diff --git a/README.md b/README.md
index 95aea12..adf6d72 100644
--- a/README.md
+++ b/README.md
@@ -39,7 +39,7 @@
 
 | Группа     | Дата       | 
 | :---       | :---:      | 
-| А-01-19    | 06.03.2023 | 
+| А-01-19    | 13.03.2023 | 
 | А-03-19    | 06.03.2023 | 
 
 * [Задание](labs/OATD_LR3.md) 
diff --git a/labs/OATD_LR3.md b/labs/OATD_LR3.md
index f1fded4..efec3be 100644
--- a/labs/OATD_LR3.md
+++ b/labs/OATD_LR3.md
@@ -63,7 +63,7 @@
 Обратить внимание, что разные виды регуляризации работают с разными методами нахождения экстремума.
 
 **Метод опорных векторов (SVM):**
-* функция потерь (параметр loss: ‘hinge’, ‘squared_hinge’), 
+* функция потерь (параметр kernel: ‘linear’, ‘rbf’), 
 * регуляризация (параметр penalty: ‘L1’, ‘L2’)
 Обратить внимание, что разные виды регуляризации работают с разными функциями потерь
 
diff --git a/lections/notebooks/lec5_text2vec_classifier.ipynb b/lections/notebooks/lec5_text2vec_classifier.ipynb
index 8d2999f..d202ef1 100644
--- a/lections/notebooks/lec5_text2vec_classifier.ipynb
+++ b/lections/notebooks/lec5_text2vec_classifier.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "id": "3dda6a69",
    "metadata": {},
    "outputs": [],
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "id": "7fd6636b",
    "metadata": {},
    "outputs": [
@@ -45,7 +45,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "id": "be870586",
    "metadata": {},
    "outputs": [],
@@ -55,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 4,
    "id": "599d6406",
    "metadata": {},
    "outputs": [
@@ -84,7 +84,7 @@
        " ('lady', 0.8845519423484802)]"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -96,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "id": "2db71cfb",
    "metadata": {},
    "outputs": [
@@ -106,7 +106,7 @@
        "0.60927683"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -117,7 +117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "id": "7788acf5",
    "metadata": {},
    "outputs": [],
@@ -139,7 +139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "id": "0565dd1a",
    "metadata": {},
    "outputs": [],
@@ -149,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "id": "a681a1d6",
    "metadata": {},
    "outputs": [
@@ -355,7 +355,7 @@
        "[5 rows x 23297 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -369,7 +369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 9,
    "id": "b20aef46",
    "metadata": {},
    "outputs": [
@@ -410,10 +410,129 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "id": "0d6af65a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
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    "source": [
     "text_data = ['Hello world I love python', 'This is a great computer game! 00 000 zyxel']\n",
     "# Векторизуем с помощью обученного CountVectorizer\n",
@@ -424,10 +543,82 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "id": "11dda58a",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "hello :  [-0.77069    0.12827    0.33137    0.0050893 -0.47605   -0.50116\n",
+      "  1.858      1.0624    -0.56511    0.13328   -0.41918   -0.14195\n",
+      " -2.8555    -0.57131   -0.13418   -0.44922    0.48591   -0.6479\n",
+      " -0.84238    0.61669   -0.19824   -0.57967   -0.65885    0.43928\n",
+      " -0.50473  ]\n",
+      "love :  [-0.62645  -0.082389  0.070538  0.5782   -0.87199  -0.14816   2.2315\n",
+      "  0.98573  -1.3154   -0.34921  -0.8847    0.14585  -4.97     -0.73369\n",
+      " -0.94359   0.035859 -0.026733 -0.77538  -0.30014   0.48853  -0.16678\n",
+      " -0.016651 -0.53164   0.64236  -0.10922 ]\n",
+      "python :  [-0.25645  -0.22323   0.025901  0.22901   0.49028  -0.060829  0.24563\n",
+      " -0.84854   1.5882   -0.7274    0.60603   0.25205  -1.8064   -0.95526\n",
+      "  0.44867   0.013614  0.60856   0.65423   0.82506   0.99459  -0.29403\n",
+      " -0.27013  -0.348    -0.7293    0.2201  ]\n",
+      "world :  [ 0.10301   0.095666 -0.14789  -0.22383  -0.14775  -0.11599   1.8513\n",
+      "  0.24886  -0.41877  -0.20384  -0.08509   0.33246  -4.6946    0.84096\n",
+      " -0.46666  -0.031128 -0.19539  -0.037349  0.58949   0.13941  -0.57667\n",
+      " -0.44426  -0.43085  -0.52875   0.25855 ]\n",
+      "Hello world I love python :  [ -1.55058002  -0.081683     0.27991899   0.58846928  -1.00551002\n",
+      "  -0.82613902   6.18642995   1.44844997  -0.71108004  -1.14717001\n",
+      "  -0.78294002   0.58841    -14.32649982  -1.41929996  -1.09575997\n",
+      "  -0.430875     0.87234702  -0.806399     0.27203003   2.23921998\n",
+      "  -1.23571999  -1.31071102  -1.96934     -0.17641005  -0.1353    ]\n",
+      "computer :  [ 0.64005  -0.019514  0.70148  -0.66123   1.1723   -0.58859   0.25917\n",
+      " -0.81541   1.1708    1.1413   -0.15405  -0.11369  -3.8414   -0.87233\n",
+      "  0.47489   1.1541    0.97678   1.1107   -0.14572  -0.52013  -0.52234\n",
+      " -0.92349   0.34651   0.061939 -0.57375 ]\n",
+      "game :  [ 1.146     0.3291    0.26878  -1.3945   -0.30044   0.77901   1.3537\n",
+      "  0.37393   0.50478  -0.44266  -0.048706  0.51396  -4.3136    0.39805\n",
+      "  1.197     0.10287  -0.17618  -1.2881   -0.59801   0.26131  -1.2619\n",
+      "  0.39202   0.59309  -0.55232   0.005087]\n",
+      "great :  [-8.4229e-01  3.6512e-01 -3.8841e-01 -4.6118e-01  2.4301e-01  3.2412e-01\n",
+      "  1.9009e+00 -2.2630e-01 -3.1335e-01 -1.0970e+00 -4.1494e-03  6.2074e-01\n",
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+      " -2.7928e-01]\n",
+      "zyxel :  [ 0.79234   0.067376 -0.22639  -2.2272    0.30057  -0.85676  -1.7268\n",
+      " -0.78626   1.2042   -0.92348  -0.83987  -0.74233   0.29689  -1.208\n",
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+      " -0.10225   1.7102   -0.95599  -1.3867  ]\n",
+      "This is a great computer game! 00 000 zyxel :  [  1.73610002   0.74208201   0.35545996  -4.74411008   1.41543998\n",
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+      "\n",
+      "[2 rows x 25 columns]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Андрей\\AppData\\Local\\Temp\\ipykernel_29476\\129113310.py:17: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
+      "  glove_data=glove_data.append(pd.DataFrame([one_doc]))\n"
+     ]
+    }
+   ],
    "source": [
     "# Создадим датафрейм, в который будем сохранять вектор документа\n",
     "glove_data=pd.DataFrame()\n",
@@ -451,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "ff68d8dc",
    "metadata": {},
    "outputs": [],
@@ -485,10 +676,129 @@
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    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "id": "b778776c",
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-   "outputs": [],
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+     "execution_count": 13,
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    "source": [
     "\n",
     "glove_data\n"
@@ -496,20 +806,412 @@
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   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "id": "cb6edbdf",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
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+       "        -2.23464306])"
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+     "metadata": {},
+     "output_type": "execute_result"
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    "source": [
     "one_doc"
    ]
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   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "id": "1bdb459e",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
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+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1657 rows × 25 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          0          1          2          3          4          5   \\\n",
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+       "\n",
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+       "0   21.189778 -10.895375   2.659030  -3.848115  ...   0.726191  11.634998   \n",
+       "\n",
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+       "0  -0.656084  12.178709  -6.118551  -3.286376  3.450946   2.055343  0.463787   \n",
+       "..       ...        ...        ...        ...       ...        ...       ...   \n",
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+       "0  -5.447248   1.293007  -7.882002  -2.527453  0.298939  -6.107062  3.365051   \n",
+       "\n",
+       "           24  \n",
+       "0  -16.556824  \n",
+       "0  -58.948193  \n",
+       "0   -2.279330  \n",
+       "0   -7.844431  \n",
+       "0  -12.644626  \n",
+       "..        ...  \n",
+       "0  -21.542710  \n",
+       "0   -0.504730  \n",
+       "0  -13.863438  \n",
+       "0  -10.156440  \n",
+       "0  -15.641826  \n",
+       "\n",
+       "[1657 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "train_data_glove = text2vec(twenty_train['data']);\n",
     "train_data_glove"
@@ -517,17 +1219,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 16,
    "id": "3a7ea7c6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<1657x23297 sparse matrix of type '<class 'numpy.int64'>'\n",
+       "\twith 106580 stored elements in Compressed Sparse Row format>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "train_data\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "id": "5ac20e79",
    "metadata": {},
    "outputs": [],
@@ -537,17 +1251,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "08164a25",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
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+      ],
+      "text/plain": [
+       "KNeighborsClassifier()"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "clf.fit(train_data_glove, twenty_train['target'])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "e459faaf",
    "metadata": {},
    "outputs": [],
@@ -557,17 +1285,392 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "d8144e75",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
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+       "0   15.131499   -0.904470   2.185990  -1.459807  ...   0.968499   4.725793   \n",
+       "0   56.409175  -56.250411  -4.028209 -11.687558  ...  22.884424  12.940570   \n",
+       "0    6.139770   -0.612219  -2.251460  -0.465165  ...   0.377958   1.957450   \n",
+       "0   32.153890  -40.169293  13.089525 -21.306493  ...   6.497279   8.340729   \n",
+       "\n",
+       "           17         18         19         20         21         22  \\\n",
+       "0   -0.921720  12.187404  -5.547615  -4.133999   3.588260  -0.497106   \n",
+       "0   -2.249193  -0.163590  -1.177710  -2.496928  -5.074085  -2.666947   \n",
+       "0   -0.766260   3.549431  -1.643443  -0.825730  -2.968016  -0.808924   \n",
+       "0   -6.193988  -3.990476  -2.345854  -5.534376  -8.925422   1.553300   \n",
+       "0  -19.537098  28.903925 -34.643949 -69.894146 -94.992145 -48.601895   \n",
+       "..        ...        ...        ...        ...        ...        ...   \n",
+       "0   -4.682214  -5.648911  -1.026898   3.719006   2.449941  -6.487197   \n",
+       "0   -0.726944   1.328612  -3.144209   1.643127  -1.259245  -0.880740   \n",
+       "0    1.058664  21.879058 -20.897253   2.537755   3.774890 -11.495336   \n",
+       "0   -1.705220  -0.509700   0.016110   1.461620   1.589069   2.267340   \n",
+       "0    4.996109  23.442078  -3.701088 -11.671505   9.209790 -10.002501   \n",
+       "\n",
+       "           23         24  \n",
+       "0   -2.542142 -11.362855  \n",
+       "0    0.662050  -3.590550  \n",
+       "0   -0.000160  -7.468189  \n",
+       "0    0.905790 -12.824533  \n",
+       "0  -29.098555 -91.934770  \n",
+       "..        ...        ...  \n",
+       "0    1.340930  -7.325196  \n",
+       "0   -6.713165  -3.115454  \n",
+       "0   -2.609774 -36.597559  \n",
+       "0    0.447919  -0.469250  \n",
+       "0   -0.815266 -17.024052  \n",
+       "\n",
+       "[1102 rows x 25 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "test_data_glove"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "a69830f0",
    "metadata": {},
    "outputs": [],
@@ -577,10 +1680,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "id": "9ac5cf20",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[225  35  59]\n",
+      " [ 26 313  50]\n",
+      " [ 56  98 240]]\n",
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "           0       0.73      0.71      0.72       319\n",
+      "           1       0.70      0.80      0.75       389\n",
+      "           2       0.69      0.61      0.65       394\n",
+      "\n",
+      "    accuracy                           0.71      1102\n",
+      "   macro avg       0.71      0.71      0.70      1102\n",
+      "weighted avg       0.71      0.71      0.70      1102\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "print (confusion_matrix(twenty_test['target'], predict))\n",
     "print(classification_report(twenty_test['target'], predict))"