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stas-sl / flats.csv
Created January 26, 2020 14:15
flats
We can't make this file beautiful and searchable because it's too large.
id,url,city,city_region,city_subregion,street,source,term,house_number,korpus,floor,is_last_floor,is_owner,building_year,building_floors,building_type,kap_remont_year,price,update_date,created_at,updated_at,rooms_total,is_room,rooms_separate,longitude,latitude
1,https://realt.by/vitebsk-region/rent/flat-for-long/object/1137119/,Витебск,Железнодорожный район,Центр,,realt,длительный,,,16,1,1,,16,,,46.0,2020-01-19 03:00:00,2020-01-26 01:53:53,2020-01-26 15:27:42,2,1,,30.190732,55.19471
2,https://realt.by/grodno-region/rent/flat-for-long/object/1625135/,Гродно,,Вишневец,,realt,длительный,,,9,1,1,2005,9,панельный,,50.0,2019-12-29 03:00:00,2020-01-26 01:53:53,2020-01-26 15:27:42,3,1,,23.845851,53.646472
3,https://realt.by/gomel-region/rent/flat-for-long/object/1637301/,Гомель,Центральный район,Волотова,,realt,длительный,,,3,0,1,,15,панельный,,50.0,2020-01-21 03:00:00,2020-01-26 01:53:53,2020-01-26 15:27:42,3,1,,31.028744,52.471726
4,https://realt.by/gomel-region/rent/flat-for-long/object/1637303/,Гомель,Центральный
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@stas-sl
stas-sl / pandas_image.ipynb
Last active June 12, 2024 07:55
Displaying inline images in pandas DataFrame
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import Ember from 'ember';
export default Ember.Component.extend({
classNames: ['grid-stack'],
didInsertElement() {
var options = {
width: this.get('width'),
cellHeight: this.get('cellHeight'),
verticalMargin: this.get('verticalMargin'),
This file has been truncated, but you can view the full file.
I0623 14:13:34.760556 10365 solver.cpp:280] Solving mixed_lstm
I0623 14:13:34.760573 10365 solver.cpp:281] Learning Rate Policy: fixed
I0623 14:13:34.771200 10365 solver.cpp:338] Iteration 0, Testing net (#0)
I0623 14:14:33.060055 10365 solver.cpp:393] Test loss: 4.63185
I0623 14:14:33.060313 10365 solver.cpp:406] Test net output #0: loss1/accuracy = 0.483626
I0623 14:14:33.060333 10365 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.92
I0623 14:14:33.060346 10365 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.726
I0623 14:14:33.060359 10365 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.492
I0623 14:14:33.060370 10365 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.391
I0623 14:14:33.060381 10365 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.353
This file has been truncated, but you can view the full file.
I0612 14:48:17.543987 6181 solver.cpp:280] Solving mixed_lstm
I0612 14:48:17.543999 6181 solver.cpp:281] Learning Rate Policy: fixed
I0612 14:48:17.554961 6181 solver.cpp:338] Iteration 0, Testing net (#0)
I0612 14:49:17.784380 6181 solver.cpp:393] Test loss: 2.74935
I0612 14:49:17.784834 6181 solver.cpp:406] Test net output #0: loss1/accuracy = 0.62328
I0612 14:49:17.784857 6181 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.772
I0612 14:49:17.784870 6181 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.65
I0612 14:49:17.784883 6181 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.535
I0612 14:49:17.784894 6181 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.498
I0612 14:49:17.784906 6181 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.509
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I0607 01:01:32.950039 32403 solver.cpp:280] Solving mixed_lstm
I0607 01:01:32.950052 32403 solver.cpp:281] Learning Rate Policy: fixed
I0607 01:01:32.960855 32403 solver.cpp:338] Iteration 0, Testing net (#0)
I0607 01:02:33.209952 32403 solver.cpp:393] Test loss: 2.53488
I0607 01:02:33.210446 32403 solver.cpp:406] Test net output #0: loss1/accuracy = 0.638494
I0607 01:02:33.210469 32403 solver.cpp:406] Test net output #1: loss1/accuracy01 = 0.796
I0607 01:02:33.210481 32403 solver.cpp:406] Test net output #2: loss1/accuracy02 = 0.667
I0607 01:02:33.210494 32403 solver.cpp:406] Test net output #3: loss1/accuracy03 = 0.569
I0607 01:02:33.210505 32403 solver.cpp:406] Test net output #4: loss1/accuracy04 = 0.476
I0607 01:02:33.210517 32403 solver.cpp:406] Test net output #5: loss1/accuracy05 = 0.534
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I0525 00:09:25.737826 5272 solver.cpp:280] Solving mixed_lstm
I0525 00:09:25.737840 5272 solver.cpp:281] Learning Rate Policy: fixed
I0525 00:09:26.273970 5272 solver.cpp:229] Iteration 0, loss = 27.9836
I0525 00:09:26.274058 5272 solver.cpp:245] Train net output #0: loss1/accuracy = 0
I0525 00:09:26.274077 5272 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0
I0525 00:09:26.274091 5272 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0
I0525 00:09:26.274102 5272 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0
I0525 00:09:26.274114 5272 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0
I0525 00:09:26.274126 5272 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125
I0525 00:09:26.274138 5272 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0
This file has been truncated, but you can view the full file.
I0525 00:09:25.737826 5272 solver.cpp:280] Solving mixed_lstm
I0525 00:09:25.737840 5272 solver.cpp:281] Learning Rate Policy: fixed
I0525 00:09:26.273970 5272 solver.cpp:229] Iteration 0, loss = 27.9836
I0525 00:09:26.274058 5272 solver.cpp:245] Train net output #0: loss1/accuracy = 0
I0525 00:09:26.274077 5272 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0
I0525 00:09:26.274091 5272 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0
I0525 00:09:26.274102 5272 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0
I0525 00:09:26.274114 5272 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0
I0525 00:09:26.274126 5272 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0.125
I0525 00:09:26.274138 5272 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0
This file has been truncated, but you can view the full file.
I0510 15:24:57.106338 10926 solver.cpp:280] Solving mixed_lstm
I0510 15:24:57.106350 10926 solver.cpp:281] Learning Rate Policy: fixed
I0510 15:24:57.414443 10926 solver.cpp:229] Iteration 0, loss = 28.4505
I0510 15:24:57.414511 10926 solver.cpp:245] Train net output #0: loss1/accuracy = 0
I0510 15:24:57.414530 10926 solver.cpp:245] Train net output #1: loss1/accuracy01 = 0
I0510 15:24:57.414542 10926 solver.cpp:245] Train net output #2: loss1/accuracy02 = 0
I0510 15:24:57.414554 10926 solver.cpp:245] Train net output #3: loss1/accuracy03 = 0
I0510 15:24:57.414566 10926 solver.cpp:245] Train net output #4: loss1/accuracy04 = 0
I0510 15:24:57.414578 10926 solver.cpp:245] Train net output #5: loss1/accuracy05 = 0
I0510 15:24:57.414590 10926 solver.cpp:245] Train net output #6: loss1/accuracy06 = 0