diff --git a/Projekt_2/fixed/algorytm.ipynb b/Projekt_2/fixed/algorytm.ipynb
index ce19495..3a241a2 100644
--- a/Projekt_2/fixed/algorytm.ipynb
+++ b/Projekt_2/fixed/algorytm.ipynb
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -32,12 +32,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 68,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n"
+     ]
+    }
+   ],
    "source": [
     "rand_coeffs = (random.randrange(-10, 10), random.randrange(-10, 10), random.randrange(-10,10))\n",
-    "coeffs = [2, -5, 4] # a, b, c"
+    "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)"
    ]
   },
   {
@@ -50,16 +63,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def eval_2nd_degree(coeffs, x):\n",
-    "    a = (coeffs[0]*(x*x))\n",
-    "    b = coeffs[1]*x\n",
+    "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",
+    "\n",
     "    y = a+b+c\n",
     "    return y\n",
+    "\n",
+    "#eval_2nd_degree(coeffs, 3, 4)\n",
     "    "
    ]
   },
@@ -73,12 +89,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [],
    "source": [
     "def eval_2nd_degree_jitter(coeffs, x, j):\n",
-    "    y = eval_2nd_degree(coeffs, x)\n",
+    "    y = eval_2nd_degree(coeffs, x, k)\n",
     "    \n",
     "    interval_min = y-j\n",
     "    interval_max = y+j\n",
@@ -96,35 +112,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[-2.75898766 -5.65016775 -2.64800795 -0.87530755 -8.01730191 -0.07202148\n",
-      "  5.28994963 -0.73685206 -8.69865485  7.11491828  3.53780935  5.6983085\n",
-      " -3.07830759  1.28764607  9.45509424  6.95589075 -9.90311757 -2.30741362\n",
-      "  0.85246282 -4.39966538  0.79658004  3.29939139 -1.77080971 -8.61248702\n",
-      " -8.08537204 -8.46573256  5.29314956  8.89274736 -5.489419   -9.54545965\n",
-      "  0.57426227  1.98898479  0.28600353 -3.3779621  -6.74739724 -9.74245438\n",
-      "  6.70940724 -8.87576881  2.22682743  0.5484296  -5.13845562 -6.39800992\n",
-      "  3.76662938  0.84736151  1.02810373  6.79544869  2.20334512  3.65087577\n",
-      " -9.40928261 -0.51270946  9.95897626  4.22555829  3.24859186  7.78895395\n",
-      "  2.92230082  2.48586306 -7.51038001  4.53569191  1.27276562  0.75734371\n",
-      " -3.47811458 -2.76641217  0.3687908   5.73108176 -3.10855301  2.15342573\n",
-      "  9.14193125  9.51722955 -0.73921274  9.39733669 -8.08203669 -1.68282233\n",
-      "  0.63287602  2.9995979  -0.17714689  1.99519815  3.651222    3.90420391\n",
-      "  4.96344081  9.64202473  8.79805941 -5.23101012 -9.18668126 -9.79448374\n",
-      " -5.26154655  6.52710444 -7.58126901 -2.83931574 -9.23639541  4.23583082\n",
-      " -3.42590506 -3.22933046 -5.75833418  8.25154698 -8.96797588 -0.79300038\n",
-      "  0.30593884 -6.6657409  -6.58569143 -9.75881443]\n"
+      "[ 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"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -167,12 +183,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [],
    "source": [
     "def loss_mse(ys, y_bar):\n",
-    "    return sum((ys - y_bar)*(ys - y_bar)) / len(ys)\n"
+    "    return sum((ys - y_bar)*(ys - y_bar)) / len(ys)"
    ]
   },
   {
@@ -185,22 +201,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def calc_gradient_2nd_poly_for_GD(coeffs, inputs_x, outputs_y, lr): \n",
+    "def calc_gradient_2nd_poly_for_GD(coeffs, inputs_x, outputs_y, lr, k): \n",
     "    a_s = []\n",
     "    b_s = []\n",
     "    c_s = []\n",
     "        \n",
-    "    y_bars = eval_2nd_degree(coeffs, inputs_x)\n",
+    "    y_bars = eval_2nd_degree(coeffs, inputs_x, k)\n",
     "\n",
+    "\n",
+    "    # tu zamiast 2 dodałem k i w potencial_b k-1, czy tylko to powinno byś zmiaenione?\n",
     "    for x,y,y_bar in list(zip(inputs_x, outputs_y, y_bars)):    # take tuple of (x datapoint, actual y label, predicted y label)\n",
-    "        x_squared = x**2        \n",
+    "        x_squared = x**k      \n",
     "        partial_a = x_squared * (y - y_bar)\n",
     "        a_s.append(partial_a)\n",
-    "        partial_b = x * (y-y_bar)\n",
+    "        partial_b = x**(k-1) * (y-y_bar)\n",
     "        b_s.append(partial_b)\n",
     "        partial_c = (y-y_bar)\n",
     "        c_s.append(partial_c)\n",
@@ -220,12 +238,10 @@
     "    new_model_coeffs = (a_new, b_new, c_new)\n",
     "    \n",
     "    #update with these new coeffs:\n",
-    "    new_y_bar = eval_2nd_degree(new_model_coeffs, inputs_x)\n",
+    "    new_y_bar = eval_2nd_degree(new_model_coeffs, inputs_x, k)\n",
     "    \n",
     "    updated_model_loss = loss_mse(outputs_y, new_y_bar)\n",
-    "    return updated_model_loss, new_model_coeffs, new_y_bar\n",
-    "    \n",
-    "    "
+    "    return updated_model_loss, new_model_coeffs, new_y_bar"
    ]
   },
   {
@@ -238,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 74,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -246,7 +262,7 @@
     "    losses = []\n",
     "    rand_coeffs_to_test = rand_coeffs\n",
     "    for i in range(epochs):\n",
-    "        loss = calc_gradient_2nd_poly_for_GD(rand_coeffs_to_test, hundred_xs, ys, lr)\n",
+    "        loss = calc_gradient_2nd_poly_for_GD(rand_coeffs_to_test, hundred_xs, ys, lr, k)\n",
     "        rand_coeffs_to_test = loss[1]\n",
     "        losses.append(loss[0])\n",
     "    #print(losses)\n",
@@ -263,12 +279,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 75,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -299,21 +315,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Początkowe współczynniki (-5, -5, 4)\n",
-      "Wyznaczone współczynniki (1.9655100088406912, -4.317400439992125, 4.040348505930957)\n",
+      "Początkowe współczynniki (7, 5, -6)\n",
+      "Wyznaczone współczynniki (2.1546532016162714, -4.490001059494276, -5.122120484596819)\n",
       "Oryginalne współczynniki [2, -5, 4]\n"
      ]
     }
    ],
    "source": [
-    "\n",
     "print(\"Początkowe współczynniki {}\".format(rand_coeffs))\n",
     "print(\"Wyznaczone współczynniki {}\".format(GD[1]))\n",
     "print(\"Oryginalne współczynniki {}\".format(coeffs))"
@@ -329,12 +344,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1440x720 with 1 Axes>"
       ]
@@ -358,7 +373,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.8.9 64-bit",
+   "display_name": "Python 3.10.4 64-bit",
    "language": "python",
    "name": "python3"
   },
@@ -372,12 +387,12 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.9"
+   "version": "3.10.4"
   },
   "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
-    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
    }
   }
  },