765 -1.95408492  0.23432606\n",
            "  -0.30438618 -0.93666051 -0.54190793 -7.56589893  2.3466108  -1.22852258]]\n",
            "\n",
            " j = [[3.17617536]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07706499]\n",
            " [ 0.6025896 ]\n",
            " [-0.10209319]\n",
            " [-0.57762793]]\n",
            "\n",
            "Gradient Vector Norm: 0.8444704141615541\n",
            "m_0_0: [[ 0.07718108]\n",
            " [ 0.63889777]\n",
            " [-0.09422468]\n",
            " [-0.62190625]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07718108]\n",
            " [ 0.63889777]\n",
            " [-0.09422468]\n",
            " [-0.62190625]]\n",
            "v_0_0: [[3.35741027e+01]\n",
            " [2.20302019e+05]\n",
            " [2.21309007e+05]\n",
            " [2.33303809e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.54586132e+01]\n",
            " [2.32667546e+05]\n",
            " [2.33731056e+05]\n",
            " [2.46399125e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.07081369]\n",
            " [ 0.39690652]\n",
            " [ 0.52590036]\n",
            " [ 1.09293734]]\n",
            "\n",
            "\n",
            "****************** Iteration 292 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.2361029   1.47516009  2.86418166 -1.88160008  4.26820219  1.38853813\n",
            "  -1.3895466  -0.62945729  0.27631261  2.91318732  1.77352937  2.25932495\n",
            "   1.14125613  3.35248162 -3.43139052 -2.34983891 -1.9471804   0.23240457\n",
            "  -0.30619995 -0.93268736 -0.54160007 -7.56270122  2.34236297 -1.23000685]]\n",
            "\n",
            " j = [[3.1749112]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07702623]\n",
            " [ 0.59393472]\n",
            " [-0.10359568]\n",
            " [-0.56736973]]\n",
            "\n",
            "Gradient Vector Norm: 0.8314637497955166\n",
            "m_0_0: [[ 0.07715011]\n",
            " [ 0.62990516]\n",
            " [-0.09609888]\n",
            " [-0.61099895]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07715011]\n",
            " [ 0.62990516]\n",
            " [-0.09609888]\n",
            " [-0.61099895]]\n",
            "v_0_0: [[3.32384210e+01]\n",
            " [2.18099002e+05]\n",
            " [2.19095917e+05]\n",
            " [2.30970774e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.50843969e+01]\n",
            " [2.30211657e+05]\n",
            " [2.31263938e+05]\n",
            " [2.43798293e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.07732611]\n",
            " [ 0.3962501 ]\n",
            " [ 0.52600028]\n",
            " [ 1.09355606]]\n",
            "\n",
            "\n",
            "****************** Iteration 293 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.23732865  1.47500359  2.86631686 -1.88612618  4.26552535  1.39176808\n",
            "  -1.38419216 -0.62949994  0.27938464  2.91354272  1.77334749  2.25756117\n",
            "   1.13604649  3.35557396 -3.43066019 -2.35798531 -1.94032929  0.23052953\n",
            "  -0.30797853 -0.92874849 -0.54130527 -7.55953003  2.3381849  -1.23146484]]\n",
            "\n",
            " j = [[3.17366431]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07698483]\n",
            " [ 0.58530327]\n",
            " [-0.10507344]\n",
            " [-0.55732895]]\n",
            "\n",
            "Gradient Vector Norm: 0.8186345739825239\n",
            "m_0_0: [[ 0.07711706]\n",
            " [ 0.62098478]\n",
            " [-0.09789379]\n",
            " [-0.60026495]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07711706]\n",
            " [ 0.62098478]\n",
            " [-0.09789379]\n",
            " [-0.60026495]]\n",
            "v_0_0: [[3.29060960e+01]\n",
            " [2.15918016e+05]\n",
            " [2.16904958e+05]\n",
            " [2.28661069e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.47143360e+01]\n",
            " [2.27783039e+05]\n",
            " [2.28824215e+05]\n",
            " [2.41226343e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.08387034]\n",
            " [ 0.39559954]\n",
            " [ 0.5261026 ]\n",
            " [ 1.09416715]]\n",
            "\n",
            "\n",
            "****************** Iteration 294 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.23854016  1.47486807  2.86845448 -1.89063243  4.26281767  1.39496026\n",
            "  -1.37891781 -0.62956221  0.28238381  2.91388127  1.77313495  2.25584376\n",
            "   1.13090263  3.35862434 -3.42993612 -2.36604563 -1.93352938  0.22870255\n",
            "  -0.30972052 -0.92484168 -0.54102109 -7.55638335  2.33407754 -1.23289425]]\n",
            "\n",
            " j = [[3.17243427]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07694278]\n",
            " [ 0.57685519]\n",
            " [-0.10636754]\n",
            " [-0.54733938]]\n",
            "\n",
            "Gradient Vector Norm: 0.8059631247746815\n",
            "m_0_0: [[ 0.0770822 ]\n",
            " [ 0.61215886]\n",
            " [-0.09958854]\n",
            " [-0.58967983]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.0770822 ]\n",
            " [ 0.61215886]\n",
            " [-0.09958854]\n",
            " [-0.58967983]]\n",
            "v_0_0: [[3.25770943e+01]\n",
            " [2.13758839e+05]\n",
            " [2.14735909e+05]\n",
            " [2.26374462e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.43483802e+01]\n",
            " [2.25381362e+05]\n",
            " [2.26411557e+05]\n",
            " [2.38682923e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.09044637]\n",
            " [ 0.39495481]\n",
            " [ 0.52620725]\n",
            " [ 1.09477064]]\n",
            "\n",
            "\n",
            "****************** Iteration 295 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.23974141  1.47474917  2.87059001 -1.89512186  4.26007734  1.3981114\n",
            "  -1.37372582 -0.62964743  0.28530757  2.91419983  1.77288904  2.25416805\n",
            "   1.12582041  3.3616296  -3.42922223 -2.37402386 -1.92678462  0.22691904\n",
            "  -0.31143008 -0.92097074 -0.54075114 -7.55326494  2.33003644 -1.23429945]]\n",
            "\n",
            " j = [[3.17122068]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07689643]\n",
            " [ 0.56829396]\n",
            " [-0.10777655]\n",
            " [-0.53770453]]\n",
            "\n",
            "Gradient Vector Norm: 0.7934815874298129\n",
            "m_0_0: [[ 0.07704505]\n",
            " [ 0.60338588]\n",
            " [-0.10122614]\n",
            " [-0.57928477]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07704505]\n",
            " [ 0.60338588]\n",
            " [-0.10122614]\n",
            " [-0.57928477]]\n",
            "v_0_0: [[3.22513825e+01]\n",
            " [2.11621254e+05]\n",
            " [2.12588550e+05]\n",
            " [2.24110720e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.39864795e+01]\n",
            " [2.23006298e+05]\n",
            " [2.24025634e+05]\n",
            " [2.36167687e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.09705413]\n",
            " [ 0.39431595]\n",
            " [ 0.52631418]\n",
            " [ 1.09536665]]\n",
            "\n",
            "\n",
            "****************** Iteration 296 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.2409244   1.4746544   2.87273182 -1.89958834  4.25730929  1.40122788\n",
            "  -1.36861132 -0.62974768  0.28816379  2.91450455  1.77261624  2.25254157\n",
            "   1.12080585  3.3645963  -3.42851027 -2.38191426 -1.92008742  0.22518603\n",
            "  -0.31310095 -0.91712815 -0.54048773 -7.55016774  2.32606726 -1.23567234]]\n",
            "\n",
            " j = [[3.17002313]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07685268]\n",
            " [ 0.56017717]\n",
            " [-0.10874292]\n",
            " [-0.52784897]]\n",
            "\n",
            "Gradient Vector Norm: 0.7811237784101849\n",
            "m_0_0: [[ 0.07700657]\n",
            " [ 0.59474414]\n",
            " [-0.1027295 ]\n",
            " [-0.56899761]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07700657]\n",
            " [ 0.59474414]\n",
            " [-0.1027295 ]\n",
            " [-0.56899761]]\n",
            "v_0_0: [[3.19289277e+01]\n",
            " [2.09505044e+05]\n",
            " [2.10462664e+05]\n",
            " [2.21869616e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.36285851e+01]\n",
            " [2.20657527e+05]\n",
            " [2.21666123e+05]\n",
            " [2.33680295e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.10369364]\n",
            " [ 0.39368289]\n",
            " [ 0.52642328]\n",
            " [ 1.09595518]]\n",
            "\n",
            "\n",
            "****************** Iteration 297 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24210395  1.47456876  2.8748638  -1.90404317  4.25450562  1.40429776\n",
            "  -1.36358304 -0.62987638  0.29094058  2.91478391  1.77230559  2.25094864\n",
            "   1.11584566  3.36751255 -3.42781513 -2.38972955 -1.91345213  0.22348842\n",
            "  -0.31474667 -0.9133279  -0.54024462 -7.54710522  2.32215645 -1.23702851]]\n",
            "\n",
            " j = [[3.16884122]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.0767984 ]\n",
            " [ 0.55143938]\n",
            " [-0.11033589]\n",
            " [-0.51886739]]\n",
            "\n",
            "Gradient Vector Norm: 0.7690128459466232\n",
            "m_0_0: [[ 0.07696494]\n",
            " [ 0.58608319]\n",
            " [-0.10425078]\n",
            " [-0.55897157]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07696494]\n",
            " [ 0.58608319]\n",
            " [-0.10425078]\n",
            " [-0.55897157]]\n",
            "v_0_0: [[3.16096974e+01]\n",
            " [2.07409997e+05]\n",
            " [2.08358038e+05]\n",
            " [2.19650922e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.32746484e+01]\n",
            " [2.18334729e+05]\n",
            " [2.19332706e+05]\n",
            " [2.31220411e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.11036476]\n",
            " [ 0.39305575]\n",
            " [ 0.52653458]\n",
            " [ 1.09653641]]\n",
            "\n",
            "\n",
            "****************** Iteration 298 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24325087  1.47452064  2.87701705 -1.90846405  4.2516832   1.40734446\n",
            "  -1.35862345 -0.63000582  0.29366414  2.9150605   1.77197975  2.24941837\n",
            "   1.1109638   3.37040204 -3.42710706 -2.3974469  -1.90685071  0.22185384\n",
            "  -0.31634257 -0.90954251 -0.53999424 -7.54405129  2.31832747 -1.2383379 ]]\n",
            "\n",
            " j = [[3.16767458]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07675908]\n",
            " [ 0.54414602]\n",
            " [-0.11048663]\n",
            " [-0.50863278]]\n",
            "\n",
            "Gradient Vector Norm: 0.7569025367652867\n",
            "m_0_0: [[ 0.07692377]\n",
            " [ 0.57769576]\n",
            " [-0.10549795]\n",
            " [-0.54890381]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07692377]\n",
            " [ 0.57769576]\n",
            " [-0.10549795]\n",
            " [-0.54890381]]\n",
            "v_0_0: [[3.12936593e+01]\n",
            " [2.05335900e+05]\n",
            " [2.06274458e+05]\n",
            " [2.17454416e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.29246219e+01]\n",
            " [2.16037594e+05]\n",
            " [2.17025067e+05]\n",
            " [2.28787702e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.11706766]\n",
            " [ 0.3924343 ]\n",
            " [ 0.52664781]\n",
            " [ 1.0971102 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 299 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24442212  1.47445352  2.87913025 -1.91289447  4.24881036  1.41032217\n",
            "  -1.35376638 -0.63018881  0.2962861   2.91529014  1.77159559  2.24789216\n",
            "   1.10611127  3.37321885 -3.42644372 -2.40511306 -1.90033813  0.22022624\n",
            "  -0.31793883 -0.90582565 -0.53978987 -7.54105726  2.3145314  -1.23965977]]\n",
            "\n",
            " j = [[3.16652283]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07668458]\n",
            " [ 0.53423319]\n",
            " [-0.11326965]\n",
            " [-0.50132519]]\n",
            "\n",
            "Gradient Vector Norm: 0.7452802098567707\n",
            "m_0_0: [[ 0.07687593]\n",
            " [ 0.56900324]\n",
            " [-0.10705229]\n",
            " [-0.53938809]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07687593]\n",
            " [ 0.56900324]\n",
            " [-0.10705229]\n",
            " [-0.53938809]]\n",
            "v_0_0: [[3.09807816e+01]\n",
            " [2.03282544e+05]\n",
            " [2.04211713e+05]\n",
            " [2.15279874e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.25784583e+01]\n",
            " [2.13765810e+05]\n",
            " [2.14742897e+05]\n",
            " [2.26381842e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.12380188]\n",
            " [ 0.39181896]\n",
            " [ 0.52676332]\n",
            " [ 1.09767702]]\n",
            "\n",
            "\n",
            "****************** Iteration 300 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24550354  1.4744795   2.88132567 -1.91724724  4.24595187  1.4133226\n",
            "  -1.34894363 -0.63031822  0.2989062   2.91556125  1.77124065  2.24648563\n",
            "   1.10138403  3.37605527 -3.42570915 -2.41263663 -1.89380444  0.21871577\n",
            "  -0.3194369  -0.90206981 -0.53952425 -7.53802072  2.31086304 -1.24087602]]\n",
            "\n",
            " j = [[3.16538559]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07667504]\n",
            " [ 0.52981141]\n",
            " [-0.11055719]\n",
            " [-0.48859726]]\n",
            "\n",
            "Gradient Vector Norm: 0.7331639393414029\n",
            "m_0_0: [[ 0.07683575]\n",
            " [ 0.56116488]\n",
            " [-0.10775327]\n",
            " [-0.52922992]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07683575]\n",
            " [ 0.56116488]\n",
            " [-0.10775327]\n",
            " [-0.52922992]]\n",
            "v_0_0: [[3.06710325e+01]\n",
            " [2.01249721e+05]\n",
            " [2.02169596e+05]\n",
            " [2.13127078e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.22361114e+01]\n",
            " [2.11519075e+05]\n",
            " [2.12485889e+05]\n",
            " [2.24002508e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.13056823]\n",
            " [ 0.39120888]\n",
            " [ 0.5268802 ]\n",
            " [ 1.09823612]]\n",
            "\n",
            "\n",
            "****************** Iteration 301 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24672574  1.47437095  2.8833557  -1.92169846  4.24297817  1.41616041\n",
            "  -1.34429258 -0.63060916  0.30132578  2.91569514  1.77073917  2.24496338\n",
            "   1.09658593  3.37872521 -3.42513719 -2.42020453 -1.88747195  0.21709778\n",
            "  -0.32103789 -0.89849221 -0.53941358 -7.53514893  2.30712762 -1.24222588]]\n",
            "\n",
            " j = [[3.16426251]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.0765278 ]\n",
            " [ 0.51446304]\n",
            " [-0.11880571]\n",
            " [-0.48734088]]\n",
            "\n",
            "Gradient Vector Norm: 0.7225957757506556\n",
            "m_0_0: [[ 0.07677416]\n",
            " [ 0.55182451]\n",
            " [-0.10996376]\n",
            " [-0.52085211]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07677416]\n",
            " [ 0.55182451]\n",
            " [-0.10996376]\n",
            " [-0.52085211]]\n",
            "v_0_0: [[3.03643808e+01]\n",
            " [1.99237227e+05]\n",
            " [2.00147900e+05]\n",
            " [2.10995809e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.18975352e+01]\n",
            " [2.09297087e+05]\n",
            " [2.10253742e+05]\n",
            " [2.21649382e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.13736494]\n",
            " [ 0.39060578]\n",
            " [ 0.5270001 ]\n",
            " [ 1.09878928]]\n",
            "\n",
            "\n",
            "****************** Iteration 302 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24761494  1.47459439  2.8857284  -1.92588633  4.24015674  1.41921633\n",
            "  -1.33953053 -0.63061841  0.30395584  2.91605879  1.77045334  2.24380722\n",
            "   1.09211766  3.38161129 -3.424247   -2.42743504 -1.88088414  0.21583159\n",
            "  -0.3223307  -0.89464661 -0.53901317 -7.53201645  2.30372205 -1.24321852]]\n",
            "\n",
            " j = [[3.16315323]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07665883]\n",
            " [ 0.52188407]\n",
            " [-0.10424344]\n",
            " [-0.46288142]]\n",
            "\n",
            "Gradient Vector Norm: 0.7094825361682279\n",
            "m_0_0: [[ 0.07675109]\n",
            " [ 0.54583642]\n",
            " [-0.10881969]\n",
            " [-0.50925797]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07675109]\n",
            " [ 0.54583642]\n",
            " [-0.10881969]\n",
            " [-0.50925797]]\n",
            "v_0_0: [[3.00607957e+01]\n",
            " [1.97244857e+05]\n",
            " [1.98146421e+05]\n",
            " [2.08885853e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.15626850e+01]\n",
            " [2.07099551e+05]\n",
            " [2.08046159e+05]\n",
            " [2.19322151e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.14419555]\n",
            " [ 0.39000607]\n",
            " [ 0.52711939]\n",
            " [ 1.09933299]]\n",
            "\n",
            "\n",
            "****************** Iteration 303 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24915649  1.47417843  2.88738656 -1.93056328  4.23693263  1.42169849\n",
            "  -1.33524494 -0.63126641  0.30594453  2.91588897  1.76963108  2.24201327\n",
            "   1.08714359  3.38391816 -3.42403813 -2.43512438 -1.87499056  0.21395979\n",
            "  -0.32417235 -0.89146102 -0.53924729 -7.52950843  2.29981757 -1.24487534]]\n",
            "\n",
            " j = [[3.16205742]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07620269]\n",
            " [ 0.48196431]\n",
            " [-0.1371422 ]\n",
            " [-0.48736001]]\n",
            "\n",
            "Gradient Vector Norm: 0.7031530493903948\n",
            "m_0_0: [[ 0.07664141]\n",
            " [ 0.533062  ]\n",
            " [-0.11448419]\n",
            " [-0.50487838]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07664141]\n",
            " [ 0.533062  ]\n",
            " [-0.11448419]\n",
            " [-0.50487838]]\n",
            "v_0_0: [[2.97602458e+01]\n",
            " [1.95272411e+05]\n",
            " [1.96164957e+05]\n",
            " [2.06796997e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.12315153e+01]\n",
            " [2.04926173e+05]\n",
            " [2.05862845e+05]\n",
            " [2.17020505e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.15105248]\n",
            " [ 0.3894173 ]\n",
            " [ 0.52724555]\n",
            " [ 1.09987487]]\n",
            "\n",
            "\n",
            "****************** Iteration 304 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24927202  1.47517083  2.89055951 -1.9341422   4.23447771  1.42527715\n",
            "  -1.33019618 -0.63061039  0.30909151  2.91679544  1.76985985  2.2416971\n",
            "   1.08342393  3.38732376 -3.42240185 -2.44159549 -1.86778848  0.21349959\n",
            "  -0.32475721 -0.88697801 -0.53816591 -7.52575822  2.29715895 -1.24504262]]\n",
            "\n",
            " j = [[3.16097484]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07698445]\n",
            " [ 0.54255174]\n",
            " [-0.06931708]\n",
            " [-0.40864043]]\n",
            "\n",
            "Gradient Vector Norm: 0.68708140554426\n",
            "m_0_0: [[ 0.07671002]\n",
            " [ 0.53495995]\n",
            " [-0.10545077]\n",
            " [-0.48563079]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07671002]\n",
            " [ 0.53495995]\n",
            " [-0.10545077]\n",
            " [-0.48563079]]\n",
            "v_0_0: [[2.94627026e+01]\n",
            " [1.93319690e+05]\n",
            " [1.94203308e+05]\n",
            " [2.04729029e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.09039842e+01]\n",
            " [2.02776667e+05]\n",
            " [2.03703510e+05]\n",
            " [2.14744137e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.15795181]\n",
            " [ 0.3888233 ]\n",
            " [ 0.52736237]\n",
            " [ 1.10039885]]\n",
            "\n",
            "\n",
            "****************** Iteration 305 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.25240235  1.473195    2.89048327 -1.94001427  4.2302894   1.42638349\n",
            "  -1.32703241 -0.63280009  0.30955442  2.91533858  1.76774887  2.23833656\n",
            "   1.07719534  3.38824327 -3.42384341 -2.4504341  -1.86355723  0.21013865\n",
            "  -0.32794543 -0.88537999 -0.53993513 -7.52475459  2.29201484 -1.24832287]]\n",
            "\n",
            " j = [[3.15990552]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07510416]\n",
            " [ 0.38770912]\n",
            " [-0.21742922]\n",
            " [-0.55181797]]\n",
            "\n",
            "Gradient Vector Norm: 0.7125570366000172\n",
            "m_0_0: [[ 0.07638885]\n",
            " [ 0.50550978]\n",
            " [-0.12784646]\n",
            " [-0.49886823]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07638885]\n",
            " [ 0.50550978]\n",
            " [-0.12784646]\n",
            " [-0.49886823]]\n",
            "v_0_0: [[2.91681320e+01]\n",
            " [1.91386494e+05]\n",
            " [1.92261275e+05]\n",
            " [2.02681741e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.05800441e+01]\n",
            " [2.00650746e+05]\n",
            " [2.01567871e+05]\n",
            " [2.12492750e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.16485855]\n",
            " [ 0.38825904]\n",
            " [ 0.52750475]\n",
            " [ 1.10093996]]\n",
            "\n",
            "\n",
            "****************** Iteration 306 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24893282  1.47772538  2.89747157 -1.94083737  4.22978728  1.43274421\n",
            "  -1.32001964 -0.62884974  0.31565521  2.91896535  1.77064059  2.24172052\n",
            "   1.07660331  3.39443847 -3.41860818 -2.45387931 -1.85303183  0.21321124\n",
            "  -0.32538162 -0.87761169 -0.53553449 -7.51786193  2.29246052 -1.244752  ]]\n",
            "\n",
            " j = [[3.15885103]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07900513]\n",
            " [ 0.70142918]\n",
            " [ 0.10407745]\n",
            " [-0.21306749]]\n",
            "\n",
            "Gradient Vector Norm: 0.7446305018310728\n",
            "m_0_0: [[ 0.0769121 ]\n",
            " [ 0.54469366]\n",
            " [-0.08146168]\n",
            " [-0.44170808]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.0769121 ]\n",
            " [ 0.54469366]\n",
            " [-0.08146168]\n",
            " [-0.44170808]]\n",
            "v_0_0: [[2.88765131e+01]\n",
            " [1.89472634e+05]\n",
            " [1.90338663e+05]\n",
            " [2.00654925e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.02596616e+01]\n",
            " [1.98548134e+05]\n",
            " [1.99455644e+05]\n",
            " [2.10266042e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.17184931]\n",
            " [ 0.38764783]\n",
            " [ 0.52759595]\n",
            " [ 1.1014216 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 307 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.25994795  1.46798378  2.8889063  -1.95271243  4.2210878   1.42741507\n",
            "  -1.32173166 -0.63846334  0.3091472   2.91134485  1.76243452  2.23038053\n",
            "   1.06378274  3.38888855 -3.42808719 -2.46895287 -1.85653005  0.2022455\n",
            "  -0.33539083 -0.88353083 -0.54474491 -7.52401803  2.2807839  -1.25618147]]\n",
            "\n",
            " j = [[3.15782054]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.07017122]\n",
            " [-0.01628482]\n",
            " [-0.60822612]\n",
            " [-0.93587254]]\n",
            "\n",
            "Gradient Vector Norm: 1.1184746847060096\n",
            "m_0_0: [[ 0.07556393]\n",
            " [ 0.43249797]\n",
            " [-0.18681457]\n",
            " [-0.54054097]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07556393]\n",
            " [ 0.43249797]\n",
            " [-0.18681457]\n",
            " [-0.54054097]]\n",
            "v_0_0: [[2.85877972e+01]\n",
            " [1.87577908e+05]\n",
            " [1.88435280e+05]\n",
            " [1.98648384e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.99427744e+01]\n",
            " [1.96468547e+05]\n",
            " [1.97366555e+05]\n",
            " [2.08063731e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.17875379]\n",
            " [ 0.38715996]\n",
            " [ 0.52780621]\n",
            " [ 1.10201412]]\n",
            "\n",
            "\n",
            "****************** Iteration 308 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.23861832  1.49011811  2.91502245 -1.93987879  4.23058609  1.44802985\n",
            "  -1.30424101 -0.61787631  0.3305599   2.92874855  1.77889159  2.2519969\n",
            "   1.07841024  3.40939772 -3.40476977 -2.45785135 -1.82892544  0.22270789\n",
            "  -0.31727463 -0.85903266 -0.52363176 -7.5011617   2.29632075 -1.23407917]]\n",
            "\n",
            " j = [[3.15686252]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.08972705]\n",
            " [1.56607898]\n",
            " [0.98479123]\n",
            " [0.70786916]]\n",
            "\n",
            "Gradient Vector Norm: 1.982812859309704\n",
            "m_0_0: [[ 0.07839655]\n",
            " [ 0.65921417]\n",
            " [ 0.04750659]\n",
            " [-0.29085895]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07839655]\n",
            " [ 0.65921417]\n",
            " [ 0.04750659]\n",
            " [-0.29085895]]\n",
            "v_0_0: [[2.83019998e+01]\n",
            " [1.85702153e+05]\n",
            " [1.86550937e+05]\n",
            " [1.96661905e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.96293875e+01]\n",
            " [1.94411742e+05]\n",
            " [1.95300333e+05]\n",
            " [2.05885515e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.18595487]\n",
            " [ 0.38641242]\n",
            " [ 0.52775246]\n",
            " [ 1.10233462]]\n",
            "\n",
            "\n",
            "****************** Iteration 309 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.29026339  1.44034043  2.86284082 -1.98276224  4.19892082  1.40995012\n",
            "  -1.33035598 -0.66551391  0.28874535  2.88960179  1.73958654  2.19933457\n",
            "   1.03125057  3.37094471 -3.45550706 -2.50559727 -1.87170751  0.17234197\n",
            "  -0.36256598 -0.9033088  -0.57100358 -7.54388898  2.25060144 -1.28759459]]\n",
            "\n",
            " j = [[3.15623357]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.04518291]\n",
            " [-2.04408935]\n",
            " [-2.62662015]\n",
            " [-2.99167948]]\n",
            "\n",
            "Gradient Vector Norm: 4.475446604930234\n",
            "m_0_0: [[ 0.07175382]\n",
            " [ 0.11855346]\n",
            " [-0.48731876]\n",
            " [-0.83102305]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07175382]\n",
            " [ 0.11855346]\n",
            " [-0.48731876]\n",
            " [-0.83102305]]\n",
            "v_0_0: [[2.80190002e+01]\n",
            " [1.83845174e+05]\n",
            " [1.84685496e+05]\n",
            " [1.94695376e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.93193640e+01]\n",
            " [1.92377441e+05]\n",
            " [1.93256763e+05]\n",
            " [2.03731202e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.19258054]\n",
            " [ 0.38627727]\n",
            " [ 0.52830672]\n",
            " [ 1.10325519]]\n",
            "\n",
            "\n",
            "****************** Iteration 310 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.17507014  1.55497537  2.98961522 -1.89823281  4.26125948  1.5058982\n",
            "  -1.25706099 -0.55725983  0.39125112  2.97965056  1.82764941  2.31656653\n",
            "   1.12549049  3.46712394 -3.33699785 -2.41859633 -1.75378396  0.28398406\n",
            "  -0.26284586 -0.79049914 -0.46187213 -7.43680434  2.34506846 -1.16819434]]\n",
            "\n",
            " j = [[3.15731867]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.14713813]\n",
            " [6.2138995 ]\n",
            " [5.65725237]\n",
            " [5.52080343]]\n",
            "\n",
            "Gradient Vector Norm: 10.055743214925386\n",
            "m_0_0: [[0.08683068]\n",
            " [1.33762267]\n",
            " [0.74159547]\n",
            " [0.43934224]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.08683068]\n",
            " [1.33762267]\n",
            " [0.74159547]\n",
            " [0.43934224]]\n",
            "v_0_0: [[2.77390267e+01]\n",
            " [1.82007108e+05]\n",
            " [1.82838961e+05]\n",
            " [1.92748727e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.90129320e+01]\n",
            " [1.90365722e+05]\n",
            " [1.91235778e+05]\n",
            " [2.01600646e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.20064062]\n",
            " [ 0.38474439]\n",
            " [ 0.52745881]\n",
            " [ 1.10276594]]\n",
            "\n",
            "\n",
            "****************** Iteration 311 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.44580476  1.28929628  2.70237808 -2.10840297  4.10604397  1.29165153\n",
            "  -1.41400458 -0.80969818  0.15968557  2.77075253  1.62096883  2.040875\n",
            "   0.89278448  3.25170305 -3.61004812 -2.64308366 -2.00781981  0.02094692\n",
            "  -0.49851972 -1.04120848 -0.71483209 -7.67639217  2.11539584 -1.44874567]]\n",
            "\n",
            " j = [[3.16776322]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.08983659]\n",
            " [-12.98467331]\n",
            " [-13.57826226]\n",
            " [-14.21951812]]\n",
            "\n",
            "Gradient Vector Norm: 23.562124549936843\n",
            "m_0_0: [[ 0.05149723]\n",
            " [-1.52683652]\n",
            " [-2.12237608]\n",
            " [-2.49242983]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.05149723]\n",
            " [-1.52683652]\n",
            " [-2.12237608]\n",
            " [-2.49242983]]\n",
            "v_0_0: [[2.74617171e+01]\n",
            " [1.80188723e+05]\n",
            " [1.81012415e+05]\n",
            " [1.90823261e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.87097022e+01]\n",
            " [1.88377317e+05]\n",
            " [1.89238441e+05]\n",
            " [1.99495137e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.20544604]\n",
            " [ 0.38650332]\n",
            " [ 0.52989823]\n",
            " [ 1.10555609]]\n",
            "\n",
            "\n",
            "****************** Iteration 312 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 0.18712304  1.9141362   3.38450792 -1.62849443  4.46013327  1.80357078\n",
            "  -1.03212387 -0.21764921  0.71012799  3.2617535   2.10432174  2.68529848\n",
            "   1.42576898  3.76573195 -2.96631047 -2.13800593 -1.3911203   0.63530676\n",
            "   0.05118471 -0.44087818 -0.12001392 -7.10436658  2.64484155 -0.79276368]]\n",
            "\n",
            " j = [[3.23074953]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.46675335]\n",
            " [32.10378796]\n",
            " [31.62028413]\n",
            " [32.19082264]]\n",
            "\n",
            "Gradient Vector Norm: 55.380163337008\n",
            "m_0_0: [[0.13454845]\n",
            " [5.19928837]\n",
            " [4.62615597]\n",
            " [4.44422066]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.13454845]\n",
            " [5.19928837]\n",
            " [4.62615597]\n",
            " [4.44422066]]\n",
            "v_0_0: [[2.71892785e+01]\n",
            " [1.78397142e+05]\n",
            " [1.79212290e+05]\n",
            " [1.88925391e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.84119711e+01]\n",
            " [1.86419601e+05]\n",
            " [1.87271405e+05]\n",
            " [1.97421302e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.21806692]\n",
            " [ 0.38048233]\n",
            " [ 0.52455315]\n",
            " [ 1.10055495]]\n",
            "\n",
            "\n",
            "****************** Iteration 313 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-1.32092285  0.42914244  1.7699403  -2.78350257  3.60746364  0.594878\n",
            "  -1.92712406 -1.62646    -0.59240685  2.09444559  0.95271712  1.14983929\n",
            "   0.14480033  2.55136213 -4.49470484 -3.36122251 -2.83760445 -0.82900655\n",
            "  -1.25980273 -1.85699959 -1.53383783 -8.4553631   1.37590289 -2.35560022]]\n",
            "\n",
            " j = [[3.59577224]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.8568361 ]\n",
            " [-75.12121798]\n",
            " [-75.8430614 ]\n",
            " [-78.12844788]]\n",
            "\n",
            "Gradient Vector Norm: 132.2881547593635\n",
            "m_0_0: [[ -0.06372846]\n",
            " [-10.8648129 ]\n",
            " [-11.46768751]\n",
            " [-12.07031305]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.06372846]\n",
            " [-10.8648129 ]\n",
            " [-11.46768751]\n",
            " [-12.07031305]]\n",
            "v_0_0: [[2.69247274e+01]\n",
            " [1.76669603e+05]\n",
            " [1.77477688e+05]\n",
            " [1.87097178e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.81228765e+01]\n",
            " [1.84531391e+05]\n",
            " [1.85375437e+05]\n",
            " [1.95422993e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.21205843]\n",
            " [ 0.39312844]\n",
            " [ 0.53787056]\n",
            " [ 1.11420709]]\n",
            "\n",
            "\n",
            "****************** Iteration 314 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 2.30275451  4.00123115  5.66029501 -0.01972953  5.64718426  3.51017079\n",
            "   0.23815464  1.76052761  2.54625093  4.90186021  3.71984296  4.83943422\n",
            "   3.21185014  5.47963576 -0.81675367 -0.44166653  0.66095437  2.68914491\n",
            "   1.88916173  1.55997931  1.86680375 -5.19718155  4.41766231  1.39990678]]\n",
            "\n",
            " j = [[5.73615646]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  2.32614475]\n",
            " [182.7320834 ]\n",
            " [182.60695159]\n",
            " [187.21488662]]\n",
            "\n",
            "Gradient Vector Norm: 319.04723442179\n",
            "m_0_0: [[ 0.41424618]\n",
            " [27.85456636]\n",
            " [27.34724031]\n",
            " [27.78672689]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.41424618]\n",
            " [27.85456636]\n",
            " [27.34724031]\n",
            " [27.78672689]]\n",
            "v_0_0: [[2.67095897e+01]\n",
            " [1.75236817e+05]\n",
            " [1.76036365e+05]\n",
            " [1.85576700e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.78857559e+01]\n",
            " [1.82953432e+05]\n",
            " [1.83788188e+05]\n",
            " [1.93748636e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.25128038]\n",
            " [ 0.36056757]\n",
            " [ 0.50597541]\n",
            " [ 1.08264339]]\n",
            "\n",
            "\n",
            "****************** Iteration 315 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ -6.506826    -4.67910019  -3.78686552  -6.75043416   0.67905558\n",
            "   -3.56639746  -5.0113788   -6.47194022  -5.0755462   -1.92087263\n",
            "   -3.00753872  -4.13028491  -4.25544289  -1.62916191  -9.75292043\n",
            "   -7.55910443  -7.82168597  -5.86426589  -5.76751069  -6.73295019\n",
            "   -6.39722495 -13.10636361  -2.98450613  -7.72995949]]\n",
            "\n",
            " j = [[18.50886753]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -5.40955108]\n",
            " [-443.9384264 ]\n",
            " [-445.49013336]\n",
            " [-457.61029892]]\n",
            "\n",
            "Gradient Vector Norm: 777.8041104807969\n",
            "m_0_0: [[ -0.75051327]\n",
            " [-66.50403219]\n",
            " [-67.22023442]\n",
            " [-69.29267827]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.75051327]\n",
            " [-66.50403219]\n",
            " [-67.22023442]\n",
            " [-69.29267827]]\n",
            "v_0_0: [[2.67351262e+01]\n",
            " [1.75455262e+05]\n",
            " [1.76260615e+05]\n",
            " [1.85815005e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.79001310e+01]\n",
            " [1.83100868e+05]\n",
            " [1.83941315e+05]\n",
            " [1.93912045e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.18023807]\n",
            " [ 0.43827681]\n",
            " [ 0.58434188]\n",
            " [ 1.16132167]]\n",
            "\n",
            "\n",
            "****************** Iteration 316 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[15.00450534 16.52045874 19.29200621  9.67304953 12.80085081 13.72392898\n",
            "   7.82121706 13.63180259 13.54386788 14.74127481 13.41916389 17.77225515\n",
            "  13.96746167 15.73918558 12.07281221  9.80086715 12.91386739 15.02123714\n",
            "  12.92749716 13.53081169 13.78522975  6.21807049 15.08301355 14.56368849]]\n",
            "\n",
            " j = [[94.90020356]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  13.48200514]\n",
            " [1086.46989423]\n",
            " [1088.42562556]\n",
            " [1117.18200216]]\n",
            "\n",
            "Gradient Vector Norm: 1900.8852053400242\n",
            "m_0_0: [[  2.09599041]\n",
            " [164.09075309]\n",
            " [163.90893757]\n",
            " [168.00225781]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  2.09599041]\n",
            " [164.09075309]\n",
            " [163.90893757]\n",
            " [168.00225781]]\n",
            "v_0_0: [[2.82854195e+01]\n",
            " [1.85504878e+05]\n",
            " [1.86344713e+05]\n",
            " [1.96437811e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[2.95051226e+01]\n",
            " [1.93504083e+05]\n",
            " [1.94380132e+05]\n",
            " [2.04908458e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.37316933]\n",
            " [ 0.25176388]\n",
            " [ 0.39845591]\n",
            " [ 0.97575281]]\n",
            "\n",
            "\n",
            "****************** Iteration 317 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-36.14999696 -33.88894317 -35.57987653 -29.39361924 -16.03430721\n",
            "  -27.38275778 -22.6815673  -34.17394186 -30.72566171 -24.87959267\n",
            "  -25.64435554 -34.3123112  -29.37739479 -25.553697   -39.82428261\n",
            "  -31.50020228 -36.37425167 -34.64526239 -31.53077968 -34.64356987\n",
            "  -34.20609082 -39.72475718 -27.88882431 -38.45092014]]\n",
            "\n",
            " j = [[510.18393066]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -31.44029016]\n",
            " [-2552.69350388]\n",
            " [-2559.05544907]\n",
            " [-2627.47656356]]\n",
            "\n",
            "Gradient Vector Norm: 4468.739251745603\n",
            "m_0_0: [[  -4.6112657 ]\n",
            " [-379.2660983 ]\n",
            " [-380.68393975]\n",
            " [-391.09350646]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  -4.6112657 ]\n",
            " [-379.2660983 ]\n",
            " [-380.68393975]\n",
            " [-391.09350646]]\n",
            "v_0_0: [[3.78874838e+01]\n",
            " [2.48812270e+05]\n",
            " [2.49968914e+05]\n",
            " [2.63509764e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[3.95042053e+01]\n",
            " [2.59429501e+05]\n",
            " [2.60635500e+05]\n",
            " [2.74754161e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.00634209]\n",
            " [ 0.6240735 ]\n",
            " [ 0.77129178]\n",
            " [ 1.34881256]]\n",
            "\n",
            "\n",
            "****************** Iteration 318 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[66.34573823 67.1168819  74.37256445 48.87316017 41.73366613 54.98888832\n",
            "  38.44620572 61.6132335  57.98139433 54.50847096 52.62489811 70.04715559\n",
            "  57.46212381 57.19048259 64.16346768 51.23752861 62.39943977 64.86884995\n",
            "  57.54717588 61.89190847 61.95422279 52.33767633 58.20615354 67.77204276]]\n",
            "\n",
            " j = [[1749.84299963]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  58.57013873]\n",
            " [4739.06804989]\n",
            " [4749.39063356]\n",
            " [4875.70187002]]\n",
            "\n",
            "Gradient Vector Norm: 8294.056700919773\n",
            "m_0_0: [[  8.02501518]\n",
            " [644.40073134]\n",
            " [645.33097491]\n",
            " [662.26556883]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  8.02501518]\n",
            " [644.40073134]\n",
            " [645.33097491]\n",
            " [662.26556883]]\n",
            "v_0_0: [[7.18132205e+01]\n",
            " [4.70911807e+05]\n",
            " [4.73036338e+05]\n",
            " [4.98599354e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[7.48456713e+01]\n",
            " [4.90796960e+05]\n",
            " [4.93011204e+05]\n",
            " [5.19653667e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.47013863]\n",
            " [ 0.16416119]\n",
            " [ 0.311751  ]\n",
            " [ 0.88946107]]\n",
            "\n",
            "\n",
            "****************** Iteration 319 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-60.04917996 -57.43895495 -61.21295887 -47.64853049 -29.50869043\n",
            "  -46.58493973 -36.9288422  -56.50828933 -51.40626002 -43.38978803\n",
            "  -43.89492077 -58.6458451  -49.63066611 -44.84306847 -64.06904574\n",
            "  -50.80088767 -59.39561226 -57.84920943 -52.30173001 -57.14683829\n",
            "  -56.6266586  -61.18604142 -47.96681883 -63.21901924]]\n",
            "\n",
            " j = [[1410.30869036]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -52.42719983]\n",
            " [-4252.84625902]\n",
            " [-4263.08769665]\n",
            " [-4376.90272851]]\n",
            "\n",
            "Gradient Vector Norm: 7444.504289629823\n",
            "m_0_0: [[  -4.06542782]\n",
            " [-335.04866674]\n",
            " [-336.3527594 ]\n",
            " [-345.56809064]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  -4.06542782]\n",
            " [-335.04866674]\n",
            " [-336.3527594 ]\n",
            " [-345.56809064]]\n",
            "v_0_0: [[9.85812011e+01]\n",
            " [6.47069702e+05]\n",
            " [6.50045142e+05]\n",
            " [6.85186135e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.02700614e+02]\n",
            " [6.74108805e+05]\n",
            " [6.77208579e+05]\n",
            " [7.13818009e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.26955963]\n",
            " [ 0.36820003]\n",
            " [ 0.51611468]\n",
            " [ 1.09396885]]\n",
            "\n",
            "\n",
            "****************** Iteration 320 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ -3.86976113  -2.07612377  -0.94623222  -4.74956844   2.15460445\n",
            "   -1.43566912  -3.42360463  -4.00590003  -2.78447014   0.12399673\n",
            "   -0.9944078   -1.44489605  -2.03285612   0.51038509  -7.07173988\n",
            "   -5.45147384  -5.25593214  -3.30408453  -3.47683668  -4.2340974\n",
            "   -3.91972487 -10.72508585  -0.77704987  -4.99664991]]\n",
            "\n",
            " j = [[8.26451988]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -3.09113242]\n",
            " [-256.12218922]\n",
            " [-257.21822338]\n",
            " [-264.29707398]]\n",
            "\n",
            "Gradient Vector Norm: 449.02370612582814\n",
            "m_0_0: [[  -3.87056874]\n",
            " [-319.26337123]\n",
            " [-320.5258522 ]\n",
            " [-329.3138873 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  -3.87056874]\n",
            " [-319.26337123]\n",
            " [-320.5258522 ]\n",
            " [-329.3138873 ]]\n",
            "v_0_0: [[9.76909401e+01]\n",
            " [6.41254991e+05]\n",
            " [6.44206303e+05]\n",
            " [6.79032803e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.01730641e+02]\n",
            " [6.67772072e+05]\n",
            " [6.70845426e+05]\n",
            " [7.07112067e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.07768631]\n",
            " [ 0.56354621]\n",
            " [ 0.71178357]\n",
            " [ 1.28977931]]\n",
            "\n",
            "\n",
            "****************** Iteration 321 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[49.91854143 50.93036939 56.75547096 36.3234545  32.47016919 41.79199342\n",
            "  28.65562422 46.26187407 43.76789361 41.78574189 40.08014953 53.32146783\n",
            "  43.53903973 43.93353536 47.49966293 37.96767911 46.57955406 48.91948952\n",
            "  43.26996148 46.42662805 46.54389928 37.58819594 44.40419321 50.74765655]]\n",
            "\n",
            " j = [[994.58594691]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  44.14509355]\n",
            " [3570.4934291 ]\n",
            " [3578.15368218]\n",
            " [3673.26817938]]\n",
            "\n",
            "Gradient Vector Norm: 6248.716268761209\n",
            "m_0_0: [[  5.73256372]\n",
            " [458.68798884]\n",
            " [459.21005468]\n",
            " [471.20252603]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  5.73256372]\n",
            " [458.68798884]\n",
            " [459.21005468]\n",
            " [471.20252603]]\n",
            "v_0_0: [[1.16201924e+02]\n",
            " [7.62326674e+05]\n",
            " [7.65796077e+05]\n",
            " [8.07171466e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.20957070e+02]\n",
            " [7.93522158e+05]\n",
            " [7.97133534e+05]\n",
            " [8.40202062e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.33830125]\n",
            " [ 0.30608738]\n",
            " [ 0.45461624]\n",
            " [ 1.0327482 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 322 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-20.82036381 -18.77910919 -19.12670191 -17.69687714  -7.40217243\n",
            "  -15.05505937 -13.52879333 -19.84667882 -17.45242039 -13.00451711\n",
            "  -13.93876273 -18.70354652 -16.39748093 -13.17084003 -24.26750298\n",
            "  -19.14039049 -21.58417987 -19.76157311 -18.20872681 -20.19480203\n",
            "  -19.82166567 -25.94674124 -15.01739074 -22.56352408]]\n",
            "\n",
            " j = [[169.48958798]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -17.97624253]\n",
            " [-1461.96680083]\n",
            " [-1465.81460304]\n",
            " [-1505.08976387]]\n",
            "\n",
            "Gradient Vector Norm: 2559.604992928371\n",
            "m_0_0: [[ 0.99080247]\n",
            " [74.5570309 ]\n",
            " [74.20512313]\n",
            " [75.94406805]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.99080247]\n",
            " [74.5570309 ]\n",
            " [74.20512313]\n",
            " [75.94406805]]\n",
            "v_0_0: [[1.18271357e+02]\n",
            " [7.76076877e+05]\n",
            " [7.79624241e+05]\n",
            " [8.21752704e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.23060830e+02]\n",
            " [8.07504596e+05]\n",
            " [8.11195613e+05]\n",
            " [8.55030094e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.38295864]\n",
            " [ 0.26460286]\n",
            " [ 0.41342157]\n",
            " [ 0.99168307]]\n",
            "\n",
            "\n",
            "****************** Iteration 323 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-32.16726892 -29.96046487 -31.29746315 -26.36331128 -13.79904593\n",
            "  -24.17256866 -20.29403204 -30.4507294  -27.27171253 -21.79298723\n",
            "  -22.60380913 -30.25668409 -26.01274297 -22.3297043  -35.77879813\n",
            "  -28.30286903 -32.51566009 -30.77837846 -28.07034883 -30.87979272\n",
            "  -30.46675063 -36.13688423 -24.54965299 -34.32298635]]\n",
            "\n",
            " j = [[403.81677895]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -27.94061025]\n",
            " [-2269.18152932]\n",
            " [-2274.87268186]\n",
            " [-2335.7018663 ]]\n",
            "\n",
            "Gradient Vector Norm: 3972.4695615744536\n",
            "m_0_0: [[  -4.79548007]\n",
            " [-394.19068114]\n",
            " [-395.61043786]\n",
            " [-406.38511882]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  -4.79548007]\n",
            " [-394.19068114]\n",
            " [-395.61043786]\n",
            " [-406.38511882]]\n",
            "v_0_0: [[1.24895421e+02]\n",
            " [8.19807956e+05]\n",
            " [8.23578456e+05]\n",
            " [8.68090209e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.29900535e+02]\n",
            " [8.52661302e+05]\n",
            " [8.56582902e+05]\n",
            " [9.02878439e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.17258427]\n",
            " [ 0.47804887]\n",
            " [ 0.62714542]\n",
            " [ 1.20552513]]\n",
            "\n",
            "\n",
            "****************** Iteration 324 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[26.58859415 27.94125957 31.73287085 18.50333964 19.31657699 23.04698343\n",
            "  14.74736364 24.45941678 23.5796668  23.71630124 22.26414812 29.56756185\n",
            "  23.76824412 25.10341201 23.83231495 19.12684251 24.1062986  26.26825562\n",
            "  22.99373202 24.4591904  24.65727905 16.63793304 24.80448353 26.56951811]]\n",
            "\n",
            " j = [[287.05172091]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  23.65798279]\n",
            " [1910.83035033]\n",
            " [1914.70337602]\n",
            " [1965.50451887]]\n",
            "\n",
            "Gradient Vector Norm: 3343.819576432776\n",
            "m_0_0: [[ 0.8952125 ]\n",
            " [66.81352515]\n",
            " [66.45232491]\n",
            " [67.99280872]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.8952125 ]\n",
            " [66.81352515]\n",
            " [66.45232491]\n",
            " [67.99280872]]\n",
            "v_0_0: [[1.29243468e+02]\n",
            " [8.48122603e+05]\n",
            " [8.52003562e+05]\n",
            " [8.98041387e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.34368980e+02]\n",
            " [8.81757283e+05]\n",
            " [8.85792152e+05]\n",
            " [9.33655736e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.2111981 ]\n",
            " [ 0.44247264]\n",
            " [ 0.59184221]\n",
            " [ 1.17034158]]\n",
            "\n",
            "\n",
            "****************** Iteration 325 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[16.86263968 18.35725097 21.3008708  11.07480172 13.83338087 15.23203991\n",
            "   8.94867502 15.37019917 15.16315496 16.18330474 14.83690792 19.66485197\n",
            "  15.52645328 17.25301193 13.96550664 11.27306166 14.73665236 16.8252542\n",
            "  14.5408736  15.30073141 15.53291157  7.90360628 16.6338788  16.48995183]]\n",
            "\n",
            " j = [[118.64750977]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  15.11708214]\n",
            " [1218.93103117]\n",
            " [1221.22430882]\n",
            " [1253.55072007]]\n",
            "\n",
            "Gradient Vector Norm: 2132.7915053221227\n",
            "m_0_0: [[  3.73958643]\n",
            " [297.23702636]\n",
            " [297.40672169]\n",
            " [305.10439099]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  3.73958643]\n",
            " [297.23702636]\n",
            " [297.40672169]\n",
            " [305.10439099]]\n",
            "v_0_0: [[1.30236295e+02]\n",
            " [8.54499305e+05]\n",
            " [8.58397414e+05]\n",
            " [9.04774867e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.35347505e+02]\n",
            " [8.88034696e+05]\n",
            " [8.92085789e+05]\n",
            " [9.40283356e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.37191618]\n",
            " [ 0.28476313]\n",
            " [ 0.43440136]\n",
            " [ 1.01301974]]\n",
            "\n",
            "\n",
            "****************** Iteration 326 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-26.45079024 -24.32569789 -25.16134912 -22.00233662 -10.5809224\n",
            "  -19.57504012 -16.88038891 -25.10804045 -22.32179718 -17.3647245\n",
            "  -18.23894843 -24.4360202  -21.17307962 -17.71165861 -29.97732968\n",
            "  -23.69494822 -26.99914507 -25.2281386  -23.10255973 -25.49104658\n",
            "  -25.10261924 -30.99840185 -19.7502044  -28.39845459]]\n",
            "\n",
            " j = [[273.0680603]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -22.91973509]\n",
            " [-1862.43777484]\n",
            " [-1867.18989531]\n",
            " [-1917.14935909]]\n",
            "\n",
            "Gradient Vector Norm: 3260.5305625983083\n",
            "m_0_0: [[  -1.59227788]\n",
            " [-134.69793388]\n",
            " [-135.51260171]\n",
            " [-139.34635903]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  -1.59227788]\n",
            " [-134.69793388]\n",
            " [-135.51260171]\n",
            " [-139.34635903]]\n",
            "v_0_0: [[1.34187075e+02]\n",
            " [8.80641057e+05]\n",
            " [8.84677421e+05]\n",
            " [9.32481735e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.39398627e+02]\n",
            " [9.14843362e+05]\n",
            " [9.19036490e+05]\n",
            " [9.68697426e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.30448574]\n",
            " [ 0.35517689]\n",
            " [ 0.5050792 ]\n",
            " [ 1.08380967]]\n",
            "\n",
            "\n",
            "****************** Iteration 327 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ -7.03176652  -5.18866548  -4.32877618  -7.17466903   0.36306422\n",
            "   -3.96807529  -5.29815676  -6.95996963  -5.51471685  -2.32363077\n",
            "   -3.4100402   -4.66383147  -4.72106992  -2.03416794 -10.27525831\n",
            "   -8.02069115  -8.28373076  -6.37400185  -6.22577053  -7.20029968\n",
            "   -6.88371859 -13.55529317  -3.43898232  -8.27322053]]\n",
            "\n",
            " j = [[21.16162561]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -5.86605995]\n",
            " [-480.91580884]\n",
            " [-482.50507539]\n",
            " [-495.56843017]]\n",
            "\n",
            "Gradient Vector Norm: 842.4450383376656\n",
            "m_0_0: [[  -2.44703429]\n",
            " [-203.94150887]\n",
            " [-204.91109645]\n",
            " [-210.59077326]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  -2.44703429]\n",
            " [-203.94150887]\n",
            " [-204.91109645]\n",
            " [-210.59077326]]\n",
            "v_0_0: [[1.33189310e+02]\n",
            " [8.74147447e+05]\n",
            " [8.78158758e+05]\n",
            " [9.25612798e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.38308396e+02]\n",
            " [9.07745003e+05]\n",
            " [9.11910488e+05]\n",
            " [9.61188408e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.20045012]\n",
            " [ 0.46220396]\n",
            " [ 0.61236922]\n",
            " [ 1.19120986]]\n",
            "\n",
            "\n",
            "****************** Iteration 328 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[22.45552625 23.87018849 27.30429932 15.34149749 16.98188604 19.73010441\n",
            "  12.28840375 20.59734994 20.00597556 20.5157881  19.10735582 25.35969107\n",
            "  20.2615199  21.771149   19.64152504 15.78142056 20.13381716 22.25551432\n",
            "  19.40126303 20.57291467 20.78104378 12.93094188 21.32959596 22.28640394]]\n",
            "\n",
            " j = [[206.40761091]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  20.02938231]\n",
            " [1616.87934943]\n",
            " [1620.09131242]\n",
            " [1663.0521989 ]]\n",
            "\n",
            "Gradient Vector Norm: 2829.335343084994\n",
            "m_0_0: [[  2.04824903]\n",
            " [160.22266279]\n",
            " [160.08938533]\n",
            " [164.13782118]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  2.04824903]\n",
            " [160.22266279]\n",
            " [160.08938533]\n",
            " [164.13782118]]\n",
            "v_0_0: [[1.35869179e+02]\n",
            " [8.91548960e+05]\n",
            " [8.95624129e+05]\n",
            " [9.44014096e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.41037057e+02]\n",
            " [9.25459641e+05]\n",
            " [9.29689812e+05]\n",
            " [9.79920325e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.2866849 ]\n",
            " [ 0.37892886]\n",
            " [ 0.5293529 ]\n",
            " [ 1.10830438]]\n",
            "\n",
            "\n",
            "****************** Iteration 329 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.3909384   1.35649173  2.79750784 -2.10628014  4.10351895  1.37095828\n",
            "  -1.33500331 -0.75360082  0.23418342  2.82032916  1.66079788  2.09792316\n",
            "   0.9031468   3.32883861 -3.53667237 -2.66406846 -1.87937893  0.07369827\n",
            "  -0.45450585 -0.94277481 -0.65277535 -7.58807084  2.13781017 -1.39077855]]\n",
            "\n",
            " j = [[3.14868796]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[-0.03373515]\n",
            " [-8.43667829]\n",
            " [-8.93967042]\n",
            " [-9.38074913]]\n",
            "\n",
            "Gradient Vector Norm: 15.462691872715046\n",
            "m_0_0: [[  1.63185219]\n",
            " [126.49079457]\n",
            " [126.28357418]\n",
            " [129.43410712]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  1.63185219]\n",
            " [126.49079457]\n",
            " [126.28357418]\n",
            " [129.43410712]]\n",
            "v_0_0: [[1.34510499e+02]\n",
            " [8.82634183e+05]\n",
            " [8.86668687e+05]\n",
            " [9.34574835e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.39573610e+02]\n",
            " [9.15857431e+05]\n",
            " [9.20043798e+05]\n",
            " [9.69753183e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.35574791]\n",
            " [ 0.31284203]\n",
            " [ 0.46352461]\n",
            " [ 1.04258581]]\n",
            "\n",
            "\n",
            "****************** Iteration 330 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-18.51079317 -16.49934487 -16.63892029 -15.94464881  -6.11076876\n",
            "  -13.18971936 -12.13964575 -17.68732428 -15.44695363 -11.21416104\n",
            "  -12.17635627 -16.3512925  -14.45049464 -11.29781613 -21.91951113\n",
            "  -17.29388878 -19.33784827 -17.51900552 -16.20241959 -18.00686212\n",
            "  -17.65217827 -23.86173312 -13.0836185  -20.1694724 ]]\n",
            "\n",
            " j = [[134.16843717]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -15.94603238]\n",
            " [-1297.49396905]\n",
            " [-1300.94276645]\n",
            " [-1335.80600074]]\n",
            "\n",
            "Gradient Vector Norm: 2271.6898179226046\n",
            "m_0_0: [[  -1.88372472]\n",
            " [-158.30615815]\n",
            " [-159.16169395]\n",
            " [-163.61391446]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  -1.88372472]\n",
            " [-158.30615815]\n",
            " [-159.16169395]\n",
            " [-163.61391446]]\n",
            "v_0_0: [[1.35708153e+02]\n",
            " [8.90642747e+05]\n",
            " [8.94726521e+05]\n",
            " [9.43072864e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.40763361e+02]\n",
            " [9.23819710e+05]\n",
            " [9.28055608e+05]\n",
            " [9.78202880e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.27636282]\n",
            " [ 0.39519403]\n",
            " [ 0.5461325 ]\n",
            " [ 1.12529918]]\n",
            "\n",
            "\n",
            "****************** Iteration 331 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 4.18916567  5.87088951  7.71316702  1.38848894  6.68247065  5.05386709\n",
            "   1.39912739  3.52691146  4.19956837  6.36814037  5.15797108  6.7614948\n",
            "   4.78140565  7.0282278   1.11119571  1.02905321  2.53910233  4.52063126\n",
            "   3.52580584  3.37386881  3.64481575 -3.47180071  5.9836227   3.35600862]]\n",
            "\n",
            " j = [[10.96242929]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  3.9888833 ]\n",
            " [317.43796125]\n",
            " [317.68583961]\n",
            " [325.95180311]]\n",
            "\n",
            "Gradient Vector Norm: 554.9339069402143\n",
            "m_0_0: [[ -0.70920312]\n",
            " [-63.15733427]\n",
            " [-63.79218724]\n",
            " [-65.70077094]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.70920312]\n",
            " [-63.15733427]\n",
            " [-63.79218724]\n",
            " [-65.70077094]]\n",
            "v_0_0: [[1.34510183e+02]\n",
            " [8.82743988e+05]\n",
            " [8.86788499e+05]\n",
            " [9.34704581e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.39468814e+02]\n",
            " [9.15285769e+05]\n",
            " [9.19479378e+05]\n",
            " [9.69161855e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.24633675]\n",
            " [ 0.42820174]\n",
            " [ 0.57939589]\n",
            " [ 1.15866811]]\n",
            "\n",
            "\n",
            "****************** Iteration 332 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[13.31939714 14.86877351 17.50863799  8.35928635 11.82731221 12.39232325\n",
            "   6.84540118 12.05961941 12.10210631 13.44006727 12.12995769 16.05783212\n",
            "  12.51604351 14.39979721 10.37479736  8.39752179 11.33976805 13.38529522\n",
            "  11.46070187 11.97438192 12.21093722  4.73004675 13.65228973 12.81831801]]\n",
            "\n",
            " j = [[75.82451859]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 12.00710888]\n",
            " [966.99671386]\n",
            " [968.73310031]\n",
            " [994.34782992]]\n",
            "\n",
            "Gradient Vector Norm: 1691.862358996266\n",
            "m_0_0: [[  1.83405928]\n",
            " [142.87347535]\n",
            " [142.71287027]\n",
            " [146.30894923]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  1.83405928]\n",
            " [142.87347535]\n",
            " [142.71287027]\n",
            " [146.30894923]]\n",
            "v_0_0: [[1.34606788e+02]\n",
            " [8.83267375e+05]\n",
            " [8.87305052e+05]\n",
            " [9.35244811e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.39517547e+02]\n",
            " [9.15490960e+05]\n",
            " [9.19675941e+05]\n",
            " [9.69364651e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.32397313]\n",
            " [ 0.35354061]\n",
            " [ 0.50498856]\n",
            " [ 1.08436667]]\n",
            "\n",
            "\n",
            "****************** Iteration 333 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ -7.15960295  -5.31189971  -4.45852885  -7.28059684   0.28331971\n",
            "   -4.06428414  -5.36620502  -7.07884367  -5.62079895  -2.42170505\n",
            "   -3.50874058  -4.79341635  -4.83641331  -2.13136543 -10.40151439\n",
            "   -8.13673006  -8.39211317  -6.49791842  -6.33752158  -7.3116268\n",
            "   -7.00178526 -13.66258826  -3.5508028   -8.40530498]]\n",
            "\n",
            " j = [[21.83682271]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -5.97695779]\n",
            " [-489.89488754]\n",
            " [-491.48824744]\n",
            " [-504.77780628]]\n",
            "\n",
            "Gradient Vector Norm: 858.1340547637097\n",
            "m_0_0: [[ 0.27185587]\n",
            " [16.31980278]\n",
            " [15.87264673]\n",
            " [16.09159813]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.27185587]\n",
            " [16.31980278]\n",
            " [15.87264673]\n",
            " [16.09159813]]\n",
            "v_0_0: [[1.33617961e+02]\n",
            " [8.76834671e+05]\n",
            " [8.80847608e+05]\n",
            " [9.28440369e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.38442138e+02]\n",
            " [9.08492138e+05]\n",
            " [9.12649960e+05]\n",
            " [9.61961023e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.3355255 ]\n",
            " [ 0.34497962]\n",
            " [ 0.49668112]\n",
            " [ 1.07616333]]\n",
            "\n",
            "\n",
            "****************** Iteration 334 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ -9.46129267  -7.57965776  -6.92639928  -9.03962629  -1.01529102\n",
            "   -5.91295246  -6.73754084  -9.22979045  -7.61208414  -4.20433027\n",
            "   -5.26659293  -7.13685148  -6.78772289  -3.98849064 -12.73612155\n",
            "   -9.99692477 -10.60768582  -8.73260958  -8.33800582  -9.47792068\n",
            "   -9.16089004 -15.72869917  -5.48494504 -10.79062155]]\n",
            "\n",
            " j = [[36.40886763]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -7.99804363]\n",
            " [-653.62249475]\n",
            " [-655.58765268]\n",
            " [-673.24741224]]\n",
            "\n",
            "Gradient Vector Norm: 1144.7023992245602\n",
            "m_0_0: [[  -1.38212403]\n",
            " [-117.66865673]\n",
            " [-118.41941315]\n",
            " [-121.77620395]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[  -1.38212403]\n",
            " [-117.66865673]\n",
            " [-118.41941315]\n",
            " [-121.77620395]]\n",
            "v_0_0: [[1.32921468e+02]\n",
            " [8.72338548e+05]\n",
            " [8.76337084e+05]\n",
            " [9.23688587e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.37670794e+02]\n",
            " [9.03507481e+05]\n",
            " [9.07648887e+05]\n",
            " [9.56692273e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.2766286 ]\n",
            " [ 0.40687594]\n",
            " [ 0.55883009]\n",
            " [ 1.13841432]]\n",
            "\n",
            "\n",
            "****************** Iteration 335 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 7.61241387  9.2461574  11.39024284  3.99710365  8.60676817  7.80914084\n",
            "   3.44588501  6.72644443  7.16513744  9.02016319  7.77130301 10.24739585\n",
            "   7.67722353  9.79560101  4.58648692  3.78412553  5.84756272  7.84445427\n",
            "   6.50049514  6.60387535  6.85764444 -0.3921903   8.85625733  6.90402079]]\n",
            "\n",
            " j = [[27.59508029]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  6.99598802]\n",
            " [561.04767091]\n",
            " [561.8636174 ]\n",
            " [576.64391871]]\n",
            "\n",
            "Gradient Vector Norm: 981.3421250648065\n",
            "m_0_0: [[ 0.29349838]\n",
            " [18.0746088 ]\n",
            " [17.63719296]\n",
            " [17.90782059]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.29349838]\n",
            " [18.0746088 ]\n",
            " [17.63719296]\n",
            " [17.90782059]]\n",
            "v_0_0: [[1.32081692e+02]\n",
            " [8.66762907e+05]\n",
            " [8.70730620e+05]\n",
            " [9.17776883e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.36752151e+02]\n",
            " [8.97411973e+05]\n",
            " [9.01519986e+05]\n",
            " [9.50229822e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.28917748]\n",
            " [ 0.39733606]\n",
            " [ 0.54954231]\n",
            " [ 1.12922891]]\n",
            "\n",
            "\n",
            "****************** Iteration 336 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 5.04108901  6.71268496  8.63312274  2.03217182  7.15617058  5.74376848\n",
            "   1.91372191  4.32350507  4.94047791  7.02868357  5.80753698  7.62942738\n",
            "   5.49746734  7.72079114  1.97831807  1.70627513  3.37214088  5.34797555\n",
            "   4.26567387  4.18362095  4.44556703 -2.70049481  6.69562826  4.2392666 ]]\n",
            "\n",
            " j = [[14.24215759]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  4.73810793]\n",
            " [378.13738953]\n",
            " [378.53764982]\n",
            " [388.4354299 ]]\n",
            "\n",
            "Gradient Vector Norm: 661.1982838489313\n",
            "m_0_0: [[ 1.18242029]\n",
            " [90.08716494]\n",
            " [89.81728433]\n",
            " [92.01334245]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 1.18242029]\n",
            " [90.08716494]\n",
            " [89.81728433]\n",
            " [92.01334245]]\n",
            "v_0_0: [[1.30985372e+02]\n",
            " [8.59525157e+05]\n",
            " [8.63456222e+05]\n",
            " [9.10107935e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.35569126e+02]\n",
            " [8.89603726e+05]\n",
            " [8.93672356e+05]\n",
            " [9.41956618e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.33995339]\n",
            " [ 0.34957933]\n",
            " [ 0.50203716]\n",
            " [ 1.08182597]]\n",
            "\n",
            "\n",
            "****************** Iteration 337 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ -8.03999     -6.17767376  -5.3980986   -7.95839153  -0.21811096\n",
            "   -4.76762978  -5.8860855   -7.90127836  -6.37989942  -3.1030823\n",
            "   -4.1818628   -5.68938181  -5.58691736  -2.83826163 -11.29247493\n",
            "   -8.85581235  -9.23086768  -7.35251981  -7.10308133  -8.1349149\n",
            "   -7.82657218 -14.44847357  -4.29319581  -9.31741426]]\n",
            "\n",
            " j = [[26.91035646]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -6.74924961]\n",
            " [-552.45449151]\n",
            " [-554.18014705]\n",
            " [-569.13183945]]\n",
            "\n",
            "Gradient Vector Norm: 967.6146979087955\n",
            "m_0_0: [[ -0.40391369]\n",
            " [-38.42116635]\n",
            " [-38.98220194]\n",
            " [-40.21569393]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.40391369]\n",
            " [-38.42116635]\n",
            " [-38.98220194]\n",
            " [-40.21569393]]\n",
            "v_0_0: [[1.30131042e+02]\n",
            " [8.53981965e+05]\n",
            " [8.57892816e+05]\n",
            " [9.04245966e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.34637784e+02]\n",
            " [8.83557358e+05]\n",
            " [8.87603651e+05]\n",
            " [9.35562119e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.3225485 ]\n",
            " [ 0.37001661]\n",
            " [ 0.52272556]\n",
            " [ 1.10261476]]\n",
            "\n",
            "\n",
            "****************** Iteration 338 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-2.36798175 -0.58771582  0.68762697 -3.62841575  2.97751944 -0.20837444\n",
            "  -2.50225089 -2.60045572 -1.47034816  1.29026429  0.14926789  0.08586762\n",
            "  -0.78231401  1.74153138 -5.53741192 -4.2790322  -3.76273002 -1.84546592\n",
            "  -2.17369491 -2.7914599  -2.50491043 -9.35277683  0.4705833  -3.43907461]]\n",
            "\n",
            " j = [[4.87243287]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -1.76798968]\n",
            " [-148.92066438]\n",
            " [-149.72059542]\n",
            " [-153.89378473]]\n",
            "\n",
            "Gradient Vector Norm: 261.30450386785185\n",
            "m_0_0: [[ -0.67672889]\n",
            " [-60.52106595]\n",
            " [-61.12988064]\n",
            " [-62.95131209]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.67672889]\n",
            " [-60.52106595]\n",
            " [-61.12988064]\n",
            " [-62.95131209]]\n",
            "v_0_0: [[1.28860989e+02]\n",
            " [8.45663919e+05]\n",
            " [8.49538050e+05]\n",
            " [8.95440339e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.33277589e+02]\n",
            " [8.74648327e+05]\n",
            " [8.78655241e+05]\n",
            " [9.26130792e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.2932394 ]\n",
            " [ 0.40237298]\n",
            " [ 0.55533281]\n",
            " [ 1.1353216 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 339 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 6.58164438  8.23218607 10.28934265  3.20446603  8.02052611  6.98488779\n",
            "   2.83622432  5.76344636  6.27582398  8.22227014  6.98330067  9.19833086\n",
            "   6.7993281   8.96724956  3.54294199  2.94368306  4.86383419  6.8438595\n",
            "   5.60423995  5.63891112  5.89175021 -1.31318725  7.98756438  5.83607059]]\n",
            "\n",
            " j = [[21.62147701]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  6.09161228]\n",
            " [487.7882066 ]\n",
            " [488.44735833]\n",
            " [501.27962396]]\n",
            "\n",
            "Gradient Vector Norm: 853.1333573868897\n",
            "m_0_0: [[ 0.67693935]\n",
            " [49.14078856]\n",
            " [48.78556715]\n",
            " [49.89487512]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.67693935]\n",
            " [49.14078856]\n",
            " [48.78556715]\n",
            " [49.89487512]]\n",
            "v_0_0: [[1.27943456e+02]\n",
            " [8.39586653e+05]\n",
            " [8.43428478e+05]\n",
            " [8.88998748e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.32283270e+02]\n",
            " [8.68065246e+05]\n",
            " [8.72037385e+05]\n",
            " [9.19153389e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.32266759]\n",
            " [ 0.37600142]\n",
            " [ 0.52921157]\n",
            " [ 1.10930013]]\n",
            "\n",
            "\n",
            "****************** Iteration 340 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.61900542  1.13672324  2.56621545 -2.29559132  3.96064694  1.19919647\n",
            "  -1.45674523 -0.96581848  0.04466575  2.64514967  1.48439335  1.86687534\n",
            "   0.6972796   3.15528644 -3.76189347 -2.87128031 -2.07259932 -0.14726711\n",
            "  -0.6538866  -1.14134457 -0.8634853  -7.77948559  1.93830981 -1.62634305]]\n",
            "\n",
            " j = [[3.18648814]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.23166682]\n",
            " [-24.46064259]\n",
            " [-24.97050628]\n",
            " [-25.81586582]]\n",
            "\n",
            "Gradient Vector Norm: 43.45528525839341\n",
            "m_0_0: [[ 0.49521811]\n",
            " [34.42050233]\n",
            " [34.03435247]\n",
            " [34.75272693]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.49521811]\n",
            " [34.42050233]\n",
            " [34.03435247]\n",
            " [34.75272693]]\n",
            "v_0_0: [[1.26664559e+02]\n",
            " [8.31196770e+05]\n",
            " [8.35000429e+05]\n",
            " [8.80115426e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.30916586e+02]\n",
            " [8.59099376e+05]\n",
            " [8.63030720e+05]\n",
            " [9.09660191e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.344308  ]\n",
            " [ 0.35743341]\n",
            " [ 0.51089373]\n",
            " [ 1.09108135]]\n",
            "\n",
            "\n",
            "****************** Iteration 341 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ -5.67378768  -3.84408415  -2.85498073  -6.15698886   1.11024821\n",
            "   -2.8620032   -4.47000818  -5.68967573  -4.32934925  -1.26990351\n",
            "   -2.37585741  -3.27969426  -3.58663215  -0.92438218  -8.88964991\n",
            "   -6.95394095  -6.94120109  -5.05494922  -5.04705898  -5.90058665\n",
            "   -5.60550929 -12.3184082   -2.30841387  -6.86485612]]\n",
            "\n",
            " j = [[14.63138373]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -4.67048639]\n",
            " [-384.04904884]\n",
            " [-385.37873589]\n",
            " [-395.82492031]]\n",
            "\n",
            "Gradient Vector Norm: 672.8369958370603\n",
            "m_0_0: [[ -0.53792279]\n",
            " [-49.2734079 ]\n",
            " [-49.8482652 ]\n",
            " [-51.36280252]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.53792279]\n",
            " [-49.2734079 ]\n",
            " [-49.8482652 ]\n",
            " [-51.36280252]]\n",
            "v_0_0: [[1.25616047e+02]\n",
            " [8.24359739e+05]\n",
            " [8.28135592e+05]\n",
            " [8.72881045e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.29789308e+02]\n",
            " [8.51746906e+05]\n",
            " [8.55648202e+05]\n",
            " [9.01880204e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.32069959]\n",
            " [ 0.38412824]\n",
            " [ 0.53783837]\n",
            " [ 1.11812369]]\n",
            "\n",
            "\n",
            "****************** Iteration 342 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 1.71850637  3.44113745  5.0761406  -0.51334741  5.27546504  3.07971238\n",
            "  -0.06030229  1.21882052  2.06900187  4.45586416  3.26892423  4.24712759\n",
            "   2.67553029  5.04412594 -1.3892842  -0.98840823  0.18464419  2.12235284\n",
            "   1.3774119   1.0630479   1.3300837  -5.67759861  3.9004133   0.79633032]]\n",
            "\n",
            " j = [[4.69615305]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  1.82148749]\n",
            " [141.86813031]\n",
            " [141.74409962]\n",
            " [145.34461088]]\n",
            "\n",
            "Gradient Vector Norm: 247.68191274698734\n",
            "m_0_0: [[ -0.06604073]\n",
            " [-11.04510026]\n",
            " [-11.52979224]\n",
            " [-12.02131984]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.06604073]\n",
            " [-11.04510026]\n",
            " [-11.52979224]\n",
            " [-12.02131984]]\n",
            "v_0_0: [[1.24393065e+02]\n",
            " [8.16317407e+05]\n",
            " [8.20055150e+05]\n",
            " [8.64363485e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.28483010e+02]\n",
            " [8.43157273e+05]\n",
            " [8.47017910e+05]\n",
            " [8.92783067e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.31778649]\n",
            " [ 0.39014255]\n",
            " [ 0.54410228]\n",
            " [ 1.12448503]]\n",
            "\n",
            "\n",
            "****************** Iteration 343 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 3.42357863  5.12186017  6.90642425  0.78732188  6.23514995  4.45096513\n",
            "   0.95774259  2.81234777  3.54531686  5.7766159   4.5707321   5.98332449\n",
            "   4.11906411  6.42149044  0.341136    0.38598502  1.83007262  3.77787976\n",
            "   2.85916853  2.67035097  2.93002702 -4.14495121  5.33197323  2.56348771]]\n",
            "\n",
            " j = [[8.51207981]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  3.31904433]\n",
            " [263.1867193 ]\n",
            " [263.34287735]\n",
            " [270.18521184]]\n",
            "\n",
            "Gradient Vector Norm: 460.0302001172483\n",
            "m_0_0: [[ 0.61097628]\n",
            " [43.80126365]\n",
            " [43.44474168]\n",
            " [44.4199865 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.61097628]\n",
            " [43.80126365]\n",
            " [43.44474168]\n",
            " [44.4199865 ]]\n",
            "v_0_0: [[1.23259295e+02]\n",
            " [8.08846906e+05]\n",
            " [8.12548093e+05]\n",
            " [8.56449851e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.27270117e+02]\n",
            " [8.35166551e+05]\n",
            " [8.38988174e+05]\n",
            " [8.84318482e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.34486516]\n",
            " [ 0.36617795]\n",
            " [ 0.52038694]\n",
            " [ 1.10086698]]\n",
            "\n",
            "\n",
            "****************** Iteration 344 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ -3.11563521  -1.32178715  -0.10716305  -4.2076476    2.54804976\n",
            "   -0.803195    -2.94081364  -3.29878634  -2.11342641   0.7117893\n",
            "   -0.42310047  -0.67466059  -1.42256176   1.143463    -6.29264731\n",
            "   -4.89500933  -4.46899677  -2.5710461   -2.82411095  -3.48696753\n",
            "   -3.20467046 -10.01717508  -0.16166876  -4.21343267]]\n",
            "\n",
            " j = [[6.32176814]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -2.42338334]\n",
            " [-202.00710761]\n",
            " [-202.91233474]\n",
            " [-208.49108209]]\n",
            "\n",
            "Gradient Vector Norm: 354.1958373692942\n",
            "m_0_0: [[ 4.10435665e-03]\n",
            " [-5.36041060e+00]\n",
            " [-5.82667360e+00]\n",
            " [-6.16222722e+00]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 4.10435665e-03]\n",
            " [-5.36041060e+00]\n",
            " [-5.82667360e+00]\n",
            " [-6.16222722e+00]]\n",
            "v_0_0: [[1.22085430e+02]\n",
            " [8.01166505e+05]\n",
            " [8.04834346e+05]\n",
            " [8.48320038e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.26017049e+02]\n",
            " [8.26967140e+05]\n",
            " [8.30753100e+05]\n",
            " [8.75639197e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.34504797]\n",
            " [ 0.36912525]\n",
            " [ 0.52358329]\n",
            " [ 1.10415963]]\n",
            "\n",
            "\n",
            "****************** Iteration 345 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-2.25422429 -0.4724543   0.8181018  -3.55123324  3.03220144 -0.10995048\n",
            "  -2.42591898 -2.49370432 -1.36727522  1.37907782  0.23444624  0.2025395\n",
            "  -0.69383674  1.8397511  -5.41816211 -4.20167661 -3.63656071 -1.73463478\n",
            "  -2.07557359 -2.67424752 -2.39623932 -9.24229655  0.56121813 -3.32061281]]\n",
            "\n",
            " j = [[4.68407902]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -1.6667194 ]\n",
            " [-140.70831406]\n",
            " [-141.47064139]\n",
            " [-145.41043473]]\n",
            "\n",
            "Gradient Vector Norm: 246.9002723873068\n",
            "m_0_0: [[ -0.33006039]\n",
            " [-32.42999129]\n",
            " [-32.95546716]\n",
            " [-34.01186872]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.33006039]\n",
            " [-32.42999129]\n",
            " [-32.95546716]\n",
            " [-34.01186872]]\n",
            "v_0_0: [[1.20892355e+02]\n",
            " [7.93352828e+05]\n",
            " [7.96986142e+05]\n",
            " [8.40048279e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.24745380e+02]\n",
            " [8.18638200e+05]\n",
            " [8.22387313e+05]\n",
            " [8.66821906e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.33027229]\n",
            " [ 0.3870466 ]\n",
            " [ 0.54175347]\n",
            " [ 1.1224253 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 346 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 2.72491995  4.43471807  6.16057157  0.24962968  5.83724871  3.89244812\n",
            "   0.5446303   2.15958319  2.94257852  5.23573304  4.03643758  5.27234367\n",
            "   3.52373381  5.86016655 -0.36605068 -0.18420664  1.16386766  3.09972376\n",
            "   2.25164881  2.01662449  2.27537171 -4.76894883  4.74300382  1.83967772]]\n",
            "\n",
            " j = [[6.67250957]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  2.70606061]\n",
            " [213.53273058]\n",
            " [213.58337096]\n",
            " [219.10557537]]\n",
            "\n",
            "Gradient Vector Norm: 373.1335676403787\n",
            "m_0_0: [[ 0.27716381]\n",
            " [16.76255308]\n",
            " [16.35230046]\n",
            " [16.6116201 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.27716381]\n",
            " [16.76255308]\n",
            " [16.35230046]\n",
            " [16.6116201 ]]\n",
            "v_0_0: [[1.19756659e+02]\n",
            " [7.85875262e+05]\n",
            " [7.89472459e+05]\n",
            " [8.32127869e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.23534115e+02]\n",
            " [8.10663941e+05]\n",
            " [8.14374604e+05]\n",
            " [8.58375483e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.34274065]\n",
            " [ 0.37773789]\n",
            " [ 0.5326933 ]\n",
            " [ 1.11346044]]\n",
            "\n",
            "\n",
            "****************** Iteration 347 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 0.215714    1.96246101  3.47008371 -1.66788641  4.42158329  1.87693148\n",
            "  -0.95060888 -0.18533431  0.77160203  3.29231654  2.12004839  2.71764619\n",
            "   1.39662838  3.83543652 -2.91121138 -2.21187819 -1.25175407  0.66357466\n",
            "   0.07081214 -0.34517069 -0.07845276 -7.02150433  2.63457372 -0.76068497]]\n",
            "\n",
            " j = [[3.22988934]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.50270525]\n",
            " [35.03989926]\n",
            " [34.68494552]\n",
            " [35.44240602]]\n",
            "\n",
            "Gradient Vector Norm: 60.72278685001883\n",
            "m_0_0: [[ 0.3222721 ]\n",
            " [20.41802232]\n",
            " [20.01882947]\n",
            " [20.37777728]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.3222721 ]\n",
            " [20.41802232]\n",
            " [20.01882947]\n",
            " [20.37777728]]\n",
            "v_0_0: [[1.18561620e+02]\n",
            " [7.78028788e+05]\n",
            " [7.81589765e+05]\n",
            " [8.23819152e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.22262816e+02]\n",
            " [8.02316894e+05]\n",
            " [8.05989037e+05]\n",
            " [8.49536718e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.3573134 ]\n",
            " [ 0.36634036]\n",
            " [ 0.52154409]\n",
            " [ 1.10240603]]\n",
            "\n",
            "\n",
            "****************** Iteration 348 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-2.86801968 -1.07596695  0.16328446 -4.02413543  2.68208521 -0.60030199\n",
            "  -2.78848595 -3.0671767  -1.89660842  0.90389755 -0.23507649 -0.42202313\n",
            "  -1.21725797  1.34690031 -6.03925751 -4.7033489  -4.22101584 -2.33038733\n",
            "  -2.60934196 -3.24806991 -2.97129083 -9.79009375  0.04354115 -3.95646611]]\n",
            "\n",
            " j = [[5.78780347]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  -2.20519234]\n",
            " [-184.32595322]\n",
            " [-185.17999306]\n",
            " [-190.27881136]]\n",
            "\n",
            "Gradient Vector Norm: 323.2314585338434\n",
            "m_0_0: [[ -0.18322079]\n",
            " [-20.53077279]\n",
            " [-21.02093503]\n",
            " [-21.75354045]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.18322079]\n",
            " [-20.53077279]\n",
            " [-21.02093503]\n",
            " [-21.75354045]]\n",
            "v_0_0: [[1.17424632e+02]\n",
            " [7.70588260e+05]\n",
            " [7.74116784e+05]\n",
            " [8.15943021e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.21052545e+02]\n",
            " [7.94396102e+05]\n",
            " [7.98033641e+05]\n",
            " [8.41152128e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.34898707]\n",
            " [ 0.37785782]\n",
            " [ 0.53330961]\n",
            " [ 1.11426543]]\n",
            "\n",
            "\n",
            "****************** Iteration 349 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 0.34972077  2.09542205  3.61624834 -1.56835192  4.49431803  1.98655168\n",
            "  -0.86840564 -0.06000895  0.88881111  3.39625153  2.22182671  2.85435721\n",
            "   1.50792587  3.94536532 -2.77417334 -2.10780714 -1.11795385  0.79381214\n",
            "   0.18705859 -0.21613076  0.04779664 -6.89881801  2.7457504  -0.62162685]]\n",
            "\n",
            " j = [[3.28798555]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.6207475 ]\n",
            " [44.60539988]\n",
            " [44.27769625]\n",
            " [45.2944166 ]]\n",
            "\n",
            "Gradient Vector Norm: 77.47338631264489\n",
            "m_0_0: [[-0.02242713]\n",
            " [-7.50353826]\n",
            " [-7.96120877]\n",
            " [-8.34394904]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[-0.02242713]\n",
            " [-7.50353826]\n",
            " [-7.96120877]\n",
            " [-8.34394904]]\n",
            "v_0_0: [[1.16254239e+02]\n",
            " [7.62902274e+05]\n",
            " [7.66395221e+05]\n",
            " [8.07804106e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.19808976e+02]\n",
            " [7.86229741e+05]\n",
            " [7.89829493e+05]\n",
            " [8.32504548e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.34796261]\n",
            " [ 0.382089  ]\n",
            " [ 0.53778863]\n",
            " [ 1.11883787]]\n",
            "\n",
            "\n",
            "****************** Iteration 350 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 1.56363512  3.2921314   4.91965345 -0.6427656   5.17711388  2.96305249\n",
            "  -0.14334452  1.07448988  1.94000452  4.33654651  3.14853025  4.09047976\n",
            "   2.53532276  4.92619073 -1.54206265 -1.12989871  0.05414438  1.97247955\n",
            "   1.24195111  0.92856527  1.1869289  -5.80734594  3.76474591  0.63652098]]\n",
            "\n",
            " j = [[4.46315984]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  1.68696123]\n",
            " [130.98106052]\n",
            " [130.85361477]\n",
            " [134.17889037]]\n",
            "\n",
            "Gradient Vector Norm: 228.6602877048709\n",
            "m_0_0: [[ 0.31945054]\n",
            " [20.1933815 ]\n",
            " [19.80175593]\n",
            " [20.16061884]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.31945054]\n",
            " [20.1933815 ]\n",
            " [19.80175593]\n",
            " [20.16061884]]\n",
            "v_0_0: [[1.15120155e+02]\n",
            " [7.55444812e+05]\n",
            " [7.58902496e+05]\n",
            " [7.99906105e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.18603949e+02]\n",
            " [7.78306265e+05]\n",
            " [7.81868586e+05]\n",
            " [8.24113056e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.36262889]\n",
            " [ 0.37064432]\n",
            " [ 0.52659149]\n",
            " [ 1.10773384]]\n",
            "\n",
            "\n",
            "****************** Iteration 351 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-1.53340848  0.2405872   1.59857632 -3.00918609  3.43009003  0.4751055\n",
            "  -1.98919171 -1.81979936 -0.73974504  1.93780415  0.78322483  0.93726429\n",
            "  -0.08983774  2.42689216 -4.68361546 -3.63210875 -2.92795729 -1.03440099\n",
            "  -1.4497722  -1.98688049 -1.71840304 -8.58790145  1.16253611 -2.57305037]]\n",
            "\n",
            " j = [[3.75978575]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -1.03263241]\n",
            " [-89.33218002]\n",
            " [-89.96088061]\n",
            " [-92.51723149]]\n",
            "\n",
            "Gradient Vector Norm: 156.95127549114858\n",
            "m_0_0: [[ 0.04903395]\n",
            " [-1.7117308 ]\n",
            " [-2.15077137]\n",
            " [-2.37495122]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.04903395]\n",
            " [-1.7117308 ]\n",
            " [-2.15077137]\n",
            " [-2.37495122]]\n",
            "v_0_0: [[1.13979617e+02]\n",
            " [7.47970166e+05]\n",
            " [7.51394400e+05]\n",
            " [7.91992638e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.17393370e+02]\n",
            " [7.70372288e+05]\n",
            " [7.73899080e+05]\n",
            " [8.15713257e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.36489166]\n",
            " [ 0.37161943]\n",
            " [ 0.52781392]\n",
            " [ 1.10904863]]\n",
            "\n",
            "\n",
            "****************** Iteration 352 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-1.21513703  0.55468111  1.94120087 -2.76755039  3.60805315  0.73182367\n",
            "  -1.79828108 -1.52232478 -0.46370276  2.18437347  1.02597193  1.26146314\n",
            "   0.17870696  2.68467781 -4.36017722 -3.37722523 -2.61894236 -0.72531893\n",
            "  -1.17327619 -1.6857245  -1.41955109 -8.30088713  1.42919316 -2.24311188]]\n",
            "\n",
            " j = [[3.4833449]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.75296105]\n",
            " [-66.67438456]\n",
            " [-67.24857509]\n",
            " [-69.1979959 ]]\n",
            "\n",
            "Gradient Vector Norm: 117.28927485356708\n",
            "m_0_0: [[ -0.11136505]\n",
            " [-14.70426155]\n",
            " [-15.17033212]\n",
            " [-15.73956016]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.11136505]\n",
            " [-14.70426155]\n",
            " [-15.17033212]\n",
            " [-15.73956016]]\n",
            "v_0_0: [[1.12845490e+02]\n",
            " [7.40534919e+05]\n",
            " [7.43925680e+05]\n",
            " [7.84120595e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.16190476e+02]\n",
            " [7.62485982e+05]\n",
            " [7.65977253e+05]\n",
            " [8.07363633e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.35972596]\n",
            " [ 0.38003914]\n",
            " [ 0.53648069]\n",
            " [ 1.1178071 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 353 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 1.15027453  2.88613874  4.47984849 -0.9626466   4.9398504   2.63369199\n",
            "  -0.38654212  0.68829222  1.58403872  4.01654086  2.83198367  3.67002514\n",
            "   2.18173914  4.59505754 -1.95982349 -1.46973891 -0.3372338   1.5713519\n",
            "   0.88237044  0.54346131  0.79988804 -6.17517279  3.41544958  0.20840958]]\n",
            "\n",
            " j = [[3.93239505]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[  1.32446894]\n",
            " [101.62019999]\n",
            " [101.43364449]\n",
            " [103.98038693]]\n",
            "\n",
            "Gradient Vector Norm: 177.2826115672971\n",
            "m_0_0: [[0.17580175]\n",
            " [8.56063075]\n",
            " [8.1504632 ]\n",
            " [8.20442926]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.17580175]\n",
            " [8.56063075]\n",
            " [8.1504632 ]\n",
            " [8.20442926]]\n",
            "v_0_0: [[1.11734578e+02]\n",
            " [7.33232837e+05]\n",
            " [7.36589311e+05]\n",
            " [7.76387509e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.15012541e+02]\n",
            " [7.54743728e+05]\n",
            " [7.58198671e+05]\n",
            " [7.99164431e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.36792223]\n",
            " [ 0.37511222]\n",
            " [ 0.53180053]\n",
            " [ 1.11321828]]\n",
            "\n",
            "\n",
            "****************** Iteration 354 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.1547394   1.60055846  3.08113049 -1.96061583  4.20285506  1.58586749\n",
            "  -1.16372157 -0.53127228  0.45518868  3.00579225  1.83512013  2.34142771\n",
            "   1.07491968  3.54239747 -3.28328402 -2.52528659 -1.59248495  0.30437363\n",
            "  -0.25191783 -0.68422969 -0.42420121 -7.34617155  2.31854804 -1.14396355]]\n",
            "\n",
            " j = [[3.13921568]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.17859544]\n",
            " [8.79442444]\n",
            " [8.39822339]\n",
            " [8.46786114]]\n",
            "\n",
            "Gradient Vector Norm: 14.819197885385067\n",
            "m_0_0: [[0.17636049]\n",
            " [8.60738949]\n",
            " [8.20001524]\n",
            " [8.25711563]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.17636049]\n",
            " [8.60738949]\n",
            " [8.20001524]\n",
            " [8.25711563]]\n",
            "v_0_0: [[1.10617551e+02]\n",
            " [7.25901282e+05]\n",
            " [7.29224123e+05]\n",
            " [7.68624351e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.13829350e+02]\n",
            " [7.46977945e+05]\n",
            " [7.50397266e+05]\n",
            " [7.90941486e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.37618718]\n",
            " [ 0.3701327 ]\n",
            " [ 0.5270675 ]\n",
            " [ 1.10857606]]\n",
            "\n",
            "\n",
            "****************** Iteration 355 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-1.47433302  0.30061051  1.66677247 -2.96972128  3.45763393  0.52632034\n",
            "  -1.94960697 -1.76446486 -0.68628604  1.9837461   0.82711737  0.99798735\n",
            "  -0.04425195  2.47796159 -4.62153734 -3.59260243 -2.86179038 -0.97675892\n",
            "  -1.39887791 -1.92565586 -1.66197112 -8.53026943  1.20940034 -2.51144532]]\n",
            "\n",
            " j = [[3.70017714]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.98008428]\n",
            " [-85.068748  ]\n",
            " [-85.67696835]\n",
            " [-88.11215468]]\n",
            "\n",
            "Gradient Vector Norm: 149.47222872887485\n",
            "m_0_0: [[ -0.05492847]\n",
            " [-10.12783801]\n",
            " [-10.57538148]\n",
            " [-11.01673843]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ -0.05492847]\n",
            " [-10.12783801]\n",
            " [-10.57538148]\n",
            " [-11.01673843]]\n",
            "v_0_0: [[1.09520981e+02]\n",
            " [7.18714636e+05]\n",
            " [7.22005288e+05]\n",
            " [7.61015745e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.12668227e+02]\n",
            " [7.39367957e+05]\n",
            " [7.42753171e+05]\n",
            " [7.82884651e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.37359979]\n",
            " [ 0.3760219 ]\n",
            " [ 0.53320291]\n",
            " [ 1.11480156]]\n",
            "\n",
            "\n",
            "****************** Iteration 356 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 0.19483767  1.94595414  3.45854337 -1.69650014  4.39698034  1.86864646\n",
            "  -0.95311416 -0.20452458  0.75887093  3.27662859  2.10143749  2.69765493\n",
            "   1.36887667  3.826267   -2.92755475 -2.24716757 -1.25101928  0.64392892\n",
            "   0.05166973 -0.3522137  -0.09573931 -7.02991781  2.61081659 -0.78147897]]\n",
            "\n",
            " j = [[3.21963147]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.48591177]\n",
            " [33.69357654]\n",
            " [33.35968971]\n",
            " [34.09785436]]\n",
            "\n",
            "Gradient Vector Norm: 58.403987704017005\n",
            "m_0_0: [[ 0.05323958]\n",
            " [-1.3635551 ]\n",
            " [-1.78836724]\n",
            " [-1.99381987]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.05323958]\n",
            " [-1.3635551 ]\n",
            " [-1.78836724]\n",
            " [-1.99381987]]\n",
            "v_0_0: [[1.08428132e+02]\n",
            " [7.11538842e+05]\n",
            " [7.14796363e+05]\n",
            " [7.53417214e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.11511929e+02]\n",
            " [7.31775670e+05]\n",
            " [7.35125838e+05]\n",
            " [7.74845102e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.37612059]\n",
            " [ 0.37681889]\n",
            " [ 0.53424582]\n",
            " [ 1.11593408]]\n",
            "\n",
            "\n",
            "****************** Iteration 357 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 0.46353791  2.21119755  3.74799135 -1.49273181  4.54696279  2.08549777\n",
            "  -0.79182195  0.04660996  0.99197832  3.48477816  2.30630205  2.97139047\n",
            "   1.59542523  4.04400493 -2.65441402 -2.03229317 -0.98979712  0.90488815\n",
            "   0.28508014 -0.09776376  0.15659444 -6.78744719  2.83583785 -0.50290816]]\n",
            "\n",
            " j = [[3.34714283]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.72203749]\n",
            " [52.82392145]\n",
            " [52.53647156]\n",
            " [53.78728699]]\n",
            "\n",
            "Gradient Vector Norm: 91.89146370011528\n",
            "m_0_0: [[0.18699916]\n",
            " [9.47394021]\n",
            " [9.07660052]\n",
            " [9.1624015 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.18699916]\n",
            " [9.47394021]\n",
            " [9.07660052]\n",
            " [9.1624015 ]]\n",
            "v_0_0: [[1.07349064e+02]\n",
            " [7.04451357e+05]\n",
            " [7.07676001e+05]\n",
            " [7.45911972e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.10370781e+02]\n",
            " [7.24280618e+05]\n",
            " [7.27596030e+05]\n",
            " [7.66908288e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.38502036]\n",
            " [ 0.37125284]\n",
            " [ 0.52892537]\n",
            " [ 1.11070281]]\n",
            "\n",
            "\n",
            "****************** Iteration 358 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-1.01771238  0.75194396  2.16021953 -2.62529194  3.7106017   0.89601934\n",
            "  -1.67414756 -1.33766619 -0.28942286  2.33750662  1.1748354   1.46335961\n",
            "   0.33928873  2.84904962 -4.15667858 -3.23010502 -2.41489569 -0.53319474\n",
            "  -1.00237676 -1.49145277 -1.23284572 -8.11675429  1.59090071 -2.03792138]]\n",
            "\n",
            " j = [[3.34932135]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.57861419]\n",
            " [-52.54031069]\n",
            " [-53.06609045]\n",
            " [-54.62728328]]\n",
            "\n",
            "Gradient Vector Norm: 92.52550500013241\n",
            "m_0_0: [[ 0.03387649]\n",
            " [-2.92890997]\n",
            " [-3.35193768]\n",
            " [-3.59553545]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.03387649]\n",
            " [-2.92890997]\n",
            " [-3.35193768]\n",
            " [-3.59553545]]\n",
            "v_0_0: [[1.06278922e+02]\n",
            " [6.97434449e+05]\n",
            " [7.00627401e+05]\n",
            " [7.38482694e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.09239766e+02]\n",
            " [7.16864407e+05]\n",
            " [7.20146312e+05]\n",
            " [7.59056224e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.38664095]\n",
            " [ 0.37298249]\n",
            " [ 0.53090032]\n",
            " [ 1.11276627]]\n",
            "\n",
            "\n",
            "****************** Iteration 359 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.49277844  1.26969377  2.72453612 -2.22585942  4.00499041  1.3187771\n",
            "  -1.36006995 -0.84707504  0.16542966  2.74411073  1.57535789  1.99799126\n",
            "   0.78293878  3.27362553 -3.6235793  -2.80837666 -1.90676762 -0.02345348\n",
            "  -0.54627766 -0.99568523 -0.74012284 -7.64414749  2.03115974 -1.49379684]]\n",
            "\n",
            " j = [[3.15043826]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.11747412]\n",
            " [-15.18143579]\n",
            " [-15.61908572]\n",
            " [-16.18086382]]\n",
            "\n",
            "Gradient Vector Norm: 27.13422166599091\n",
            "m_0_0: [[ 3.60636835e-03]\n",
            " [-5.37941513e+00]\n",
            " [-5.80536728e+00]\n",
            " [-6.11260113e+00]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 3.60636835e-03]\n",
            " [-5.37941513e+00]\n",
            " [-5.80536728e+00]\n",
            " [-6.11260113e+00]]\n",
            "v_0_0: [[1.05216270e+02]\n",
            " [6.90462409e+05]\n",
            " [6.93623566e+05]\n",
            " [7.31100485e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.08117390e+02]\n",
            " [7.09500471e+05]\n",
            " [7.12748791e+05]\n",
            " [7.51259058e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.38681437]\n",
            " [ 0.3761757 ]\n",
            " [ 0.53433852]\n",
            " [ 1.11629242]]\n",
            "\n",
            "\n",
            "****************** Iteration 360 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 0.43451151  2.18394698  3.72047081 -1.51917582  4.5261533   2.06487651\n",
            "  -0.80605359  0.01953332  0.96849614  3.46235273  2.28312825  2.94229631\n",
            "   1.56749499  4.02300612 -2.6822727  -2.06183545 -1.0109199   0.8769401\n",
            "   0.25950455 -0.12097555  0.13008075 -6.81015606  2.80939859 -0.53268392]]\n",
            "\n",
            " j = [[3.32991062]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.69700492]\n",
            " [50.80153167]\n",
            " [50.51748414]\n",
            " [51.71951519]]\n",
            "\n",
            "Gradient Vector Norm: 88.36405316062195\n",
            "m_0_0: [[0.14228608]\n",
            " [5.85677423]\n",
            " [5.459203  ]\n",
            " [5.45382214]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.14228608]\n",
            " [5.85677423]\n",
            " [5.459203  ]\n",
            " [5.45382214]]\n",
            "v_0_0: [[1.04168966e+02]\n",
            " [6.83583593e+05]\n",
            " [6.86712851e+05]\n",
            " [7.23816230e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.07011702e+02]\n",
            " [7.02238358e+05]\n",
            " [7.05453013e+05]\n",
            " [7.43568930e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.39369158]\n",
            " [ 0.3726812 ]\n",
            " [ 0.53108866]\n",
            " [ 1.11313007]]\n",
            "\n",
            "\n",
            "****************** Iteration 361 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.47744185  1.28570913  2.74339882 -2.21700788  4.01064398  1.33288711\n",
            "  -1.34890625 -0.83271716  0.17977521  2.75601219  1.58638016  2.01391861\n",
            "   0.79371072  3.28760971 -3.60696298 -2.80006413 -1.88743888 -0.0084037\n",
            "  -0.53318422 -0.97850253 -0.72526245 -7.62814364  2.04267296 -1.47769483]]\n",
            "\n",
            " j = [[3.14711232]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.10370883]\n",
            " [-14.06252104]\n",
            " [-14.49226371]\n",
            " [-15.02072533]]\n",
            "\n",
            "Gradient Vector Norm: 25.167700542008706\n",
            "m_0_0: [[0.0930871 ]\n",
            " [1.87291517]\n",
            " [1.46890966]\n",
            " [1.35891264]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.0930871 ]\n",
            " [1.87291517]\n",
            " [1.46890966]\n",
            " [1.35891264]]\n",
            "v_0_0: [[1.03127384e+02]\n",
            " [6.76749734e+05]\n",
            " [6.79847822e+05]\n",
            " [7.16580324e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.05912792e+02]\n",
            " [6.95028335e+05]\n",
            " [6.98210101e+05]\n",
            " [7.35934725e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.39821411]\n",
            " [ 0.37155792]\n",
            " [ 0.53020969]\n",
            " [ 1.11233804]]\n",
            "\n",
            "\n",
            "****************** Iteration 362 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.73743694  1.02994634  2.46570003 -2.41698349  3.86259368  1.12482867\n",
            "  -1.5029667  -1.0756858  -0.04471246  2.55463773  1.38748523  1.74935482\n",
            "   0.57231384  3.07851494 -3.87021312 -3.01198275 -2.13571257 -0.26075405\n",
            "  -0.75926284 -1.22200755 -0.96895269 -7.86055851  1.82359308 -1.74703864]]\n",
            "\n",
            " j = [[3.21457944]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.33188749]\n",
            " [-32.54536399]\n",
            " [-33.01471232]\n",
            " [-34.03506612]]\n",
            "\n",
            "Gradient Vector Norm: 57.512327564145856\n",
            "m_0_0: [[ 0.00809218]\n",
            " [-5.01074066]\n",
            " [-5.42781474]\n",
            " [-5.71988311]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.00809218]\n",
            " [-5.01074066]\n",
            " [-5.42781474]\n",
            " [-5.71988311]]\n",
            "v_0_0: [[1.02097211e+02]\n",
            " [6.69992829e+05]\n",
            " [6.73060244e+05]\n",
            " [7.09426104e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.04826482e+02]\n",
            " [6.87903132e+05]\n",
            " [6.91052545e+05]\n",
            " [7.28390540e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.39860929]\n",
            " [ 0.37457862]\n",
            " [ 0.53347436]\n",
            " [ 1.11568905]]\n",
            "\n",
            "\n",
            "****************** Iteration 363 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[ 1.42027830e-01  1.89706929e+00  3.41033065e+00 -1.74682866e+00\n",
            "   4.35677593e+00  1.83247507e+00 -9.77506944e-01 -2.53780313e-01\n",
            "   7.16943344e-01  3.23582143e+00  2.05871729e+00  2.64497028e+00\n",
            "   1.31635130e+00  3.78926837e+00 -2.97743067e+00 -2.30404437e+00\n",
            "  -1.28596692e+00  5.93208851e-01  4.95194469e-03 -3.92352883e-01\n",
            "  -1.43624891e-01 -7.06953569e+00  2.56166043e+00 -8.35486394e-01]]\n",
            "\n",
            " j = [[3.19922198]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.44058393]\n",
            " [30.03467577]\n",
            " [29.71114109]\n",
            " [30.36367404]]\n",
            "\n",
            "Gradient Vector Norm: 52.02865046175492\n",
            "m_0_0: [[0.09459053]\n",
            " [1.99834263]\n",
            " [1.59997643]\n",
            " [1.49682832]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.09459053]\n",
            " [1.99834263]\n",
            " [1.59997643]\n",
            " [1.49682832]]\n",
            "v_0_0: [[1.01078180e+02]\n",
            " [6.63301922e+05]\n",
            " [6.66338469e+05]\n",
            " [7.02341063e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.03752475e+02]\n",
            " [6.80851357e+05]\n",
            " [6.83968244e+05]\n",
            " [7.20923383e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.40325245]\n",
            " [ 0.37336771]\n",
            " [ 0.53250705]\n",
            " [ 1.1148076 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 364 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.14232851  1.61729904  3.10649832 -1.96541255  4.19497829  1.60482623\n",
            "  -1.14611944 -0.51952102  0.47135864  3.01556763  1.84120766  2.35560568\n",
            "   1.07431524  3.56049448 -3.26539867 -2.53562572 -1.55772689  0.317208\n",
            "  -0.24230092 -0.65881018 -0.41017517 -7.32384016  2.32211808 -1.13007492]]\n",
            "\n",
            " j = [[3.13708749]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.19100596]\n",
            " [9.81835021]\n",
            " [9.45124107]\n",
            " [9.56555781]]\n",
            "\n",
            "Gradient Vector Norm: 16.651196300974664\n",
            "m_0_0: [[0.11387362]\n",
            " [3.56234414]\n",
            " [3.17022936]\n",
            " [3.11057422]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.11387362]\n",
            " [3.56234414]\n",
            " [3.17022936]\n",
            " [3.11057422]]\n",
            "v_0_0: [[1.00067763e+02]\n",
            " [6.56669866e+05]\n",
            " [6.59675978e+05]\n",
            " [6.95318567e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.02688156e+02]\n",
            " [6.73865543e+05]\n",
            " [6.76950373e+05]\n",
            " [7.13526306e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.40887106]\n",
            " [ 0.37119791]\n",
            " [ 0.5305805 ]\n",
            " [ 1.11296638]]\n",
            "\n",
            "\n",
            "****************** Iteration 365 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-6.90508832e-01  1.07754188e+00  2.51965449e+00 -2.38546459e+00\n",
            "   3.88447166e+00  1.16515237e+00 -1.47207897e+00 -1.03181988e+00\n",
            "  -2.56036621e-03  2.59096488e+00  1.42222669e+00  1.79762188e+00\n",
            "   6.08748933e-01  3.11873616e+00 -3.82102921e+00 -2.98018334e+00\n",
            "  -2.08372276e+00 -2.14941039e-01 -7.18841985e-01 -1.17374596e+00\n",
            "  -9.24242679e-01 -7.81511097e+00  1.86096393e+00 -1.69807987e+00]]\n",
            "\n",
            " j = [[3.19710112]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.29026031]\n",
            " [-29.16704788]\n",
            " [-29.62066536]\n",
            " [-30.54581085]]\n",
            "\n",
            "Gradient Vector Norm: 51.58712349222592\n",
            "m_0_0: [[ 0.03304683]\n",
            " [-2.98353426]\n",
            " [-3.38794959]\n",
            " [-3.62070279]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.03304683]\n",
            " [-2.98353426]\n",
            " [-3.38794959]\n",
            " [-3.62070279]]\n",
            "v_0_0: [[9.90679284e+01]\n",
            " [6.50111675e+05]\n",
            " [6.53087992e+05]\n",
            " [6.88374712e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.01635525e+02]\n",
            " [6.66960966e+05]\n",
            " [6.70014422e+05]\n",
            " [7.06215687e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.41051004]\n",
            " [ 0.37302454]\n",
            " [ 0.53264999]\n",
            " [ 1.11512062]]\n",
            "\n",
            "\n",
            "****************** Iteration 366 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.13981105  1.62067891  3.11160681 -1.96637589  4.1933242   1.6085702\n",
            "  -1.14272004 -0.51717008  0.47454243  3.01748777  1.84238297  2.35849766\n",
            "   1.0742331   3.56406537 -3.26179921 -2.53764449 -1.55080556  0.31982189\n",
            "  -0.240359   -0.65374284 -0.40736526 -7.31939406  2.32286822 -1.12724624]]\n",
            "\n",
            " j = [[3.13668234]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.19348524]\n",
            " [10.02337689]\n",
            " [ 9.66206622]\n",
            " [ 9.78513818]]\n",
            "\n",
            "Gradient Vector Norm: 17.017930942821177\n",
            "m_0_0: [[ 0.06513451]\n",
            " [-0.38215203]\n",
            " [-0.77794643]\n",
            " [-0.9395346 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.06513451]\n",
            " [-0.38215203]\n",
            " [-0.77794643]\n",
            " [-0.9395346 ]]\n",
            "v_0_0: [[9.80776234e+01]\n",
            " [6.43611563e+05]\n",
            " [6.46558045e+05]\n",
            " [6.81491922e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[1.00593482e+02]\n",
            " [6.60121300e+05]\n",
            " [6.63143365e+05]\n",
            " [6.98973356e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.41375711]\n",
            " [ 0.37325972]\n",
            " [ 0.53312765]\n",
            " [ 1.11568251]]\n",
            "\n",
            "\n",
            "****************** Iteration 367 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.026897    1.73239955  3.2339362  -1.88159732  4.25541773  1.70016256\n",
            "  -1.07445154 -0.41165167  0.57275674  3.10492194  1.92822592  2.47363153\n",
            "   1.1687956   3.65597714 -3.14672182 -2.44851491 -1.43975831  0.42953745\n",
            "  -0.14235114 -0.54605671 -0.30121095 -7.21689107  2.41703438 -1.01011458]]\n",
            "\n",
            " j = [[3.15356319]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.29277416]\n",
            " [18.06893976]\n",
            " [17.72869372]\n",
            " [18.06824012]]\n",
            "\n",
            "Gradient Vector Norm: 31.102092895618334\n",
            "m_0_0: [[0.11066244]\n",
            " [3.30806633]\n",
            " [2.9233816 ]\n",
            " [2.86202034]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.11066244]\n",
            " [3.30806633]\n",
            " [2.9233816 ]\n",
            " [2.86202034]]\n",
            "v_0_0: [[9.70977044e+01]\n",
            " [6.37178712e+05]\n",
            " [6.40095608e+05]\n",
            " [6.74680268e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.95628870e+01]\n",
            " [6.53355839e+05]\n",
            " [6.56346791e+05]\n",
            " [6.91809509e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.41930231]\n",
            " [ 0.37121342]\n",
            " [ 0.53132343]\n",
            " [ 1.11396203]]\n",
            "\n",
            "\n",
            "****************** Iteration 368 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.54152374  1.2257068   2.68308504 -2.27603427  3.96379831  1.28743372\n",
            "  -1.38043774 -0.89260122  0.1278565   2.70629188  1.53485017  1.94981623\n",
            "   0.73166037  3.24128574 -3.66831422 -2.86598035 -1.93343789 -0.0700313\n",
            "  -0.58973463 -1.02940428 -0.78380637 -7.6780384   1.98406636 -1.54334227]]\n",
            "\n",
            " j = [[3.15607399]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ -0.15903481]\n",
            " [-18.52999622]\n",
            " [-18.95129865]\n",
            " [-19.58753031]]\n",
            "\n",
            "Gradient Vector Norm: 32.9576867513566\n",
            "m_0_0: [[ 0.05672299]\n",
            " [-1.05954618]\n",
            " [-1.45155445]\n",
            " [-1.62788979]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.05672299]\n",
            " [-1.05954618]\n",
            " [-1.45155445]\n",
            " [-1.62788979]]\n",
            "v_0_0: [[9.61269803e+01]\n",
            " [6.30810359e+05]\n",
            " [6.33698243e+05]\n",
            " [6.67937302e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.85424988e+01]\n",
            " [6.46661622e+05]\n",
            " [6.49622075e+05]\n",
            " [6.84721507e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.42215933]\n",
            " [ 0.37187221]\n",
            " [ 0.53222391]\n",
            " [ 1.11494567]]\n",
            "\n",
            "\n",
            "****************** Iteration 369 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.31239176  1.45195409  2.93008369 -2.10251254  4.09137989  1.4724096\n",
            "  -1.24288593 -0.67847725  0.32663851  2.88373127  1.70942961  2.18328426\n",
            "   0.9246925   3.42700358 -3.43533083 -2.68302763 -1.71041051  0.1525231\n",
            "  -0.39072529 -0.81227095 -0.56862341 -7.4711599   2.17585624 -1.30576422]]\n",
            "\n",
            " j = [[3.12869499]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.04230859]\n",
            " [-2.21690016]\n",
            " [-2.59823191]\n",
            " [-2.79723466]]\n",
            "\n",
            "Gradient Vector Norm: 4.414948141738459\n",
            "m_0_0: [[ 0.05384011]\n",
            " [-1.29101698]\n",
            " [-1.68088994]\n",
            " [-1.86175876]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.05384011]\n",
            " [-1.29101698]\n",
            " [-1.68088994]\n",
            " [-1.86175876]]\n",
            "v_0_0: [[9.51657284e+01]\n",
            " [6.24502304e+05]\n",
            " [6.27361328e+05]\n",
            " [6.61258007e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.75325838e+01]\n",
            " [6.40034226e+05]\n",
            " [6.42964357e+05]\n",
            " [6.77704075e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.42488514]\n",
            " [ 0.37267908]\n",
            " [ 0.53327204]\n",
            " [ 1.11607644]]\n",
            "\n",
            "\n",
            "****************** Iteration 370 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.04268228  1.71818821  3.22061045 -1.89800688  4.24182413  1.68998822\n",
            "  -1.08114773 -0.42643444  0.56053169  3.09259429  1.91498961  2.45806913\n",
            "   1.15210548  3.6454717  -3.16118092 -2.467316   -1.44828713  0.41447541\n",
            "  -0.15645171 -0.55692554 -0.31537383 -7.22784018  2.40173149 -1.02613196]]\n",
            "\n",
            " j = [[3.14958594]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.27928338]\n",
            " [16.98274191]\n",
            " [16.64798083]\n",
            " [16.96327708]]\n",
            "\n",
            "Gradient Vector Norm: 29.21300322165165\n",
            "m_0_0: [[0.09892876]\n",
            " [2.3637348 ]\n",
            " [1.98488421]\n",
            " [1.90324841]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.09892876]\n",
            " [2.3637348 ]\n",
            " [1.98488421]\n",
            " [1.90324841]]\n",
            "v_0_0: [[9.42148511e+01]\n",
            " [6.18260165e+05]\n",
            " [6.21090487e+05]\n",
            " [6.54648304e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.65340485e+01]\n",
            " [6.33479288e+05]\n",
            " [6.36379281e+05]\n",
            " [6.70763159e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.42991955]\n",
            " [ 0.37119416]\n",
            " [ 0.53202796]\n",
            " [ 1.11491451]]\n",
            "\n",
            "\n",
            "****************** Iteration 371 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.4033525   1.36321122  2.83491118 -2.17488462  4.03695527  1.40096402\n",
            "  -1.29535751 -0.76351267  0.24885336  2.81319413  1.63916297  2.09101423\n",
            "   0.84541537  3.35504472 -3.52658101 -2.76049115 -1.7936265   0.06438699\n",
            "  -0.47003686 -0.89528772 -0.65353744 -7.55072079  2.09808777 -1.39980118]]\n",
            "\n",
            " j = [[3.13369673]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[-0.03733286]\n",
            " [-8.6640854 ]\n",
            " [-9.05485918]\n",
            " [-9.42291004]]\n",
            "\n",
            "Gradient Vector Norm: 15.679587935292028\n",
            "m_0_0: [[ 0.07167644]\n",
            " [ 0.15817076]\n",
            " [-0.22306446]\n",
            " [-0.36198328]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07167644]\n",
            " [ 0.15817076]\n",
            " [-0.22306446]\n",
            " [-0.36198328]]\n",
            "v_0_0: [[9.32727165e+01]\n",
            " [6.12078314e+05]\n",
            " [6.14880402e+05]\n",
            " [6.48102709e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.55452028e+01]\n",
            " [6.26990924e+05]\n",
            " [6.29861281e+05]\n",
            " [6.63893013e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.43358593]\n",
            " [ 0.37109428]\n",
            " [ 0.5321685 ]\n",
            " [ 1.11513664]]\n",
            "\n",
            "\n",
            "****************** Iteration 372 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.38328236  1.38343608  2.85764194 -2.16101767  4.04667713  1.41790965\n",
            "  -1.2825204  -0.74477445  0.26668153  2.8286891   1.65407754  2.11162384\n",
            "   0.86131691  3.37197208 -3.50570394 -2.74630306 -1.77207676  0.08396374\n",
            "  -0.45272533 -0.87506639 -0.63449988 -7.53163221  2.11426627 -1.37888375]]\n",
            "\n",
            " j = [[3.13149546]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[-0.01959293]\n",
            " [-7.22461743]\n",
            " [-7.60945031]\n",
            " [-7.93752079]]\n",
            "\n",
            "Gradient Vector Norm: 13.156878480947313\n",
            "m_0_0: [[ 0.05342256]\n",
            " [-1.31838688]\n",
            " [-1.70034163]\n",
            " [-1.87709078]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.05342256]\n",
            " [-1.31838688]\n",
            " [-1.70034163]\n",
            " [-1.87709078]]\n",
            "v_0_0: [[9.23399932e+01]\n",
            " [6.05958053e+05]\n",
            " [6.08732177e+05]\n",
            " [6.41622312e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.45667146e+01]\n",
            " [6.20570354e+05]\n",
            " [6.23411374e+05]\n",
            " [6.57094635e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.43633269]\n",
            " [ 0.37193107]\n",
            " [ 0.53324526]\n",
            " [ 1.11629446]]\n",
            "\n",
            "\n",
            "****************** Iteration 373 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.10577534  1.65735275  3.15653167 -1.95056507  4.2014914   1.64173391\n",
            "  -1.11617018 -0.48545248  0.50730269  3.04357504  1.86557429  2.39435254\n",
            "   1.09533836  3.59671419 -3.2236467  -2.5242913  -1.50245772  0.35348909\n",
            "  -0.21167911 -0.61239105 -0.37394224 -7.28132343  2.34669198 -1.09116691]]\n",
            "\n",
            " j = [[3.13848042]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.22422027]\n",
            " [12.52912786]\n",
            " [12.19212447]\n",
            " [12.39310402]]\n",
            "\n",
            "Gradient Vector Norm: 21.43049803733443\n",
            "m_0_0: [[0.0875821 ]\n",
            " [1.45111607]\n",
            " [1.07815159]\n",
            " [0.97694818]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.0875821 ]\n",
            " [1.45111607]\n",
            " [1.07815159]\n",
            " [0.97694818]]\n",
            "v_0_0: [[9.14170960e+01]\n",
            " [5.99900042e+05]\n",
            " [6.02646341e+05]\n",
            " [6.35207625e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.35989915e+01]\n",
            " [6.14218143e+05]\n",
            " [6.17029990e+05]\n",
            " [6.50368429e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.44085901]\n",
            " [ 0.37100529]\n",
            " [ 0.53255898]\n",
            " [ 1.11568875]]\n",
            "\n",
            "\n",
            "****************** Iteration 374 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.31312242  1.45346712  2.93530457 -2.11036719  4.08301604  1.47590481\n",
            "  -1.23898207 -0.67925127  0.32829806  2.88291386  1.70681298  2.18341296\n",
            "   0.91855658  3.43003987 -3.43349717 -2.6936874  -1.70006791  0.15226579\n",
            "  -0.39201674 -0.80636482 -0.56826836 -7.4665067   2.17184016 -1.30593412]]\n",
            "\n",
            " j = [[3.12688791]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.04224028]\n",
            " [-2.21066868]\n",
            " [-2.57874531]\n",
            " [-2.76988553]]\n",
            "\n",
            "Gradient Vector Norm: 4.383039296723078\n",
            "m_0_0: [[0.07851374]\n",
            " [0.71875912]\n",
            " [0.34677221]\n",
            " [0.22758144]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.07851374]\n",
            " [0.71875912]\n",
            " [0.34677221]\n",
            " [0.22758144]]\n",
            "v_0_0: [[9.05029429e+01]\n",
            " [5.93901091e+05]\n",
            " [5.96619945e+05]\n",
            " [6.28855625e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.26409088e+01]\n",
            " [6.07930914e+05]\n",
            " [6.10713996e+05]\n",
            " [6.43711185e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.4449376 ]\n",
            " [ 0.37054437]\n",
            " [ 0.53233712]\n",
            " [ 1.11554693]]\n",
            "\n",
            "\n",
            "****************** Iteration 375 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.39278103  1.37541207  2.85105172 -2.17266424  4.03650219  1.4126837\n",
            "  -1.28565823 -0.75372205  0.25979511  2.82114753  1.64555325  2.10248493\n",
            "   0.84996237  3.36643788 -3.51380313 -2.76000221 -1.7746392   0.07501711\n",
            "  -0.46138295 -0.88008408 -0.642802   -7.53700753  2.10424676 -1.38836849]]\n",
            "\n",
            " j = [[3.13118652]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[-0.02760919]\n",
            " [-7.86670956]\n",
            " [-8.24508472]\n",
            " [-8.58577727]]\n",
            "\n",
            "Gradient Vector Norm: 14.268247087955311\n",
            "m_0_0: [[ 0.05728915]\n",
            " [-0.99833462]\n",
            " [-1.37159918]\n",
            " [-1.53509031]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.05728915]\n",
            " [-0.99833462]\n",
            " [-1.37159918]\n",
            " [-1.53509031]]\n",
            "v_0_0: [[8.95979211e+01]\n",
            " [5.87962699e+05]\n",
            " [5.90654425e+05]\n",
            " [6.22567806e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.16928468e+01]\n",
            " [6.01710095e+05]\n",
            " [6.04464758e+05]\n",
            " [6.37124319e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.44792897]\n",
            " [ 0.37118787]\n",
            " [ 0.5332192 ]\n",
            " [ 1.11650852]]\n",
            "\n",
            "\n",
            "****************** Iteration 376 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.16887441  1.59650615  3.09244089 -2.00315033  4.16108032  1.59341145\n",
            "  -1.15131228 -0.54450004  0.4540071   2.99450055  1.81610093  2.33064297\n",
            "   1.03857132  3.54788864 -3.28612964 -2.58125517 -1.55669048  0.29250339\n",
            "  -0.26692486 -0.66790499 -0.43253883 -7.33485669  2.29164827 -1.15619773]]\n",
            "\n",
            " j = [[3.13044781]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.16912361]\n",
            " [8.07316795]\n",
            " [7.7338888 ]\n",
            " [7.8203503 ]]\n",
            "\n",
            "Gradient Vector Norm: 13.644616463064077\n",
            "m_0_0: [[0.07965604]\n",
            " [0.8159659 ]\n",
            " [0.44949842]\n",
            " [0.33599782]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.07965604]\n",
            " [0.8159659 ]\n",
            " [0.44949842]\n",
            " [0.33599782]]\n",
            "v_0_0: [[8.87022279e+01]\n",
            " [5.82083723e+05]\n",
            " [5.84748479e+05]\n",
            " [6.16342740e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[9.07549912e+01]\n",
            " [5.95554412e+05]\n",
            " [5.98280836e+05]\n",
            " [6.30606257e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.45210967]\n",
            " [ 0.37065921]\n",
            " [ 0.53292864]\n",
            " [ 1.11629696]]\n",
            "\n",
            "\n",
            "****************** Iteration 377 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.26746568  1.49979251  2.98787706 -2.07991001  4.10387864  1.514961\n",
            "  -1.20930541 -0.63666955  0.36910513  2.9180586   1.74037159  2.23044085\n",
            "   0.95393704  3.46898854 -3.38564615 -2.66285017 -1.64951889  0.19687408\n",
            "  -0.3527484  -0.75946513 -0.52483981 -7.42237269  2.2081514  -1.25825094]]\n",
            "\n",
            " j = [[3.12538436]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.0826414 ]\n",
            " [1.06978916]\n",
            " [0.71712274]\n",
            " [0.6180204 ]]\n",
            "\n",
            "Gradient Vector Norm: 1.4309062454059234\n",
            "m_0_0: [[0.08025312]\n",
            " [0.86673055]\n",
            " [0.50302328]\n",
            " [0.39240233]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.08025312]\n",
            " [0.86673055]\n",
            " [0.50302328]\n",
            " [0.39240233]]\n",
            "v_0_0: [[8.78152739e+01]\n",
            " [5.76262898e+05]\n",
            " [5.78900999e+05]\n",
            " [6.10179316e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.98267233e+01]\n",
            " [5.89462466e+05]\n",
            " [5.92160994e+05]\n",
            " [6.24155755e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.45634341]\n",
            " [ 0.37009476]\n",
            " [ 0.53260179]\n",
            " [ 1.11604862]]\n",
            "\n",
            "\n",
            "****************** Iteration 378 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.37605151  1.39322899  2.87259104 -2.16430437  4.04103531  1.42847129\n",
            "  -1.27327219 -0.73818206  0.27554649  2.83386964  1.65700404  2.12006381\n",
            "   0.86083515  3.38201287 -3.49530386 -2.75251167 -1.751985    0.09154155\n",
            "  -0.44725974 -0.86044328 -0.62652005 -7.51887079  2.11625903 -1.37066103]]\n",
            "\n",
            " j = [[3.12867009]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[-0.0126211 ]\n",
            " [-6.64484098]\n",
            " [-7.01252519]\n",
            " [-7.31620074]]\n",
            "\n",
            "Gradient Vector Norm: 12.11843116035915\n",
            "m_0_0: [[ 0.06167827]\n",
            " [-0.63558376]\n",
            " [-1.00008641]\n",
            " [-1.14931828]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.06167827]\n",
            " [-0.63558376]\n",
            " [-1.00008641]\n",
            " [-1.14931828]]\n",
            "v_0_0: [[8.69371227e+01]\n",
            " [5.70500710e+05]\n",
            " [5.73112481e+05]\n",
            " [6.04078058e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.89080929e+01]\n",
            " [5.83434654e+05]\n",
            " [5.86105637e+05]\n",
            " [6.17773241e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.45961401]\n",
            " [ 0.37051081]\n",
            " [ 0.53325495]\n",
            " [ 1.11677975]]\n",
            "\n",
            "\n",
            "****************** Iteration 379 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.21515602  1.55222636  3.0463842  -2.04291545  4.13007303  1.55853921\n",
            "  -1.17654352 -0.58785471  0.41520473  2.95840039  1.77941617  2.28406916\n",
            "   0.99605801  3.51257481 -3.33156181 -2.62462633 -1.59477581  0.2478515\n",
            "  -0.30756893 -0.70762407 -0.47538041 -7.37333092  2.25073095 -1.20378932]]\n",
            "\n",
            " j = [[3.12623129]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.12876672]\n",
            " [4.81165558]\n",
            " [4.47280136]\n",
            " [4.47650469]]\n",
            "\n",
            "Gradient Vector Norm: 7.950701644303036\n",
            "m_0_0: [[ 0.07509596]\n",
            " [ 0.45386411]\n",
            " [ 0.09449114]\n",
            " [-0.02415369]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07509596]\n",
            " [ 0.45386411]\n",
            " [ 0.09449114]\n",
            " [-0.02415369]]\n",
            "v_0_0: [[8.60679173e+01]\n",
            " [5.64795935e+05]\n",
            " [5.67381556e+05]\n",
            " [5.98037478e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.79992310e+01]\n",
            " [5.77469625e+05]\n",
            " [5.80113266e+05]\n",
            " [6.11457089e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.46361661]\n",
            " [ 0.37021218]\n",
            " [ 0.53319292]\n",
            " [ 1.1167952 ]]\n",
            "\n",
            "\n",
            "****************** Iteration 380 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.25095342  1.51739105  3.00917873 -2.07173084  4.10823916  1.53053021\n",
            "  -1.1971287  -0.6213484   0.38462745  2.93057773  1.75162048  2.24780666\n",
            "   0.96462876  3.48434466 -3.36737491 -2.65552401 -1.62711373  0.21319101\n",
            "  -0.33882386 -0.74005883 -0.50877701 -7.4044623   2.21998061 -1.24076187]]\n",
            "\n",
            " j = [[3.12457084]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.09741911]\n",
            " [2.2748667 ]\n",
            " [1.93283073]\n",
            " [1.87020524]]\n",
            "\n",
            "Gradient Vector Norm: 3.5239198700953502\n",
            "m_0_0: [[0.07956059]\n",
            " [0.81806463]\n",
            " [0.46215906]\n",
            " [0.3547181 ]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.07956059]\n",
            " [0.81806463]\n",
            " [0.46215906]\n",
            " [0.3547181 ]]\n",
            "v_0_0: [[8.52073331e+01]\n",
            " [5.59148027e+05]\n",
            " [5.61707778e+05]\n",
            " [5.92057138e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.70997911e+01]\n",
            " [5.71566725e+05]\n",
            " [5.74183328e+05]\n",
            " [6.05206749e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.46787901]\n",
            " [ 0.36967115]\n",
            " [ 0.53288797]\n",
            " [ 1.11656721]]\n",
            "\n",
            "\n",
            "****************** Iteration 381 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.35357037  1.41670732  2.90029325 -2.1515745   4.04873501  1.44881516\n",
            "  -1.25757612 -0.7172906   0.29621408  2.85099443  1.67279294  2.14351099\n",
            "   0.87658558  3.40216555 -3.47098076 -2.74036148 -1.72384818  0.11365529\n",
            "  -0.42815281 -0.83543055 -0.60486505 -7.49561511  2.13310162 -1.34698266]]\n",
            "\n",
            " j = [[3.12604166]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.00738846]\n",
            " [-5.01580355]\n",
            " [-5.37189918]\n",
            " [-5.62782169]]\n",
            "\n",
            "Gradient Vector Norm: 9.256782253211325\n",
            "m_0_0: [[ 0.06512616]\n",
            " [-0.34870901]\n",
            " [-0.70465259]\n",
            " [-0.84178986]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.06512616]\n",
            " [-0.34870901]\n",
            " [-0.70465259]\n",
            " [-0.84178986]]\n",
            "v_0_0: [[8.43552603e+01]\n",
            " [5.53556798e+05]\n",
            " [5.56090989e+05]\n",
            " [5.86136884e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.62096465e+01]\n",
            " [5.65725667e+05]\n",
            " [5.68315567e+05]\n",
            " [5.99021963e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.47138607]\n",
            " [ 0.36990295]\n",
            " [ 0.53335533]\n",
            " [ 1.11711103]]\n",
            "\n",
            "\n",
            "****************** Iteration 382 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24381672  1.52530436  3.01922263 -2.0692467   4.10892222  1.53776131\n",
            "  -1.19138709 -0.61476548  0.39159087  2.93589588  1.75613323  2.25544852\n",
            "   0.96847871  3.49141997 -3.3591283  -2.65375603 -1.61594152  0.22031261\n",
            "  -0.33291415 -0.73079297 -0.50171348 -7.39602591  2.22461377 -1.23310916]]\n",
            "\n",
            " j = [[3.12403879]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.10385444]\n",
            " [2.80169442]\n",
            " [2.46609304]\n",
            " [2.42032387]]\n",
            "\n",
            "Gradient Vector Norm: 4.449703349355227\n",
            "m_0_0: [[ 0.07287182]\n",
            " [ 0.28137168]\n",
            " [-0.07050346]\n",
            " [-0.18936711]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07287182]\n",
            " [ 0.28137168]\n",
            " [-0.07050346]\n",
            " [-0.18936711]]\n",
            "v_0_0: [[8.35118155e+01]\n",
            " [5.48021309e+05]\n",
            " [5.50530140e+05]\n",
            " [5.80275573e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.53289024e+01]\n",
            " [5.59945398e+05]\n",
            " [5.62508817e+05]\n",
            " [5.92901465e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.47533043]\n",
            " [ 0.36971495]\n",
            " [ 0.53340233]\n",
            " [ 1.11723399]]\n",
            "\n",
            "\n",
            "****************** Iteration 383 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.24967391  1.51997131  3.01413219 -2.07520597  4.10393859  1.53379085\n",
            "  -1.19415782 -0.62028654  0.38690494  2.93124555  1.75118284  2.24967441\n",
            "   0.96241831  3.48733746 -3.3645696  -2.66047826 -1.61944861  0.21472271\n",
            "  -0.33815367 -0.73504496 -0.50702849 -7.40028171  2.21901247 -1.23904943]]\n",
            "\n",
            " j = [[3.12352916]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.09878969]\n",
            " [2.39421269]\n",
            " [2.06028769]\n",
            " [2.00500762]]\n",
            "\n",
            "Gradient Vector Norm: 3.742573274805518\n",
            "m_0_0: [[0.07805539]\n",
            " [0.70393988]\n",
            " [0.35565477]\n",
            " [0.24950783]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.07805539]\n",
            " [0.70393988]\n",
            " [0.35565477]\n",
            " [0.24950783]]\n",
            "v_0_0: [[8.26767950e+01]\n",
            " [5.42541153e+05]\n",
            " [5.45024881e+05]\n",
            " [5.74472858e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.44573365e+01]\n",
            " [5.54225412e+05]\n",
            " [5.56762629e+05]\n",
            " [5.86844803e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.47957711]\n",
            " [ 0.36924216]\n",
            " [ 0.53316401]\n",
            " [ 1.11707114]]\n",
            "\n",
            "\n",
            "****************** Iteration 384 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.33400698  1.43730297  2.92485759 -2.1410947   4.05471474  1.46674672\n",
            "  -1.24374244 -0.69914945  0.31429489  2.86580611  1.68629938  2.16399879\n",
            "   0.88986782  3.41989601 -3.44962954 -2.7305501  -1.69858492  0.13294024\n",
            "  -0.41159775 -0.81321221 -0.58597074 -7.4750286   2.14749107 -1.32631816]]\n",
            "\n",
            " j = [[3.12395017]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.02480545]\n",
            " [-3.5964073 ]\n",
            " [-3.94143101]\n",
            " [-4.1553355 ]]\n",
            "\n",
            "Gradient Vector Norm: 6.762873082216072\n",
            "m_0_0: [[ 0.06740541]\n",
            " [-0.15612956]\n",
            " [-0.50376239]\n",
            " [-0.63146083]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.06740541]\n",
            " [-0.15612956]\n",
            " [-0.50376239]\n",
            " [-0.63146083]]\n",
            "v_0_0: [[8.18500332e+01]\n",
            " [5.37115871e+05]\n",
            " [5.39574787e+05]\n",
            " [5.68728302e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.35947663e+01]\n",
            " [5.48565150e+05]\n",
            " [5.51076481e+05]\n",
            " [5.80851438e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.48326324]\n",
            " [ 0.36934756]\n",
            " [ 0.53350331]\n",
            " [ 1.11748541]]\n",
            "\n",
            "\n",
            "****************** Iteration 385 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.25950495  1.51115849  3.00596875 -2.08569359  4.0950077   1.52734055\n",
            "  -1.19861749 -0.62957638  0.37914248  2.92338565  1.74270206  2.24004741\n",
            "   0.9518944   3.48067021 -3.3735454  -2.67239679 -1.62466908  0.20537259\n",
            "  -0.34700157 -0.74179101 -0.51589942 -7.40711827  2.20939082 -1.24897487]]\n",
            "\n",
            " j = [[3.1226155]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.09030385]\n",
            " [1.71252993]\n",
            " [1.38224932]\n",
            " [1.31146147]]\n",
            "\n",
            "Gradient Vector Norm: 2.5634855016246414\n",
            "m_0_0: [[ 0.07198509]\n",
            " [ 0.21760234]\n",
            " [-0.12656005]\n",
            " [-0.24287637]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.07198509]\n",
            " [ 0.21760234]\n",
            " [-0.12656005]\n",
            " [-0.24287637]]\n",
            "v_0_0: [[8.10316144e+01]\n",
            " [5.31744741e+05]\n",
            " [5.34179058e+05]\n",
            " [5.63041036e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.27412646e+01]\n",
            " [5.42963789e+05]\n",
            " [5.45449466e+05]\n",
            " [5.74920390e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.48722007]\n",
            " [ 0.36919991]\n",
            " [ 0.53358899]\n",
            " [ 1.11764557]]\n",
            "\n",
            "\n",
            "****************** Iteration 386 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.25475889  1.51627174  3.01224978 -2.08356309  4.09597528  1.53186824\n",
            "  -1.19510637 -0.625196    0.38361264  2.92692997  1.74583048  2.24507293\n",
            "   0.954819    3.48512473 -3.36823318 -2.67055374 -1.6179798   0.21007854\n",
            "  -0.34303164 -0.73607258 -0.5112745  -7.40186706  2.21269511 -1.24392305]]\n",
            "\n",
            " j = [[3.12234367]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[0.09454036]\n",
            " [2.05867381]\n",
            " [1.7317799 ]\n",
            " [1.67156165]]\n",
            "\n",
            "Gradient Vector Norm: 3.1686362563389983\n",
            "m_0_0: [[0.07649615]\n",
            " [0.58581664]\n",
            " [0.24510794]\n",
            " [0.14001123]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[0.07649615]\n",
            " [0.58581664]\n",
            " [0.24510794]\n",
            " [0.14001123]]\n",
            "v_0_0: [[8.02213876e+01]\n",
            " [5.26427336e+05]\n",
            " [5.28837298e+05]\n",
            " [5.57410654e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.18966642e+01]\n",
            " [5.37420806e+05]\n",
            " [5.39881095e+05]\n",
            " [5.69051153e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.49144648]\n",
            " [ 0.36880036]\n",
            " [ 0.5334222 ]\n",
            " [ 1.11755277]]\n",
            "\n",
            "\n",
            "****************** Iteration 387 ********************\n",
            "\n",
            "\n",
            "\n",
            "Error Vector: \n",
            "[[-0.31945211  1.45295476  2.94404036 -2.13446136  4.05779596  1.48058423\n",
            "  -1.2330203  -0.68571236  0.32797944  2.87668434  1.69592646  2.17939776\n",
            "   0.8989099   3.43351515 -3.4333716  -2.72476531 -1.67821197  0.14736553\n",
            "  -0.39941277 -0.79575873 -0.57179889 -7.45899061  2.1576699  -1.3108345 ]]\n",
            "\n",
            " j = [[3.12231826]]\n",
            "\n",
            "\n",
            "Gradient Vector : [[ 0.03779305]\n",
            " [-2.53544863]\n",
            " [-2.87026084]\n",
            " [-3.05185488]]\n",
            "\n",
            "Gradient Vector Norm: 4.897156684792644\n",
            "m_0_0: [[ 0.06875553]\n",
            " [-0.03843642]\n",
            " [-0.37796581]\n",
            " [-0.49836199]]\n",
            "\n",
            "\n",
            "m_0_0_corr: [[ 0.06875553]\n",
            " [-0.03843642]\n",
            " [-0.37796581]\n",
            " [-0.49836199]]\n",
            "v_0_0: [[7.94191880e+01]\n",
            " [5.21163127e+05]\n",
            " [5.23549007e+05]\n",
            " [5.51836640e+05]]\n",
            "\n",
            "\n",
            "v_0_0_corr: [[8.10607841e+01]\n",
            " [5.31935579e+05]\n",
            " [5.34370775e+05]\n",
            " [5.63243114e+05]]\n",
            "\n",
            "theta_new : \n",
            " [[-0.49526475]\n",
            " [ 0.36882671]\n",
            " [ 0.53368073]\n",
            " [ 1.11788479]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "y_pred = Xs @ opt_thetas\n",
        "r2_score(y, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S6Nj8fqZJWI6",
        "outputId": "891d6581-c17f-487a-8de6-91926937eaf9"
      },
      "id": "S6Nj8fqZJWI6",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9891191536205268"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.grid()\n",
        "plt.plot(loss, marker = 'o', color='b');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 719
        },
        "id": "Zv7IPmEG5RVM",
        "outputId": "faea8eaa-319e-45f8-edf1-0547781d11dd"
      },
      "id": "Zv7IPmEG5RVM",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1030x830 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "figure, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 15))\n",
        "\n",
        "ax1.set_title('Learning Rate = 0.01 (β1 = 0.8 & β2 = 0.99)', fontweight='bold', fontsize=15)\n",
        "ax1.set_xlabel('Theta 0', fontweight='bold', fontsize=12)\n",
        "ax1.set_ylabel('Loss', fontweight='bold', fontsize=12)\n",
        "ax1.grid()\n",
        "\n",
        "\n",
        "ax1.plot(thetas_upd[0], loss, marker = 'o', color='b')\n",
        "\n",
        "ax2.set_title('Learning Rate = 0.01 (β1 = 0.8 & β2 = 0.99)', fontweight='bold', fontsize=15)\n",
        "ax2.set_xlabel('Theta 1', fontweight='bold', fontsize=12)\n",
        "ax2.set_ylabel('Loss', fontweight='bold', fontsize=12)\n",
        "ax2.grid()\n",
        "\n",
        "ax2.plot(thetas_upd[1], loss, marker = 'o', color='b');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GQW-0di76BVj",
        "outputId": "086df31c-2676-410b-a2b3-b2872028679e"
      },
      "id": "GQW-0di76BVj",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "figure, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 15))\n",
        "\n",
        "ax1.set_title('Learning Rate = 0.01 (β1 = 0.8 & β2 = 0.99)', fontweight='bold', fontsize=15)\n",
        "ax1.set_xlabel('Theta 2', fontweight='bold', fontsize=12)\n",
        "ax1.set_ylabel('Loss', fontweight='bold', fontsize=12)\n",
        "ax1.grid()\n",
        "\n",
        "\n",
        "ax1.plot(thetas_upd[2], loss, marker = 'o', color='b')\n",
        "\n",
        "ax2.set_title('Learning Rate = 0.01 (β1 = 0.8 & β2 = 0.99)', fontweight='bold', fontsize=15)\n",
        "ax2.set_xlabel('Theta 3', fontweight='bold', fontsize=12)\n",
        "ax2.set_ylabel('Loss', fontweight='bold', fontsize=12)\n",
        "ax2.grid()\n",
        "\n",
        "ax2.plot(thetas_upd[3], loss, marker = 'o', color='b');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "ZNj16rVwB8G6",
        "outputId": "81376ab8-b3aa-445d-bded-3bf7d469a33e"
      },
      "id": "ZNj16rVwB8G6",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6b51a395",
      "metadata": {
        "id": "6b51a395"
      },
      "source": [
        "# Task 2\n",
        "## Repeat task 1 using BFGS optimizer instead of Adam.\n",
        "### All the requirments stated in task 1 must be also fulfilled in task 2.\n",
        "### Compare the results from task 1 and task 2 and write your conclusion. "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def grad(features, target, thetas):\n",
        "  \n",
        "  n_samp = len(features)\n",
        "\n",
        "  h_x = features @ thetas\n",
        "  err = h_x - target\n",
        "\n",
        "  cost = (err.T@err) / (2*n_samp)\n",
        "\n",
        "  grad = (features.T @ err) / n_samp\n",
        "\n",
        "  return grad, cost[0]\n"
      ],
      "metadata": {
        "id": "Gd2h7uZ-4OPU"
      },
      "id": "Gd2h7uZ-4OPU",
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def hessian_update(identity_mat, delta_x, delta_grad, hessian_old):\n",
        "\n",
        "  common_denominator = 1 / (delta_grad.T @ delta_x)\n",
        "\n",
        "  f_exp = identity_mat - ((delta_x @ delta_grad.T) * common_denominator)\n",
        "  s_exp = hessian_old @ (identity_mat - ((delta_grad @ delta_x.T) * common_denominator))\n",
        "  t_exp = ((delta_x @ delta_x.T) * common_denominator)\n",
        "\n",
        "  hessian_new = (f_exp @ s_exp) + t_exp\n",
        "\n",
        "  return hessian_new\n"
      ],
      "metadata": {
        "id": "Eiw4kA7J7p2r"
      },
      "id": "Eiw4kA7J7p2r",
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def bfgs(features, target, gradient_fun, tolerance=1E-5, lr=1.0, max_iter=500):\n",
        "  \n",
        "  thetas_upd, loss = [], []\n",
        "    \n",
        "  n_samp, n_feat = features.shape\n",
        "    \n",
        "  thetas = np.zeros((n_feat, 1))\n",
        "\n",
        "  identity_mat = np.identity(n_feat)\n",
        "  \n",
        "  hessian = np.identity(n_feat)\n",
        "\n",
        "  counter = 0\n",
        "\n",
        "  grad, cost = gradient_fun(features, target, thetas)\n",
        "\n",
        "  loss.append(cost)\n",
        "  \n",
        "  thetas_new = thetas - lr*(hessian @ grad)\n",
        "\n",
        "\n",
        "  for theta_val in thetas:\n",
        "    thetas_upd.append([theta_val[0]])\n",
        "\n",
        "  \n",
        "  while(counter < max_iter):\n",
        "    \n",
        "    grad_new, cost = gradient_fun(features, target, thetas_new)\n",
        "\n",
        "    loss.append(cost)\n",
        "\n",
        "    if (abs(loss[-2] - loss[-1]) < tolerance):\n",
        "      break\n",
        "    \n",
        "    print('theta_old: {}\\ntheta_new: {}\\n'.format(thetas.T, thetas_new.T))\n",
        "\n",
        "    print('grad_old: {}\\ngrad_new: {}\\n'.format(grad.T, grad_new.T))\n",
        "\n",
        "    norm = np.linalg.norm(grad_new)\n",
        "\n",
        "    print('norm: ', norm, '\\n')\n",
        "\n",
        "    if(abs(norm) < tolerance):\n",
        "      break\n",
        "\n",
        "    delta_x  = thetas_new - thetas\n",
        "\n",
        "    delta_grad = grad_new - grad\n",
        "\n",
        "    hessian = hessian_update(identity_mat, delta_x, delta_grad, hessian)\n",
        "\n",
        "    print('B_inv ', hessian, '\\n')\n",
        "          \n",
        "    thetas = thetas_new\n",
        "    grad = grad_new\n",
        "\n",
        "    thetas_new = thetas - lr*(hessian @ grad)\n",
        "\n",
        "    for idx, value in enumerate(thetas_new):\n",
        "      thetas_upd[idx] = np.append(thetas_upd[idx], value)\n",
        "\n",
        "    counter += 1    \n",
        "\n",
        "    print('*'*100,'\\n\\n')\n",
        "  \n",
        "\n",
        "  print('Found solution after {} iterations.'.format(counter))\n",
        "  print('thetas_min = {}\\nGradient = {}'.format(thetas_new.T, grad_new.T))\n",
        "  \n",
        "  return thetas_upd, thetas_new, loss"
      ],
      "metadata": {
        "id": "eYzwkz4N44Im"
      },
      "id": "eYzwkz4N44Im",
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "thetas_upd, opt_thetas, loss = bfgs(Xs, y, grad, tolerance=1E-4, lr=0.5, max_iter=500)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6Js5wFkk2hKj",
        "outputId": "40e397e7-2e34-4e2b-dcbb-ef39f234045a"
      },
      "id": "6Js5wFkk2hKj",
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "theta_old: [[0. 0. 0. 0.]]\n",
            "theta_new: [[  81.22916667 6579.14583333 6594.16666667 6776.60416667]]\n",
            "\n",
            "grad_old: [[  -162.45833333 -13158.29166667 -13188.33333333 -13553.20833333]]\n",
            "grad_new: [[1.59953909e+06 1.29587540e+08 1.29883482e+08 1.33347167e+08]]\n",
            "\n",
            "norm:  226818594.788175 \n",
            "\n",
            "B_inv  [[ 0.99995027 -0.00402883 -0.00403803 -0.00414571]\n",
            " [-0.00402883  0.67360232 -0.32714308 -0.33586734]\n",
            " [-0.00403803 -0.32714308  0.67210981 -0.33663437]\n",
            " [-0.00414571 -0.33586734 -0.33663437  0.65438859]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  81.22916667 6579.14583333 6594.16666667 6776.60416667]]\n",
            "theta_new: [[  40.61216862 3287.69755513 3295.19957506 3392.95445106]]\n",
            "\n",
            "grad_old: [[1.59953909e+06 1.29587540e+08 1.29883482e+08 1.33347167e+08]]\n",
            "grad_new: [[  799770.26508705 64793740.05512758 64941683.99921253 66673668.12307587]]\n",
            "\n",
            "norm:  113409297.34571147 \n",
            "\n",
            "B_inv  [[ 0.99995027 -0.00402883 -0.00403803 -0.0041457 ]\n",
            " [-0.00402883  0.67360202 -0.32714352 -0.33586707]\n",
            " [-0.00403803 -0.32714352  0.67210924 -0.33663423]\n",
            " [-0.0041457  -0.33586707 -0.33663423  0.65438947]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  40.61216862 3287.69755513 3295.19957506 3392.95445106]]\n",
            "theta_new: [[  19.58468138 1671.93267713 1702.76195809 1616.4593107 ]]\n",
            "\n",
            "grad_old: [[  799770.26508705 64793740.05512758 64941683.99921253 66673668.12307587]]\n",
            "grad_new: [[  399871.63077522 32397308.40310284 32472168.74263753 33335114.25477924]]\n",
            "\n",
            "norm:  56704647.731172524 \n",
            "\n",
            "B_inv  [[ 0.99995027 -0.0040289  -0.00403799 -0.00414607]\n",
            " [-0.0040289   0.67361071 -0.3271261  -0.3358793 ]\n",
            " [-0.00403799 -0.3271261   0.67213513 -0.33663656]\n",
            " [-0.00414607 -0.3358793  -0.33663656  0.65435206]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  19.58468138 1671.93267713 1702.76195809 1616.4593107 ]]\n",
            "theta_new: [[  22.56513679  425.06616793 -420.80771633 2447.38633275]]\n",
            "\n",
            "grad_old: [[  399871.63077522 32397308.40310284 32472168.74263753 33335114.25477924]]\n",
            "grad_new: [[  200201.46461115 16190664.41712724 16205577.18962601 16702167.30417261]]\n",
            "\n",
            "norm:  28350675.82263185 \n",
            "\n",
            "B_inv  [[ 0.99995023 -0.00401994 -0.00402145 -0.0041543 ]\n",
            " [-0.00401994  0.67318462 -0.32838714 -0.33465354]\n",
            " [-0.00402145 -0.32838714  0.66907566 -0.33419216]\n",
            " [-0.0041543  -0.33465354 -0.33419216  0.65290756]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  22.56513679  425.06616793 -420.80771633 2447.38633275]]\n",
            "theta_new: [[  -252.60123165   6745.55293239  27872.79578733 -32602.36312642]]\n",
            "\n",
            "grad_old: [[  200201.46461115 16190664.41712724 16205577.18962601 16702167.30417261]]\n",
            "grad_new: [[  83933.78804991 7373086.34389597 7896751.19056841 6740796.89628905]]\n",
            "\n",
            "norm:  12734459.913089579 \n",
            "\n",
            "B_inv  [[ 9.99918745e-01 -3.35124121e-03 -5.63451165e-04 -8.20686420e-03]\n",
            " [-3.35124121e-03  6.59074920e-01 -4.02219947e-01 -2.48499931e-01]\n",
            " [-5.63451165e-04 -4.02219947e-01  2.90795713e-01  1.10648101e-01]\n",
            " [-8.20686420e-03 -2.48499931e-01  1.10648101e-01  1.31288730e-01]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -252.60123165   6745.55293239  27872.79578733 -32602.36312642]]\n",
            "theta_new: [[    23.52935001   2837.25148311 -10401.2406713    4472.18525743]]\n",
            "\n",
            "grad_old: [[  83933.78804991 7373086.34389597 7896751.19056841 6740796.89628905]]\n",
            "grad_new: [[  -236032.63368005 -19164050.57226562 -19430543.15286089\n",
            "  -19620471.64475366]]\n",
            "\n",
            "norm:  33612876.40946975 \n",
            "\n",
            "B_inv  [[ 9.99918885e-01 -3.45556948e-03 -4.41717841e-04 -8.21070974e-03]\n",
            " [-3.45556948e-03  6.62000324e-01 -3.89755207e-01 -2.62188597e-01]\n",
            " [-4.41717841e-04 -3.89755207e-01  2.54375199e-01  1.30115769e-01]\n",
            " [-8.21070974e-03 -2.62188597e-01  1.30115769e-01  1.27747091e-01]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[    23.52935001   2837.25148311 -10401.2406713    4472.18525743]]\n",
            "theta_new: [[    78.51155866 -12976.36588956   2692.89878619   8544.54561704]]\n",
            "\n",
            "grad_old: [[  -236032.63368005 -19164050.57226562 -19430543.15286089\n",
            "  -19620471.64475366]]\n",
            "grad_new: [[ -116511.77185464 -9714232.91801936 -9569649.40278673 -9673850.46170244]]\n",
            "\n",
            "norm:  16719493.671345092 \n",
            "\n",
            "B_inv  [[ 0.99991505 -0.00247106 -0.00114051 -0.00853137]\n",
            " [-0.00247106  0.41287758 -0.21695717 -0.17872848]\n",
            " [-0.00114051 -0.21695717  0.13901819  0.06963094]\n",
            " [-0.00853137 -0.17872848  0.06963094  0.10128266]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[    78.51155866 -12976.36588956   2692.89878619   8544.54561704]]\n",
            "theta_new: [[  -395.4702308   89675.83320943 -49182.48107234 -36988.98872633]]\n",
            "\n",
            "grad_old: [[ -116511.77185464 -9714232.91801936 -9569649.40278673 -9673850.46170244]]\n",
            "grad_new: [[  178180.54275046 15986780.7402227  14281025.80364815 14796285.63897883]]\n",
            "\n",
            "norm:  26047776.746902715 \n",
            "\n",
            "B_inv  [[ 9.99907723e-01 -8.48642854e-04 -1.99319133e-03 -9.22713145e-03]\n",
            " [-8.48642854e-04  5.38060055e-02 -2.83972919e-02 -2.46288785e-02]\n",
            " [-1.99319133e-03 -2.83972919e-02  4.01399077e-02 -1.13939994e-02]\n",
            " [-9.22713145e-03 -2.46288785e-02 -1.13939994e-02  3.52236436e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -395.4702308   89675.83320943 -49182.48107234 -36988.98872633]]\n",
            "theta_new: [[  -197.94230105  44638.22260166 -24339.36147045 -18529.24687867]]\n",
            "\n",
            "grad_old: [[  178180.54275046 15986780.7402227  14281025.80364815 14796285.63897883]]\n",
            "grad_new: [[  90347.6373187  8093625.21436999 7247661.60345522 7502030.02461607]]\n",
            "\n",
            "norm:  13203180.867302192 \n",
            "\n",
            "B_inv  [[ 9.99907724e-01 -8.47998534e-04 -1.99420225e-03 -9.22684878e-03]\n",
            " [-8.47998534e-04  5.34630584e-02 -2.80586605e-02 -2.46129794e-02]\n",
            " [-1.99420225e-03 -2.80586605e-02  3.98706731e-02 -1.14640288e-02]\n",
            " [-9.22684878e-03 -2.46129794e-02 -1.14640288e-02  3.52682243e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -197.94230105  44638.22260166 -24339.36147045 -18529.24687867]]\n",
            "theta_new: [[   -99.20154096  22325.04490055 -12183.96370368  -9256.25772512]]\n",
            "\n",
            "grad_old: [[  90347.6373187  8093625.21436999 7247661.60345522 7502030.02461607]]\n",
            "grad_new: [[  45111.84038212 4041731.36497605 3618398.23723018 3745929.73447537]]\n",
            "\n",
            "norm:  6592603.6498178 \n",
            "\n",
            "B_inv  [[ 9.99907722e-01 -8.47876089e-04 -1.99416260e-03 -9.22699491e-03]\n",
            " [-8.47876089e-04  5.34871053e-02 -2.80957926e-02 -2.46014491e-02]\n",
            " [-1.99416260e-03 -2.80957926e-02  3.99039931e-02 -1.14603227e-02]\n",
            " [-9.22699491e-03 -2.46014491e-02 -1.14603227e-02  3.52544876e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[   -99.20154096  22325.04490055 -12183.96370368  -9256.25772512]]\n",
            "theta_new: [[  -49.92211631 11162.44702799 -6090.64785093 -4628.32102128]]\n",
            "\n",
            "grad_old: [[  45111.84038212 4041731.36497605 3618398.23723018 3745929.73447537]]\n",
            "grad_new: [[  22558.67783307 2021095.08211704 1809452.5480204  1873184.62670614]]\n",
            "\n",
            "norm:  3296706.44646578 \n",
            "\n",
            "B_inv  [[ 9.99907721e-01 -8.47755630e-04 -1.99424559e-03 -9.22702880e-03]\n",
            " [-8.47755630e-04  5.34850975e-02 -2.80907918e-02 -2.46042531e-02]\n",
            " [-1.99424559e-03 -2.80907918e-02  3.98991318e-02 -1.14604110e-02]\n",
            " [-9.22702880e-03 -2.46042531e-02 -1.14604110e-02  3.52571577e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -49.92211631 11162.44702799 -6090.64785093 -4628.32102128]]\n",
            "theta_new: [[  -25.31218927  5581.40701821 -3045.13348734 -2313.52670708]]\n",
            "\n",
            "grad_old: [[  22558.67783307 2021095.08211704 1809452.5480204  1873184.62670614]]\n",
            "grad_new: [[  11279.25431173 1010536.91608838  904713.91944757  936582.95327528]]\n",
            "\n",
            "norm:  1648334.609465947 \n",
            "\n",
            "B_inv  [[ 9.99907719e-01 -8.47479999e-04 -1.99439318e-03 -9.22714578e-03]\n",
            " [-8.47479999e-04  5.34852402e-02 -2.80915089e-02 -2.46037078e-02]\n",
            " [-1.99439318e-03 -2.80915089e-02  3.98998720e-02 -1.14604434e-02]\n",
            " [-9.22714578e-03 -2.46037078e-02 -1.14604434e-02  3.52566808e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -25.31218927  5581.40701821 -3045.13348734 -2313.52670708]]\n",
            "theta_new: [[  -13.04263986  2790.87786978 -1522.28927089 -1156.20332779]]\n",
            "\n",
            "grad_old: [[  11279.25431173 1010536.91608838  904713.91944757  936582.95327528]]\n",
            "grad_new: [[  5639.66642175 505268.957348   452357.59043747 468291.85024347]]\n",
            "\n",
            "norm:  824168.1695671842 \n",
            "\n",
            "B_inv  [[ 9.99907714e-01 -8.46948039e-04 -1.99468394e-03 -9.22736594e-03]\n",
            " [-8.46948039e-04  5.34852328e-02 -2.80914013e-02 -2.46038107e-02]\n",
            " [-1.99468394e-03 -2.80914013e-02  3.98997567e-02 -1.14604302e-02]\n",
            " [-9.22736594e-03 -2.46038107e-02 -1.14604302e-02  3.52567675e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -13.04263986  2790.87786978 -1522.28927089 -1156.20332779]]\n",
            "theta_new: [[  -6.94199906 1395.61353319 -760.87394184 -577.53493493]]\n",
            "\n",
            "grad_old: [[  5639.66642175 505268.957348   452357.59043747 468291.85024347]]\n",
            "grad_new: [[  2819.86651122 252634.45447071 226178.76063886 234145.91191124]]\n",
            "\n",
            "norm:  412084.04368736356 \n",
            "\n",
            "B_inv  [[ 9.99907705e-01 -8.45903635e-04 -1.99525380e-03 -9.22779912e-03]\n",
            " [-8.45903635e-04  5.34852312e-02 -2.80914200e-02 -2.46038036e-02]\n",
            " [-1.99525380e-03 -2.80914200e-02  3.98997776e-02 -1.14604260e-02]\n",
            " [-9.22779912e-03 -2.46038036e-02 -1.14604260e-02  3.52567618e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -6.94199906 1395.61353319 -760.87394184 -577.53493493]]\n",
            "theta_new: [[  -3.92518731  697.98139116 -380.16566245 -288.20095724]]\n",
            "\n",
            "grad_old: [[  2819.86651122 252634.45447071 226178.76063886 234145.91191124]]\n",
            "grad_new: [[  1409.9661037  126317.22842771 113089.38229755 117072.95588147]]\n",
            "\n",
            "norm:  206042.02384304517 \n",
            "\n",
            "B_inv  [[ 9.99907687e-01 -8.43856463e-04 -1.99637096e-03 -9.22864806e-03]\n",
            " [-8.43856463e-04  5.34852290e-02 -2.80914206e-02 -2.46038254e-02]\n",
            " [-1.99637096e-03 -2.80914206e-02  3.98997789e-02 -1.14604133e-02]\n",
            " [-9.22864806e-03 -2.46038254e-02 -1.14604133e-02  3.52567803e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -3.92518731  697.98139116 -380.16566245 -288.20095724]]\n",
            "theta_new: [[  -2.44962221  349.16533784 -189.81150345 -143.53361064]]\n",
            "\n",
            "grad_old: [[  1409.9661037  126317.22842771 113089.38229755 117072.95588147]]\n",
            "grad_new: [[  705.01524221 63158.61410306 56544.69092962 58536.47762632]]\n",
            "\n",
            "norm:  103021.01177494995 \n",
            "\n",
            "B_inv  [[ 9.99907653e-01 -8.39843151e-04 -1.99856105e-03 -9.23031237e-03]\n",
            " [-8.39843151e-04  5.34852242e-02 -2.80914276e-02 -2.46038622e-02]\n",
            " [-1.99856105e-03 -2.80914276e-02  3.98997879e-02 -1.14603893e-02]\n",
            " [-9.23031237e-03 -2.46038622e-02 -1.14603893e-02  3.52568116e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[  -2.44962221  349.16533784 -189.81150345 -143.53361064]]\n",
            "theta_new: [[ -1.74403048 174.75733024 -94.63435759 -71.19963436]]\n",
            "\n",
            "grad_old: [[  705.01524221 63158.61410306 56544.69092962 58536.47762632]]\n",
            "grad_new: [[  352.53917389 31579.30698845 28272.34536943 29268.23846472]]\n",
            "\n",
            "norm:  51510.50581440328 \n",
            "\n",
            "B_inv  [[ 9.99907590e-01 -8.31975482e-04 -2.00285449e-03 -9.23357510e-03]\n",
            " [-8.31975482e-04  5.34852149e-02 -2.80914403e-02 -2.46039353e-02]\n",
            " [-2.00285449e-03 -2.80914403e-02  3.98998046e-02 -1.14603420e-02]\n",
            " [-9.23357510e-03 -2.46039353e-02 -1.14603420e-02  3.52568739e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[ -1.74403048 174.75733024 -94.63435759 -71.19963436]]\n",
            "theta_new: [[ -1.42278795  87.55334482 -47.04572343 -35.03234524]]\n",
            "\n",
            "grad_old: [[  352.53917389 31579.30698845 28272.34536943 29268.23846472]]\n",
            "grad_new: [[  176.30051442 15789.65338336 14136.17254106 14634.11885239]]\n",
            "\n",
            "norm:  25755.252756174796 \n",
            "\n",
            "B_inv  [[ 9.99907476e-01 -8.16551746e-04 -2.01127134e-03 -9.23997144e-03]\n",
            " [-8.16551746e-04  5.34851967e-02 -2.80914653e-02 -2.46040784e-02]\n",
            " [-2.01127134e-03 -2.80914653e-02  3.98998373e-02 -1.14602494e-02]\n",
            " [-9.23997144e-03 -2.46040784e-02 -1.14602494e-02  3.52569957e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[ -1.42278795  87.55334482 -47.04572343 -35.03234524]]\n",
            "theta_new: [[ -1.29309519  43.95136991 -23.25134588 -16.94840588]]\n",
            "\n",
            "grad_old: [[  176.30051442 15789.65338336 14136.17254106 14634.11885239]]\n",
            "grad_new: [[  88.18057129 7894.82649689 7068.08605313 7317.05897334]]\n",
            "\n",
            "norm:  12877.626089576914 \n",
            "\n",
            "B_inv  [[ 9.99907296e-01 -7.86315151e-04 -2.02777179e-03 -9.25251120e-03]\n",
            " [-7.86315151e-04  5.34851609e-02 -2.80915144e-02 -2.46043590e-02]\n",
            " [-2.02777179e-03 -2.80915144e-02  3.98999015e-02 -1.14600678e-02]\n",
            " [-9.25251120e-03 -2.46043590e-02 -1.14600678e-02  3.52572346e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[ -1.29309519  43.95136991 -23.25134588 -16.94840588]]\n",
            "theta_new: [[ -1.25856486  22.15039945 -11.35409762  -7.90614704]]\n",
            "\n",
            "grad_old: [[  88.18057129 7894.82649689 7068.08605313 7317.05897334]]\n",
            "grad_new: [[  44.11999758 3947.41289151 3534.0426648  3658.52888897]]\n",
            "\n",
            "norm:  6438.812491316691 \n",
            "\n",
            "B_inv  [[ 9.99907108e-01 -7.27039573e-04 -2.06011950e-03 -9.27709566e-03]\n",
            " [-7.27039573e-04  5.34850907e-02 -2.80916105e-02 -2.46049091e-02]\n",
            " [-2.06011950e-03 -2.80916105e-02  3.99000274e-02 -1.14597119e-02]\n",
            " [-9.27709566e-03 -2.46049091e-02 -1.14597119e-02  3.52577030e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[ -1.25856486  22.15039945 -11.35409762  -7.90614704]]\n",
            "theta_new: [[-1.27101542 11.24993005 -5.40541473 -3.38473384]]\n",
            "\n",
            "grad_old: [[  44.11999758 3947.41289151 3534.0426648  3658.52888897]]\n",
            "grad_new: [[  22.08911879 1973.70577692 1767.02069225 1829.263563  ]]\n",
            "\n",
            "norm:  3219.405183071039 \n",
            "\n",
            "B_inv  [[ 9.99907374e-01 -6.10836394e-04 -2.12353504e-03 -9.32529695e-03]\n",
            " [-6.10836394e-04  5.34849526e-02 -2.80917987e-02 -2.46059872e-02]\n",
            " [-2.12353504e-03 -2.80917987e-02  3.99002739e-02 -1.14590142e-02]\n",
            " [-9.32529695e-03 -2.46059872e-02 -1.14590142e-02  3.52586212e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.27101542 11.24993005 -5.40541473 -3.38473384]]\n",
            "theta_new: [[-1.30636799  5.79970924 -2.43101479 -1.12374839]]\n",
            "\n",
            "grad_old: [[  22.08911879 1973.70577692 1767.02069225 1829.263563  ]]\n",
            "grad_new: [[ 11.07309592 986.85161996 883.50916992 914.63034942]]\n",
            "\n",
            "norm:  1609.7005506248818 \n",
            "\n",
            "B_inv  [[ 9.99910329e-01 -3.83033765e-04 -2.24785925e-03 -9.41981370e-03]\n",
            " [-3.83033765e-04  5.34846803e-02 -2.80921668e-02 -2.46081001e-02]\n",
            " [-2.24785925e-03 -2.80921668e-02  3.99007568e-02 -1.14576467e-02]\n",
            " [-9.41981370e-03 -2.46081001e-02 -1.14576467e-02  3.52604211e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.30636799  5.79970924 -2.43101479 -1.12374839]]\n",
            "theta_new: [[-1.35259486  3.07460932 -0.94375578  0.00701894]]\n",
            "\n",
            "grad_old: [[ 11.07309592 986.85161996 883.50916992 914.63034942]]\n",
            "grad_new: [[  5.56450624 493.42338885 441.75237763 457.31267942]]\n",
            "\n",
            "norm:  804.8463544637794 \n",
            "\n",
            "B_inv  [[ 9.99925480e-01  6.35431404e-05 -2.49160164e-03 -9.60519269e-03]\n",
            " [ 6.35431404e-05  5.34841404e-02 -2.80928852e-02 -2.46122396e-02]\n",
            " [-2.49160164e-03 -2.80928852e-02  3.99017017e-02 -1.14549672e-02]\n",
            " [-9.60519269e-03 -2.46122396e-02 -1.14549672e-02  3.52639494e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.35259486  3.07460932 -0.94375578  0.00701894]]\n",
            "theta_new: [[-1.40369381  1.71206366 -0.20006515  0.57267427]]\n",
            "\n",
            "grad_old: [[  5.56450624 493.42338885 441.75237763 457.31267942]]\n",
            "grad_new: [[  2.80963249 246.7070581  220.87199905 228.65179632]]\n",
            "\n",
            "norm:  402.41564392893866 \n",
            "\n",
            "B_inv  [[ 9.99991144e-01  9.38983265e-04 -2.96949982e-03 -9.96894893e-03]\n",
            " [ 9.38983265e-04  5.34830587e-02 -2.80942806e-02 -2.46203448e-02]\n",
            " [-2.96949982e-03 -2.80942806e-02  3.99035472e-02 -1.14497189e-02]\n",
            " [-9.96894893e-03 -2.46203448e-02 -1.14497189e-02  3.52708655e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.40369381  1.71206366 -0.20006515  0.57267427]]\n",
            "theta_new: [[-1.45667579  1.03078351  0.17184639  0.85577301]]\n",
            "\n",
            "grad_old: [[  2.80963249 246.7070581  220.87199905 228.65179632]]\n",
            "grad_new: [[  1.43160487 123.34463578 110.42799937 114.31741424]]\n",
            "\n",
            "norm:  201.1933470883804 \n",
            "\n",
            "B_inv  [[ 1.00025807  0.00265507 -0.00390661 -0.01068335]\n",
            " [ 0.00265507  0.05348085 -0.02809697 -0.0246362 ]\n",
            " [-0.00390661 -0.02809697  0.03990714 -0.01143945]\n",
            " [-0.01068335 -0.0246362  -0.01143945  0.03528442]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.45667579  1.03078351  0.17184639  0.85577301]]\n",
            "theta_new: [[-1.5100606   0.69011414  0.35787915  0.99759731]]\n",
            "\n",
            "grad_old: [[  1.43160487 123.34463578 110.42799937 114.31741424]]\n",
            "grad_new: [[ 0.74196719 61.65524447 55.19867675 57.14264649]]\n",
            "\n",
            "norm:  100.56886023633778 \n",
            "\n",
            "B_inv  [[ 1.00131236  0.00601861 -0.00574458 -0.01208878]\n",
            " [ 0.00601861  0.05347617 -0.02810204 -0.02466712]\n",
            " [-0.00574458 -0.02810204  0.03991408 -0.01141938]\n",
            " [-0.01208878 -0.02466712 -0.01141938  0.03531098]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.5100606   0.69011414  0.35787915  0.99759731]]\n",
            "theta_new: [[-1.56313151  0.51970823  0.45099423  1.06879674]]\n",
            "\n",
            "grad_old: [[ 0.74196719 61.65524447 55.19867675 57.14264649]]\n",
            "grad_new: [[ 0.39645055 30.79483126 27.56994456 28.5407    ]]\n",
            "\n",
            "norm:  50.23098849605172 \n",
            "\n",
            "B_inv  [[ 1.00541821  0.01260873 -0.00935028 -0.01486235]\n",
            " [ 0.01260873  0.05346568 -0.02811127 -0.02472716]\n",
            " [-0.00935028 -0.02811127  0.03992729 -0.01138033]\n",
            " [-0.01486235 -0.02472716 -0.01138033  0.03536298]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.56313151  0.51970823  0.45099423  1.06879674]]\n",
            "theta_new: [[-1.61558843  0.43435383  0.49769321  1.10471255]]\n",
            "\n",
            "grad_old: [[ 0.39645055 30.79483126 27.56994456 28.5407    ]]\n",
            "grad_new: [[ 0.22284192 15.33443309 13.72854927 14.21174931]]\n",
            "\n",
            "norm:  25.01282431926758 \n",
            "\n",
            "B_inv  [[ 1.02127828  0.02550409 -0.01642357 -0.02036605]\n",
            " [ 0.02550409  0.05344005 -0.02812653 -0.02484253]\n",
            " [-0.01642357 -0.02812653  0.03995166 -0.01130488]\n",
            " [-0.02036605 -0.02484253 -0.01130488  0.03546461]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.61558843  0.43435383  0.49769321  1.10471255]]\n",
            "theta_new: [[-1.66747115  0.39137183  0.52126724  1.12304755]]\n",
            "\n",
            "grad_old: [[ 0.22284192 15.33443309 13.72854927 14.21174931]]\n",
            "grad_new: [[0.13488293 7.54630082 6.75598588 6.99358507]]\n",
            "\n",
            "norm:  12.309282141839471 \n",
            "\n",
            "B_inv  [[ 1.08202007  0.05062285 -0.03026928 -0.0313783 ]\n",
            " [ 0.05062285  0.0533707  -0.02814566 -0.0250592 ]\n",
            " [-0.03026928 -0.02814566  0.03999351 -0.01116166]\n",
            " [-0.0313783  -0.0250592  -0.01116166  0.03566208]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.66747115  0.39137183  0.52126724  1.12304755]]\n",
            "theta_new: [[-1.71947901  0.36928472  0.53343868  1.13271699]]\n",
            "\n",
            "grad_old: [[0.13488293 7.54630082 6.75598588 6.99358507]]\n",
            "grad_new: [[0.08915139 3.54151417 3.17057897 3.28188992]]\n",
            "\n",
            "norm:  5.776992561917488 \n",
            "\n",
            "B_inv  [[ 1.30985131  0.09875927 -0.05705529 -0.05357041]\n",
            " [ 0.09875927  0.05316502 -0.0281426  -0.02544422]\n",
            " [-0.05705529 -0.0281426   0.0400526  -0.01090121]\n",
            " [-0.05357041 -0.02544422 -0.01090121  0.03603862]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.71947901  0.36928472  0.53343868  1.13271699]]\n",
            "theta_new: [[-1.77438997  0.35710686  0.54020899  1.13830465]]\n",
            "\n",
            "grad_old: [[0.08915139 3.54151417 3.17057897 3.28188992]]\n",
            "grad_new: [[0.06338617 1.33064531 1.1912322  1.23282882]]\n",
            "\n",
            "norm:  2.1710678931413154 \n",
            "\n",
            "B_inv  [[ 2.11003125  0.18607293 -0.10651953 -0.09760481]\n",
            " [ 0.18607293  0.05253688 -0.02799786 -0.02603681]\n",
            " [-0.10651953 -0.02799786  0.04008581 -0.01047793]\n",
            " [-0.09760481 -0.02603681 -0.01047793  0.03671467]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.77438997  0.35710686  0.54020899  1.13830465]]\n",
            "theta_new: [[-1.84145215  0.34898111  0.54479553  1.1423303 ]]\n",
            "\n",
            "grad_old: [[0.06338617 1.33064531 1.1912322  1.23282882]]\n",
            "grad_new: [[ 0.04555541 -0.14782393 -0.13241096 -0.1374297 ]]\n",
            "\n",
            "norm:  0.24565584595167986 \n",
            "\n",
            "B_inv  [[ 4.4837983   0.32270722 -0.18646858 -0.17747175]\n",
            " [ 0.32270722  0.05079754 -0.02735858 -0.02665041]\n",
            " [-0.18646858 -0.02735858  0.03991828 -0.00996192]\n",
            " [-0.17747175 -0.02665041 -0.00996192  0.03774955]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.84145215  0.34898111  0.54479553  1.1423303 ]]\n",
            "theta_new: [[-1.94427105  0.34174256  0.54897901  1.14633734]]\n",
            "\n",
            "grad_old: [[ 0.04555541 -0.14782393 -0.13241096 -0.1374297 ]]\n",
            "grad_new: [[ 0.02876702 -1.46947328 -1.31565527 -1.3623385 ]]\n",
            "\n",
            "norm:  2.3973097931828864 \n",
            "\n",
            "B_inv  [[ 9.91242268e+00  4.86336763e-01 -2.88078847e-01 -2.98384478e-01]\n",
            " [ 4.86336763e-01  4.69310171e-02 -2.56196845e-02 -2.66454647e-02]\n",
            " [-2.88078847e-01 -2.56196845e-02  3.91998048e-02 -9.69035612e-03]\n",
            " [-2.98384478e-01 -2.66454647e-02 -9.69035612e-03  3.89289227e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-1.94427105  0.34174256  0.54897901  1.14633734]]\n",
            "theta_new: [[-2.12227358  0.33422586  0.55348482  1.15119436]]\n",
            "\n",
            "grad_old: [[ 0.02876702 -1.46947328 -1.31565527 -1.3623385 ]]\n",
            "grad_new: [[ 0.00983792 -2.84952397 -2.55118308 -2.64136131]]\n",
            "\n",
            "norm:  4.648140308682831 \n",
            "\n",
            "B_inv  [[ 1.95524755e+01  6.10609105e-01 -3.77763590e-01 -4.44122083e-01]\n",
            " [ 6.10609105e-01  4.02363565e-02 -2.22481268e-02 -2.50829090e-02]\n",
            " [-3.77763590e-01 -2.22481268e-02  3.75632922e-02 -1.02125087e-02]\n",
            " [-4.44122083e-01 -2.50829090e-02 -1.02125087e-02  3.97047940e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-2.12227358  0.33422586  0.55348482  1.15119436]]\n",
            "theta_new: [[-2.41689427  0.32704349  0.55807269  1.15705216]]\n",
            "\n",
            "grad_old: [[ 0.00983792 -2.84952397 -2.55118308 -2.64136131]]\n",
            "grad_new: [[-0.01086397 -4.18150643 -3.74367304 -3.87581291]]\n",
            "\n",
            "norm:  6.8207131289392136 \n",
            "\n",
            "B_inv  [[ 3.27051931e+01  5.93989836e-01 -3.98527992e-01 -5.65742625e-01]\n",
            " [ 5.93989836e-01  3.16093454e-02 -1.75024573e-02 -2.13422162e-02]\n",
            " [-3.98527992e-01 -1.75024573e-02  3.50205627e-02 -1.19780283e-02]\n",
            " [-5.65742625e-01 -2.13422162e-02 -1.19780283e-02  3.93415732e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-2.41689427  0.32704349  0.55807269  1.15705216]]\n",
            "theta_new: [[-2.83968959  0.32223642  0.56165504  1.16317713]]\n",
            "\n",
            "grad_old: [[-0.01086397 -4.18150643 -3.74367304 -3.87581291]]\n",
            "grad_new: [[-0.02945636 -5.09710789 -4.56338151 -4.72433273]]\n",
            "\n",
            "norm:  8.314153418344286 \n",
            "\n",
            "B_inv  [[ 4.50478029e+01  3.65224389e-01 -3.01681453e-01 -5.91453793e-01]\n",
            " [ 3.65224389e-01  2.48117944e-02 -1.32739639e-02 -1.62874872e-02]\n",
            " [-3.01681453e-01 -1.32739639e-02  3.24932954e-02 -1.46781986e-02]\n",
            " [-5.91453793e-01 -1.62874872e-02 -1.46781986e-02  3.74961963e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-2.83968959  0.32223642  0.56165504  1.16317713]]\n",
            "theta_new: [[-3.33087945  0.32208887  0.56284972  1.1680377 ]]\n",
            "\n",
            "grad_old: [[-0.02945636 -5.09710789 -4.56338151 -4.72433273]]\n",
            "grad_new: [[-0.04006752 -5.17633945 -4.63430218 -4.79767852]]\n",
            "\n",
            "norm:  8.443374406632683 \n",
            "\n",
            "B_inv  [[ 5.04630990e+01 -5.97016466e-05 -1.14620293e-01 -4.92853741e-01]\n",
            " [-5.97016466e-05  2.45918506e-02 -1.23253714e-02 -1.26271051e-02]\n",
            " [-1.14620293e-01 -1.23253714e-02  3.15513195e-02 -1.68994110e-02]\n",
            " [-4.92853741e-01 -1.26271051e-02 -1.68994110e-02  3.50145336e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-3.33087945  0.32208887  0.56284972  1.1680377 ]]\n",
            "theta_new: [[-3.7679377   0.32688541  0.56122349  1.17031864]]\n",
            "\n",
            "grad_old: [[-0.04006752 -5.17633945 -4.63430218 -4.79767852]]\n",
            "grad_new: [[-0.03954979 -4.35457587 -3.89858078 -4.03597633]]\n",
            "\n",
            "norm:  7.102952357696826 \n",
            "\n",
            "B_inv  [[ 4.72517221e+01 -2.97178519e-01  5.48090413e-02 -3.38236900e-01]\n",
            " [-2.97178519e-01  3.15001620e-02 -1.54214607e-02 -1.25894133e-02]\n",
            " [ 5.48090413e-02 -1.54214607e-02  3.28566198e-02 -1.72706935e-02]\n",
            " [-3.38236900e-01 -1.25894133e-02 -1.72706935e-02  3.34881543e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-3.7679377   0.32688541  0.56122349  1.17031864]]\n",
            "theta_new: [[-4.05630239  0.33412747  0.55792541  1.17013236]]\n",
            "\n",
            "grad_old: [[-0.03954979 -4.35457587 -3.89858078 -4.03597633]]\n",
            "grad_new: [[-0.03096755 -3.07411523 -2.75220092 -2.84917234]]\n",
            "\n",
            "norm:  5.014333086563441 \n",
            "\n",
            "B_inv  [[ 3.99630598e+01 -3.93715141e-01  1.24024125e-01 -2.26978702e-01]\n",
            " [-3.93715141e-01  4.09461849e-02 -2.03574167e-02 -1.55641806e-02]\n",
            " [ 1.24024125e-01 -2.03574167e-02  3.53932939e-02 -1.58996535e-02]\n",
            " [-2.26978702e-01 -1.55641806e-02 -1.58996535e-02  3.36349343e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-4.05630239  0.33412747  0.55792541  1.17013236]]\n",
            "theta_new: [[-4.19536727  0.34078155  0.55460955  1.16873118]]\n",
            "\n",
            "grad_old: [[-0.03096755 -3.07411523 -2.75220092 -2.84917234]]\n",
            "grad_new: [[-0.02043897 -1.88415457 -1.68684815 -1.74627183]]\n",
            "\n",
            "norm:  3.0733333959935356 \n",
            "\n",
            "B_inv  [[ 3.38260765e+01 -3.23956032e-01  9.98752105e-02 -1.95949510e-01]\n",
            " [-3.23956032e-01  4.83210565e-02 -2.45402984e-02 -1.93047356e-02]\n",
            " [ 9.98752105e-02 -2.45402984e-02  3.77307718e-02 -1.39287232e-02]\n",
            " [-1.95949510e-01 -1.93047356e-02 -1.39287232e-02  3.48832568e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-4.19536727  0.34078155  0.55460955  1.16873118]]\n",
            "theta_new: [[-4.24172718  0.34543952  0.55217274  1.16725212]]\n",
            "\n",
            "grad_old: [[-0.02043897 -1.88415457 -1.68684815 -1.74627183]]\n",
            "grad_new: [[-0.01199825 -1.04571067 -0.93620417 -0.96917951]]\n",
            "\n",
            "norm:  1.7057086332439044 \n",
            "\n",
            "B_inv  [[ 3.07451520e+01 -1.88629858e-01  3.30127603e-02 -2.21976634e-01]\n",
            " [-1.88629858e-01  5.22295680e-02 -2.69802261e-02 -2.22481645e-02]\n",
            " [ 3.30127603e-02 -2.69802261e-02  3.92139627e-02 -1.22633850e-02]\n",
            " [-2.21976634e-01 -2.22481645e-02 -1.22633850e-02  3.63584393e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "theta_old: [[-4.24172718  0.34543952  0.55217274  1.16725212]]\n",
            "theta_new: [[-4.2480235   0.34820568  0.55067746  1.1661663 ]]\n",
            "\n",
            "grad_old: [[-0.01199825 -1.04571067 -0.93620417 -0.96917951]]\n",
            "grad_new: [[-0.00653984 -0.54552574 -0.48839805 -0.50559848]]\n",
            "\n",
            "norm:  0.8898334553866678 \n",
            "\n",
            "B_inv  [[ 3.01437537e+01 -5.79291519e-02 -3.69496996e-02 -2.70312409e-01]\n",
            " [-5.79291519e-02  5.34643741e-02 -2.79505550e-02 -2.40374002e-02]\n",
            " [-3.69496996e-02 -2.79505550e-02  3.99021890e-02 -1.11773163e-02]\n",
            " [-2.70312409e-01 -2.40374002e-02 -1.11773163e-02  3.75728616e-02]] \n",
            "\n",
            "**************************************************************************************************** \n",
            "\n",
            "\n",
            "Found solution after 42 iterations.\n",
            "thetas_min = [[-4.24261456  0.34969722  0.54985122  1.16549479]]\n",
            "Gradient = [[-0.00341178 -0.27488053 -0.24609476 -0.25476087]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "y_pred = Xs @ opt_thetas\n",
        "r2_score(y, y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qzK3_jdoRYai",
        "outputId": "3cc3d5b4-fe48-49c1-fc6b-807e1e5c6d7a"
      },
      "id": "qzK3_jdoRYai",
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9896128170947136"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.grid()\n",
        "plt.plot(loss, marker = 'o', color='m');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "outputId": "6e07ca8e-e812-4e8a-ed96-790eb4ee639f",
        "id": "ShD4zcDZR92W"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1030x830 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "id": "ShD4zcDZR92W"
    },
    {
      "cell_type": "code",
      "source": [
        "figure, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 15))\n",
        "\n",
        "ax1.set_title('Learning Rate = 0.5 & tolerance=0.0001', fontweight='bold', fontsize=15)\n",
        "ax1.set_xlabel('Theta 0', fontweight='bold', fontsize=12)\n",
        "ax1.set_ylabel('Loss', fontweight='bold', fontsize=12)\n",
        "ax1.grid()\n",
        "\n",
        "\n",
        "ax1.plot(thetas_upd[0], loss[:-1], marker = 'o', color='m')\n",
        "\n",
        "ax2.set_title('Learning Rate = 0.5 & tolerance=0.0001', fontweight='bold', fontsize=15)\n",
        "ax2.set_xlabel('Theta 1', fontweight='bold', fontsize=12)\n",
        "ax2.set_ylabel('Loss', fontweight='bold', fontsize=12)\n",
        "ax2.grid()\n",
        "\n",
        "ax2.plot(thetas_upd[1], loss[:-1], marker = 'o', color='m');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c2d1e531-3b17-41f9-dcb7-af7a6b5a2cfd",
        "id": "mTuBSg7aR92X"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "id": "mTuBSg7aR92X"
    },
    {
      "cell_type": "code",
      "source": [
        "figure, (ax1, ax2) = plt.subplots(2, 1, figsize=(10, 15))\n",
        "\n",
        "ax1.set_title('Learning Rate = 0.5 & tolerance=0.0001', fontweight='bold', fontsize=15)\n",
        "ax1.set_xlabel('Theta 2', fontweight='bold', fontsize=12)\n",
        "ax1.set_ylabel('Loss', fontweight='bold', fontsize=12)\n",
        "ax1.grid()\n",
        "\n",
        "\n",
        "ax1.plot(thetas_upd[2], loss[:-1], marker = 'o', color='m')\n",
        "\n",
        "ax2.set_title('Learning Rate = 0.5 & tolerance=0.0001', fontweight='bold', fontsize=15)\n",
        "ax2.set_xlabel('Theta 3', fontweight='bold', fontsize=12)\n",
        "ax2.set_ylabel('Loss', fontweight='bold', fontsize=12)\n",
        "ax2.grid()\n",
        "\n",
        "ax2.plot(thetas_upd[3], loss[:-1], marker = 'o', color='m');"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "7e5e122a-857e-40f8-cd7a-9f4873d5b362",
        "id": "WHvz7XT4R92X"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1500 with 2 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ],
      "id": "WHvz7XT4R92X"
    },
    {
      "cell_type": "markdown",
      "source": [
        "<font size='5'>**Conclusion**</font>\n",
        "> After comparing the convergence rates of the BFGS method and the Adam optimizer, with both methods using the same learning rate and tolerance value, it can be concluded that the BFGS method is significantly faster in achieving convergence than the Adam optimizer. The <b><font color='purple'>BFGS method</font></b> converges after only <b><font color='purple'>43 iterations</font></b> while the <b><font color='orange'>Adam optimizer</font></b> converges after <b><font color='orange'>388 iterations</font></b> iterations, which means that the BFGS method is approximately <b><font color='red'>9 times</font></b> faster in achieving convergence than the Adam optimizer. Therefore, for optimization problems where fast convergence is a critical factor, the BFGS method could be a more suitable choice over the Adam optimizer, especially when using the same learning rate and tolerance value for both methods."
      ],
      "metadata": {
        "id": "wnv8mq0XURoN"
      },
      "id": "wnv8mq0XURoN"
    },
    {
      "cell_type": "markdown",
      "id": "c72c6406",
      "metadata": {
        "id": "c72c6406"
      },
      "source": [
        "<font color='red'><b><b><i>Notes:</i></b></font>\n",
        "    \n",
        "<font color='red'><b><b><i>You are allowed to use the code you developed during practical sessions. However, it should be your own code.</i></b></font>\n",
        "\n",
        "<font color='red'><b><i>Any copied code from the internet will be considered as cheeeting</i></b></i></b></font>\n",
        "\n",
        "<font color='red'><b><i>Any code exchange between trainees will be considered as cheeeting</i></b></font>"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "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": "3.9.12"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}