|
17 | 17 | { |
18 | 18 | "cell_type": "code", |
19 | 19 | "execution_count": 1, |
| 20 | + "metadata": {}, |
| 21 | + "outputs": [ |
| 22 | + { |
| 23 | + "name": "stdout", |
| 24 | + "output_type": "stream", |
| 25 | + "text": [ |
| 26 | + "Requirement already satisfied: scikit-learn==0.21.3 in c:\\users\\kumar apurv\\anaconda3\\envs\\ch3\\lib\\site-packages (0.21.3)\n", |
| 27 | + "Requirement already satisfied: scipy>=0.17.0 in c:\\users\\kumar apurv\\anaconda3\\envs\\ch3\\lib\\site-packages (from scikit-learn==0.21.3) (1.5.4)\n", |
| 28 | + "Requirement already satisfied: joblib>=0.11 in c:\\users\\kumar apurv\\anaconda3\\envs\\ch3\\lib\\site-packages (from scikit-learn==0.21.3) (1.0.1)\n", |
| 29 | + "Requirement already satisfied: numpy>=1.11.0 in c:\\users\\kumar apurv\\anaconda3\\envs\\ch3\\lib\\site-packages (from scikit-learn==0.21.3) (1.19.5)\n" |
| 30 | + ] |
| 31 | + } |
| 32 | + ], |
| 33 | + "source": [ |
| 34 | + "# To install only the requirements of this notebook, uncomment the lines below and run this cell\n", |
| 35 | + "\n", |
| 36 | + "# ===========================\n", |
| 37 | + "\n", |
| 38 | + "!pip install scikit-learn==0.21.3\n", |
| 39 | + "\n", |
| 40 | + "# ===========================" |
| 41 | + ] |
| 42 | + }, |
| 43 | + { |
| 44 | + "cell_type": "code", |
| 45 | + "execution_count": 2, |
| 46 | + "metadata": {}, |
| 47 | + "outputs": [], |
| 48 | + "source": [ |
| 49 | + "# To install the requirements for the entire chapter, uncomment the lines below and run this cell\n", |
| 50 | + "\n", |
| 51 | + "# ===========================\n", |
| 52 | + "\n", |
| 53 | + "# try :\n", |
| 54 | + "# import google.colab\n", |
| 55 | + "# !curl https://raw.githubusercontent.com/practical-nlp/practical-nlp/master/Ch3/ch3-requirements.txt | xargs -n 1 -L 1 pip install\n", |
| 56 | + "# except ModuleNotFoundError :\n", |
| 57 | + "# !pip install -r \"ch3-requirements.txt\"\n", |
| 58 | + "\n", |
| 59 | + "# ===========================" |
| 60 | + ] |
| 61 | + }, |
| 62 | + { |
| 63 | + "cell_type": "code", |
| 64 | + "execution_count": 3, |
20 | 65 | "metadata": { |
21 | 66 | "colab": { |
22 | 67 | "base_uri": "https://localhost:8080/", |
|
33 | 78 | "['dog bites man', 'man bites dog', 'dog eats meat', 'man eats food']" |
34 | 79 | ] |
35 | 80 | }, |
36 | | - "execution_count": 1, |
| 81 | + "execution_count": 3, |
37 | 82 | "metadata": {}, |
38 | 83 | "output_type": "execute_result" |
39 | 84 | } |
|
46 | 91 | }, |
47 | 92 | { |
48 | 93 | "cell_type": "code", |
49 | | - "execution_count": 2, |
| 94 | + "execution_count": 4, |
50 | 95 | "metadata": { |
51 | 96 | "colab": { |
52 | 97 | "base_uri": "https://localhost:8080/", |
|
79 | 124 | }, |
80 | 125 | { |
81 | 126 | "cell_type": "code", |
82 | | - "execution_count": 3, |
| 127 | + "execution_count": 5, |
83 | 128 | "metadata": { |
84 | 129 | "colab": {}, |
85 | 130 | "colab_type": "code", |
|
102 | 147 | }, |
103 | 148 | { |
104 | 149 | "cell_type": "code", |
105 | | - "execution_count": 4, |
| 150 | + "execution_count": 6, |
106 | 151 | "metadata": { |
107 | 152 | "colab": { |
108 | 153 | "base_uri": "https://localhost:8080/", |
|
126 | 171 | "[[0, 0, 1, 0, 0, 0], [0, 1, 0, 0, 0, 0], [1, 0, 0, 0, 0, 0]]" |
127 | 172 | ] |
128 | 173 | }, |
129 | | - "execution_count": 4, |
| 174 | + "execution_count": 6, |
130 | 175 | "metadata": {}, |
131 | 176 | "output_type": "execute_result" |
132 | 177 | } |
|
138 | 183 | }, |
139 | 184 | { |
140 | 185 | "cell_type": "code", |
141 | | - "execution_count": 5, |
| 186 | + "execution_count": 7, |
142 | 187 | "metadata": { |
143 | 188 | "colab": { |
144 | 189 | "base_uri": "https://localhost:8080/", |
|
159 | 204 | " [0, 0, 0, 0, 0, 0]]" |
160 | 205 | ] |
161 | 206 | }, |
162 | | - "execution_count": 5, |
| 207 | + "execution_count": 7, |
163 | 208 | "metadata": {}, |
164 | 209 | "output_type": "execute_result" |
165 | 210 | } |
|
171 | 216 | }, |
172 | 217 | { |
173 | 218 | "cell_type": "code", |
174 | | - "execution_count": 6, |
| 219 | + "execution_count": 8, |
175 | 220 | "metadata": { |
176 | 221 | "colab": { |
177 | 222 | "base_uri": "https://localhost:8080/", |
|
192 | 237 | " [0, 0, 0, 0, 0, 0]]" |
193 | 238 | ] |
194 | 239 | }, |
195 | | - "execution_count": 6, |
| 240 | + "execution_count": 8, |
196 | 241 | "metadata": {}, |
197 | 242 | "output_type": "execute_result" |
198 | 243 | } |
|
227 | 272 | }, |
228 | 273 | { |
229 | 274 | "cell_type": "code", |
230 | | - "execution_count": 7, |
| 275 | + "execution_count": 9, |
231 | 276 | "metadata": { |
232 | 277 | "colab": {}, |
233 | 278 | "colab_type": "code", |
|
243 | 288 | }, |
244 | 289 | { |
245 | 290 | "cell_type": "code", |
246 | | - "execution_count": 8, |
| 291 | + "execution_count": 10, |
247 | 292 | "metadata": { |
248 | 293 | "colab": { |
249 | 294 | "base_uri": "https://localhost:8080/", |
|
307 | 352 | "name": "python", |
308 | 353 | "nbconvert_exporter": "python", |
309 | 354 | "pygments_lexer": "ipython3", |
310 | | - "version": "3.6.7" |
| 355 | + "version": "3.6.13" |
311 | 356 | } |
312 | 357 | }, |
313 | 358 | "nbformat": 4, |
|
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