diff --git a/Bayes.ipynb b/Bayes.ipynb
index 8a0d0e7..747fff1 100644
--- a/Bayes.ipynb
+++ b/Bayes.ipynb
@@ -98,7 +98,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Preparing data...\n"
+      "Loading prepared data...\n"
      ]
     },
     {
@@ -940,7 +940,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Boxploty dla tempa gatunków"
+    "### Boxplot dla tempa gatunków"
    ]
   },
   {
@@ -957,11 +957,13 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "scrolled": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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n0NBQ1a9fP101pKVgwYIKCAhI8lrevHmdF8C37icp+X1x/Phx7d69W5L09NNPJ/u7GjZsqPvuu0/Xrl3Tn3/+KSnhi4PVq1crLCxMjRo1um0ZwzCUL18+SUrxUT2NGjVyduu+Wbly5SRJdrs9yesTJkzQwYMH1aVLFz366KPJrvNudevW7bbXEgOkpCQh4bfffpMktWnTRgUKFLhtuVy5cik4ONg5r2EYzmmFCxd23k/7zTff6OWXX9b169fVtGlTdenSJcX6OnTo4NyfN0vsXn748OE0v7xZt26dJKlz587JDvBUoEABZ/fr1atXp7qujLhy5Yo2bdok6X/34t6qZMmSznuJ16xZc9v0qlWrJrs/UpO4TR07dkz2c92lSxfnlxuJYmNjtWHDBkkpfz4qVaqkypUryzAM57njZtWqVUvSPThRSsd5osDAwGQ/H4nbkVI9hQsX1mOPPSZJSeopXbq0JGnEiBH6888/FRcX55zWpEkTTZs2Tf369Ut2nQCQUdxzCwC38PHxSdLCkyjxgjS51pubL1ZvDhkpuX79uqKioiQltCwmx9vbWxUrVnT+fPToUUlKsdVNknMQmMyS0n1xiaEluX1x8/2Eifti//79ztcSW7mTc/36dUm6LUDlzp1b586d0z///KMjR47o+PHjOnTokHbv3u1sHU4cMfdWN4ftmyUOIuRwOJyvbdmyRV9//bVKliypt956K8U671ZK90InvqdnzpzR5cuXlT9/fh0+fDjVZW6edv78eV28eFH33Xefc1rTpk3Vvn17LVq0SEeOHFHRokX1wQcfpFrfzb0VblasWDH5+PjIbrc770VPTnR0tPO4rlatWpp1J25jVoiIiHAGq9RqqVatmn788cdka0kuLKYmNjZWR44ckZTy59rLy0tly5ZN0tPgyJEjio2NlSS9//77KY76fPLkSUm3fz4k3TagWqLE4/zGjRvJTk9uG2NiYpwDdX3xxRf6+uuvk102cZ6b63n77bfVv39/hYWFqWfPnsqXL58eeeQR1atXT40aNcqUAe8AICWEWwC4RVrPM02uNeZOXbx40fnv9LYMJS5zpy1JGZFZ++LmVqOUukemNP+ZM2f00Ucf6ZdffknSCpQ3b15Vr15dDocj1cGe0no8TGIAv3LlioYMGSLDMDR27NhM38+enp4p1nJz1+erV68qf/78zlbvm7ur3+rmL2FiYmKShFtJevzxx7Vo0SJJCQH15q6lyUmuhThRvnz5ZLfbdfny5RTniYmJSba2lOq+cuWKDMNItuUwo27uQZGefXhz7YmSGwk4NYnd6aXUP6e37pubj/cdO3ak+XuSa4W9tTU4vZLbxpv3XUrd/VOqp0GDBlq8eLFmzpypdevWKSYmRuvXr9f69es1btw4Pfzwwxo1apQqVKhwV/UCQGoItwBggptDY3IX1aktk975XUnihX7BggWdXUXT4/r16+rRo4cOHjyoggULqnPnzqpWrZrKly+vUqVKyd3dXRMnTsyUkYz//fdfRURESJKzu29yNm/e7GxpXbNmzW2PWElJXFycYmNjkw24N4eDxADq5eWlS5cupdidVFKSe5pvvTf40qVLGjlypKSELyG2bt2qOXPmJLm/8lZXr15NcVpi4EltBOOba0ite35i3fny5cuSYHtrLXa7PcW6E2vJjHurbw60qW3/rZ/hm5fbtm1bpt7nfTduPj+tXLkyyejT6VGlShVNmDBBcXFxCgsL06ZNm7Rx40Zt27ZNW7duVc+ePbVq1aps/aIOwL2Be24BwAT58+d3Xmzf3GX3ZqdPn1aHDh30+uuvy263O7vzpTS/lDCATu/evZM8M9QVJA5yc/HiRZ05cybF+bZs2aKDBw86759dvXq1Dh48KA8PDy1cuFADBw5U06ZNVbZsWed9z6dOncqUGn18fFSzZs0U/0vsou3t7e187U5b9lK6X3XXrl2SEgbqSQwWiV1/d+7cmeL6Elv5ChQocFur7ciRI3Xq1ClVrlzZOVjXZ5995hzwJzkptdIdO3bMGchSa3Hz9vZ2dgNPrQUycVpWdlEtVaqUszUzPbUk3iuaET4+Ps7jZO/evcnOc/36dWfX5UQlS5Z0Hs/JDRaVKDw8XHv37s3yL7jy58/vHAgstXr27t2b5NYAh8Oho0eP6u+//5aU0Jpcq1Ytvfzyy1qwYIEWLFggm82mM2fOZOqgdwCQiHALACZJHIwpcTTkW/3yyy/OZ3H6+PioQYMGcnNz04kTJ5wDLt3s2rVrWrx4sUJDQ9N13292Kl++vDM8zJ8/P9l5tm7dqq5du6pVq1b6559/JCUMRCUltKolF4TOnj3rHMDo5ntn70bVqlX17bffpvhf4iBIN893p/dkJvdeOxwOffvtt5ISBtxJ1LhxY0nSDz/8kKSFNlFsbKxzuVsHEFu+fLl++ukneXp66sMPP1SbNm30+OOPKy4uTm+99Zbz3uZbLV26NNl7lxN/T40aNdJ8Pmli3d9++63zPtKbXbp0ST/88IOk2wckS2zFzYzjN1++fM7n/6Z0z2hERIRz4K7kBke7G82aNZOU8F4ntx3Lli27bf97e3urdu3aadbapUsXPf300/rll18ypdbUJA7gNn/+/GSPCbvdrh49eqhNmzb66quvJCV88da8eXP16NEj2S+xAgMDna3SKd0jDwAZQbgFAJP06dNHuXPn1pYtWzRq1KgkXUI3bNigiRMnSpLzMSwlS5ZU69atJUmDBg3S9u3bnfNfunRJgwYNUlRUlIoXL+58PI4rSXwEyowZMzRz5swkwWfLli3O6TVq1HCOUpzYennp0iV99dVXScLCP//8o169ejnvRU6tS62rCAkJ0YIFC5wX9tHR0Ro0aJB27typAgUK6Pnnn3fO27lzZ/n6+urs2bPq16+fs8u0JJ07d04DBgzQvn375OXlpVdffdU57eTJk86Rkvv27asqVapIShioqECBAtq/f78+/fTTZOvbsWOH3nvvPee+jI+P1/z58zVv3jxJ0uuvv57mNr7wwgvy8vLSvn37NGDAAJ07d845LSIiQv369dPZs2fl6+urHj16JFk2sZtq4sBJGfXKK6/Iw8NDoaGheu+995J0Fd6zZ49eeOEFXb9+XZUrV1abNm0y5Xc+//zz8vLy0vbt2zVy5Mgkx+Wvv/6qjz76KNnlXn31Vbm7u+vHH3/UuHHjkrTO7tu3T3379lVcXJyKFy/uPA9kpb59+ypfvnzaunWr3n77bZ0/f9457cSJE+rbt68uXLggHx8fde3aVVLCIFp+fn5yOBx64403kvSqiI2N1cSJExUdHa18+fKpVq1aWb4NAO493HMLACapUKGCPvroIw0aNEgLFizQsmXLVK5cOZ07d06RkZGSEp6ve/OjW4YPH67IyEht3rxZnTp1UunSpZUvXz4dPnxY165dU8GCBTV58mTnCKmu5Mknn9SRI0c0ZcoUjR8/XtOnT1eZMmV0/vx556irZcuW1RdffOFcpkmTJgoMDNT27ds1duxYzZw5U76+vjpz5oyioqJks9lUr149bdy4UadPn86ywYkyg6enp4KCgjRq1Ch9+eWX8vX11aFDh3TlyhV5eXlp0qRJSUa8zZ8/v6ZNm6a+fftq+/btat68uSpUqCAPDw/t379fcXFxKliwoMaPH+9s1Y6Pj9fgwYNlt9tVqVIl57OSpYRRcYcNG6bBgwfr66+/VuPGjVW3bt0kNfr5+Wnx4sX6+eefVa5cOZ06dUpnzpyRm5ubhg4dmq5HI5UsWVKTJ0/WgAED9Ntvv6lhw4aqUKGCHA6HDhw4oPj4eBUrVkxTp069bbTtqlWr6u+//9aoUaP07bffqkuXLnruuefuep8HBgbqgw8+0Lvvvqvvv/9eK1asUPny5XXlyhXn6Mh+fn6aOnVqmgOPpVfRokX18ccfa+DAgfruu++cv/PcuXM6efKkqlSpogMHDiguLi7JI8UefvhhjR49WiNGjNC8efP03XffqXz58oqJidHRo0dlGIYKFy6s2bNnZ1qtqSldurQ+++wzvf766/rxxx/13//+VxUqVFBcXJyOHDmiGzduKF++fJoxY0aS+5knTpyoTp06afPmzWratKlKlCihvHnz6vjx47p8+bLc3d01atSodD0zGADuFC23AGCili1bavny5Wrfvr3uu+8+7d27V3a7XXXq1NFnn32mcePGJQlr3t7emjt3rkaPHq2HH35Y58+f14EDB1S4cGEFBwdrxYoVqT72xGwvv/yyFi5cqNatW8vb21t79uzRhQsXVLVqVQ0YMEBLlixJcqHs7u6uefPm6a233lKVKlV09epV7du3Tx4eHmrVqpXmz5+vL774Qrlz59bFixfTNRKzWWw2m6ZMmaIBAwYob9682rt3r7y9vfXcc89p+fLltwVNKSHs/fjjj3rppZdUsWJFRURE6MiRIypbtqxefPFFrVixIkmX5NmzZ2vz5s3y8PDQhx9+eNsIum3atFGjRo1kGIYGDx58W3fn7t27a8KECUkeVdOiRQt999136tmzZ7q3NSgoSP/5z3/Us2dPlShRQocPH1ZkZKSqVKmiN998U8uXL0/2EUdjx47VY489Jg8PDx0+fPi2e1PvRps2bbR8+XJ16NBBhQsX1v79+3XhwgXVrFlTw4cP1+LFi1WyZMkM/56bNW3aVEuWLFGrVq3k5eWlPXv2yM3NTf369dOCBQuc8906Gvmzzz6r5cuXq2PHjipSpIj279+vyMhIlS9fXn369NGKFSuc969nh4YNGzrfx1KlSunw4cM6evSoihcvri5dumjFihWqWbNmkmUqVKigZcuWqXPnzipevLhOnjypAwcOKH/+/M7ty46WZwD3JpvhajdmAQCAbNWkSROdOHFCY8aMUfv27c0uJ0e7cuWKAgMDJSXcfpDS82kBAHeOllsAAIBM8s477+iZZ57R0qVLk52+fv16SVLhwoUJtgCQyQi3AAAAmaRSpUras2ePJkyY4HzEU6ItW7Y4B/u6+V56AEDmYEApAMiBFi9enOIjhtIyefLkO37EDYAEHTt21I8//qiwsDC1bdtWJUqUUMGCBXX27Fnn6MEtWrRQv379TK4UAHIewi0A5ECRkZF3PbhSSs9ABZC23Llza/78+frxxx+1bNkyHT16VHv37tV9992nBg0aqG3bti75qC4AyAkYUAoAAAAAYHnccwsAAAAAsLwc2S35woUYxcfTIA0AAAAAOYmbm0333eeV7LQcGW7j4w3CLQAAAADcQ+iWDAAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyPMwuwKrCw7crLGxbhtcTExMtSfLy8s7QegICasrfPzDD9QAAAACAFZkebuPi4jRkyBCdOHFCbm5uGj16tDw8PDRkyBDZbDZVrFhRI0aMkJtbzmxkjo62S8p4uAUAAACAe5nNMAzDzAJWr16tlStXatKkSfrjjz/03XffKS4uTr169VKdOnU0fPhw1a9fX82aNUv3Os+di1Z8vKmblW4hIbMlScHBvU2uBAAAAABcm5ubTfffn3zDoOnNoWXLlpXD4VB8fLyio6Pl4eGhnTt3qnbt2pKkBg0aaOPGjSZXCQAAAAA5i91uV0jILGdvUqszvVtyvnz5dOLECbVs2VIXLlzQtGnT9Pfff8tms0mSvLy8ZLff2c5OKcm7Ik9Pd0lSkSI+JlcCAAAA4F6ybt0viog4pi1b/lD79u3NLifDTA+38+bNU1BQkN58801FRkaqR48eiouLc06PiYlR/vz572idVuqWHBfnkCSdOZMzvi0BAAAA4Prsdrs2bdokwzC0adMm1ar1mLy9Xb/BzaW7JefPn18+Pgk7sUCBArpx44aqVq2qTZs2SZI2bNigWrVqmVkiAAAAAOQooaFrlTj8kmEYCg1dZ25BmcD0cNuzZ0/t3LlTXbp0UY8ePfT6669r+PDhmjJlijp27Ki4uDi1aNHC7DIBAAAAIMfYsSNcDkdCL1KHw6F//w0zuaKMM71bspeXlyZNmnTb6/PnzzehGgAAAADI+apV81dY2DY5HA65u7urevUAs0vKMNNbbgEAAAAA2SsoqLFzEF+bzaagoEbmFpQJCLcAAAAAcI/x8fGRv3+gbDabAgJqWmIwqbSY3i0ZAAAAAJD9goIa6+zZ0zmi1VYi3AIAAADAPcnHx0fBwX3MLiPT0C0ZAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOXxKCAAAAAXEB6+XWFh2zK8npiYaEmSl5d3htcVEFBT/v6BGV4PAGQHwi0AAEAOEh1tl5Q54RYArIRwCwAA4AL8/QMzpZU0JGS2JCk4uHeG1wUAVsI9twAAAAAAyyPcAgAAAAAsj3ALAAAAALA8wi0AAAAAwPIItwAAAAAAyyPcAgAAAAAsj3ALAAAAALA8wi0AAAAAwPIItwAAAAAAyyPcAgAAAAAsj3ALAAAAALA8wi0AAAAAwPIItwAAAAAAyyPcAgAAAAAsj3ALAAAAALA8wi0AAAAAwPIItwAAAAAAyyPcAgAAAAAsj3ALAAAAALA8wi0AAAAAwPIItwAAAAAAyyPcAgAAAHA5drtdISGzFB1tN7sUWAThFgAAAIDLCQ1dq4iIYwoNXWd2KbAIwi0AAAAAl2K32xUevl2GYSgsbButt0gXwi0AAAAAlxIaulaGYUiSDMOg9RbpQrgFAAAA4FJ27AiXw+GQJDkcDv37b5jJFcEKPMwuAACAjAoP366wsG0ZXk9MTLQkycvLO8PrCgioKX//wAyvBwDuRdWq+SssbJscDofc3d1VvXqA2SXBAmi5BQDg/0VH27mvCwBcQFBQY9lsNkmSzWZTUFAjcwuCJdByCwCwPH//wExpJQ0JmS1JCg7uneF1AQDuno+Pj/z9A7V9+xYFBNSUt7eP2SXBAgi3AAAAAFxOUFBjnT17mlZbpBvhFgAAAIDL8fHxUXBwH7PLgIVwzy0AAAAAwPIItwAAAAAAyyPcAgAAAAAsj3tuAQAAAMBCeL578gi3AAAAAHAPSny2e2aEW1dAuAUAAAAAC+H57snjnlsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAOBy7Ha7QkJmOZ/FCqSF59wC97Dw8O0KC9uW4fXExERLypwHgAcE1MyU57YBAABrCw1dq4iIYwoNXacnnmhtdjmwAFpuAWRYdLSdb1UBAECmsdvtCg/fLsMwFBa2jesMpAstt8A9zN8/MFNaSUNCZkuSgoN7Z3hdAGBFq1b9pKioSLPLkCRnHYnnZlfg61tUzZu3MrsMWEho6FoZhiFJMgyD1lukC+EWAAAgg6KiInX6ZKQezF/E7FLk45FPkuQWfcPkShKcunzG7BJgQTt2hMvhcEiSHA6H/v03jHCLNBFuAQAAMsGD+Yvo+Ufbm12Gy5nz1yLFm10ELKdaNX+FhW2Tw+GQu7u7qlcPMLskWAD33AIAAABwKUFBjWWz2SRJNptNQUGNzC0IlkC4BQAAAOBSfHx85O8fKJvNpoCAmvL29jG7JFgA3ZIBAAAAuJygoMY6e/Y0rbZIN8ItAABIE8/FBpDdfHx8FBzcx+wyYCGEWwAAkG0Sn1WZGeEWAICbEW4BAECaeC42AMDVMaAUAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyPMwuAAByuvDw7QoL25ahdcTEREuSvLy8M1xPQEBN+fsHZng9AAAAroRwCwAWEB1tl5Q54RYAACAnItwCQBbz9w/McEtpSMhsSVJwcO/MKAkAACDH4Z5bAAAAAIDlEW4BAAAAAJbnEt2Sp0+frt9++01xcXHq3LmzateurSFDhshms6lixYoaMWKE3NzI4QAAAACA5JmeGDdt2qTt27fr22+/VUhIiE6dOqVx48Zp4MCB+uabb2QYhtasWWN2mQAAAAAAF2Z6y21oaKj8/Pz08ssvKzo6WoMGDdL333+v2rVrS5IaNGigP/74Q82aNTO5UgAAAABpyYxH4Ek8Bg93zvRwe+HCBZ08eVLTpk3T8ePH1b9/fxmGIZvNJkny8vKS3W6/o3Xef791HpXh6ekuSSpSxMfkSoC7x3Gc9djH2YP9nPVy6j729HSXQzfMLsNleXq657j3HCnz8cnj/KxnRGK4LViwQIbX5eOTh2MwGTntnGx6uC1YsKDKlSunXLlyqVy5csqdO7dOnTrlnB4TE6P8+fPf0TrPnYtWfLyR2aVmibg4hyTpzJk7C/CAK+E4znrs4+zBfs56OXUfx8U5zL/Xy4XFxTly3HuOlJUtW0Vly1bJ8HoSH4PXqVPPDK9LynnnncxgxXOym5stxcZM08/DDz/8sH7//XcZhqGoqChdvXpVdevW1aZNmyRJGzZsUK1atUyuEgAAAADgykxvuW3cuLH+/vtvPffcczIMQ8OHD1eJEiX03nvvacKECSpXrpxatGhhdpkAAAAAABdmeriVpEGDBt322vz5802oBAAAAABgRaZ3SwYAAAAAIKMItwAAAAAAyyPcAgAAAAAsj3ALAAAAALA8wi0AAAAAwPIItwAAAAAAy3OJRwFlp1WrflJUVKTZZTgl1hISMtvkShL4+hZV8+atzC4DAAAAAO7IPRduo6IidSIyUl73+ZpdiiTJlstLknTxWrzJlUgxF6LMLgEAAAAA7so9F24lyes+X/k3DTa7DJcTvjrE7BIAAAAA4K7ck+EWAAAgM0VHRyvm8mXN+WuR2aW4nMjLp+Wl/GaXAeAewIBSAAAAAADLo+UWAAAgg7y9vZVfefT8o+3NLsXlzPlrkeK9ueQEkPVouQUAAAAAWB7hFgAAAABgeYRbAAAAAIDlEW4BAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDlEW4BAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDlEW4BAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDleZhdAAAAQE5w6vIZzflrkdllKPp6jCTJO7eXyZUkOHX5jB7wLmp2GQDuAYRbAACADPL1TQhv8SbXIUn2mCuSpHz3FzC5kgQPeBd17h8AyEqEWwAAgAxq3ryV2SU4hYTMliQFB/c2uRIAyF7ccwsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPAaXgssLDtyssbFuG1xMTEy1J8vLyzvC6AgJqyt8/MMPrAQAAAJC5CLfI8aKj7ZIyJ9wCAAAAcE2EW7gsf//ATGkl5ZEIAAAAQM7HPbcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMtjtGQAgGlWrfpJUVGRZpfhlFhL4ijrZvP1LarmzVuZXQYAAJZAuAUAmCYqKlKnIk+pUMGiZpciScqTy0eSFHvVZnIl0vmLrhP6AQCwAsItAMBUhQoW1VON+pldhsv5cd10SYbZZQAAYBnccwsAAAAAsDzCLQAAAADA8gi3AAAAAADL455bAAAAAMgGPCUgbRl5UgDhFgAAAACyQVRUpCKjIlXA19fsUiRJnl5ekqQrije5kgSXoqIytDzhFgAAAACySQFfX9Xv3s3sMlzS71/Pz9DyhFvAgujSkraMdGkBAACA9RBuAQuKiorU8VMRylM4n9mlSJKMvDZJ0tkb50yuJMG1s1fMLgEAAADZjHALWFSewvlUuu1DZpfhko4u22l2CQAAAMhm91y4jY6OVozdrvDVIWaX4nJiLkTJw8fH7DIAAAAA4I7xnFsAAAAAgOXdcy233t7euuGRT/5Ng80uxeWErw6Rdx6+7wAAAABgPSQZAAAAAIDl3XMttwAA3Et4dFjaeHQYAOQMhFsAAHKwqKhInToZofvz5zW7FElSnv+/8oiLPmtuIf/v3OWrZpcAAMgkhFsAAHK4+/Pn1dP1KptdhktasXGP2SUAADIJ99wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDwPswsAAAAAYL5Vq35SVFSk2WU4JdYSEjLb5Er+x9e3qJo3b2V2GUgB4RYAAACAoqIidfp0hB580NvsUiRJPj4JnUzd3C6YXEmCU6eizS4BaSDcAgAAAJAkPfigt/r0CTS7DJc0a9Z2xcdnbB3R0dG6HGPX71/Pz5yicpiLUVGK9/K56+W55xYAAAAAYHm03AIAAABANvD29pabdz7V797N7FJc0u9fz1e+DLS/Em4BIAWuNLAGg2oAAACkjnALACmIiorU6RMRetDLy+xS5GOzSZLcLp43uZIEp2JizC4BAAAgCcItAKTiQS8v9fF/yOwyXM6s8J3K4JgaAAAAmYoBpQAAAAAAlnfX4bZ169aKjHSNe9EAAAAAAPe2VLslT506NcVphw8f1pw5c1SgQAFJ0iuvvHLXRZw7d07t2rXTnDlz5OHhoSFDhshms6lixYoaMWKE3NxoYAYAAAAApCzVcLt48WJFRUXJ19dXuXLlSjLN4XBozZo18vDwkM1mu+twGxcXp+HDhytPnjySpHHjxmngwIGqU6eOhg8frjVr1qhZs2Z3tW4AAAAAwL0h1SbRlStXqmXLlsqdO7c++eQTrVq1yvlfnjx59NVXX2nVqlX673//e9cFfPTRR+rUqZMeeOABSdLOnTtVu3ZtSVKDBg20cePGu143AAAAAODekGrLrY+PjyZMmKD//Oc/evHFF9WhQwe99tprcnd3z5RfvnTpUhUqVEj169fXjBkzJEmGYcj2/4+88PLykt1uv+P13n+/d4rTPD3dpWuM8ZkST093FSniY3YZmcrTM+F4zUnb5enpLt0wuwrXlhnHsqenuxyZVE9OlFn7OPYq5+SUZNY+jsukenKqnPa3Lyf+3UP28PR0l4M/fKnK6PnC09NdiuPvXmoyso/T9SigJ598UrVq1dKwYcPUrl07ffTRR84AmhFLliyRzWbTn3/+qd27d2vw4ME6f/5/z3CMiYlR/vz573i9585FKz7eSHZaXByf2NTExTl05sydf6HgyhLf85y0XRzHacuMYzkuzsGQ8qnIrH0sZfzvSU6VefsYqclpf/ty4t89ZI+4OIcY6iZ1GT1fcE5OW1r72M3NlmJjZrqfc+vr66vZs2fr66+/Vrdu3XT9+vU7r/QWCxYscP47ODhYI0eO1CeffKJNmzapTp062rBhgx599NEM/x4AgGuKjo6W3R6tH9dNN7sUl3PuYqR8HCn3RAIAAEnd8Xcz3bt318KFC9W/f3/nSMmZafDgwZoyZYo6duyouLg4tWjRItN/BwAAAAAgZ0l3y60kXbt2TQcOHFBcXJzq1aunAwcOOKfVrFkzQ4WEhIQ4/z1//vwMrQsAYA3e3t7K5e6jpxr1M7sUl/PjuunKlTf5W2wAAMDt0h1uV69eraFDhyo6Oto56JNhJPzRtdls2r17d5YVCQAAAABAatIdbqdOnapatWppwIAB8vFh9D0AAIDMFB6+XWFh2zK8nqioSElSSMjsDK8rIKCm/P0DM7weAMgO6Q63R44c0fjx41WhQoWsrAcAAAAZ4O1NIwSAe1O6w225cuUUFRVFuAUAAMgC/v6BtJICQAakO9y++OKLGjlypPr06aPSpUsrV65cSaZndEApAAAAAADuVrrD7WuvvSZJGjFixG3TGFAKyF7R0dG6Fn1FR5ftNLsUl3Tt7BVFe+c2uwzAJURHRyv68hWt2LjH7FJc0rnLV+StaLPLAABkgnSH2zVr1mRlHQAAAAAA3LV0h9vixYunOO3UqVOZUkx2ibkQpfDVIWnPmA1iryZ8W5wrr7fJlSTsl4JFi5pdBtLB29tb1/JcV+m2D5ldiks6umynvD3M/0wBrsDb21u5dU1P16tsdikuacXGPfL05nwBADlBusNtRESEPvroI+3bt08Oh0OSZBiGYmNjdf78ee3atSvLisxMvr6uFd6iLsVIkgrel9/kSqSCRYu63P4BAAAAgPRId7gdOXKkTpw4odatW2v69Ol64YUXdPToUf38888aNWpUVtaYqZo3b2V2CUkkPoMuOLi3yZUAAAAAgHWlO9xu375dM2bMUK1atbR27Vo1bNhQNWrUULly5bRmzRq1b98+K+sEAAAAACBF6Q63N27ccN53W7ZsWe3Zs0c1atRQ69at9e2332ZZgQAAAEBmCQ/frrCwbRleT0xMwrgpXl4Zu2c7IKAmzzcGMolbemcsXbq0wsLCJCWE2x07dkiSrl69qitXrmRNdQAAAIALio62KzrabnYZAG6S7pbbLl26aMiQIYqPj1eLFi3Utm1b5c2bV1u3blVAQEBW1ggAAABkCn//wExpKWXcFMD1pDvcdu7cWYUKFVKhQoVUsWJFffDBBwoJCVHhwoX13nvvZWWNAAAAALJYdHS0YmKiNWvWdrNLcUmRkdHy8vI0uwykIt3hVpJatGjh/PczzzyjZ555JtMLAgAAAADgTt1RuP377781ffp0HTp0SCEhIVq6dKlKliypNm3aZFF5AAAAALKDt7e38uePU58+DHCVnFmztis+PmMDiCFrpXtAqfXr16tPnz4qWrSozp49q/j4eNlsNr3zzjtasmRJVtYIAAAAAECq0h1up06dqkGDBmn06NFyd3eXJL3yyisaPHiw5syZk2UFAgAAAACQlnSH2wMHDqhBgwa3vd64cWNFRERkalEAAAAAANyJdN9ze9999ykiIkIlS5ZM8vqOHTtUuHDhTC8MAMyWMGpkjGaF7zS7FJcTGRMjL49cZpcBAADglO6W2w4dOuj999/X+vXrJUnHjh3T4sWLNXr0aLVt2zbLCgQAAAAAIC3pbrnt16+f7Ha7Xn31VcXGxqp3797y8PBQr1699NJLL2VljQBgCm9vb+W/Eas+/g+ZXYrLmRW+U/HejBgJAABcR7rDrc1m09tvv62XX35ZBw8elKenp8qUKaM8efJkZX0AAAAAAKTpjp5za7fbdfToUcXFxSkuLk67du1yTqtZs2amFwcAAAAAQHqkO9z+8MMPGjFihGJjY2UYRpJpNptNu3fvzvTiAAAAAABIj3SH288++0xPP/20evbsSVdkAAAAAIBLSXe4vXTpknr37q0yZcpkYTkAAADICLvdrh9+WKi2bTvK29vH7HIAINuk+1FAjz/+uEJDQ7OyFgAAAGRQaOhaRUQcU2joOrNLAYBsle6W20GDBql169b673//q1KlSsnNLWkuHj16dKYXBwAAgPSz2+0KD98uwzAUFrZNQUGNaL0FcM9Id7gdO3asYmJidPXqVR07dizJNJvNlumFAQAA4M6Ehq51DvxpGIZCQ9fpiSdam1wVAGSPdIfbdevW6csvv1T9+vWzsh4AAADcpR07wuVwOCRJDodD//4bRrgFcM9I9z239913n4oVK5aVtQAAACADqlXzl7u7uyTJ3d1d1asHmFwRAGSfdLfcDhgwQGPHjtXIkSNVsmTJrKwJFrdq1U+Kioo0uwynxFpCQmabXMn/+PoWVfPmrcwuAwCQwwQFNVZ4+HZJCbeNBQU1MrcgAMhG6Q6306dPV0REhJo3by5Jzm8FE+3YsSNzK4NlRUVFKurkMT3ok8vsUiRJ3u7xkiSb/ZTJlSQ4ZY81uwQAQA7l4+Mjf/9Abd++RQEBNRlMCsA9Jd3htm/fvllZB3KYB31yqVed4maX4ZLmbjohw+wiAAA5VlBQY509e5pWWwD3nHSH27Zt26Zrvt69e2vcuHF64IEH7rooAAAA3B0fHx8FB/cxuwwAyHbpHlAqvbZt26br169n9moBAAAAAEhRpodbAAAAAACyG+EWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYXqaHW5vNltmrBAAAAAAgVel+zm16ubu7Z/YqAQAAAGSDU6eiNWvWdrPLkCRFR8dKkry9c5lcSYJTp6L1wAP3ZXg9l6Ki9PvX8zOhooy7Fh0tScrj7W1yJQkuRUUpn2/Ru17+rsPtjBkz1KlTJ+XPnz/J63///fddFwMAAADAHL7/Hyri400u5P/Z7ZGSpHz5Mh4oM8MDD9zn3Ed3K6PLZzZ7TIwkqZB3/jTmzB75fItmaB+lGm5PnjyZ4rQvv/xSNWvWVLFixSTJ+X8A2ePa2Ss6umyn2WVIkm5ciZMkeeTzNLmSBNfOXpEevN/sMgAAsJTmzVuZXUISISGzJUnBwb1NriTzsI+zVqrhtkmTJineQ2sYhoKDg2UYhmw2m3bv3p0lBQK4nat96xd1NeGb1cL5XSRQPni/y+0jwEznLl/Vio17zC5DknTlesKXYflyu8aXYecuX9WDrtEbDwCQQamG288++0wjR45UpUqV9NJLL8nDI2F2wzDUp08fjR07Vr6+vtlSKID/4Vs/AOnlal/0XItJ+DKswP2FTa4kwYPerrePAAB3J9Vw+8QTT+jhhx/WsGHD9OGHH+rjjz9WxYoVJSWMily9enWVLFkyWwoFAORM5y9G6sd1080uQ5J09ZpdkpQ3j4/JlSTslwfzPpjh9fBlGADgXpHmgFJFihTRzJkztWDBAnXt2lUvvPCC+vTpkx21AQByuP+1mBmm1pHowuWEcFvgPvP7qT6Y90FaFAEAuAPpHi25a9euqlevngYPHqw1a9bI4XBkZV0AgHsArYoAACCzuN3JzGXLltW3336roKAgFSlSxHkPLgAAAAAAZrqjcLtx40b9+eefeuWVV7RmzRrNmTNHf/31V1bVBgAAAABAuqQ73P7www/q27evDh065Hzt0qVL6tOnj37++ecsKQ4AAAAAgPRId7/iGTNmaMSIEWrfvr3ztY8//li1atXSF198oZYtW2ZJgQAAAAAApCXdLbcnTpzQo48+etvrdevW1bFjxzK1KAAAAAAA7kS6w22pUqW0fv36217/448/VLQojyoAAAAAAJgn3d2Se/furXfffVe7du1S9erVJUk7duzQihUrNHz48CwrEAAAAACAtKQ73LZp00a5cuXS119/rZ9//lmenp4qV66cJk6cqKZNm2ZljQAAAAAApOqOHlTbqlUrNW3aVLly5ZIknTx5UsWKFcuSwgAAAAAASK9033N75swZdenSRVOnTnW+9uyzzyo4OFjnz5/PkuIAAAAAAEiPdIfbMWPGyGazqV27ds7X5s+fr/j4eH344YdZUhwAAAAAAOmR7m7Jf/75pxYsWKAyZco4Xytfvrzee+899ezZMwtKAwAAAAAgfdLdcmuz2XT16tXbXnc4HIqLi8vUogAAAAAAuBPpbrkNCgrS2LFjNWHCBOcgUpGRkfrwww/12GOPZVmBsJ7o6GjF2K9r7qYTZpfikk7Zr8vLFm12GQAAAECOku5wO2zYMPXq1UuPP/64ChUqJEk6f/68qlatqvHjx2dZgQAAAAAApCXVcLtr1y5VqlRJ7u7uuv/++7Vs2TJt3LhR+/fvl4eHh8qXL6969erJZrNlV72wAG9vb/kY0epVp7jZpbikuZtOyPD2NrsMAAAAIEdJNdx269ZNP/30kx588EF1795dU6dOVf369VW/fv3sqg8AAAAAgDSlGm49PT21aNEi1alTR5s3b9bmzZtVoECBZOd95JFHsqRAAAAAAADSkmq47d27tyZOnKjPP/9cNptNr7zySrLz2Ww27d69O0sKBAAAAAAgLamG2759+6pbt26y2+1q2LChli1b5hxMCgAAAAAAV5HmaMn58uVTvnz59PXXX6tixYry8Ej3AMsAAAAAAGSLdCfVwMBALV68WPv371dsbOxt00ePHp2phQEAAACJVq36SVFRkWaX4ZRYS0jIbJMrSeDrW1TNm7cyuwzAVOkOt0OGDNGqVatUpUoV5c6dO8k0HgUEAACArBQVFanIyBMuc4tc4vXw9etXTa5EOn/+vNklAC4h3eF2/fr1mjBhgpo1a5aV9QAAAADJKlSokFq2bGl2GS7n559/NrsEwCW4pXdGb29vlS1bNitrAQAAAADgrqQ73Pbt21cff/yxTp48mZX1AAAAAABwx9LdLfmhhx7SpEmT9Pjjj8vNze22+2x37NiR6cUBAAAAAJAe6Q63w4YNU5kyZfT0008rX758WVkTALiMUzExmhW+0+wyFP3/o9R758plciUJTsXE6IGCrjGoCwAAgHQH4TYiIkIrVqxQmTJlsrAcAHAdvr5FJUnxJtchSfb/f+REPhcJlA8ULOTcPwAAAK4g3eG2WrVqOnr0aKaH27i4OA0bNkwnTpxQbGys+vfvrwoVKmjIkCGy2WyqWLGiRowYITe3dN8eDACZwpWeF5j4HMXg4N4mVwIAAOCa0h1uu3btqnfeeUft27dXqVKl5OGRdNHWrVvfVQErVqxQwYIF9cknn+jChQtq27atKleurIEDB6pOnToaPny41qxZwyOIAAAAAAApSne4ffPNNyVJX3755W3TbDbbXYfbJ554Qi1atHD+7O7urp07d6p27dqSpAYNGuiPP/4g3AIAAAAAUpTucLtnz54sKcDLy0uSFB0drddee00DBw7URx995ByN2cvLS3a7PUt+NwAAAAAgZ0h3uM1KkZGRevnll9WlSxe1bt1an3zyiXNaTEyM8ufPf0fru/9+78wuMct4erpLkooU8TG5kszj6emuG2YX4eI8Pd1z3Hsu5azj2NWwj7MH+znrsY9xtzw93XX9utlVuK6cdm0hcb7IDjltH5sebs+ePavnn39ew4cPV926dSVJVatW1aZNm1SnTh1t2LBBjz766B2t89y5aMXHG1lRbqaLi3NIks6cyTmt03FxDtnSnu2eFhfnyHHvuZSzjmNXwz7OHuznrMc+xt1KPHaQvJx2bSFxvsgOVtzHbm62FBszTR+CeNq0abp8+bK++OILBQcHKzg4WAMHDtSUKVPUsWNHxcXFJbknFwAAAACAW5necvvuu+/q3Xffve31+fPnm1ANAAAAAMCKTG+5BQAAAAAgowi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDzCLQAAAADA8gi3AAAAAADLI9wCAAAAACyPcAsAAAAAsDwPswuwqvDw7QoL25bh9URFRUqSQkJmZ2g9AQE15e8fmOF6AAAAXFF0dLTs9kv6+eefzS7F5Zw/f14+PgXMLgMwHeHWZN7ePmaXAAAAAACWR7i9S/7+gbSUpuKUPVZzN50wuwxJUvR1hyTJO7e7yZUkOGWPlS/faQAAcEe8vb3l6emuli1bml2Ky/n555+VO3des8sATEe4Rabz9S0qSTJMriNR9JWErt9ePg+aXEkCX5//7SMAAAAAmYNwi0zXvHkrs0tIIvF+5uDg3iZXAgAAACCrMFoyAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyGC0ZuIeFh29XWNi2DK8nKirhcUuJI1NnREBATZ4hDQAAgDtGuAWQYd7ePmaXAAAAgHsc4Ra4h/n7B9JKCgAAgByBe24BAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDlEW4BAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDlEW4BAAAAAJZHuAUAAAAAWJ6H2QUAAAAA6XH+/Hn9/PPPZpchSbp69aokKW/evCZXkrBfihYtbnYZgOkItwAAAHB5vr5FzS4hiYsXL0qSChYsZG4hkooWLe5y+wcwA+EWAAAALq9581Zml5BESMhsSVJwcG+TKwGQiHALALC88PDtCgvbluH1REVFSvrfRWtGBATUlL9/YIbXAwAA0odwCwDA//P29jG7BAAAcJcItwAAy/P3D6SVFACAexyPAgIAAAAAWB7hFgAAAABgeYRbAAAAAIDlcc8tAAAAgEzDCPYwC+EWAAAAgMthBHvcKcItAAAAgEzDCPYwC+EWAACkiW6GALKb3W7XDz8sVNu2HWnFRbowoBQAAMg23t4+XKQCSJfQ0LWKiDim0NB1ZpcCi6DlFgAApIluhgCyk91uV3j4dhmGobCwbQoKasQXY0gTLbcAAAAAXEpo6FoZhiFJMgyD1lukC+EWAAAAgEvZsSNcDodDkuRwOPTvv2EmVwQrINwCAAAAcCnVqvnL3d1dkuTu7q7q1QNMrghWQLgFAAAA4FKCghrLZrNJkmw2m4KCGplbECyBcAsAAADApfj4+MjfP1A2m00BATUZTArpwmjJAAAAAFxOUFBjnT17mlZbpBvhFgAAAIDL8fHxUXBwH7PLgIXQLRkAAAAAYHmEWwAAAACA5RFuAQAAAACWxz23AAAAAGAh4eHbFRa2LcPriYqKlCSFhMzO8LoCAmrK3z8ww+vJCFpuAQAAALgcu92ukJBZio62m11KjuXt7ZOjHrNEyy0AAAAAlxMaulYREccUGrpOTzzR2uxyXIq/f6DpraSuiJZbAAAAAC7FbrcrPHy7DMNQWNg2Wm+RLoRbAAAAAC4lNHStDMOQJBmGodDQdeYWBEsg3AIAAABwKTt2hMvhcEiSHA6H/v03zOSKYAWEWwAAAAAupVo1f7m7u0uS3N3dVb16gMkVwQoItwAAAABcSlBQY9lsNkmSzWZTUFAjcwuCJRBuAQAAALgUHx8f+fsHymazKSCgZo56XA2yDo8CAgAAAOBygoIa6+zZ07TaIt0ItwAAAABcjo+Pj4KD+5hdBiyEbskAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAABwOXa7XSEhsxQdbTe7FFgE4RYAAACAywkNXauIiGMKDV1ndimwCMItAAAAAJdit9sVHr5dhmEoLGwbrbdIF8ItAAAAAJcSGrpWhmFIkgzDoPUW6UK4BQAAAOBSduwIl8PhkCQ5HA79+2+YyRXBCgi3AAAAAFxKtWr+cnd3lyS5u7urevUAkyuCFbhkuI2Pj9fw4cPVsWNHBQcH6+jRo2aXBAAAACCbBAU1ls1mkyTZbDYFBTUytyBYgkuG29WrVys2NlYLFy7Um2++qQ8//NDskgAAAABkEx8fH/n7B8pmsykgoKa8vX3MLgkW4GF2AcnZunWr6tevL0mqUaOGduzYYXJFAHD3wsO3KyxsW4bWERUVKUkKCZmd4XoCAmrK3z8ww+sBACArBQU11tmzp2m1Rbq5ZLiNjo6Wt7e382d3d3fduHFDHh7pK/f++73Tngn3DE/PhPs1ihThGz+Yw8cnj/M4vFsFChSQpAyvJ7EePg8AkDFcX2S9IkV89MYbr5tdBizEJcOtt7e3YmJinD/Hx8enO9hK0rlz0YqPN7KiNFhQXFzCSHtnzvB8NJijbNkqKlu2itllJMHnAQAyhusLwBxubrYUGzNd8p7bmjVrasOGDZKkf/75R35+fiZXBAAAAABwZS7ZctusWTP98ccf6tSpkwzD0NixY80uCQAAAADgwlwy3Lq5uWnUqFFmlwEAADKZ3W7XDz8sVNu2HRn9FACQqVyyWzIAAMiZQkPXKiLimEJD15ldCgAghyHcAgCAbGG32xUevl2GYSgsbJuioxmIBwCQeQi3AAAgW4SGrpVhJDzNwDAMWm8BAJmKcAsAALLFjh3hcjgSHp/icDj0779hJlcEAMhJCLcAACBbVKvmL3d3d0mSu7u7qlcPMLkiAEBOQrgFAADZIiiosWw2myTJZrMpKKiRuQUBAHIUwi0AAMgWPj4+8vcPlM1mU0BATR4FBADIVC75nFsAAJAzBQU11tmzp2m1BQBkOsItXFZ4+HaFhW3L8HqioiIlSSEhszO8roCAmvL3D8zwegDgXuXj46Pg4D5mlwEAyIEIt8jx6PYGAAAA5HyEW7gsf/9AWkkBAAAApAsDSgEAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMuzGYZhmF1EZjt3Llrx8TluswAAAJBB4eHbFRa2LcPriYqKlCT5+hbN0HoCAmrK3z8ww/UA9wo3N5vuv9872Wke2VwLAAAAYHne3j5mlwDgFrTcAgAAAAAsIbWWW+65BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAuEN2u10hIbMUHW03uxQA/49wCwAAANyh0NC1iog4ptDQdWaXAuD/EW4BAACAO2C32xUevl2GYSgsbButt4CLINwCAAAAdyA0dK0Mw5AkGYZB6y3gIgi3AAAAwB3YsSNcDodDkuRwOPTvv2EmVwRAItwCAAAAd6RaNX+5u7tLktzd3VW9eoDJFQGQTA63drtdL774orp166aOHTtq+/btkqR//vlH7du3V6dOnTR16lQzSwQAAACSCApqLJvNJkmy2WwKCmpkbkEAJJkcbufOnatHH31U8+fP17hx4zRq1ChJ0ogRI/Tpp5/q22+/VVhYmHbu3GlmmQAAAICTj4+P/P0DZbPZFBBQU97ePmaXBECSh5m/vGfPnsqVK5ekhPsVcufOrejoaMXGxqpUqVKSpKCgIP3555966KGHzCwVAAAAcAoKaqyzZ0/Tagu4kGwLt4sWLdJXX32V5LWxY8fK399fZ86c0dtvv61hw4YpOjpa3t7eznm8vLwUERGRXWUCAAAAafLx8VFwcB+zywBwk2wLt+3bt1f79u1ve33v3r164403NGjQINWuXVvR0dGKiYlxTo+JiVH+/Pnv6Hfdf7932jMBAAAAAHIMU7slHzhwQAMGDNBnn32mypUrS5K8vb3l6empY8eOqWTJkgoNDdUrr7xyR+s9dy5a8fFGVpQMAAAAADCJm5stxcZMm5H4BGoT9O/fX3v37lXx4sUlJQTbL7/8Uv/884/Gjh0rh8OhoKAgvf7663e0XsItAAAAAOQ8LhtuswrhFgAAAAByntTCramPAgIAAAAAIDMQbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYHuEWAAAAAGB5hFsAAAAAgOURbgEAAAAAlke4BQAAAABYnofZBWQFNzeb2SUAAAAAADJZalnPZhiGkY21AAAAAACQ6eiWDAAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMItAAAAAMDyCLcAAAAAAMsj3AIAAAAALI9wCwAAAACwPMJtJjh+/LgqVaqko0eP3jZt6dKlatCggQlVWVtq+zSzbNq0SZUqVdKNGzcytB7e4/Q5d+6cfvrpJ7PLcEkTJ05UcHAwx9Jdyqxz8JQpU9S5c+fMLu+ekB3nbNy9Ozn/ZtbfRgA5Q3BwsCZOnGh2GenmYXYBgFkCAwMVGhoqDw8+Btlh/PjxiouLU6tWrcwuxWW1atVKjRo1MruMHIV9mj2KFi2q0NBQFSpUyOxSkAzOvwDuFVzV456VK1cuFSlSxOwy7hmGYZhdgsvLkyeP8uTJY3YZOQr7NHu4u7tzPnVhnH8B3CvolpyJVq1apYYNG6pmzZoaM2bMbV16kuvqM2TIEL311lvOn1evXq0nn3xSAQEBatu2rTZs2OCctnfvXnXt2lU1atTQY489pg8//DBHdBuKiIhQv379FBgYqAYNGmjatGm3zXPw4EH16dNHgYGBql69ujp37qz9+/c7p0+aNEn169dX9erV1bFjR23fvj3Nabe+H6nVsX37dnXp0kUBAQGqUaOGevfuraioqKzaJVkupW09deqUBgwYoNq1a6tOnToaNWqUrl+/Lin57p03d1UZMmSIxowZozfeeEM1atRQixYttHTpUkkJ3T2XLVumlStXqkmTJpKkSpUq6bPPPtOjjz6qnj17qmXLlpo5c2aS9Xfo0EFz587N0n1hhgMHDqhz584KCAhQr169dPHiRUm37+PUjutdu3apW7duCggI0OOPP67Fixc7px08eFC9e/dWzZo1FRQUpClTpig+Pj7bts8sv/32m5o1ayZ/f3/169dPFy5cSLJPN23apMcee0wLFixQnTp1VLduXU2dOjXJOm7cuKExY8bo4YcfVt26dTVr1izntPj4eM2aNUtNmzaVv7+/unXrpj179jinV6pUSd9//72aNWumwMBAvfHGG4qOjs6ejTfZzd2SUztfDxkyRJUqVbrtv82bNys4ODjZaSdOnDB568yRuE/XrFmjJk2aKDAwUB9++KH27t2rdu3aqUaNGnrxxRd15coVSdLChQv1+OOPKzAwUJ07d1Z4eLik5M+/af1Nvdcl7vsVK1aoQYMGqlWrlkaNGqW4uDhJCdcEnTt3Vo0aNdSkSRMtWLDAueyQIUP0/vvvq3///vL399czzzyjLVu2mLUp2S5x333++ed65JFHNHTo0FSvbePj4zV+/HjVqVNHderU0RdffKFmzZpp06ZNkqRr167pnXfe0cMPP6z69etr0aJFqlq1qo4fPy4p7WN5//796t69u/z9/dWsWTPNmTPnnviyJ7n3Ye3atWrbtq38/f3VsmVL/fzzz875HQ6HJk+erPr166tmzZrq37+/Tp8+fdt6T5w4oaCgIH300UfZuTl3hHCbiRYtWqQJEyZo2rRpWr16taZMmXJHy+/Zs0dvv/22XnjhBa1cuVIdOnTQK6+8ot27d0uS3n77bZUrV04rV67UZ599puXLlye5oLWi2NhY9e7dWx4eHlq4cKE++OADzZo1SytXrnTOYxiGXnrpJRUrVkzLly/Xd999p/j4eH388ceSpF9//VULFizQ+PHj9dNPP6lq1ap67bXXFB8fn+q09NSxYsUKRUdHq1+/fqpXr55+/PFHzZ49W8ePH9eXX36Zrfsqs6S0rcuWLVOPHj105coVff3115o0aZI2bNigDz/8MN3r/u6771SlShUtXbpUQUFBGjlypC5evKjnn39eLVu2VIsWLZIcs2vWrNE333yjd955R08++WSSE+2JEyf077//qmXLlpm6/WaLjY1V3759VaJECS1dulRNmzbVokWLbpsvtWP3/Pnz6tmzp8qVK6dly5bp9ddf18iRI7VlyxadP39eXbp00QMPPKBFixZp5MiRWrBggebMmWPC1mavpUuX6tNPP1VISIh27dqlGTNm3DbPxYsXtWTJEs2ZM0ejR4/W3Llz9c033zinJwaCZcuWqV+/fvrkk0+0d+9eSdLnn3+uOXPmaOjQoVq2bJlKlCihPn36JAmwkydP1rBhw/T1119r//79evfdd7N4q11LWufrd955R6Ghoc7/mjRpoho1aigwMFBTpkxxvr5hwwZVq1ZNLVq0UPHixU3eKnPNnDlTX3zxhUaOHKm5c+fqtdde09tvv62ZM2fq77//1pIlS/Tbb79p0qRJzmOzQYMG6tGjh06fPn3b+Tet9wj/8/nnn2vChAn6/PPPtXr1an322Wc6ePCgevTooUceeUTLli3Tq6++qk8++STJ369FixapfPnyWrZsmerUqaO+ffvq7NmzJm5J9tuyZYuWLFmiHj16pHptO336dP3www8aP3685s6dq3Xr1ikiIsK5njFjxmjr1q2aNWuWJk6cqFmzZsnhcEhK+3xz7do19enTRzVq1NCKFSv07rvv6quvvtL8+fOzf4eYJPF9qFGjhl599VU988wzWr58uTp27Ki33noryZdg33//vcaMGaNFixbp+vXrGjx4cJJ1XbhwQX369FGDBg1um+ZSDGRYRESE4efnZ6xZs8b52tKlS43atWsbS5YsMerXr28YhmH89ddfhp+fnxEXF+ecb/Dgwcabb75pGIZhvPXWW8bo0aOTrHvIkCHG0KFDDcMwjJo1axqffvqpcePGDcMwDOPff/81IiIisnTbstratWuNgIAA4/Lly87Xli9fbsydO9fw8/Mzjhw5YsTExBgzZswwoqOjnfN8++23RqNGjQzDMIy5c+cadevWNY4dO2YYhmHY7XZj48aNRlxcXKrTbn4/Uqrj119/NU6fPm3MmjXLiI+Pd04bP3680bVrV8MwjCTvsRWktK3Lli0z/P39jQsXLjhfX79+vVGlShXj8uXLyW5nt27djAkTJhiGkXAst23b1jnNbrcbfn5+xubNm53TE491wzAMPz8/IyQkxPnzkSNHDD8/P+d7NXPmTKNbt26Zt+EuYu3atUaNGjWSHM+vvvqq0a1btyT7OLVjd/78+UajRo2c5wLDMIyQkBBj06ZNxldffWXUr1/fiI2NdU775ptvjDp16mTTFma/xHPwunXrnK998MEHRs+ePZM9B+/cudM536RJk4ynn37aMAzDmDx5svHYY48ZDofDOb1WrVrG8uXLjfj4eKN27drGggULnNNiY2ONhg0bGvPnzzcMI+GYnjt3rnP6n3/+aVSuXDnJZyqnSnwPdu/ener5+mbffPONUbt2bePkyZO3Tfvkk0+MZs2aGXa7PUvrdmXJHde1a9c2Jk2a5Pz5xRdfNEaOHGl07tw5ybFnGAnn56lTpxqGkfT8m9bf1OSuVe41ift+1apVztcWL15s1K5d2xg7dqzx3HPPJZn/k08+Mdq1a2cYRsK+bt26tXOaw+EwmjRpctv7k1Ml7rvffvvNMIy0r22DgoKM7777zjnt4MGDhp+fn/HXX38Z0dHRxkMPPWT8/vvvzukbNmww/Pz8jIiIiDSP5e+//z7Je2EYCdfnjz/+eOZutAu69X14+eWXjQEDBiSZZ+DAgcarr75qxMfHG48++qjx/fffO6cdPXrU+PTTTw2Hw2F069bNGDdunNGxY0fj1VdfTXLt4Yq45zYTVa9e3fnvqlWr6uLFizp//ny6lz948KD27dunJUuWOF+Li4uTv7+/JOmNN97QmDFjtHDhQjVo0EBPPvmkqlWrlnkbYIIDBw6oVKlS8vHxcb729NNP6/jx4xo3bpwkKV++fOrSpYuWL1+uHTt26NChQ9q1a5cKFiwoSWrdurWWLl2qZs2aqXr16mrSpImee+45eXh4pDotPXUkatu2rebNm6fdu3frwIED2rt3r/N9sZqUtnXGjBkqVaqUc79KUs2aNeVwOHTkyJF0rbtkyZLOf3t7e0tSql3nb26RKV26tKpXr66ff/5Zffv21U8//aQOHTqkc6us48CBAypZsqS8vLycr1WrVk2///57kvlSO3YPHDigypUry93d3Tl/t27dJEn/+c9/VLVqVXl6ejqnBQYG6sKFCzp//nyOHvDn5uPPx8fH2aX+Zrlz51bVqlWdP1erVi1JC2/x4sXl5va/Tk2J6zl37pwuXryogIAA5zRPT09Vq1ZNBw8edL4WGBiYZN3x8fE6fPhwktdzsrx586Z6vk4UHh6ucePGacqUKSpatGiSaatXr1ZISIi+++4753nkXlaiRAnnv3Pnzq1ixYo5f86TJ49iY2N18OBBTZgwQZMmTXJOi42N1YMPPnjb+tL6m4r/ufXzfPHiRe3atSvJeSBxvpu7Jt+8nJubm6pWrapDhw5lfcEuJPHve2rXtufPn9fp06eTXD+XK1dOBQoUkCQdOnRIcXFxSabfvG/TOpYPHTqkAwcOJFkmPj5esbGxio2NVa5cubJk213Jze/DrddUgYGB+v77753XBw899JBzWqlSpfTGG284f16wYIHi4uLUrVu3JNcerohwm4luviAy/r8//80XmDab7bZlbr7wdzgc6t27t9q1a5dknsQPX9euXdW4cWOtWbNG69at00svvaT+/fvr1VdfzdTtyE4375+UxMTE6LnnnlOBAgXUtGlTPfXUUzp06JDzgvT+++/X0qVL9eeff2r9+vVauHChFixYoCVLlsjX1zfFaemtIyoqSs8++6yqVKmioKAgdejQQevWrdPWrVsztvEmSWlbc+fOfdtriV1/4uPj0zx+U1q3kcq9Lbf+zqeeekorV65Uy5YttW/fPrVo0SLFZa3s1n2S3IjdqR3XqR2vyb2Pid3wc/p9t7f+wU3u2Lt1nvj4+CTn7pv/fbPk9quU8BlJ/Jzcuv7E/Z3SOnOi69evp3q+lhK6tg0YMEC9evVSw4YNkyx/9OhRDRkyRO+8846qVKmS3eW7pFvPD8kdTw6HQ4MHD1ZQUFCS1/Ply3fbvGn9TcX/JPd5Tk58fHyS88Ct75nD4Uj2b2hOlnjOTO3aNnE/3XquTvw5uek3/zutY/nGjRuqXbu23n///dvqu1eelJH4PqR0beBwONJ1Le7n56f+/fvrtddec14Tu6p75y9uNti3b5/z3+Hh4SpSpEiS1rHEg+fm+7MSb4iXpLJlyyoiIkKlS5d2/rd8+XL9+uuvun79usaMGSObzabg4GDNnj1br7zyiuWfG1qmTBlFRETcds/azYO8bN68WadOnVJISIj69OmjevXq6eTJk84T3Lp167Rw4ULVr19f7777rn755RfFxMRo69atqU5LTx1DhgzRr7/+Ki8vL82cOVM9evRQrVq1FBERYdkBCVLa1pkzZ+rYsWPOwY0k6Z9//pG7u7tKlSolT09PxcTEOLfbMIwkx29a0vOHvVWrVtq9e7cWL16sevXq6b777kv/hllExYoVdezYMV26dMn52q5du26bL7Vjt3Tp0tq7d2+Si62hQ4dq0qRJKl++vHbt2uUc+ERKGPykYMGCObrVNr2uXLmiY8eOOX/+999/ValSpTSX8/HxUZEiRRQWFuZ8LS4uTjt37lTZsmWdryXeRyZJO3bskKenp8qVK5dJ1bu+tM7X8fHxeuutt1SiRAm99tprSZa9evWqXn31VTVu3DhH9trISmXLltWpU6eSXD/MmTNHmzdvlpT0/JvWe4T/uXnAuB07dqhw4cKqUaNGkvOAlHCOTek84HA4tGfPnnSdZ3Ki1K5t8+fPrwceeEA7d+50zh8REaHLly9LkvPa4+bpO3bscP47rWO5bNmyOnLkiIoXL+783bt379bMmTPvqS8dJal8+fIpHrc+Pj4qVKhQkmuRI0eOqF69es5rwqCgIDVt2lRNmjTR+++/79Lni3vrnc1iY8aM0T///KONGzdq8uTJev7555NMr1ixovLkyaOpU6cqIiJCc+fOTXIg9ezZU7/88ovmzZuno0eP6ttvv9W0adNUqlQp5c6dW9u2bdPo0aN18OBB7d27Vxs2bEjShcCKgoKC9OCDD+rdd9/VwYMHtX79eoWEhCS5GCxYsKCuXr2qX3/9VcePH9eiRYu0YMECxcbGSpJz8IBffvlFx48f18qVKxUbG6vKlSunOi09dTRo0EAFCxbU6dOn9ccffygiIkIzZszQqlWrnL/falLa1mHDhqlMmTIaNGiQ9uzZo02bNmnMmDFq1aqV7rvvPlWvXl3R0dGaOXOmIiIi9PHHHycJaGnJly+fTp48meoo0w888IAeeeQRzZ07V08++WRmbK7LqVevnooVK6Zhw4bpwIEDWrx4sf773//eNl9qx+7TTz+tmJgYjR07VocPH9aPP/6oH3/8UfXr19dTTz2l+Ph4DR8+XAcPHtSaNWs0ZcoUderU6Z77Y56Sd999V/v27dN///tfhYSEqGvXrula7vnnn9fUqVO1Zs0aHTx4UMOHD9f169f11FNPOeeZOnWqNm3apLCwMH3wwQd6+umnk3zJmdNVrlw51fP15MmTtWfPHo0cOVIXLlzQmTNndObMGcXExGj48OGKi4vTm2++qbNnzzqnXbt2zeStcn29evVSSEiIli1bpmPHjmnq1KlasmSJ82/pzefftP6m4n/Gjh2rf//9V3/++acmT56sLl26qGvXrtq3b58mTJigw4cP64cfftA333zjvDVEknMApEOHDmns2LG6cuVKjv2blpbUrm2lhKcuTJ06VX/88Yf27NmjoUOHSkr4QsbLy0vt2rXTuHHj9M8//+iff/7RBx984Jye1rH89NNPKzY21nm988cff2jUqFHObs/3kp49e+rXX3/VvHnzdOTIEc2bN0+//vqr8+9f9+7dNWXKFP3xxx86ePCgRo0apapVq952u8LgwYO1a9eu23pAupJ7o00+m3Tr1k0vv/yyYmNj1b59e/Xs2VM//PCDc7q3t7dGjx6tiRMnatGiRWratKm6d++uU6dOSZJq1Kih8ePHa+rUqRo/fryKFy+usWPHqlGjRpKkiRMnatSoUc5vtBs3bqz33nsvuzczU7m7u+uLL77QqFGj1LZtW91///16+eWX1bRpU3366aeSEu4JeOWVVzR69Ghdv35dfn5+GjFihIYOHaqTJ0+qSZMmGjhwoD7++GOdPn1apUqV0qeffqpy5cqpXLlyKU47c+ZMmnW0atVKDodDf//9twYOHCgp4d7qoUOHauLEiZa86EptW/39/TV69Gh17NhR+fLlU+vWrfXmm29KSrgndvDgwZo1a5amTZumdu3a3dEf62eeeUb//e9/9fTTT+uvv/5Kcb4nn3xS//zzjx5//PEMb6sr8vT01IwZM/Tuu++qXbt2qly5srp06XJb621qx7UkzZgxQx988IEWLlyoYsWKaezYsapZs6YkadasWRozZozatGmjQoUKqXv37nrxxRezfVtdVaNGjdS1a1flzZtXr7/+utq0aZOu5Xr27Kno6GiNGDFCdrtdNWrUUEhIiAoXLuycp23btho6dKguXbqkp556SsOGDcuirXBNRYoUSfV8vWLFCp09e1atWrVKstwrr7yiFStWSNJtXZXHjRt3W5dGJNWqVSudO3dOU6dO1enTp1WuXDl9/vnnzq6Dt55/U3uP8D9PPvmkXnzxRTkcDnXq1En9+/eXm5ubpk+fro8//lhz5sxRsWLFNHjwYLVv3965XKNGjbRlyxZNnjxZVatW1bx58+7JQCWlfW37/PPP6/Tp0xowYIDc3d3Vp08fbd++3dnbcfDgwRoxYoR69eolb29vdevWTRMmTJCnp2ea14fFihXTrFmzNG7cOLVt21b58+dX27Zt9frrr5u4R8xRvXp1jR8/XpMnT9b48eNVtmxZffbZZ3rsscckSS+88IIuX76sN998U3FxcQoKCtLw4cNvW0/JkiX1/PPPa/z48WratKlL3qtvM1y5XRnAPWfq1Kk6cOCAPvvsM7NLQQ6zadMmde/eXTt37syS+60qVaqkuXPnql69epm+bld39OhRNW/eXGvXrk0y4BFgRcePH9fjjz+uVatWqXTp0ne07JAhQ3Tjxg2NHz8+i6rLWRIf+5V428z58+dVt25drVmzRiVKlNDq1atVt25d5yCM4eHh6tKlS5IADNyMllsALmHv3r3avXu3QkJCNHHiRLPLAZBOUVFR+v333+Xp6cl93QDuSOJgiW+//bZsNpsmTZqk6tWrO0cJnzp1qn777Tf169dPMTEx+uSTT9SkSROCLVLEDVgAXMKuXbs0cuRItW7d+p5s+QKsat68eZo4caL69eunPHnymF0OAAsZPny43N3d1alTJ3Xo0EHx8fH6/PPPndPHjx+vEydOqE2bNurVq5dKlCjhvO8WSA7dkgEAAAAAlkfLLQAAAADA8gi3AAAAAADLI9wCAAAAACyP0ZIBADBBfHy8Fi5cqB9++EGHDh3S9evXVbp0aT355JPq1auXcufObXaJAABYCgNKAQCQzW7cuKF+/fpp165devnll1W3bl3lzp1b27dv12effaaSJUtq7ty5stlsZpcKAIBl0HILAEA2mzNnjjZt2qQlS5aoUqVKztdLlCihgIAAtWzZUuvXr1ejRo3MKxIAAIvhnlsAALKRYRj65ptv1KZNmyTBNlGpUqX0008/qWHDhpKkLVu2qFOnTvL399fjjz+uTz/9VNevX3fOX6lSJS1evFhdu3aVv7+/nnjiCS1cuNA5fciQIRo4cKCCg4P18MMP65tvvpEkff/992rRooX8/f3VunVrLVu2LIu3HACArEW4BQAgGx0/flyRkZF69NFHU5yndOnSstls2r17t3r37q1mzZpp5cqVGjNmjNauXauRI0cmmX/8+PHq2rWrli1bplq1amnkyJE6ceKEc/rPP/+sZs2a6fvvv1ezZs30zTffaOLEiXr99df1448/qk+fPvrggw8IuAAAS6NbMgAA2ejs2bOSpPvuuy/J608//bQiIiKcP7du3VpXrlxRw4YN1bt3b0kJoff9999Xly5d9Prrr+uBBx6QJD377LNq1aqVJGnQoEFatGiRwsPDVbx4cUlSkSJF1L17d+e6p02bpldeeUVPPPGEpITW4pMnT2ratGlq27ZtFm05AABZi3ALAEA2KliwoCTp0qVLSV6fNm2a4uLiJEmDBw9WbGysdu/eraNHjyowMNA5X+I4kAcPHnSG2zJlyjin58+fX5Kc65IS7uVNdP78eUVFRemjjz7S+PHjna/fuHFDDodDsbGxypUrVyZsKQAA2YtwCwBANipVqpQKFy6sLVu2OFtbJalYsWLOf+fJk0eS5OnpqTZt2uiFF164bT1FihRx/ju5MHrzwxAS15e4Tkl67733VLt27duW8/Dg0gAAYE3ccwsAQDZyd3dX165dtXTpUh08ePC26bGxsTp//rwkqUKFCjp48KBKly7t/O/8+fP66KOPFBMTc1e/38fHR76+vjp+/HiS9W7cuFGzZ8+WmxuXBgAAa+IvGAAA2axv376qW7euOnfurLlz52r//v2KiIjQypUr9eyzz+rQoUN6+OGH9cILLyg8PFzjxo3TwYMHtXnzZg0ePFh2uz1Jy+2d6t+/v+bNm6eFCxfq2LFjWrlypT788MMMrRMAALPR9wgAgGzm4eGhL774QsuXL9fSpUs1bdo0XblyRcWKFVNQUJCmTJnivI92+vTpmjRpkr755hv5+PiocePGGjRoUIZ+f+fOnRUbG6vZs2dr9OjR8vX11UsvvaS+fftmwtYBAGAOm3HzTTkAAAAAAFgQ3ZIBAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDlEW4BAAAAAJZHuAUAAAAAWB7hFgAAAABgeYRbAAAAAIDlEW4BAAAAAJb3fySIcb62wNfHAAAAAElFTkSuQmCC\n",
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\n",
       "text/plain": [
        "<Figure size 1152x648 with 1 Axes>"
       ]
@@ -973,23 +975,39 @@
    "source": [
     "f, ax = plt.subplots(figsize=(16, 9));\n",
     "\n",
-    "sns.boxplot(x = \"label\", y = \"mfcc4_mean\", data = data[[\"label\", \"mfcc4_mean\"]], palette = 'pastel');\n",
+    "sns.boxplot(x = \"label\", y = \"tempo\", data = data[[\"label\", \"tempo\"]], palette = 'pastel');\n",
     "\n",
-    "plt.title('mfcc_mean4 boxplot for genres', fontsize = 25)\n",
+    "plt.title('Zależność pomiędzy tempem a gatunkiem', fontsize = 25)\n",
     "plt.xticks(fontsize = 14)\n",
     "plt.yticks(fontsize = 10);\n",
     "plt.xlabel(\"Genre\", fontsize = 15)\n",
     "plt.ylabel(\"mfcc4_mean4\", fontsize = 15);"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Boxplot dla średnich melowych współczynników cepstralnych sygnału dla poszczególnych gatunków"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Interesujące wyniki pojawiły się także na wykresie pokazującym zależność pomiędzy MFCC mean, czyli średnimi wartościami dla melowych współczynników cepstralnych sygnału a gatunkami muzycznimi. \n",
+    "\n",
+    "Najwyższe wartości MFCC_mean dotyczą metalu oraz bluesa, podczas gdy najniższe wartości uzyskiwane są w przypadu popu i muzyki klasycznej. Z kolei najwięcej obserwacji odstających pojawia się w przypadku reggae."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1152x648 with 1 Axes>"
       ]
@@ -1002,7 +1020,7 @@
     "f, ax = plt.subplots(figsize=(16, 9));\n",
     "sns.boxplot(x = \"label\", y = \"mfcc4_mean\", data = data[[\"label\", \"mfcc4_mean\"]], palette = 'pastel');\n",
     "\n",
-    "plt.title('mfcc_mean4 boxplot for genres', fontsize = 25)\n",
+    "plt.title('Zależność między MFCC a gatunkiem', fontsize = 25)\n",
     "plt.xticks(fontsize = 14)\n",
     "plt.yticks(fontsize = 10);\n",
     "plt.xlabel(\"Genre\", fontsize = 15)\n",
@@ -1020,7 +1038,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "W procesie badania zależności pomiędzy dostępnymi cechami wykorzystana została mapa ciepła, która jednak pokazała, że w wielu przypadkach korelacje nie zachodzą, co jest szczególnie widoczne w przypadku średniej częstotliwości melodycznej cepstrum2 (mfcc2_mean), a jeżeli takowe korelacje zachodza to mają stosunkowo niewielkie wartości.Występowanie zależności widać w górnej oraz środkowej częsci mapy."
+    "W procesie badania zależności pomiędzy dostępnymi cechami wykorzystana została mapa ciepła, która jednak pokazała, że w wielu przypadkach korelacje nie zachodzą, co jest szczególnie widoczne w przypadku średniej częstotliwości melodycznej cepstrum2 (mfcc2_mean), a jeżeli takowe korelacje zachodza to mają stosunkowo niewielkie wartości.Występowanie zależności widać w górnej oraz środkowej częsci mapy.\n",
+    "\n",
+    "Należy jednak podkreślić, ze takie wartości nie są dobre, ponieważ niewielkie zrożnicowanie odchyleń może mieć istotny wpływ na znajdowanie korelacji, a co za tym idzie skuteczność modelu!"
    ]
   },
   {
@@ -1067,16 +1087,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Odwrotna sytuacja ma miejsce w przypadku mapy ciepła dla cech wariancji, w przypadku ktorych korelacja nie zachodzi wyłącznie dla dwóch parametrów czyli harmony i perceptr w środkowej cześci wykresu. Z kolei stosunkow wysokie wartości korelacji można zaobserwować dla parametrów \"skrajnych\", czyli pierwszych i ostatnich na liście parametrów.\n",
-    "\n",
-    "Należy jednak podkreślić, ze takie wartości nie są dobre, ponieważ niewielkie zrożnicowanie odchyleń może mieć niewielki wpływ na znajdowanie korelacji, a co za tym idzie skuteczność modelu!"
+    "Odwrotna sytuacja ma miejsce w przypadku mapy ciepła dla cech wariancji, w przypadku ktorych korelacja nie zachodzi wyłącznie dla dwóch parametrów czyli harmony i perceptr w środkowej cześci wykresu. Z kolei stosunkow wysokie wartości korelacji można zaobserwować dla parametrów \"skrajnych\", czyli pierwszych i ostatnich na liście parametrów.\n"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
    "metadata": {
-    "scrolled": true
+    "scrolled": false
    },
    "outputs": [
     {
@@ -1114,6 +1132,13 @@
     "### Wykres punktowy przedstawiający zależność pomiędzy krótkotrwałą transformatą Fouriera a częstotliwością melodyczną cepstrum"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Wykres ten jednoznacznie pokazuje, że chociaż pojawia się pewna pula obserwacji odstających to jednak wraz ze wzrosetem wartości melowego współczynnika cepstralnego sygnału rosną wartości krótkotrwałej transformaty Fouriera. Tym samym zależność pomiędzy tymi dwoma warościami, w ogólność, ma charakter liniowy."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -1123,8 +1148,8 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "<ipython-input-10-3d72438c776e>:3: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
-      "  ax1 = fig.add_subplot()\n"
+      "<ipython-input-10-3dd1710c0643>:3: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
+      "  ax = fig.add_subplot()\n"
      ]
     },
     {
@@ -1141,11 +1166,11 @@
    "source": [
     "fig = plt.figure(figsize=(10,10))\n",
     "chart = fig.add_subplot()\n",
-    "ax1 = fig.add_subplot()\n",
+    "ax = fig.add_subplot()\n",
     "colors = ['r','g','blue', 'brown','purple','gray','pink','black', 'y', 'orange']\n",
     "for genre in genre_dict:\n",
     "    genre_data = data[data[\"genre\"]==genre_dict[genre]]\n",
-    "    ax1.scatter(genre_data['chroma_stft_mean'],genre_data['mfcc12_mean'], c=colors[genre_dict[genre]-1])\n",
+    "    ax.scatter(genre_data['chroma_stft_mean'],genre_data['mfcc12_mean'], c=colors[genre_dict[genre]-1])\n",
     "plt.show()"
    ]
   },
@@ -1153,11 +1178,15 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Cechy średnie mają między sobą mniejszą korelację niż cechy wariancji, więc model powinien uzyskac lepsze wyniki (https://datascience.stackexchange.com/questions/9087/correlation-and-naive-bayes). \n",
+    "## Cechy średnie dają lepsze rezultaty niż cechy wariancji ze względu na mniejszą korelację pomiędzy poszczególnymi parametrami(https://datascience.stackexchange.com/questions/9087/correlation-and-naive-bayes). \n",
+    "\n",
+    "W toku przeprowadzanych testów okazało sie, że dokładość stworzonego i wytrenowanego modelu zależy od rodzaju cech. W przypadku cech wariancji dokładność jest niższa niż w przypadku cech średnich. Z kolei najwyższą dokładność udało się uzyskać poprzez wykorzystanie kombinacji 8 różnych kolumn.\n",
     "\n",
     "- dla var_cols accuracy = 0.3875,\n",
     "- dla mean_cols accuracy = 0.4375,\n",
-    "- dla ['mfcc4_mean', 'mfcc12_mean', 'mfcc9_var', 'mfcc1_mean', 'rms_mean', 'chroma_stft_mean', 'mfcc6_var', 'mfcc9_mean'] accuracy = 0.56125 \n"
+    "- dla ['mfcc4_mean', 'mfcc12_mean', 'mfcc9_var', 'mfcc1_mean', 'rms_mean', 'chroma_stft_mean', 'mfcc6_var', 'mfcc9_mean'] accuracy = 0.56125 \n",
+    "\n",
+    "Równocześnie uzyskane wyniki mogłyby mieć zdecydowanie wyższą dokładność jednak ograniczeniem okazał się specyfika samego datasetu, który posiada niewielkie zróżnicowanie wartości cech i niewielką korelację pomiędzy poszczególnymi cechami!"
    ]
   },
   {
@@ -1231,7 +1260,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "W procesie trenowania i testowania modelu wykorzystany został skrypt losujący kolumny i zapisujący uzyskiwane wartości accuracy w celu znalezienia najbardziej efektywnej kombinacji cech. Uzyskane w ten sposób cechy to: mfcc4, m"
+    "W procesie trenowania i testowania modelu wykorzystany został skrypt losujący kolumny i zapisujący uzyskiwane wartości accuracy w celu znalezienia najbardziej efektywnej kombinacji cech. W ten sposób wybranych zostało 8 cech, w tym sześć cech należących do kategorii średnich i dwie do wariancji."
    ]
   },
   {
@@ -1419,7 +1448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1462,14 +1491,12 @@
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-20-c3e0e5836867>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mbayes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBayes\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mbayes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mY_predicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbayes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0meval_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbayes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_predicted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m<ipython-input-18-d1179e864a3a>\u001b[0m in \u001b[0;36mtrain\u001b[1;34m(self, X, Y)\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 14\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     15\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\naive_bayes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    205\u001b[0m         \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    206\u001b[0m         \"\"\"\n\u001b[1;32m--> 207\u001b[1;33m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    208\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    209\u001b[0m         return self._partial_fit(X, y, np.unique(y), _refit=True,\n",
-      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    431\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    432\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 433\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    434\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    435\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    869\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    870\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 871\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    872\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    873\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     61\u001b[0m             \u001b[0mextra_args\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mall_args\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     62\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mextra_args\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 63\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     64\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     65\u001b[0m             \u001b[1;31m# extra_args > 0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32m~\\AppData\\Roaming\\Python\\Python38\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    671\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    672\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 673\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    674\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    675\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
+      "\u001b[1;32m<ipython-input-20-86955df8a2b3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mbayes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train_np\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mY_predicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbayes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[0meval_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbayes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_predicted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Train:\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m<ipython-input-17-d1179e864a3a>\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     17\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 18\u001b[1;33m         \u001b[0mpredictions\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclassifier\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     19\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mpredictions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py\u001b[0m in \u001b[0;36mpredict\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m     75\u001b[0m         \"\"\"\n\u001b[0;32m     76\u001b[0m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 77\u001b[1;33m         \u001b[0mX\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_check_X\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     78\u001b[0m         \u001b[0mjll\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_joint_log_likelihood\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     79\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclasses_\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mjll\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py\u001b[0m in \u001b[0;36m_check_X\u001b[1;34m(self, X)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    215\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_check_X\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 216\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    217\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    218\u001b[0m     \u001b[1;33m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    596\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
       "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order, like)\u001b[0m\n\u001b[0;32m    100\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
       "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   1897\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1898\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1899\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1900\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1901\u001b[0m     def __array_wrap__(\n",
       "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order, like)\u001b[0m\n\u001b[0;32m    100\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
@@ -1479,7 +1506,7 @@
    ],
    "source": [
     "bayes = Bayes()\n",
-    "bayes.train(X_train, Y_train)\n",
+    "bayes.train(X_train_np, Y_train)\n",
     "\n",
     "Y_predicted = bayes.predict(X_train)\n",
     "eval_result = bayes.eval(Y_train, Y_predicted)\n",
@@ -1494,9 +1521,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: 'metal'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[1;32m<ipython-input-21-ce4a1c8ea986>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      9\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mmodel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mGaussianNB\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 11\u001b[1;33m     \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     12\u001b[0m     \u001b[0mY_train_predicted\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     13\u001b[0m     \u001b[0mac\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maccuracy_score\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mY_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mY_train_predicted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\naive_bayes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    208\u001b[0m         \u001b[0mself\u001b[0m \u001b[1;33m:\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    209\u001b[0m         \"\"\"\n\u001b[1;32m--> 210\u001b[1;33m         \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_data\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    211\u001b[0m         \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcolumn_or_1d\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mwarn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    212\u001b[0m         return self._partial_fit(X, y, np.unique(y), _refit=True,\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[1;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[0;32m    430\u001b[0m                 \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    431\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 432\u001b[1;33m                 \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    433\u001b[0m             \u001b[0mout\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    434\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[1;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[0;32m    793\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"y cannot be None\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    794\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 795\u001b[1;33m     X = check_array(X, accept_sparse=accept_sparse,\n\u001b[0m\u001b[0;32m    796\u001b[0m                     \u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maccept_large_sparse\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m                     \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36minner_f\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     70\u001b[0m                           FutureWarning)\n\u001b[0;32m     71\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marg\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 72\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     73\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     74\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[1;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)\u001b[0m\n\u001b[0;32m    596\u001b[0m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcasting\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"unsafe\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    597\u001b[0m                 \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 598\u001b[1;33m                     \u001b[0marray\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    599\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    600\u001b[0m                 raise ValueError(\"Complex data not supported\\n\"\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order, like)\u001b[0m\n\u001b[0;32m    100\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__array__\u001b[1;34m(self, dtype)\u001b[0m\n\u001b[0;32m   1897\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1898\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1899\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1900\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1901\u001b[0m     def __array_wrap__(\n",
+      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\_asarray.py\u001b[0m in \u001b[0;36masarray\u001b[1;34m(a, dtype, order, like)\u001b[0m\n\u001b[0;32m    100\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0m_asarray_with_like\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlike\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlike\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    101\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
+      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'metal'"
+     ]
+    }
+   ],
    "source": [
     "# skrypt losujacy kolumny ze zbioru i sprawdzajacy accuracy na zbiorze trenujacym\n",
     "\n",
@@ -1524,6 +1572,13 @@
     "\n",
     "!sort -k1,1nr -k2,2 accuracy.txt"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -1542,7 +1597,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.8"
+   "version": "3.8.5"
   }
  },
  "nbformat": 4,