diff --git a/wyk/05_Wygladzanie.ipynb b/wyk/07_Wygladzanie.ipynb
similarity index 66%
rename from wyk/05_Wygladzanie.ipynb
rename to wyk/07_Wygladzanie.ipynb
index d3fb5f9..2515fa2 100644
--- a/wyk/05_Wygladzanie.ipynb
+++ b/wyk/07_Wygladzanie.ipynb
@@ -1,5 +1,20 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![Logo 1](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech1.jpg)\n",
+    "<div class=\"alert alert-block alert-info\">\n",
+    "<h1> Modelowanie języka</h1>\n",
+    "<h2> 07. <i>Wygładzanie w n-gramowych modelach języka</i>  [wykład]</h2> \n",
+    "<h3> Filip Graliński (2022)</h3>\n",
+    "</div>\n",
+    "\n",
+    "![Logo 2](https://git.wmi.amu.edu.pl/AITech/Szablon/raw/branch/master/Logotyp_AITech2.jpg)\n",
+    "\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -54,7 +69,7 @@
     "Rozpatrzmy przykład z 3 kolorami (wiemy, że w urnie mogą być kule\n",
     "żółte, zielone i czerwone, tj. $m=3$) i 4 losowaniami ($T=4$):\n",
     "\n",
-    "![img](./05_Wygladzanie/urna.drawio.png)\n",
+    "![img](./07_Wygladzanie/urna.drawio.png)\n",
     "\n",
     "Gdybyśmy w prosty sposób oszacowali prawdopodobieństwa, doszlibyśmy do\n",
     "wniosku, że prawdopodobieństwo wylosowania kuli czerwonej wynosi 3/4, żółtej — 1/4,\n",
@@ -168,7 +183,7 @@
     "$k_i$ to ile razy w zbiorze uczącym pojawił się $i$-ty wyraz słownika,\n",
     "$T$ — długość zbioru uczącego.\n",
     "\n",
-    "![img](./05_Wygladzanie/urna-wyrazy.drawio.png)\n",
+    "![img](./07_Wygladzanie/urna-wyrazy.drawio.png)\n",
     "\n",
     "A zatem przy użyciu wygładzania +1 w następujący sposób estymować\n",
     "będziemy prawdopodobieństwo słowa $w$:\n",
@@ -303,113 +318,11 @@
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "['<s>',\n",
-       " 'lubisz',\n",
-       " 'curry',\n",
-       " ',',\n",
-       " 'prawda',\n",
-       " '?',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'nałożę',\n",
-       " 'ci',\n",
-       " 'więcej',\n",
-       " '.',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'hey',\n",
-       " '!',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'smakuje',\n",
-       " 'ci',\n",
-       " '?',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'hey',\n",
-       " ',',\n",
-       " 'brzydalu',\n",
-       " '.',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'spójrz',\n",
-       " 'na',\n",
-       " 'nią',\n",
-       " '.',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " '-',\n",
-       " 'wariatka',\n",
-       " '.',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " '-',\n",
-       " 'zadałam',\n",
-       " 'ci',\n",
-       " 'pytanie',\n",
-       " '!',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'no',\n",
-       " ',',\n",
-       " 'tak',\n",
-       " 'lepiej',\n",
-       " '!',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " '-',\n",
-       " 'wygląda',\n",
-       " 'dobrze',\n",
-       " '!',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " '-',\n",
-       " 'tak',\n",
-       " 'lepiej',\n",
-       " '!',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'pasuje',\n",
-       " 'jej',\n",
-       " '.',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " '-',\n",
-       " 'hey',\n",
-       " '.',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " '-',\n",
-       " 'co',\n",
-       " 'do',\n",
-       " '...?',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'co',\n",
-       " 'do',\n",
-       " 'cholery',\n",
-       " 'robisz',\n",
-       " '?',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'zejdź',\n",
-       " 'mi',\n",
-       " 'z',\n",
-       " 'oczu',\n",
-       " ',',\n",
-       " 'zdziro',\n",
-       " '.',\n",
-       " '</s>',\n",
-       " '<s>',\n",
-       " 'przestań',\n",
-       " 'dokuczać']"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['<s>', 'lubisz', 'curry', ',', 'prawda', '?', '</s>', '<s>', 'nałożę', 'ci', 'więcej', '.', '</s>', '<s>', 'hey', '!', '</s>', '<s>', 'smakuje', 'ci', '?', '</s>', '<s>', 'hey', ',', 'brzydalu', '.', '</s>', '<s>', 'spójrz', 'na', 'nią', '.', '</s>', '<s>', '-', 'wariatka', '.', '</s>', '<s>', '-', 'zadałam', 'ci', 'pytanie', '!', '</s>', '<s>', 'no', ',', 'tak', 'lepiej', '!', '</s>', '<s>', '-', 'wygląda', 'dobrze', '!', '</s>', '<s>', '-', 'tak', 'lepiej', '!', '</s>', '<s>', 'pasuje', 'jej', '.', '</s>', '<s>', '-', 'hey', '.', '</s>', '<s>', '-', 'co', 'do', '...?', '</s>', '<s>', 'co', 'do', 'cholery', 'robisz', '?', '</s>', '<s>', 'zejdź', 'mi', 'z', 'oczu', ',', 'zdziro', '.', '</s>', '<s>', 'przestań', 'dokuczać']"
+     ]
     }
    ],
    "source": [
@@ -448,7 +361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,18 +372,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "48113"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "48113"
+     ]
     }
    ],
    "source": [
@@ -479,7 +389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -518,18 +428,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "926594"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "926594"
+     ]
     }
    ],
    "source": [
@@ -553,128 +460,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>liczba tokenów</th>\n",
-       "      <th>średnia częstość w części B</th>\n",
-       "      <th>estymacje +1</th>\n",
-       "      <th>estymacje +0.01</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>388334</td>\n",
-       "      <td>1.900495</td>\n",
-       "      <td>0.993586</td>\n",
-       "      <td>0.009999</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>403870</td>\n",
-       "      <td>0.592770</td>\n",
-       "      <td>1.987172</td>\n",
-       "      <td>1.009935</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>117529</td>\n",
-       "      <td>1.565809</td>\n",
-       "      <td>2.980759</td>\n",
-       "      <td>2.009870</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>62800</td>\n",
-       "      <td>2.514268</td>\n",
-       "      <td>3.974345</td>\n",
-       "      <td>3.009806</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>40856</td>\n",
-       "      <td>3.504944</td>\n",
-       "      <td>4.967931</td>\n",
-       "      <td>4.009741</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>29443</td>\n",
-       "      <td>4.454098</td>\n",
-       "      <td>5.961517</td>\n",
-       "      <td>5.009677</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>22709</td>\n",
-       "      <td>5.232023</td>\n",
-       "      <td>6.955103</td>\n",
-       "      <td>6.009612</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>18255</td>\n",
-       "      <td>6.157929</td>\n",
-       "      <td>7.948689</td>\n",
-       "      <td>7.009548</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>15076</td>\n",
-       "      <td>7.308039</td>\n",
-       "      <td>8.942276</td>\n",
-       "      <td>8.009483</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>12859</td>\n",
-       "      <td>8.045649</td>\n",
-       "      <td>9.935862</td>\n",
-       "      <td>9.009418</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   liczba tokenów  średnia częstość w części B  estymacje +1  estymacje +0.01\n",
-       "0          388334                     1.900495      0.993586         0.009999\n",
-       "1          403870                     0.592770      1.987172         1.009935\n",
-       "2          117529                     1.565809      2.980759         2.009870\n",
-       "3           62800                     2.514268      3.974345         3.009806\n",
-       "4           40856                     3.504944      4.967931         4.009741\n",
-       "5           29443                     4.454098      5.961517         5.009677\n",
-       "6           22709                     5.232023      6.955103         6.009612\n",
-       "7           18255                     6.157929      7.948689         7.009548\n",
-       "8           15076                     7.308039      8.942276         8.009483\n",
-       "9           12859                     8.045649      9.935862         9.009418"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "liczba tokenów  średnia częstość w części B  estymacje +1  estymacje +0.01\n",
+      "0          388334                     1.900495      0.993586         0.009999\n",
+      "1          403870                     0.592770      1.987172         1.009935\n",
+      "2          117529                     1.565809      2.980759         2.009870\n",
+      "3           62800                     2.514268      3.974345         3.009806\n",
+      "4           40856                     3.504944      4.967931         4.009741\n",
+      "5           29443                     4.454098      5.961517         5.009677\n",
+      "6           22709                     5.232023      6.955103         6.009612\n",
+      "7           18255                     6.157929      7.948689         7.009548\n",
+      "8           15076                     7.308039      8.942276         8.009483\n",
+      "9           12859                     8.045649      9.935862         9.009418"
+     ]
     }
    ],
    "source": [
@@ -716,128 +520,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>liczba tokenów</th>\n",
-       "      <th>średnia częstość w części B</th>\n",
-       "      <th>estymacje +1</th>\n",
-       "      <th>Good-Turing</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>388334</td>\n",
-       "      <td>1.900495</td>\n",
-       "      <td>0.993586</td>\n",
-       "      <td>1.040007</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>403870</td>\n",
-       "      <td>0.592770</td>\n",
-       "      <td>1.987172</td>\n",
-       "      <td>0.582014</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>117529</td>\n",
-       "      <td>1.565809</td>\n",
-       "      <td>2.980759</td>\n",
-       "      <td>1.603009</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>62800</td>\n",
-       "      <td>2.514268</td>\n",
-       "      <td>3.974345</td>\n",
-       "      <td>2.602293</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>40856</td>\n",
-       "      <td>3.504944</td>\n",
-       "      <td>4.967931</td>\n",
-       "      <td>3.603265</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>29443</td>\n",
-       "      <td>4.454098</td>\n",
-       "      <td>5.961517</td>\n",
-       "      <td>4.627721</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>22709</td>\n",
-       "      <td>5.232023</td>\n",
-       "      <td>6.955103</td>\n",
-       "      <td>5.627064</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>18255</td>\n",
-       "      <td>6.157929</td>\n",
-       "      <td>7.948689</td>\n",
-       "      <td>6.606847</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>15076</td>\n",
-       "      <td>7.308039</td>\n",
-       "      <td>8.942276</td>\n",
-       "      <td>7.676506</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>12859</td>\n",
-       "      <td>8.045649</td>\n",
-       "      <td>9.935862</td>\n",
-       "      <td>8.557431</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   liczba tokenów  średnia częstość w części B  estymacje +1  Good-Turing\n",
-       "0          388334                     1.900495      0.993586     1.040007\n",
-       "1          403870                     0.592770      1.987172     0.582014\n",
-       "2          117529                     1.565809      2.980759     1.603009\n",
-       "3           62800                     2.514268      3.974345     2.602293\n",
-       "4           40856                     3.504944      4.967931     3.603265\n",
-       "5           29443                     4.454098      5.961517     4.627721\n",
-       "6           22709                     5.232023      6.955103     5.627064\n",
-       "7           18255                     6.157929      7.948689     6.606847\n",
-       "8           15076                     7.308039      8.942276     7.676506\n",
-       "9           12859                     8.045649      9.935862     8.557431"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "liczba tokenów  średnia częstość w części B  estymacje +1  Good-Turing\n",
+      "0          388334                     1.900495      0.993586     1.040007\n",
+      "1          403870                     0.592770      1.987172     0.582014\n",
+      "2          117529                     1.565809      2.980759     1.603009\n",
+      "3           62800                     2.514268      3.974345     2.602293\n",
+      "4           40856                     3.504944      4.967931     3.603265\n",
+      "5           29443                     4.454098      5.961517     4.627721\n",
+      "6           22709                     5.232023      6.955103     5.627064\n",
+      "7           18255                     6.157929      7.948689     6.606847\n",
+      "8           15076                     7.308039      8.942276     7.676506\n",
+      "9           12859                     8.045649      9.935862     8.557431"
+     ]
     }
    ],
    "source": [
@@ -1008,18 +709,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "[('k', 'o', 't'), ('o', 't', 'e'), ('t', 'e', 'k')]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[('k', 'o', 't'), ('o', 't', 'e'), ('t', 'e', 'k')]"
+     ]
     }
    ],
    "source": [
@@ -1036,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1048,23 +746,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "321"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "len(histories['jork'])\n",
-    "len(histories['zielony'])"
+    "len(histories['zielony'])\n",
+    "histories['jork']"
    ]
   },
   {
@@ -1112,7 +800,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![img](./05_Wygladzanie/size-perplexity.gif \"Perplexity dla różnych rozmiarów zbioru testowego\")\n",
+    "![img](./07_Wygladzanie/size-perplexity.gif \"Perplexity dla różnych rozmiarów zbioru testowego\")\n",
     "\n"
    ]
   },
@@ -1128,7 +816,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![img](./05_Wygladzanie/size-perplexity2.gif \"Perplexity dla różnych rozmiarów zbioru uczącego\")\n",
+    "![img](./07_Wygladzanie/size-perplexity2.gif \"Perplexity dla różnych rozmiarów zbioru uczącego\")\n",
     "\n"
    ]
   },
@@ -1144,7 +832,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "![img](./05_Wygladzanie/order-perplexity.gif \"Perplexity dla różnych wartości rządu modelu\")\n",
+    "![img](./07_Wygladzanie/order-perplexity.gif \"Perplexity dla różnych wartości rządu modelu\")\n",
     "\n"
    ]
   }
@@ -1165,7 +853,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.2"
+   "version": "3.10.5"
   },
   "org": null
  },
diff --git a/wyk/05_Wygladzanie.org b/wyk/07_Wygladzanie.org
similarity index 94%
rename from wyk/05_Wygladzanie.org
rename to wyk/07_Wygladzanie.org
index e636ec7..4128060 100644
--- a/wyk/05_Wygladzanie.org
+++ b/wyk/07_Wygladzanie.org
@@ -25,7 +25,7 @@ $$p_i = \frac{k_i}{T}.$$
 Rozpatrzmy przykład z 3 kolorami (wiemy, że w urnie mogą być kule
 żółte, zielone i czerwone, tj. $m=3$) i 4 losowaniami ($T=4$):
 
-[[./05_Wygladzanie/urna.drawio.png]]
+[[./07_Wygladzanie/urna.drawio.png]]
 
 Gdybyśmy w prosty sposób oszacowali prawdopodobieństwa, doszlibyśmy do
 wniosku, że prawdopodobieństwo wylosowania kuli czerwonej wynosi 3/4, żółtej — 1/4,
@@ -85,7 +85,7 @@ losowania kul z urny: $m$ to liczba wszystkich wyrazów (czyli rozmiar słownika
 $k_i$ to ile razy w zbiorze uczącym pojawił się $i$-ty wyraz słownika,
 $T$ — długość zbioru uczącego.
 
-[[./05_Wygladzanie/urna-wyrazy.drawio.png]]
+[[./07_Wygladzanie/urna-wyrazy.drawio.png]]
 
 A zatem przy użyciu wygładzania +1 w następujący sposób estymować
 będziemy prawdopodobieństwo słowa $w$:
@@ -173,7 +173,7 @@ Stwórzmy generator, który będzie wczytywał słowa z pliku, dodatkowo:
 - dodamy specjalne tokeny na początek i koniec zdania (~<s>~ i ~</s>~).
 
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
   from itertools import islice
   import regex as re
   import sys
@@ -200,7 +200,7 @@ Stwórzmy generator, który będzie wczytywał słowa z pliku, dodatkowo:
 Zobaczmy, ile razy, średnio w drugiej połówce korpusu występują
 wyrazy, które w pierwszej wystąpiły określoną liczbę razy.
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
   from collections import Counter
 
   counterA = Counter(get_words_from_file('opensubtitlesA.pl.txt'))
@@ -210,7 +210,7 @@ wyrazy, które w pierwszej wystąpiły określoną liczbę razy.
 :results:
 :end:
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
 counterA['taki']
 #+END_SRC
 
@@ -219,7 +219,7 @@ counterA['taki']
 48113
 :end:
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
   max_r = 10
 
   buckets = {}
@@ -251,7 +251,7 @@ counterA['taki']
 Policzmy teraz jakiej liczby wystąpień byśmy oczekiwali, gdyby użyć wygładzania +1 bądź +0.01.
 (Uwaga: zwracamy liczbę wystąpień, a nie względną częstość, stąd przemnażamy przez rozmiar całego korpusu).
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
   def plus_alpha_smoothing(alpha, m, t, k):
     return t*(k + alpha)/(t + alpha * m)
 
@@ -275,7 +275,7 @@ Policzmy teraz jakiej liczby wystąpień byśmy oczekiwali, gdyby użyć wygład
 926594
 :end:
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
     import pandas as pd
 
     pd.DataFrame(data, columns=["liczba tokenów", "średnia częstość w części B", "estymacje +1", "estymacje +0.01"])
@@ -309,7 +309,7 @@ $$p(w) = \frac{\# w + 1}{|C|}\frac{N_{r+1}}{N_r}.$$
 
 **** Przykład
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
   good_turing_counts = [(ix+1)*nb_of_types[ix+1]/nb_of_types[ix]  for ix in range(0, max_r)]
 
   data2 = list(zip(nb_of_types, empirical_counts, plus_one_counts, good_turing_counts))
@@ -415,7 +415,7 @@ W metodzie Knesera-Neya w następujący sposób estymujemy prawdopodobieństwo u
 
 $$P(w) = \frac{N_{1+}(\bullet w)}{\sum_{w_j} N_{1+}(\bullet w_j)}.$$
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
   def ngrams(iter, size):
     ngram = []
     for item in iter:
@@ -433,7 +433,7 @@ $$P(w) = \frac{N_{1+}(\bullet w)}{\sum_{w_j} N_{1+}(\bullet w_j)}.$$
 :end:
 
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
   histories = { }
   for prev_token, token in ngrams(get_words_from_file('opensubtitlesA.pl.txt'), 2):
      histories.setdefault(token, set())
@@ -444,7 +444,7 @@ $$P(w) = \frac{N_{1+}(\bullet w)}{\sum_{w_j} N_{1+}(\bullet w_j)}.$$
 :results:
 :end:
 
-#+BEGIN_SRC python :session mysession :exports both :results raw drawer
+#+BEGIN_SRC ipython :session mysession :exports both :results raw drawer
   len(histories['jork'])
   len(histories['zielony'])
   histories['jork']
@@ -472,15 +472,15 @@ Knesera-Neya połączone z *przycinaniem* słownika n-gramów (wszystkie
 **** Zmiana perplexity przy zwiększaniu zbioru testowego
 
 #+CAPTION: Perplexity dla różnych rozmiarów zbioru testowego
-[[./05_Wygladzanie/size-perplexity.gif]]
+[[./07_Wygladzanie/size-perplexity.gif]]
 
 
 **** Zmiana perplexity przy zwiększaniu zbioru uczącego
 
 #+CAPTION: Perplexity dla różnych rozmiarów zbioru uczącego
-[[./05_Wygladzanie/size-perplexity2.gif]]
+[[./07_Wygladzanie/size-perplexity2.gif]]
 
 **** Zmiana perplexity przy zwiększaniu rządu modelu
 
 #+CAPTION: Perplexity dla różnych wartości rządu modelu
-[[./05_Wygladzanie/order-perplexity.gif]]
+[[./07_Wygladzanie/order-perplexity.gif]]
diff --git a/wyk/05_Wygladzanie/order-perplexity.gif b/wyk/07_Wygladzanie/order-perplexity.gif
similarity index 100%
rename from wyk/05_Wygladzanie/order-perplexity.gif
rename to wyk/07_Wygladzanie/order-perplexity.gif
diff --git a/wyk/05_Wygladzanie/size-perplexity.gif b/wyk/07_Wygladzanie/size-perplexity.gif
similarity index 100%
rename from wyk/05_Wygladzanie/size-perplexity.gif
rename to wyk/07_Wygladzanie/size-perplexity.gif
diff --git a/wyk/05_Wygladzanie/size-perplexity2.gif b/wyk/07_Wygladzanie/size-perplexity2.gif
similarity index 100%
rename from wyk/05_Wygladzanie/size-perplexity2.gif
rename to wyk/07_Wygladzanie/size-perplexity2.gif
diff --git a/wyk/05_Wygladzanie/urna-wyrazy.drawio b/wyk/07_Wygladzanie/urna-wyrazy.drawio
similarity index 100%
rename from wyk/05_Wygladzanie/urna-wyrazy.drawio
rename to wyk/07_Wygladzanie/urna-wyrazy.drawio
diff --git a/wyk/05_Wygladzanie/urna-wyrazy.drawio.png b/wyk/07_Wygladzanie/urna-wyrazy.drawio.png
similarity index 100%
rename from wyk/05_Wygladzanie/urna-wyrazy.drawio.png
rename to wyk/07_Wygladzanie/urna-wyrazy.drawio.png
diff --git a/wyk/05_Wygladzanie/urna.drawio b/wyk/07_Wygladzanie/urna.drawio
similarity index 100%
rename from wyk/05_Wygladzanie/urna.drawio
rename to wyk/07_Wygladzanie/urna.drawio
diff --git a/wyk/05_Wygladzanie/urna.drawio.png b/wyk/07_Wygladzanie/urna.drawio.png
similarity index 100%
rename from wyk/05_Wygladzanie/urna.drawio.png
rename to wyk/07_Wygladzanie/urna.drawio.png