Register
Login
Resources
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Product
Data Engine LLMs Platform Enterprise
Pricing Explore
Connect to our Discord channel
Resources  
Docs Blog Datasets Glossary Case Studies Tutorials & Webinars
Explore Pricing
Product  
Data Engine LLMs Platform Enterprise
Connect to Dagshub Discord channel
Register
Login

danaderp
/
ds4se
mirror of https://github.com/WM-SEMERU/ds4se.git

Watch 3
Issues   Discussions 0

5.0_vis.html 129 KB
Permalink History Raw

You have to be logged in to leave a comment. Sign In
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618 
  1. ---
    2. 

  2. 

  3. title: Exploration of your data
    4. 

  4. 

  5. keywords: fastai
    6. 

  6. sidebar: home_sidebar
    7. 

  7. 

  8. summary: "This module comprises all the statistical and inference techniques to describe the inner properties of software data. The submodules might include:"
    9. 

  9. ---
    10. 

  10. <!--
    11. 

  11. 

  12. #################################################
    13. 

  13. ### THIS FILE WAS AUTOGENERATED! DO NOT EDIT! ###
    14. 

  14. #################################################
    15. 

  15. # file to edit: nbs/5.0_vis.ipynb
    16. 

  16. # command to build the docs after a change: nbdev_build_docs
    17. 

  17. 

  18. -->
    19. 

  19. 

  20. <div class="container" id="notebook-container">
    21. 

  21.     
    22. 

  22. <div class="cell border-box-sizing code_cell rendered">
    23. 

  23. 

  24. </div>
    25. 

  25. <div class="cell border-box-sizing code_cell rendered">
    26. 

  26. <div class="input">
    27. 

  27. 

  28. <div class="inner_cell">
    29. 

  29.     <div class="input_area">
    30. 

  30. <div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>pip install dit
    31. 

  31. <span class="o">!</span>pip install sentencepiece
    32. 

  32. </pre></div>
    33. 

  33. 

  34.     </div>
    35. 

  35. </div>
    36. 

  36. </div>
    37. 

  37. 

  38. <div class="output_wrapper">
    39. 

  39. <div class="output">
    40. 

  40. 

  41. <div class="output_area">
    42. 

  42. 

  43. <div class="output_subarea output_stream output_stdout output_text">
    44. 

  44. <pre>Requirement already satisfied: dit in /usr/local/lib/python3.6/dist-packages (1.2.3)
    45. 

  45. Requirement already satisfied: scipy&gt;=0.15.0 in /usr/local/lib/python3.6/dist-packages (from dit) (1.4.1)
    46. 

  46. Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from dit) (2.4)
    47. 

  47. Requirement already satisfied: contextlib2 in /usr/local/lib/python3.6/dist-packages (from dit) (0.5.5)
    48. 

  48. Requirement already satisfied: numpy&gt;=1.11 in /usr/local/lib/python3.6/dist-packages (from dit) (1.17.5)
    49. 

  49. Requirement already satisfied: boltons in /usr/local/lib/python3.6/dist-packages (from dit) (20.0.0)
    50. 

  50. Requirement already satisfied: debtcollector in /usr/local/lib/python3.6/dist-packages (from dit) (2.0.0)
    51. 

  51. Requirement already satisfied: six&gt;=1.4.0 in /usr/local/lib/python3.6/dist-packages (from dit) (1.12.0)
    52. 

  52. Requirement already satisfied: prettytable in /usr/local/lib/python3.6/dist-packages (from dit) (0.7.2)
    53. 

  53. Requirement already satisfied: decorator&gt;=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx-&gt;dit) (4.4.1)
    54. 

  54. Requirement already satisfied: wrapt&gt;=1.7.0 in /usr/local/lib/python3.6/dist-packages (from debtcollector-&gt;dit) (1.11.2)
    55. 

  55. Requirement already satisfied: pbr!=2.1.0,&gt;=2.0.0 in /usr/local/lib/python3.6/dist-packages (from debtcollector-&gt;dit) (5.4.4)
    56. 

  56. Requirement already satisfied: sentencepiece in /usr/local/lib/python3.6/dist-packages (0.1.85)
    57. 

  57. </pre>
    58. 

  58. </div>
    59. 

  59. </div>
    60. 

  60. 

  61. </div>
    62. 

  62. </div>
    63. 

  63. 

  64. </div>
    65. 

  65. <div class="cell border-box-sizing code_cell rendered">
    66. 

  66. 

  67. </div>
    68. 

  68. <div class="cell border-box-sizing code_cell rendered">
    69. 

  69. 

  70. </div>
    71. 

  71. <div class="cell border-box-sizing code_cell rendered">
    72. 

  72. 

  73. <div class="output_wrapper">
    74. 

  74. <div class="output">
    75. 

  75. 

  76. <div class="output_area">
    77. 

  77. 

  78. 

  79. <div class="output_markdown rendered_html output_subarea ">
    80. 

  80. <h4 id="get_contours" class="doc_header"><code>get_contours</code><a href="https://github.com/ncoop57/ds4se/tree/master/ds4se/vis.py#L18" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>get_contours</code>(<strong><code>x_range</code></strong>, <strong><code>y_range</code></strong>, <strong><code>delta</code></strong>)</p>
    81. 

  81. </blockquote>
    82. 

  82. 

  83. </div>
    84. 

  84. 

  85. </div>
    86. 

  86. 

  87. </div>
    88. 

  88. </div>
    89. 

  89. 

  90. </div>
    91. 

  91. <div class="cell border-box-sizing code_cell rendered">
    92. 

  92. 

  93. </div>
    94. 

  94. <div class="cell border-box-sizing code_cell rendered">
    95. 

  95. 

  96. <div class="output_wrapper">
    97. 

  97. <div class="output">
    98. 

  98. 

  99. <div class="output_area">
    100. 

  100. 

  101. 

  102. <div class="output_markdown rendered_html output_subarea ">
    103. 

  103. <h4 id="visualize_gt_ngt" class="doc_header"><code>visualize_gt_ngt</code><a href="https://github.com/ncoop57/ds4se/tree/master/ds4se/vis.py#L27" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>visualize_gt_ngt</code>(<strong><code>gt</code></strong>, <strong><code>ngt</code></strong>, <strong><code>title</code></strong>, <strong><code>y_label</code></strong>)</p>
    104. 

  104. </blockquote>
    105. 

  105. 

  106. </div>
    107. 

  107. 

  108. </div>
    109. 

  109. 

  110. </div>
    111. 

  111. </div>
    112. 

  112. 

  113. </div>
    114. 

  114. <div class="cell border-box-sizing code_cell rendered">
    115. 

  115. 

  116. </div>
    117. 

  117. <div class="cell border-box-sizing code_cell rendered">
    118. 

  118. 

  119. <div class="output_wrapper">
    120. 

  120. <div class="output">
    121. 

  121. 

  122. <div class="output_area">
    123. 

  123. 

  124. 

  125. <div class="output_markdown rendered_html output_subarea ">
    126. 

  126. <h4 id="visualize_events" class="doc_header"><code>visualize_events</code><a href="https://github.com/ncoop57/ds4se/tree/master/ds4se/vis.py#L50" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>visualize_events</code>(<strong><code>events</code></strong>, <strong><code>color</code></strong>, <strong><code>title</code></strong>, <strong><code>label</code></strong>)</p>
    127. 

  127. </blockquote>
    128. 

  128. 

  129. </div>
    130. 

  130. 

  131. </div>
    132. 

  132. 

  133. </div>
    134. 

  134. </div>
    135. 

  135. 

  136. </div>
    137. 

  137. <div class="cell border-box-sizing code_cell rendered">
    138. 

  138. 

  139. </div>
    140. 

  140. <div class="cell border-box-sizing code_cell rendered">
    141. 

  141. 

  142. <div class="output_wrapper">
    143. 

  143. <div class="output">
    144. 

  144. 

  145. <div class="output_area">
    146. 

  146. 

  147. 

  148. <div class="output_markdown rendered_html output_subarea ">
    149. 

  149. <h4 id="plot_counts" class="doc_header"><code>plot_counts</code><a href="https://github.com/ncoop57/ds4se/tree/master/ds4se/vis.py#L63" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>plot_counts</code>(<strong><code>counts</code></strong>, <strong><code>x_label</code></strong>, <strong><code>y_label</code></strong>, <strong><code>top_k</code></strong>=<em><code>30</code></em>)</p>
    150. 

  150. </blockquote>
    151. 

  151. 

  152. </div>
    153. 

  153. 

  154. </div>
    155. 

  155. 

  156. </div>
    157. 

  157. </div>
    158. 

  158. 

  159. </div>
    160. 

  160. <div class="cell border-box-sizing code_cell rendered">
    161. 

  161. <div class="input">
    162. 

  162. 

  163. <div class="inner_cell">
    164. 

  164.     <div class="input_area">
    165. 

  165. <div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Create a histogram of the entropy distribution</span>
    166. 

  166. <span class="n">plt</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">LIB_entropies</span><span class="p">,</span> <span class="n">bins</span> <span class="o">=</span> <span class="mi">20</span><span class="p">)</span>
    167. 

  167. <span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">&quot;Num Files&quot;</span><span class="p">)</span>
    168. 

  168. <span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">&quot;Entropy&quot;</span><span class="p">)</span>
    169. 

  169. <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
    170. 

  170. </pre></div>
    171. 

  171. 

  172.     </div>
    173. 

  173. </div>
    174. 

  174. </div>
    175. 

  175. 

  176. <div class="output_wrapper">
    177. 

  177. <div class="output">
    178. 

  178. 

  179. <div class="output_area">
    180. 

  180. 

  181. 

  182. 

  183. <div class="output_png output_subarea ">
    184. 

  184. <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAX4AAAEGCAYAAABiq/5QAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz
    185. 

  185. AAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjMsIGh0
    186. 

  186. dHA6Ly9tYXRwbG90bGliLm9yZy+AADFEAAAQEklEQVR4nO3df5BdZX3H8fdHoiOhQVR2nEJMQ61D
    187. 

  187. x1FUZkcRrFpAiw2CQ9spjDhonaY/EX+0DrYd0bF/UH+0an/AZADBSqE1Iv7qUBjBqlNlTAKDQLBa
    188. 

  188. jRhAE8aW34rRb/+4N7qEzeaS3XNOdp/3a2Znzz3n7H2+JwmffXjuOc+TqkKS1I7HDV2AJKlfBr8k
    189. 

  189. Ncbgl6TGGPyS1BiDX5Ias2zoAiZx8MEH1+rVq4cuQ5IWlY0bN95dVVO77l8Uwb969Wo2bNgwdBmS
    190. 

  190. tKgk+c5s+x3qkaTGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY3pLPiTXJRkW5KbZ+x7b5LbktyU5BNJ
    191. 

  191. DuqqfUnS7Lrs8V8MnLDLvmuAZ1fVEcB/A2/vsH1J0iw6C/6q+gLwg132XV1VO8YvvwKs7Kp9SdLs
    192. 

  192. hnxy9/eAf93dwSRrgbUAq1at6qsmqQmrz/7sIO1uOXfNIO3qkQb5cDfJXwI7gEt3d05Vrauq6aqa
    193. 

  193. npp61FQTkqS91HuPP8nrgBOB48p1HyWpd70Gf5ITgLcBL62qB/tsW5I00uXtnJcBXwYOT7I1yRuA
    194. 

  194. fwBWANckuTHJ+V21L0maXWc9/qo6bZbdF3bVniRpMj65K0mNMfglqTEGvyQ1xuCXpMYY/JLUGINf
    195. 

  195. khpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWp
    196. 

  196. MQa/JDXG4Jekxhj8ktQYg1+SGmPwS1JjDH5JaozBL0mN6Sz4k1yUZFuSm2fse0qSa5J8Y/z9yV21
    197. 

  197. L0maXZc9/ouBE3bZdzbwuap6JvC58WtJUo86C/6q+gLwg112nwxcMt6+BHh1V+1LkmbX9xj/06rq
    198. 

  198. rvH294Cn7e7EJGuTbEiyYfv27f1UJ0kNGOzD3aoqoOY4vq6qpqtqempqqsfKJGlp6zv4v5/kFwHG
    199. 

  199. 37f13L4kNa/v4P8UcMZ4+wzgkz23L0nN6/J2zsuALwOHJ9ma5A3AucDLk3wDOH78WpLUo2VdvXFV
    200. 

  200. nbabQ8d11aYkac98cleSGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtS
    201. 

  201. Ywx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklqjMEvSY0x+CWpMQa/JDXG
    202. 

  202. 4Jekxhj8ktQYg1+SGmPwS1JjBgn+JG9OckuSm5NcluSJQ9QhSS3qPfiTHAq8EZiuqmcD+wGn9l2H
    203. 

  203. JLVqqKGeZcD+SZYBy4E7B6pDkprTe/BX1R3A+4DbgbuAe6rq6l3PS7I2yYYkG7Zv3953mZK0ZA0x
    204. 

  204. 1PNk4GTgMOAQ4IAkp+96XlWtq6rpqpqemprqu0xJWrKGGOo5Hvh2VW2vqh8DVwBHD1CHJDVpiOC/
    205. 

  205. HTgqyfIkAY4DNg9QhyQ1aYgx/uuB9cAm4GvjGtb1XYcktWrZEI1W1TnAOUO0LUmt88ldSWrMHoM/
    206. 

  206. yXuSHJjk8Uk+l2T7bHfhSJIWh0l6/K+oqnuBE4EtwK8Af95lUZKk7kwS/Ds/B1gDfKyq7umwHklS
    207. 

  207. xyb5cPczSW4DHgL+KMkU8MNuy5IkdWWPPf6qOpvRA1bT4weuHmT05K0kaRGa5MPd5cAfA+eNdx0C
    208. 

  208. THdZlCSpO5OM8X8YeJifT6twB/DXnVUkSerUJMH/jKp6D/BjgKp6EEinVUmSOjNJ8D+cZH+gAJI8
    209. 

  209. A/hRp1VJkjozyV095wBXAU9PcilwDPC6LouSJHVnj8FfVdck2QQcxWiI56yqurvzyiRJndht8Cc5
    210. 

  210. cpddd42/r0qyqqo2dVeWJKkrc/X43z/HsQKOXeBaJEk92G3wV9Wv91mIJKkfcw31HFtV1yY5Zbbj
    211. 

  211. VXVFd2VJkroy11DPS4FrgVfNcqwYrZUrSVpk5hrqOWf8/fX9lSNJ6tpuH+BKcvGM7TN6qUaS1Lm5
    212. 

  212. ntx97ozts7ouRJLUj7mCv3qrQpLUm7k+3F2Z5EOMntbduf0zVfXGTiuTJHViruCfua7uhq4LkST1
    213. 

  213. Y667ei7psxBJUj8mmZZZkrSEDBL8SQ5Ksj7JbUk2J3nREHVIUosmmY+/Cx8Erqqq307yBGD5QHVI
    214. 

  214. UnP2GPxJDgPOBFbPPL+qTtqbBpM8CXgJ48VcquphRmv6SpJ6MEmP/0rgQuDTwE8XoM3DgO3Ah5M8
    215. 

  215. F9jIaHGXB2aelGQtsBZg1apVC9CstO9ZffZnhy5hUZnPn9eWc9csYCWL2yRj/D+sqg9V1XVV9Z87
    216. 

  216. v+bR5jLgSOC8qno+8ABw9q4nVdW6qpququmpqal5NCdJmmmSHv8Hk5wDXM2MRdbnsQLXVmBrVV0/
    217. 

  217. fr2eWYJfktSNSYL/OcBrGa24tXOoZ69X4Kqq7yX5bpLDq+rrwHHArXvzXpKkx26S4P8d4JfHH8Iu
    218. 

  218. lDOBS8d39HwLcOpnSerJJMF/M3AQsG2hGq2qG4HphXo/SdLkJgn+g4DbknyVR47x79XtnJKkYU0S
    219. 

  219. /Od0XoUkqTd7DP553ropSdrHTPLk7n38fFGWJwCPBx6oqgO7LEyS1I1Jevwrdm4nCXAycFSXRUmS
    220. 

  220. uvOYZueskSuB3+ioHklSxyYZ6jllxsvHMboN84edVSRJ6tQkd/W8asb2DmALo+EeSdIiNMkYv0/V
    221. 

  221. StISstvgT/KOOX6uqurdHdQjSerYXD3+B2bZdwDwBuCpgMEvSYvQboO/qt6/czvJCuAsRpOpXQ68
    222. 

  222. f3c/J0nat805xp/kKcBbgNcAlwBHVtX/9lGY2jPU6kqu6qQuzXeVtS7+jc01xv9e4BRgHfCcqrp/
    223. 

  223. wVuXJPVurge43gocAvwVcGeSe8df9yW5t5/yJEkLba4x/sf0VK8kaXEw3CWpMQa/JDXG4Jekxhj8
    224. 

  224. ktQYg1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1ZrDgT7JfkhuSfGaoGiSpRUP2+M8CNg/YviQ1
    225. 

  225. aZDgT7ISWANcMET7ktSyPS623pEPAG8DVuzuhCRrgbUAq1at6qksLVbzXexisbW7WC3WP6/FWvfu
    226. 

  226. 9N7jT3IisK2qNs51XlWtq6rpqpqemprqqTpJWvqGGOo5BjgpyRZG6/cem+SjA9QhSU3qPfir6u1V
    227. 

  227. tbKqVgOnAtdW1el91yFJrfI+fklqzFAf7gJQVZ8HPj9kDZLUGnv8ktQYg1+SGmPwS1JjDH5JaozB
    228. 

  228. L0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhpj8EtSYwadq0e7N5+FH7acu2YBK5GWhqW2mMp82OOX
    229. 

  229. pMYY/JLUGINfkhpj8EtSYwx+SWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BLUmMMfklq
    230. 

  230. TO/Bn+TpSa5LcmuSW5Kc1XcNktSyIaZl3gG8tao2JVkBbExyTVXdOkAtktSc3nv8VXVXVW0ab98H
    231. 

  231. bAYO7bsOSWrVoAuxJFkNPB+4fpZja4G1AKtWreq1rsXOBSckzWWwD3eT/ALwceBNVXXvrseral1V
    232. 

  232. TVfV9NTUVP8FStISNUjwJ3k8o9C/tKquGKIGSWrVEHf1BLgQ2FxVf9t3+5LUuiF6/McArwWOTXLj
    233. 

  233. +Os3B6hDkprU+4e7VfUlIH23K0ka8cldSWqMwS9JjTH4JakxBr8kNcbgl6TGGPyS1BiDX5IaY/BL
    234. 

  234. UmMMfklqjMEvSY0x+CWpMQa/JDVm0BW4+jCf1ai2nLtmsLYlqSv2+CWpMQa/JDXG4Jekxhj8ktQY
    235. 

  235. g1+SGmPwS1JjDH5JaozBL0mNMfglqTEGvyQ1xuCXpMYY/JLUGINfkhozSPAnOSHJ15N8M8nZQ9Qg
    236. 

  236. Sa3qPfiT7Af8I/BK4FnAaUme1XcdktSqIXr8LwC+WVXfqqqHgcuBkweoQ5KaNMRCLIcC353xeivw
    237. 

  237. wl1PSrIWWDt+eX+Sr8+jzYOBux/rD+Vv5tHivmWvrn8J8fq9/kV7/fPMoV+abec+uwJXVa0D1i3E
    238. 

  238. eyXZUFXTC/Fei5HX7/V7/e1e/2yGGOq5A3j6jNcrx/skST0YIvi/CjwzyWFJngCcCnxqgDokqUm9
    239. 

  239. D/VU1Y4kfwr8B7AfcFFV3dJxswsyZLSIef1t8/r1CKmqoWuQJPXIJ3clqTEGvyQ1pongT7JfkhuS
    240. 

  240. fGboWvqWZEuSryW5McmGoevpW5KDkqxPcluSzUleNHRNfUly+PjvfefXvUneNHRdfUry5iS3JLk5
    241. 

  241. yWVJnjh0TfuCJsb4k7wFmAYOrKoTh66nT0m2ANNVtWgfYJmPJJcAX6yqC8Z3kS2vqv8buq6+jadK
    242. 

  242. uQN4YVV9Z+h6+pDkUOBLwLOq6qEk/wb8e1VdPGxlw1vyPf4kK4E1wAVD16J+JXkS8BLgQoCqerjF
    243. 

  243. 0B87DvifVkJ/hmXA/kmWAcuBOweuZ5+w5IMf+ADwNuCnQxcykAKuTrJxPA1GSw4DtgMfHg/1XZDk
    244. 

  244. gKGLGsipwGVDF9GnqroDeB9wO3AXcE9VXT1sVfuGJR38SU4EtlXVxqFrGdCLq+pIRrOh/kmSlwxd
    245. 

  245. UI+WAUcC51XV84EHgOamAR8PcZ0EfGzoWvqU5MmMJoA8DDgEOCDJ6cNWtW9Y0sEPHAOcNB7nvhw4
    246. 

  246. NslHhy2pX+NeD1W1DfgEo9lRW7EV2FpV149fr2f0i6A1rwQ2VdX3hy6kZ8cD366q7VX1Y+AK4OiB
    247. 

  247. a9onLOngr6q3V9XKqlrN6H91r62qZn7jJzkgyYqd28ArgJuHrao/VfU94LtJDh/vOg64dcCShnIa
    248. 

  248. jQ3zjN0OHJVkeZIw+vvfPHBN+4R9dnZOLYinAZ8Y/ZtnGfAvVXXVsCX17kzg0vFwx7eA1w9cT6/G
    249. 

  249. v/BfDvzB0LX0raquT7Ie2ATsAG7A6RuARm7nlCT93JIe6pEkPZrBL0mNMfglqTEGvyQ1xuCXpMZ4
    250. 

  250. O6ealOQnwNdm7Lq8qs6d4/yXAQ9X1X91XZvUNYNfrXqoqp73GM5/GXA/8KjgT7KsqnYsVGFS1xzq
    251. 

  251. kWYYr1/wriSbxusY/GqS1cAfAm8ez2v/a0kuTnJ+kuuB9yR5SpIrk9yU5CtJjhi/3zuT/HOSLyf5
    252. 

  252. RpLfH+//SJJXz2j30iQnD3DJapDBr1btv8siJb8749jd44ntzgP+rKq2AOcDf1dVz6uqL47PWwkc
    253. 

  253. XVVvAd4F3FBVRwB/AXxkxvsdARwLvAh4R5JDGE0V/Tr42fTRRwOf7ehapUdwqEetmmuo54rx943A
    254. 

  254. KXO8x8eq6ifj7RcDvwVQVdcmeWqSA8fHPllVDwEPJbkOeEFVXZnkn5JMjX/u4w4XqS8Gv/RoPxp/
    255. 

  255. /wlz/zfywITvt+u8KDtffwQ4ndEEgk3NIaRhOdQjTeY+YMUcx78IvAZ+dgfQ3VV17/jYyUmemOSp
    256. 

  256. jD4k/up4/8XAmwCqqsVZQzUQe/xq1f5Jbpzx+qqqmmuRlk8D68cfwJ45y/F3AhcluQl4EDhjxrGb
    257. 

  257. gOuAg4F3V9WdAFX1/SSbgSv3/jKkx87ZOaUOJXkncH9VvW+WY8sZPUtwZFXd03dtapdDPdIAkhzP
    258. 

  258. aFGQvzf01Td7/JLUGHv8ktQYg1+SGmPwS1JjDH5JaozBL0mN+X8h+epAaV5iEQAAAABJRU5ErkJg
    259. 

  259. gg==
    260. 

  260. "
    261. 

  261. >
    262. 

  262. </div>
    263. 

  263. 

  264. </div>
    265. 

  265. 

  266. </div>
    267. 

  267. </div>
    268. 

  268. 

  269. </div>
    270. 

  270. <div class="cell border-box-sizing code_cell rendered">
    271. 

  271. 

  272. </div>
    273. 

  273. <div class="cell border-box-sizing code_cell rendered">
    274. 

  274. 

  275. <div class="output_wrapper">
    276. 

  276. <div class="output">
    277. 

  277. 

  278. <div class="output_area">
    279. 

  279. 

  280. 

  281. <div class="output_markdown rendered_html output_subarea ">
    282. 

  282. <h4 id="vis_3d" class="doc_header"><code>vis_3d</code><a href="https://github.com/ncoop57/ds4se/tree/master/ds4se/vis.py#L76" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>vis_3d</code>(<strong><code>gt</code></strong>, <strong><code>ngt</code></strong>, <strong><code>src_dtype</code></strong>, <strong><code>trgt_dtype</code></strong>)</p>
    283. 

  283. </blockquote>
    284. 

  284. 

  285. </div>
    286. 

  286. 

  287. </div>
    288. 

  288. 

  289. </div>
    290. 

  290. </div>
    291. 

  291. 

  292. </div>
    293. 

  293. <div class="cell border-box-sizing code_cell rendered">
    294. 

  294. 

  295. </div>
    296. 

  296. <div class="cell border-box-sizing code_cell rendered">
    297. 

  297. 

  298. <div class="output_wrapper">
    299. 

  299. <div class="output">
    300. 

  300. 

  301. <div class="output_area">
    302. 

  302. 

  303. 

  304. <div class="output_markdown rendered_html output_subarea ">
    305. 

  305. <h4 id="reduce_dims" class="doc_header"><code>reduce_dims</code><a href="https://github.com/ncoop57/ds4se/tree/master/ds4se/vis.py#L95" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>reduce_dims</code>(<strong><code>doc_vecs</code></strong>, <strong><code>dims</code></strong>=<em><code>2</code></em>)</p>
    306. 

  306. </blockquote>
    307. 

  307. 

  308. </div>
    309. 

  309. 

  310. </div>
    311. 

  311. 

  312. </div>
    313. 

  313. </div>
    314. 

  314. 

  315. </div>
    316. 

  316. <div class="cell border-box-sizing code_cell rendered">
    317. 

  317. 

  318. </div>
    319. 

  319. <div class="cell border-box-sizing code_cell rendered">
    320. 

  320. 

  321. <div class="output_wrapper">
    322. 

  322. <div class="output">
    323. 

  323. 

  324. <div class="output_area">
    325. 

  325. 

  326. 

  327. <div class="output_markdown rendered_html output_subarea ">
    328. 

  328. <h4 id="clusterize_w_entropy" class="doc_header"><code>clusterize_w_entropy</code><a href="https://github.com/ncoop57/ds4se/tree/master/ds4se/vis.py#L107" class="source_link" style="float:right">[source]</a></h4><blockquote><p><code>clusterize_w_entropy</code>(<strong><code>gt_doc_vecs</code></strong>, <strong><code>ngt_doc_vecs</code></strong>, <strong><code>gt_entropies</code></strong>, <strong><code>ngt_entropies</code></strong>)</p>
    329. 

  329. </blockquote>
    330. 

  330. 

  331. </div>
    332. 

  332. 

  333. </div>
    334. 

  334. 

  335. </div>
    336. 

  336. </div>
    337. 

  337. 

  338. </div>
    339. 

  339. <div class="cell border-box-sizing code_cell rendered">
    340. 

  340. <div class="input">
    341. 

  341. 

  342. <div class="inner_cell">
    343. 

  343.     <div class="input_area">
    344. 

  344. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">np</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">req2src</span><span class="o">.</span><span class="n">src_vec</span><span class="o">.</span><span class="n">to_list</span><span class="p">())</span><span class="o">.</span><span class="n">shape</span>
    345. 

  345. </pre></div>
    346. 

  346. 

  347.     </div>
    348. 

  348. </div>
    349. 

  349. </div>
    350. 

  350. 

  351. <div class="output_wrapper">
    352. 

  352. <div class="output">
    353. 

  353. 

  354. <div class="output_area">
    355. 

  355. 

  356. 

  357. 

  358. <div class="output_text output_subarea output_execute_result">
    359. 

  359. <pre>(100, 100)</pre>
    360. 

  360. </div>
    361. 

  361. 

  362. </div>
    363. 

  363. 

  364. </div>
    365. 

  365. </div>
    366. 

  366. 

  367. </div>
    368. 

  368. <div class="cell border-box-sizing code_cell rendered">
    369. 

  369. <div class="input">
    370. 

  370. 

  371. <div class="inner_cell">
    372. 

  372.     <div class="input_area">
    373. 

  373. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">delta</span> <span class="o">=</span> <span class="mi">1</span>
    374. 

  374. <span class="n">x</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">delta</span><span class="p">)</span>
    375. 

  375. <span class="n">y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mf">2.0</span><span class="p">,</span> <span class="mf">10.0</span><span class="p">,</span> <span class="n">delta</span><span class="p">)</span>
    376. 

  376. <span class="n">X</span><span class="p">,</span> <span class="n">Y</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)</span>
    377. 

  377. <span class="n">Z</span> <span class="o">=</span> <span class="n">X</span> <span class="o">+</span> <span class="n">Y</span>
    378. 

  378. </pre></div>
    379. 

  379. 

  380.     </div>
    381. 

  381. </div>
    382. 

  382. </div>
    383. 

  383. 

  384. </div>
    385. 

  385. <div class="cell border-box-sizing code_cell rendered">
    386. 

  386. <div class="input">
    387. 

  387. 

  388. <div class="inner_cell">
    389. 

  389.     <div class="input_area">
    390. 

  390. <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.cm</span> <span class="k">as</span> <span class="nn">cm</span>
    391. 

  391. </pre></div>
    392. 

  392. 

  393.     </div>
    394. 

  394. </div>
    395. 

  395. </div>
    396. 

  396. 

  397. </div>
    398. 

  398. <div class="cell border-box-sizing code_cell rendered">
    399. 

  399. <div class="input">
    400. 

  400. 

  401. <div class="inner_cell">
    402. 

  402.     <div class="input_area">
    403. 

  403. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span> <span class="o">=</span> <span class="n">get_contours</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">11</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">11</span><span class="p">],</span> <span class="mi">1</span><span class="p">)</span>
    404. 

  404. </pre></div>
    405. 

  405. 

  406.     </div>
    407. 

  407. </div>
    408. 

  408. </div>
    409. 

  409. 

  410. </div>
    411. 

  411. <div class="cell border-box-sizing code_cell rendered">
    412. 

  412. <div class="input">
    413. 

  413. 

  414. <div class="inner_cell">
    415. 

  415.     <div class="input_area">
    416. 

  416. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">()</span>
    417. 

  417. <span class="n">CS</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">contourf</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">Y</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">levels</span> <span class="o">=</span> <span class="mi">12</span><span class="p">,</span> <span class="n">cmap</span> <span class="o">=</span> <span class="s1">&#39;gray&#39;</span><span class="p">)</span>
    418. 

  418. <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="s1">&#39;Simplest default with labels&#39;</span><span class="p">)</span>
    419. 

  419. </pre></div>
    420. 

  420. 

  421.     </div>
    422. 

  422. </div>
    423. 

  423. </div>
    424. 

  424. 

  425. <div class="output_wrapper">
    426. 

  426. <div class="output">
    427. 

  427. 

  428. <div class="output_area">
    429. 

  429. 

  430. 

  431. 

  432. <div class="output_text output_subarea output_execute_result">
    433. 

  433. <pre>Text(0.5, 1.0, &#39;Simplest default with labels&#39;)</pre>
    434. 

  434. </div>
    435. 

  435. 

  436. </div>
    437. 

  437. 

  438. <div class="output_area">
    439. 

  439. 

  440. 

  441. 

  442. <div class="output_png output_subarea ">
    443. 

  443. <img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAXcAAAEICAYAAACktLTqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy8QZhcZAAAgAElEQVR4nO2dfbCkdXXnP1+YQRhkxbmjLMOLuNFgzJS8jQQVEUEEhBLX3Upw465mUw4mRCHrlkGz7moSUzFlbWE2WzETVMgKowZhw2KWHQoR15hgBhhlYBAVQV4Ghhne5EVkmLN/9NNLc6f73u5+ntv9e773fKqm7lv30+d7z/d8+/Rzu3sUESRJkiRe7DbtApIkSZLmyXBPkiQxJMM9SZLEkAz3JEkSQzLckyRJDMlwT5IkMSTD3RRJvy5p/QId+0JJf7QQx27qdiUdKmmjpJ9K+mDN2/24pC/WOcaA435W0seaul1JIekVQ1zukOqyS4Y9dhPXTSZLhnuLkXSspG9LelTSQ5L+XtJrASLi4oh467RrHMRCBWYPHwaujYh9IuLPmjpok+EWEe+PiD+sjnu8pHvqV5gkHTLcW4qkfwZcCfw3YDlwAPAJ4Olp1lUQLwNumXYRSTItMtzbyy8CRMS6iHg2Ip6KiPUR8T0ASe+V9K3uhatt87cl/aA6VfGHkn6h2vwfk/QVSXtUlz1e0j2SPippm6Q7Jf36oEIknV6dAnmkOt5ren72e5LurW7z+5JOlHQK8FHg1yQ9Lum7A457hKQbq+t+GdhzmNuV9HXgzcCfV8f/RUmnSbqp0nq3pI/3HGeXrbnS/JY+ZX2z+vhIdezXzbrenpKekrSi+vr3Je2o7oypfu/nV59fKOmPJO0N/G9gZXXMxyWtrA65h6S/rn4Ht0haPagPs+oYqLeHfy/pPklbJP3HnuvuJuk8ST+StL3yxvIBt/NeSXdU9f14Lp8kkyXDvb3cDjwr6SJJp0p68RDXORk4CjiGzmmLtcC7gYOAVcC7ei77z4EVdB4RvAdYK+nQ2QeUdATweeAsYAb4S+AKSS+oLv87wGsjYp/q9u+MiKuAPwa+HBEvjIjD+hx3D+B/Av+DziOTvwH+1TC3GxEnAP8X+J3q+LcDTwD/DtgXOA34LUnvGOJ3Npvjqo/7Vsf+h94fRsTPgH8C3lR9603AXcAber6+btZ1ngBOBe6rjvnCiLiv+vHbgS9VdV8B/PmQdQ6j983AK4G3Ar/Xc2f2AeAdVa0rgYeB/z77Bqo7pT8DTq36+3pg45D1JQtMhntLiYjHgGOBAP4KeFDSFZL2m+NqfxoRj0XELcAmYH1E3BERj9LZHI+YdfmPRcTTEXEd8DXgV/sccw3wlxFxffUI4iI6p4aOAZ4FXgC8WtLSiLgzIn40pMRjgKXA+RHxTERcSic0h7ndXYiIb0TEzRGxs3p0s47nArhprgPeVJ2Xfw2dAHyTpD2B1/Lc9j8M34qIv4uIZ+nc0e1yR9iPIfV+IiKeiIibgS/w3J37+4Hfj4h7IuJp4OPAvx7wd4adwCpJe0XElspbSQFkuLeYiNgcEe+NiAPpbN4rgfPnuMoDPZ8/1efrF/Z8/XC1UXa5qzr+bF4GfKg6NfKIpEfoPBJYGRE/BM6lEw5bJX2p53TDfKwE7o3nv7PdXcPcbr+DSfoVSddKelDSo3QCbMWQtYzKdcDxwJHAzcDVdIL1GOCHEbF9hGPd3/P5k8CeA0L2eQyp9+6ez3v7+zLg8p7f62Y6d9TPWxwqf/xadewtkr4m6VUjaEsWkAx3EyLiNuBCOiHfBC+uHnZ3ORi4r8/l7gY+GRH79vxbFhHrqrouiYhj6QRGAJ/qljzP7W8BDpCkWTUMdbt9uITOaY2DIuJFwGeB7rGfAJZ1Lyhpd+AlA44zzNuofhs4FPiXwHURcWtV+9uYdUpmxOOOwlx6uxzU83lvf++mc6ql93e7Z0Tcu0vREf8nIk4C9gduo/MoMimADPeWIulVkj4k6cDq64PoPKz+xwZv5hOS9pD0RuB0Oue9Z/NXwPurTVGS9q7+mLePOs81P0HSC4Cf0Xl0sLO63gPAIZIGefAfgB3AByUtlfRO4OhhbnfA8fYBHoqIn0k6Gvg3PT+7nc5GfJqkpcB/onM6qR8PVhr+xYCfExFPAjcAZ/NcmH+bzoY7KNwfAGYkvWjQcUdkLr1dPiZpmaRfBn4D+HL1/c8Cn5T0MgBJL5F0xuwrS9pP0hnVEvA08DjP9TeZMhnu7eWnwK8A10t6gk6obwI+1NDx76fzh7T7gIuB91ePDp5HRGwA3kfnD30PAz8E3lv9+AXAnwDbquO9FPhI9bPuHcV2STf2Oe7PgXdWx3qIzsP/y4a83X78NvAHkn4K/GfgKz3HerT6+QXAvXQ2+b7POa+C+5PA31enLfqe46cT4kuB7/R8vQ8DzrdXv9t1wB3VcYc9fTWIgXpn1fhD4Brg0xHRfdHbZ+hs/eur6/8jHa/NZjfgP9DxyEN0Tj39Vs26k4ZQ/mcdyWwkHQ98sTqXnyRJC8nNPUmSxJB5w13S5yVtlbSp53vLJV2tzgtirh7yOdZJkiTJhBhmc78QOGXW984DromIV9I5X3dew3UlU6R6jnSekkmSFjPUOXdJhwBXRsSq6uvvA8dHxBZJ+wPfiIhdXr2YJEmSTIdx39luv4jYUn1+P7Ne3NCLpDV0Xk3I0qVLj1qxornXjSxZ4veuo26aUk/5uGly0wNw0003bYuIQa+96EsTb1sakgau/xGxls57mLBy5co466yz6t7k85iZmWn0eCXQ5B1gCbjpSc+Vj5ueZcuW3TX/pZ7PuM+WeaA6HUP1ceuYx6nN9u3b2b59lFdzl8+2bdumXUKjbNu2zUpTeq583PSMw7jhfgWddwqk+vi3zZQzPo7D5mZQNz3pubJx0zMqwzwVch2dl4Ifqs57fP8mnVcdniTpB8Bbqq+nTm5U5eM2cOm58nHz3LDMe849It414EcnNlxLY3SHzeXcaNeYTucRt23bZqVn+/btNn6D9JwD1q9QzY2qbNw2qtziy8fNc3NhHe7gN3CO5nTTk54rHzc9/bAP9y5OwwZ+5nQMkPRc2Th6rpdFE+6QG1UbcNOTnisfNz1dFlW4d3EaNvAbODc94Ok5Jxw9tyjDHfw2KvAcOCfcPOcYiE56Fm24d3EaNvAbODc9kJ4rHRc9iz7cwW+jAq8NBPz0pOfKp+16Mtx7cBy2thu0Fzc9kJ4rnTbryXCfRW5U5dPmgetHeq582qgnw30AjsPWRoPOhZue9FzZtE1Phvsc5EZVPm0buPlIz5VPWzyX4T4EbgPXFnOOgpseJ79Bem4aZLiPgOPAOeEWIG5LBaTnJkmG+4i4DVzJ5hwXNz3pufIpUU+G+5g4DRuUac46OAZIeq5sSvPcRMN9x44dk7y5BSc3qvJx05OeK59S9Ex8c3dsptOwgV+P3PSAp+ecKMFztcJd0jmSNkm6RdK5o1x32sKbxm2jAr8euelx81wJgdg009QzdrhLWgW8DzgaOAw4XdIrRjmGYzOdhg38euSmB9JzpTMtPXU2918Cro+IJyNiB3Ad8M5xDuTWTLeNCvy2Xjc96bnymbSeOuG+CXijpBlJy4C3AQfVKcatmY7D5tQjNz2QniudSeoZO9wjYjPwKWA9cBWwEXh29uUkrZG0QdKGp556at7jujUzN6rySc+Vj1N/YDJ6av1BNSI+FxFHRcRxwMPA7X0uszYiVkfE6r322mvoY7s103HY3Hrkpic9VzYLrafus2VeWn08mM759kuaKKqLWzNzoyqf9Fz5OPUHFs5zdZ/n/lVJtwL/Czg7Ih5poKZdcGum28C5BSJ4es6J9Nz81D0t88aIeHVEHBYR1zRVVD8cm+k4cE64ec5tqYD03Fy07r1l3JrpNnBugQjpudJJz/WndeEOns10GjbwC8T0XPm49aeu51oZ7l3cmpkbVfm46UnPlc+4elod7uDZTKdhA78euekBT88tdlof7l3cmum2UYFfj9z0uHnO8U54FGzCHTyb6TRs4NcjNz2QnnPBKty7uDXTbaMCv63XTU96rv1YhnsXt2Y6DptTj9z0QHquzViHO/g1Mzeq8knPlY9TfwZhH+5d3JrpOGxuPXLTk55rF4sm3MGvmblRlU96rnyc+tPLogr3Lm7NdBs4t0AET8854ei5RRnu4NlMx4Fzws1zbksFeHlu0YZ7F6dmgt/AuQUipOdKx8Vziz7cwaeZvTgNG/gFYnqufNrenwz3HtrezNnkRlU+bnrSc+WQ4T6LNjdzEE7DBn49ctMD6bkSyHAfQNsaOR9uGxX49chNT3puumS4z0Eb763nw3HYnHrkpgfSc9OiVrhL+l1Jt0jaJGmdpD2bKqwk2tLMYcmNqnzc9KTnJs/Y4S7pAOCDwOqIWAXsDpzZVGElUnozR8Vx2Jx65KYH0nOTpO5pmSXAXpKWAMuA++qXVDYlN3MccqMqn/Rc+ZTYn7HDPSLuBT4N/ATYAjwaEetnX07SGkkbJG146qmnxq+0MEpsZh0ch82tR2560nMLS53TMi8GzgBeDqwE9pb07tmXi4i1EbE6IlYvXbp0/EoLpLRm1iU3qvJJz5VPKf2pc1rmLcCPI+LBiHgGuAx4/XxXcjMnlNPMpnAbuPRc+aTnmqdOuP8EOEbSMkkCTgQ2D3vlaQtvmhKa2TROwwbpuTaQnmuOOufcrwcuBW4Ebq6OtXaUYzia001PblTl46YnPdcMioiJ3djy5cvj5JNPHvjzFStWTKyWSeCmZ2ZmZtolNI5bj9z0uHlu3P4cfPDBN0TE6lGuU9QrVN02EDc9bhsV+PXITY+b5ya5xRcV7uD3sNlND3ieF3XqkZseSM+NQ3Hh3sXNnG563DYq8OuRm5703GgUG+7gt4G46YHcqErHTQ+k54al6HDv4mZOt4HLjap80nPl03R/WhHu4GdO8AsQx2Fz65GbnvTcYFoT7l3czOkWILlRlU96rnya6E/rwh38zAl+AeI2cOm58nHyG9T3XCvDvYubOR0DxHHgnHDznNtSAeN7rtXhDn7mBL8AcRu49Fz5uHluHFof7l3cBs5ND+QWXzrpOS9swr2Lmznd9LhtVI6B6KbHzXPDYhfu4DdwbnrAb6Ny65GbHvDz3HxYhnsXN3O66XHcqNx65KbH0XODsA538NtA3PSA30bl1iM3PeDnuX7Yh3sXN3O6DZzjRuXUH/DT4+i5XhZNuINfIILnwDnh5jk3PeDnuS6LKty7uJnTbeAcNyqn/kB6rg2MHe6SDpW0seffY5LObbK4hcTNnOAXIG7Dlp4rHyfP1fkPsr8fEYdHxOHAUcCTwOWNVTYh3MzpFiCOG5VTfyA9VypNnZY5EfhRRNzV0PEmips5wS9AXAauS3qufNrut6bC/UxgXb8fSFojaYOkDU8//XRDN7cwuJnTMUDaPnCzceuPm+favFQoIuodQNoDuA/45Yh4YK7LLl++PE4++eRatzcpVqxYMe0SGsVND8DMzMy0S2gUtx656YHpee7II4+8ISJWj3KdJjb3U4Eb5wv2tuG2gbjpgdziSyc9N12aCPd3MeCUjANu5nTT0+aHzf1wDEQ3PW3xXK1wl7Q3cBJwWTPllInbwLnpgXZtVMPg1iM3PVC+52qFe0Q8EREzEfFoUwWVjJs53fS0ZaMaBbceuekp2XOL8hWqdXDbQNz0QPkb1ai49chND5TpuQz3MXEzp9vAlbxRjYtTf8BPT2mem2i479ixY5I3t+C4BSJ4DpwTbp5z0wPleG7im7tjMx31OGkqbaNqAqf+QHpuIZjaaRm3ZrrpAb8AmfawNU16rnym6bmpn3N3a6ajHidNJWxUTePUH0jPNcXUwx38mummB/wCxC3k03PlM2m/FRHuXdya6ajHTZNTwEN6rnQmuVQUFe7g10w3PeAXILnFl4+bnkl4rrhw7+LWTLeBc9MDucWXTnpuNIoNd/BsZuopm9ziy8dNz0J5ruhw7+LWTLeBc9MDnlu8U4/c9EDznmtFuINnM1NP2bht8eDXIzc9TXquNeHexa2Zbndabnogt/jScdMDzXiudeEOns101OOkKbf48nHTU9dzrQz3Lm7NdAtE8OuRY8A79chND4zvuVaHO3g201GPk6bc4svHzXPj0Ppw7+LWTDc94BcgjgHv1iM3PaNQ9/9Q3VfSpZJuk7RZ0uuaKmxc3JrpqMdJU27x5ePmuWGpu7l/BrgqIl4FHAZsrl9Sfdya6aYH/ALELeTTc+1n7HCX9CLgOOBzABHx84h4pKnCmsCtmY563DQ5BTyk59pMnc395cCDwBck3STpAkl7z76QpDWSNkja8Mwzz9S4ufFwa6abHvALkNziy8dNTz/qhPsS4EjgLyLiCOAJ4LzZF4qItRGxOiJWL126tMbN1cOtmW4D56YHcosvHUfP9VIn3O8B7omI66uvL6UT9sXi2MzUUza5xZePm54uY4d7RNwP3C3p0OpbJwK3NlLVAuPWTLeBc9MDnlu8U4/c9ED9Z8t8ALhY0veAw4E/rl/SZHBsZuopG7ctHvx65KRnSZ0rR8RGYHVDtUyFbdu2sWLFimmX0Rhdc7poctMDnZCfmZmZdhmN4dYjFz02r1CtQ27x5ePWo9ziy6ftnstw76HNjexH283ZDzc9jgHv1qO26slwn0Was3zcepRbfPm00XMZ7gNoYzPnwk0P+AWIY8C79ahNejLc56FNzRwGRz1OmnKLL5+2eC7DfQja0sxhcdMDfgHiFvLpucmT4T4CpTdzVBz1uGlyCnhIz02SDPcRKbmZ4+CmB/wCJLf48ilRT4b7mJTYzDq4DZybHvDc4p16VJqeiYb7jh07JnlzC05pzWyC1FM2bls8+PWoFD0T39zTnOXjdqflpgdyiy+dEvRM7bSMW8iX0MymcdTjpMlthsDTc9Ni6ufc05xl4xaI4Ncjxxly6tG09Ew93MFvA3EzJ/gFoluP3GYI0nN1KSLcu6Q5y8YtEMGvR44z5NajSekpKtzBbwNJc5aPW4/cZgjSc+NQXLh3cTSnk0Hd9IBfgLiFfHpuNIoNd/AzJ/gFiKMeN005Q2WzUJ6rFe6S7pR0s6SNkjY0VdRsHM3pZFA3PeAXIG6LUnpufprY3N8cEYdHxIL+X6pu5gS/AHEbODc94LkoOdGk54o+LdMPR3M6GtQJNz1ui1LOUH/qhnsA6yXdIGlNvwtIWiNpg6QNTb23jJs5wS9A3AbOTQ/kolQ6dfXUDfdjI+JI4FTgbEnHzb5ARKyNiNURsXrJkiU1b+75pDnLJ/WUTS5K5TOunlrhHhH3Vh+3ApcDR9c53jikOcvH7U7LTQ/kouTI2OEuaW9J+3Q/B94KbGqqsFFxC3lHczrqcdLkNkPg57lRqLO57wd8S9J3ge8AX4uIq5opa3zSnGXjFojg1yPHGXLr0TCMfRI8Iu4ADmuwlsbomnNmZmbKlTRD15grVqyYciXNsW3bNjs94NMjtxkCP8/NR+ueCjkKuYGUjZseyC2+dBw9NwjrcIc8j9gGHPU4acoZaif24d7F0ZxOBnXTA34B4hbyjp7rZdGEO/iZE/wCxFGPm6acoXawqMK9i6M5nQzqpgf8AsRtUXL03KIMd/AzJ/gFiNvAuekBz0XJhUUb7l0czelkUPAaOPDT47YouczQog938DMn+AWIy8B1cdMDuSiVRoZ7D24h33Zz9iP1lI3bDEF7e5Th3oc0Z9m43Wm56QHPGWpbjzLcB+C2gbTRnPPhqMdJk9sMQbs8l+E+D2nOsnELRPDrkeMMtaFHGe5D4LaBtMWco+Cox0mT2wxB+Z7LcB8BR3OWbtBRcNMD5QfIqOQMTY4M9xHJDaR8HPU4acoZmgwZ7mPiaM4SDToubnqgzACpg1vIl+a5iYb7jh07ihJfFzdzgl+AOOpx05QztDBMZXMvRXxTOJrTqUduesBzhpzmqATP1Q53SbtLuknSlaNcrwTxTeJmTvALEDfPuekBz0VpWjSxuZ8DbB73ymnOsnEMkNRTNm6L0rRmqFa4SzoQOA24oM5x3ALEzZzgFyBunnPTA7ko1aXu5n4+8GFg56ALSFojaYOkDTt3DrwY4BcgbiHvGCCpp2zcZggm16Oxw13S6cDWiLhhrstFxNqIWB0Rq3fbbf6bcwyQNGfZuHnOTQ94ztBC96jO5v4G4O2S7gS+BJwg6YuNVIVfgLhtII4B4qjHSZPbDMHCem7scI+Ij0TEgRFxCHAm8PWIeHdjleFnTvDcQJxw9JybHscZWogeteIVqo7mdDJoBmL5uPXIbYagec81Eu4R8Y2IOL2JYw3CzZyQG0jpuOkBvzutnKHBtGJz78XRnI4GdcJRj5OmnKH+tC7cwc+ckBtI6bjpAb87LbeQr+u5VoZ7F7eBczMn+AWIox43TTlDHVod7l3SnGXjFiBuesBzhtzmaFQswh38Bs7RnE79AT/PuekBv0VpFGzCvUuas2wcAyT1lI3jojQMduEOfgHiaE6n/oCf59z0gN+iNB+W4d7F0ZxOBnUMkNRTNm4zNBfW4Q6eAeJmTrf+uHnOTQ/4zVA/7MO9i6M5nQzqGCCOepw0uc3QbBZNuIOfOcFvA3Hrj6Pn3PS4zVCXRRXuXRzN6WTQDMTyceuR2wzBIg138DMn+G0gbj1y0wN+d1pOM7Row72LozmdDAp+PXLU46TJZYYWfbiDnznBawMBvx656QG/O622h3yGew9uA9d2c/bDqT/gqcdNU1tnKMO9D2nOsnELEDc94DlDbZujDPcBuA1cG805H079AT/PuemBdi1KY4e7pD0lfUfSdyXdIukTTRZWCmnOsnEMkNRTNm1ZlOps7k8DJ0TEYcDhwCmSjmmmrLJwC5C2mHMUnPoDfp5z0wPlL0pjh3t0eLz6cmn1LxqpqlAczVm6QUfBMUAc9ThpKnmGap1zl7S7pI3AVuDqiLi+mbLKxc2cUP4GMipu/XH0nJueEmeoVrhHxLMRcThwIHC0pFWzLyNpjaQNkjbs3Lmzzs0VhaM5SzTouGQglo9bj0qboUaeLRMRjwDXAqf0+dnaiFgdEat37txp1Uw3c0KZG0gd3Prj6Dk3PaXMUJ1ny7xE0r7V53sBJwG3DXNdt2a66SltA6lLBmL5uPWohBmqs7nvD1wr6XvAP9E5537lsFd2a6abHihnA2kKtx656QG/O61phnydZ8t8LyKOiIjXRMSqiPiDcY7j1kw3PSVsIE3j1iNHPW6apjFDRbxC1a2Zbnogt/jScdMDfndak16Uigj3Lm7NdBu43OLLx81zbnpgcotSUeEOns100+MY8G49Sj1lM4lFqbhw7+LWTLcAyS2+fNw856YHFnZRKjbcwbOZbnrcQj49Vz5uehZqhooO9y5uzXQMEKeAh/Rc6bjpgeZnqBXhDp7NdNOTW3z5OOpx0tTkDLUm3Ls4NRL8zAm5xZeOo+fc9DQxQ60Ld0hztoHc4svHUY+Tproz1Mpw7+LWTDc94LnFO/XITQ/43WmNO0OtDvcubs100+O2xYNfjxz1uGkaFYtwB79muumB3OJLx00P+N1pjYJNuHdxa6bbwOUWXz6Oetw0DYNduINnM930OAa8U4/c9IDfDM2HZbh3cWum28DlFl8+bp5z0zMX1uEOns100+MY8G49Sj3twz7cu7g10y1AcosvHzfPuemZzaIJd/Bsppset5BPz5WPm54uiyrcu7g10zFAnAIe0nOl46YHaoS7pIMkXSvpVkm3SDqnycIWGsdmuunJLb58HPW4aKqzue8APhQRrwaOAc6W9OpmypocTs0EPz2QW3zpOHrOQc/Y4R4RWyLixurznwKbgQOaKmzSODSzFzc9ucWXj6OeNmtSRNQ/iHQI8E1gVUQ8Nutna4A11ZergE21b3DhWQG0oattqLMNNULW2TRZZ7McGhH7jHKF2uEu6YXAdcAnI+KyeS67ISJW17rBCZB1NkcbaoSss2myzmYZp85az5aRtBT4KnDxfMGeJEmSTI46z5YR8Dlgc0T81+ZKSpIkSepSZ3N/A/BvgRMkbaz+vW2e66ytcXuTJOtsjjbUCFln02SdzTJynY38QTVJkiQpi0X5CtUkSRJ3MtyTJEkMWfBwb8vbFEjaU9J3JH23qvMT065pLiTtLukmSVdOu5ZBSLpT0s3V32M2TLueQUjaV9Klkm6TtFnS66Zd02wkHdrzt62Nkh6TdO6065qNpN+t5meTpHWS9px2Tf2QdE5V4y0l/R4lfV7SVkmber63XNLVkn5QfXzxMMeaxObelrcpeBo4ISIOAw4HTpF0zJRrmotz6LwquHTeHBGHF/5c4s8AV0XEq4DDKPD3GhHfr36PhwNHAU8Cl0+5rOch6QDgg8DqiFgF7A6cOd2qdkXSKuB9wNF0+n26pFdMt6r/z4XAKbO+dx5wTUS8Erim+npeFjzc2/I2BdHh8erLpdW/Iv/aLOlA4DTggmnX0nYkvQg4js7TeomIn0fEI9Otal5OBH4UEXdNu5A+LAH2krQEWAbcN+V6+vFLwPUR8WRE7KDzIsx3TrkmACLim8BDs759BnBR9flFwDuGOdZEz7lXb1NwBHD9JG93WKpTHRuBrcDVEVFkncD5wIeBndMuZB4CWC/phuptKErk5cCDwBeq01wXSNp72kXNw5nAumkXMZuIuBf4NPATYAvwaESsn25VfdkEvFHSjKRlwNuAg6Zc01zsFxFbqs/vB/Yb5koTC/fqbQq+Cpw7+/1nSiEinq0e9h4IHF09fCsKSacDWyPihmnXMgTHRsSRwKl0TscdN+2C+rAEOBL4i4g4AniCIR/2TgNJewBvB/5m2rXMpjoXfAadO8yVwN6S3j3dqnYlIjYDnwLWA1cBG4Fnp1rUkETnuetDnVGYSLi37W0Kqofl17Lrua8SeAPwdkl3Al+i8yKyL063pP5UmxwRsZXO+eGjp1tRX+4B7ul5lHYpnbAvlVOBGyPigWkX0oe3AD+OiAcj4hngMuD1U66pLxHxuYg4KiKOAx4Gbp92TXPwgKT9AaqPW4e50iSeLdOKtymQ9BJJ+1af7wWcBNw23ap2JSI+EhEHRsQhdB6efz0iituOJO0taZ/u58BbKW/xkEsAAAD+SURBVPAdQSPifuBuSYdW3zoRuHWKJc3HuyjwlEzFT4BjJC2r5v5ECvzjNICkl1YfD6Zzvv2S6VY0J1cA76k+fw/wt8NcacmClfMc3bcpuLk6nw3w0Yj4uwnc9ijsD1wkaXc6d3pfiYhin2bYAvYDLu/MOEuASyLiqumWNJAPABdXpzzuAH5jyvX0pbqTPAk4a9q19CMirpd0KXAjnWfJ3US5L+//qqQZ4Bng7FL+iC5pHXA8sELSPcB/Af4E+Iqk3wTuAn51qGPl2w8kSZL4ka9QTZIkMSTDPUmSxJAM9yRJEkMy3JMkSQzJcE+SJDEkwz1JksSQDPckSRJD/h/BFfvPMfcMAgAAAABJRU5ErkJggg==
    444. 

  444. "
    445. 

  445. >
    446. 

  446. </div>
    447. 

  447. 

  448. </div>
    449. 

  449. 

  450. </div>
    451. 

  451. </div>
    452. 

  452. 

  453. </div>
    454. 

  454. <div class="cell border-box-sizing code_cell rendered">
    455. 

  455. <div class="input">
    456. 

  456. 

  457. <div class="inner_cell">
    458. 

  458.     <div class="input_area">
    459. 

  459. <div class=" highlight hl-ipython3"><pre><span></span>ax.contour<span class="o">?</span>
    460. 

  460. </pre></div>
    461. 

  461. 

  462.     </div>
    463. 

  463. </div>
    464. 

  464. </div>
    465. 

  465. 

  466. </div>
    467. 

  467. <div class="cell border-box-sizing code_cell rendered">
    468. 

  468. <div class="input">
    469. 

  469. 

  470. <div class="inner_cell">
    471. 

  471.     <div class="input_area">
    472. 

  472. <div class=" highlight hl-ipython3"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
    473. 

  473. <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">&#39;3d&#39;</span><span class="p">)</span>
    474. 

  474. </pre></div>
    475. 

  475. 

  476.     </div>
    477. 

  477. </div>
    478. 

  478. </div>
    479. 

  479. 

  480. <div class="output_wrapper">
    481. 

  481. <div class="output">
    482. 

  482. 

  483. <div class="output_area">
    484. 

  484. 

  485. 

  486. 

  487. <div class="output_png output_subarea ">
    488. 

  488. <img src="data:image/png;base64,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
    489. 

  489. "
    490. 

  490. >
    491. 

  491. </div>
    492. 

  492. 

  493. </div>
    494. 

  494. 

  495. </div>
    496. 

  496. </div>
    497. 

  497. 

  498. </div>
    499. 

  499. <div class="cell border-box-sizing code_cell rendered">
    500. 

  500. <div class="input">
    501. 

  501. 

  502. <div class="inner_cell">
    503. 

  503.     <div class="input_area">
    504. 

  504. <div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">randrange</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">vmin</span><span class="p">,</span> <span class="n">vmax</span><span class="p">):</span>
    505. 

  505.     <span class="sd">&#39;&#39;&#39;</span>
    506. 

  506. <span class="sd">    Helper function to make an array of random numbers having shape (n, )</span>
    507. 

  507. <span class="sd">    with each number distributed Uniform(vmin, vmax).</span>
    508. 

  508. <span class="sd">    &#39;&#39;&#39;</span>
    509. 

  509.     <span class="k">return</span> <span class="p">(</span><span class="n">vmax</span> <span class="o">-</span> <span class="n">vmin</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">rand</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="o">+</span> <span class="n">vmin</span>
    510. 

  510. 

  511. <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
    512. 

  512. <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">,</span> <span class="n">projection</span><span class="o">=</span><span class="s1">&#39;3d&#39;</span><span class="p">)</span>
    513. 

  513. 

  514. <span class="n">n</span> <span class="o">=</span> <span class="mi">100</span>
    515. 

  515. 

  516. <span class="c1"># For each set of style and range settings, plot n random points in the box</span>
    517. 

  517. <span class="c1"># defined by x in [23, 32], y in [0, 100], z in [zlow, zhigh].</span>
    518. 

  518. <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">m</span><span class="p">,</span> <span class="n">zlow</span><span class="p">,</span> <span class="n">zhigh</span> <span class="ow">in</span> <span class="p">[(</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">50</span><span class="p">,</span> <span class="o">-</span><span class="mi">25</span><span class="p">),</span> <span class="p">(</span><span class="s1">&#39;b&#39;</span><span class="p">,</span> <span class="s1">&#39;^&#39;</span><span class="p">,</span> <span class="o">-</span><span class="mi">30</span><span class="p">,</span> <span class="o">-</span><span class="mi">5</span><span class="p">)]:</span>
    519. 

  519.     <span class="n">xs</span> <span class="o">=</span> <span class="n">randrange</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">23</span><span class="p">,</span> <span class="mi">32</span><span class="p">)</span>
    520. 

  520.     <span class="n">ys</span> <span class="o">=</span> <span class="n">randrange</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">100</span><span class="p">)</span>
    521. 

  521.     <span class="n">zs</span> <span class="o">=</span> <span class="n">randrange</span><span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">zlow</span><span class="p">,</span> <span class="n">zhigh</span><span class="p">)</span>
    522. 

  522.     <span class="n">ax</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">xs</span><span class="p">,</span> <span class="n">ys</span><span class="p">,</span> <span class="n">zs</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="n">c</span><span class="p">,</span> <span class="n">marker</span><span class="o">=</span><span class="n">m</span><span class="p">)</span>
    523. 

  523. 

  524. <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;X Label&#39;</span><span class="p">)</span>
    525. 

  525. <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Y Label&#39;</span><span class="p">)</span>
    526. 

  526. <span class="n">ax</span><span class="o">.</span><span class="n">set_zlabel</span><span class="p">(</span><span class="s1">&#39;Z Label&#39;</span><span class="p">)</span>
    527. 

  527. 

  528. <span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
    529. 

  529. </pre></div>
    530. 

  530. 

  531.     </div>
    532. 

  532. </div>
    533. 

  533. </div>
    534. 

  534. 

  535. <div class="output_wrapper">
    536. 

  536. <div class="output">
    537. 

  537. 

  538. <div class="output_area">
    539. 

  539. 

  540. 

  541. 

  542. <div class="output_png output_subarea ">
    543. 

  543. <img src="data:image/png;base64,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
    544. 

  544. "
    545. 

  545. >
    546. 

  546. </div>
    547. 

  547. 

  548. </div>
    549. 

  549. 

  550. </div>
    551. 

  551. </div>
    552. 

  552. 

  553. </div>
    554. 

  554. <div class="cell border-box-sizing code_cell rendered">
    555. 

  555. <div class="input">
    556. 

  556. 

  557. <div class="inner_cell">
    558. 

  558.     <div class="input_area">
    559. 

  559. <div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">nbdev.export</span> <span class="k">import</span> <span class="n">notebook2script</span>
    560. 

  560. <span class="n">notebook2script</span><span class="p">()</span>
    561. 

  561. </pre></div>
    562. 

  562. 

  563.     </div>
    564. 

  564. </div>
    565. 

  565. </div>
    566. 

  566. 

  567. <div class="output_wrapper">
    568. 

  568. <div class="output">
    569. 

  569. 

  570. <div class="output_area">
    571. 

  571. 

  572. <div class="output_subarea output_stream output_stdout output_text">
    573. 

  573. <pre>Converted 0.0_mgmnt.prep.i.ipynb.
    574. 

  574. Converted 0.1_mgmnt.prep.conv.ipynb.
    575. 

  575. Converted 0.2_mgmnt.db.mongo.ipynb.
    576. 

  576. Converted 0.3_mgmnt.prep.bpe.ipynb.
    577. 

  577. Converted 0.4_mgmnt.corpus.ipynb.
    578. 

  578. Converted 0.5_mgmnt.prep.traceability.ipynb.
    579. 

  579. Converted 0.6_mgmnt.prep.nltk.ipynb.
    580. 

  580. Converted 1.0_exp.i.ipynb.
    581. 

  581. Converted 1.1_exp.info.ipynb.
    582. 

  582. Converted 2.0_repr.i.ipynb.
    583. 

  583. Converted 2.1_repr.roberta.train.ipynb.
    584. 

  584. Converted 2.2_repr.roberta.eval.ipynb.
    585. 

  585. Converted 2.3_repr.word2vec.train.ipynb.
    586. 

  586. Converted 2.4_repr.doc2vec.train.ipynb.
    587. 

  587. Converted 2.5_repr.doc2vec.eval.ipynb.
    588. 

  588. Converted 2.6_repr.word2vec.eval.ipynb.
    589. 

  589. Converted 3.0_mining.ir.model.ipynb.
    590. 

  590. Converted 3.1_mining.ir.i.ipynb.
    591. 

  591. Converted 3.2_mining.unsupervised.eval.ipynb.
    592. 

  592. Converted 4.0_benchmark.i.ipynb.
    593. 

  593. Converted 4.1_benchmark.traceability.ipynb.
    594. 

  594. Converted 4.2_benchmark.codegen.ipynb.
    595. 

  595. Converted 5.0_vis.ipynb.
    596. 

  596. Converted 6.0_desc.stats.ipynb.
    597. 

  597. Converted 6.1_desc.metrics.se.ipynb.
    598. 

  598. Converted 7.0_inf.i.ipynb.
    599. 

  599. Converted 7.1_inf.bayesian.ipynb.
    600. 

  600. Converted 7.2_inf.causal.ipynb.
    601. 

  601. Converted aa_blog.example.ipynb.
    602. 

  602. Converted ab_templates.example.ipynb.
    603. 

  603. Converted ac_emp.eval.pp1.rq1.ipynb.
    604. 

  604. Converted ad_emp.eval.pp1.rq2.ipynb.
    605. 

  605. Converted ae_emp.eval.pp1.rq3.ipynb.
    606. 

  606. Converted af_emp.eval.pp1.rq4.ipynb.
    607. 

  607. Converted index.ipynb.
    608. 

  608. </pre>
    609. 

  609. </div>
    610. 

  610. </div>
    611. 

  611. 

  612. </div>
    613. 

  613. </div>
    614. 

  614. 

  615. </div>
    616. 

  616. </div>
    617. 

  617.  
    618. 

  618.
Prev
Next
Tip!

Press p or to see the previous file or, n or to see the next file

Comments

Write Preview
Loading...
Integrate Google Cloud Storage
Use Google Storage
Select bucket
Upload key
Finish

Use Google Cloud Storage!

Browsing data directories saved to Google Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Google Storage bucket

Service Account Key

Congratulations!

ds4se is now integrated with Google Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate AWS S3
Use S3 remote
Select bucket
Access key
Finish

Use AWS S3 as storage!

Browsing data directories saved to S3 is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Access key (If needed)

Congratulations!

ds4se is now integrated with AWS S3!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate S3 compatible storage
Use S3 like remote
Select bucket
Access key
Finish

Use any S3 compatible storage!

Browsing data directories saved to S3 compatible storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your S3 bucket

Access key (If needed)

Congratulations!

ds4se is now integrated with your S3 compatible storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes
Integrate Azure Cloud Storage
Use Azure Storage
Select bucket
Set key
Finish

Use Azure Cloud Storage!

Browsing data directories saved to Azure Cloud Storage is possible with DAGsHub. Let's configure your repository to easily display your data in the context of any commit!

Specify your Azure Storage bucket

Access key (If needed)

Congratulations!

ds4se is now integrated with Azure Cloud Storage!

Delete Storage Key

Are you sure you want to delete this access key?

No
Yes