diff --git a/Projekt_2/fixed/algorytm.ipynb b/Projekt_2/fixed/algorytm.ipynb
index 3a241a2..b033125 100644
--- a/Projekt_2/fixed/algorytm.ipynb
+++ b/Projekt_2/fixed/algorytm.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,25 +32,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2\n"
+      "Stopień: 2\n",
+      "[1, 2, 3]\n"
      ]
     }
    ],
    "source": [
     "rand_coeffs = (random.randrange(-10, 10), random.randrange(-10, 10), random.randrange(-10,10))\n",
-    "coeffs = [2, -5, 4] # a, b, c\n",
+    "coeffs = []\n",
+    "#coeffs = [2, -5, 4] # a, b, c\n",
     "#coeffs = [17, -8, 4] # a, b, c\n",
     "\n",
-    "k = input(\"Podaj stopień wielomianu: \")\n",
-    "k = int(k)\n",
-    "print(k)"
+    "k = int(input(\"Podaj stopień wielomianu: \"))\n",
+    "print('Stopień: {}'.format(k))\n",
+    "\n",
+    "for i in range(k+1):\n",
+    "    a = int(input('Podaj {} współczynnik wielomianu: '.format(i+1)))\n",
+    "    coeffs.append(a)\n",
+    "\n",
+    "print(coeffs)"
    ]
   },
   {
@@ -63,19 +70,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [],
    "source": [
     "def eval_2nd_degree(coeffs, x, k):\n",
-    "    a = (coeffs[0]*(x**k))\n",
-    "    b = coeffs[1]*(x**(k-1))\n",
-    "    c = coeffs[2]\n",
+    "    y = 0\n",
     "\n",
-    "    y = a+b+c\n",
+    "    for i in range(len(coeffs)):\n",
+    "        a = coeffs[i]*x**(k-i)\n",
+    "        y += a\n",
     "    return y\n",
     "\n",
-    "#eval_2nd_degree(coeffs, 3, 4)\n",
+    "#eval_2nd_degree(coeffs, 2, k)\n",
     "    "
    ]
   },
@@ -89,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,35 +119,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 1.93805554 -7.14146898  4.80847965  6.7923386  -4.9820572  -8.36721666\n",
-      " -3.03590771  7.10302797  3.6567952  -4.61893312 -7.77544379  4.33448858\n",
-      " -0.84984343 -0.4818275   7.11385026 -9.51525895 -4.38281891  8.47750482\n",
-      "  8.4505284  -2.03857269  6.43008266  9.91978403  9.68784877 -6.51416545\n",
-      " -9.46191372  9.53098301 -3.26192799  4.78375147 -0.48803478  4.2719198\n",
-      "  7.87940586 -9.71405924 -8.59615622 -0.99001719 -5.70862611  4.81815229\n",
-      " -4.75636826  3.00461241  8.0931194  -8.73839205  3.5703026  -1.70997658\n",
-      "  8.397044   -7.13453958 -5.68033115  6.43496878  0.63967305  3.47292048\n",
-      " -1.96283003 -5.96053698  7.02062456  6.6672641   1.14217053 -9.21276365\n",
-      "  4.7201797  -5.29291118  3.36596207  4.81029689 -6.72797697  0.21795055\n",
-      " -9.81276562  1.06328435  7.70633934  4.66017061 -2.2623938  -7.86967321\n",
-      " -2.87083371 -2.75685526  6.93841449 -2.01111387 -7.23430145  4.37277354\n",
-      "  0.88088525  8.62278739 -9.56199168  9.86296681 -6.41765534 -9.41510031\n",
-      "  3.27903273  4.84209117  3.0707047   3.82251549  6.72063776  7.53313017\n",
-      "  5.93242504  4.2171624   9.82285025 -4.81145849 -7.40363677  5.89354852\n",
-      "  9.03248351 -0.90170662 -1.86450263 -7.55012017  9.99192671 -1.20030438\n",
-      "  4.7200473  -6.71344168  1.93127689 -1.30512735]\n"
+      "[-3.97652539  5.12289902  0.81603889 -4.21468549  7.29999822  6.80214713\n",
+      "  5.12999406 -2.53404436 -5.49920761  8.378254   -7.46372758  9.86734656\n",
+      "  4.8149328   6.19203961 -6.38598843  6.71630077  1.01277528  9.94551208\n",
+      "  0.43640911  0.97816101 -1.16523849  2.44180092 -8.97602849  2.82340563\n",
+      " -7.02243684 -6.05657589 -6.7288575   1.22080707  8.1319712  -9.53796227\n",
+      " -2.7554347  -6.77738836  2.37331025 -6.13297674  9.17486304  2.5028872\n",
+      "  6.24318809  0.77218811 -9.98325959 -5.63262972  6.25902107  5.51014874\n",
+      "  6.1640148   4.72398673 -4.78847828  8.68457944 -9.88763602  6.27522868\n",
+      " -3.68361737  0.38206885  5.92106547  1.95099718  2.29306717  7.99642986\n",
+      " -1.45767721 -3.62004766 -9.65908783  2.18931831 -6.10757568  5.65887017\n",
+      " -3.93428386 -5.92527032 -5.28481895  1.2284518  -0.25110658 -5.26058855\n",
+      " -7.89031713  6.18149346  5.79201652 -8.15007796  0.68707937  5.4565716\n",
+      "  9.46307956  9.90004237 -9.24364221  4.70422637 -4.13537182  2.25176354\n",
+      "  4.15610024 -0.3163456   1.17286334 -2.64623937 -9.99866372  1.52434893\n",
+      " -8.11925897  0.39344416 -3.51950194  2.25860641  0.32732711 -6.46956416\n",
+      " -0.80515794 -4.11088739 -6.31912324  8.8255148  -4.82190706 -7.29749101\n",
+      "  0.65692064  9.14346455 -5.09824179  2.9668068 ]\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -183,7 +190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -201,7 +208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,7 +261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -279,20 +286,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1440x720 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "ename": "TypeError",
+     "evalue": "'list' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\DATA\\#Mike\\#Studia\\Magisterka\\Magisterka\\MATAM\\Matma_AI_cyber\\Projekt_2\\fixed\\algorytm.ipynb Cell 19'\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000017?line=0'>1</a>\u001b[0m GD \u001b[39m=\u001b[39m gradient_descent(\u001b[39m1500\u001b[39;49m, \u001b[39m0.0001\u001b[39;49m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000017?line=2'>3</a>\u001b[0m plt\u001b[39m.\u001b[39mfigure(figsize\u001b[39m=\u001b[39m(\u001b[39m20\u001b[39m,\u001b[39m10\u001b[39m))\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000017?line=3'>4</a>\u001b[0m plt\u001b[39m.\u001b[39mplot(xs, ys, \u001b[39m'\u001b[39m\u001b[39mg+\u001b[39m\u001b[39m'\u001b[39m, label \u001b[39m=\u001b[39m \u001b[39m'\u001b[39m\u001b[39moriginal\u001b[39m\u001b[39m'\u001b[39m)\n",
+      "\u001b[1;32mc:\\DATA\\#Mike\\#Studia\\Magisterka\\Magisterka\\MATAM\\Matma_AI_cyber\\Projekt_2\\fixed\\algorytm.ipynb Cell 17'\u001b[0m in \u001b[0;36mgradient_descent\u001b[1;34m(epochs, lr)\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000015?line=2'>3</a>\u001b[0m rand_coeffs_to_test \u001b[39m=\u001b[39m rand_coeffs\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000015?line=3'>4</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(epochs):\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000015?line=4'>5</a>\u001b[0m     loss \u001b[39m=\u001b[39m calc_gradient_2nd_poly_for_GD(rand_coeffs_to_test, hundred_xs, ys, lr, k)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000015?line=5'>6</a>\u001b[0m     rand_coeffs_to_test \u001b[39m=\u001b[39m loss[\u001b[39m1\u001b[39m]\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000015?line=6'>7</a>\u001b[0m     losses\u001b[39m.\u001b[39mappend(loss[\u001b[39m0\u001b[39m])\n",
+      "\u001b[1;32mc:\\DATA\\#Mike\\#Studia\\Magisterka\\Magisterka\\MATAM\\Matma_AI_cyber\\Projekt_2\\fixed\\algorytm.ipynb Cell 15'\u001b[0m in \u001b[0;36mcalc_gradient_2nd_poly_for_GD\u001b[1;34m(coeffs, inputs_x, outputs_y, lr, k)\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=7'>8</a>\u001b[0m     \u001b[39mglobals\u001b[39m()[\u001b[39m'\u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m_s\u001b[39m\u001b[39m'\u001b[39m \u001b[39m%\u001b[39m i] \u001b[39m=\u001b[39m []\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=9'>10</a>\u001b[0m y_bars \u001b[39m=\u001b[39m eval_2nd_degree(coeffs, inputs_x, k)\n\u001b[1;32m---> <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=12'>13</a>\u001b[0m \u001b[39mfor\u001b[39;00m x,y,y_bar \u001b[39min\u001b[39;00m \u001b[39mlist\u001b[39;49m(\u001b[39mzip\u001b[39;49m(inputs_x, outputs_y, y_bars)):    \u001b[39m# take tuple of (x datapoint, actual y label, predicted y label)\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=13'>14</a>\u001b[0m     \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=14'>15</a>\u001b[0m \u001b[39m    x_squared = x**k \u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=15'>16</a>\u001b[0m \u001b[39m    partial_a = x_squared * (y - y_bar)\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=20'>21</a>\u001b[0m \u001b[39m    c_s.append(partial_c)\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=21'>22</a>\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/DATA/%23Mike/%23Studia/Magisterka/Magisterka/MATAM/Matma_AI_cyber/Projekt_2/fixed/algorytm.ipynb#ch0000013?line=22'>23</a>\u001b[0m     \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39mlen\u001b[39m(coeffs)):\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'list' object is not callable"
+     ]
     }
    ],
    "source": [
@@ -315,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -344,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -373,7 +381,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.10.4 64-bit",
+   "display_name": "Python 3.9.1 64-bit (system)",
    "language": "python",
    "name": "python3"
   },
@@ -387,12 +395,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.9.1"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
+    "hash": "a8ea2fdcb542f7ad5e541ceaa4e664e5167814f5e1f9d57a269097b55ff58c8d"
    }
   }
  },