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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 100 new columns ({'1.2582', '0.4187', '0.37829', '-0.091543', '0.48095', '0.090771', '0.39147', '0.67575', '-0.17456', '-0.35084', '0.48402', '0.10017', '0.041849', '0.1277', '-0.39926', '-0.11333', '-0.3253', '-0.075256', '0.17528', '0.1307', '0.26044', '-0.22439', '0.46444', '0.80842', '-0.20821', '-0.059798', '0.75348', '0.054169', '-0.037454', '0.68257', '0.18824', '0.22325', '-0.3336', '0.71263', '-0.071786', '-0.56016', '0.36488', '-0.55831', '0.015313', '0.48424', '-0.038519', '0.38433', '-0.11399', '0.67198', '-0.053462', '0.19716', '0.18809', '0.70502', '0.9895', '-0.036091', '-0.22608', '0.65093', '-0.014555', '-0.58722', '0.62255', '0.25586', '-0.53529', '0.53571', '-0.16484', '0.14877', '0.31399', '0.52522', '0.50097', '-0.44104', '-0.53573', '0.055773', '0.94071', '-0.34122', '-0.13286', '0.24266', '-0.16537', '0.016964', '-0.42795', '-0.20804', '-0.7607', '0.3222', '0.13582', '-0.54722', '0.034206', '0.50237', '-0.2539', '-0.24654', '-0.35431', '0.11503', '-0.021106', '-0.20822', '-0.61738', '-0.39613', '0.2286', '0.71104', '-0.17863', '0.55068', '0.30917', '-0.27053', '0.036634', '0.0080131', '-0.0072514', '0.4292', '-0.42664', '-0.52445'}) and 784 missing columns ({'0.0.130', '213.0.3', '0.0.1', '0.0.126', '216.0.2', '227.0.1', '211.0.4', '0.0.326', '0.0.85', '188.0.1', '181.0.1', '0.0.273', '0.0.349', '224.0.2', '0.0.87', '0.0.318', '167.0.1', '3.0.3', '215.0.6', '41.0', '228.0.6', '0.0.340', '1.0.7', '0.0.125', '234.0', '0.0.242', '0.0.54', '205.0.1', '3.0.2', '198.0.2', '0.0.306', '0.0.189', '185.0.1', '6.0.2', '163.0', '225.0.2', '205.0.3', '0.0.201', '218.0.7', '54.0', '0.0.44', '0.0.235', '0.0.22', '229.0.1', '0.0.25', '1.0.9', '240.0', '222.0.5', '154.0', '196.0.2', '0.0.231', '0.0.150', '180.0', '4.0.3', '0.0.90', '193.0.4', '232.0.2', '0.0.162', '0.0.285', '243.0', '219.0.4', '225.0', '234.0.2', '0.0.254', '230.0.1', '0.0.174', '240.0.1', '0.0.202', '0.0.56', '0.0.137', '216.0.3', '0.0.240', '221.0.8', '215.0.3', '195.0', '145.0', '176.0.3', '0.0.227', '0.0.274', '222.0.8', '183.0.2', '229.0', '69.0', '0.0.102', '218.0.3', '0.0.233', '175.0.1', '228.0.7', '234.0.1', '244.0', '209.0.5', '0.0.310', '0.0.259', '0.0.293', '44.0', '0.0.122', '2.0.1', '0.0.238', '0.0.219', '223.0.12', '0.0.195', '156.0.1', '224.0.5', '188.0.3', '0.0.199', '169.0', '0.0.88', '0.0.191', '2.0', '0.0.220', '209.0.1', '192.0.1', '0.0.290', '0.0.288', '212.0.4', '173.0', '0.0.31', '0.0.34', '0.0.192', '0.0.175', '44.0.1', '136.0', '0.0.140', '161.0', '0.0.325', '0.0.3', '0.0.75', '123.0.1', '219.0.3', '0.0.86', '220.0.4', '212.0.3', '0.0.104', '224.0.3', '0.0.243', '213.0.8', '0.0.329', '209.0.4', '130.0', '213.0.7', '191.0.2', '99.0', '0.0.284', '0.0.27'
...
', '218.0.9', '235.0', '223.0.11', '0.0.245', '0.0.42', '188.0.4', '218.0.5', '106.0', '167.0', '248.0', '0.0.18', '194.0.1', '0.0.106', '0.0.92', '127.0.1', '6.0.1', '0.0.298', '244.0.2', '0.0.127', '0.0.159', '219.0.2', '0.0.212', '211.0.3', '218.0.6', '189.0.1', '12.0', '170.0', '203.0.1', '0.0.139', '200.0.2', '198.0.1', '210.0.4', '0.0.99', '202.0.2', '0.0.107', '0.0.29', '215.0', '0.0.167', '200.0.1', '0.0.260', '122.0', '72.0.1', '0.0.16', '0.0.142', '0.0.71', '185.0.2', '0.0.67', '198.0.3', '221.0.6', '221.0.2', '73.0.1', '0.0.320', '206.0.2', '237.0', '0.0.272', '228.0.5', '176.0.1', '204.0.5', '0.0.196', '233.0.2', '0.0.113', '0.0.144', '207.0.1', '0.0.341', '0.0.148', '15.0', '57.0', '0.0.218', '208.0.3', '0.0.52', '0.0.147', '216.0.6', '221.0.9', '216.0.1', '212.0', '0.0.136', '211.0.5', '223.0.2', '0.0.53', '0.0.58', '0.0.70', '206.0', '1.0.5', '56.0', '249.0', '0.0.346', '0.0.239', '205.0.4', '0.0.24', '190.0', '221.0.4', '0.0.185', '146.0', '210.0.5', '0.0.118', '0.0.256', '0.0.297', '0.0.78', '0.0.63', '217.0.2', '220.0', '0.0.163', '0.0.209', '240.0.2', '0.0.48', '7.0', '206.0.3', '0.0.250', '0.0.38', '208.0.4', '0.0.223', '0.0.276', '0.0.13', '0.0.51', '224.0.1', '219.0', '64.0', '204.0.4', '0.0.315', '0.0.280', '0.0.312', '0.0.213', '0.0.149', '0.0.308', '0.0.69', '213.0.6', '202.0.1', '1.0.1', '134.0', '0.0.55', '217.0.5', '207.0', '0.0.7', '204.0.7', '0.0.116', '211.0', '194.0.2', '0.0.20', '0.0.184', '0.0.151', '239.0.1', '0.0.111', '229.0.5', '0.0.337'}).
This happened while the csv dataset builder was generating data using
hf://datasets/habedi/nearest-neighbors-datasets/glove-100-angular/train.csv (at revision 59a77df276545051fa0919910346655fd4fed3cf)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
-0.11333: double
0.48402: double
0.090771: double
-0.22439: double
0.034206: double
-0.55831: double
0.041849: double
-0.53573: double
0.18809: double
-0.58722: double
0.015313: double
-0.014555: double
0.80842: double
-0.038519: double
0.75348: double
0.70502: double
-0.17863: double
0.3222: double
0.67575: double
0.67198: double
0.26044: double
0.4187: double
-0.34122: double
0.2286: double
-0.53529: double
1.2582: double
-0.091543: double
0.19716: double
-0.037454: double
-0.3336: double
0.31399: double
0.36488: double
0.71263: double
0.1307: double
-0.24654: double
-0.52445: double
-0.036091: double
0.55068: double
0.10017: double
0.48095: double
0.71104: double
-0.053462: double
0.22325: double
0.30917: double
-0.39926: double
0.036634: double
-0.35431: double
-0.42795: double
0.46444: double
0.25586: double
0.68257: double
-0.20821: double
0.38433: double
0.055773: double
-0.2539: double
-0.20804: double
0.52522: double
-0.11399: double
-0.3253: double
-0.44104: double
0.17528: double
0.62255: double
0.50237: double
-0.7607: double
-0.071786: double
0.0080131: double
-0.13286: double
0.50097: double
0.18824: double
-0.54722: double
-0.42664: double
0.4292: double
0.14877: double
-0.0072514: double
-0.16484: double
-0.059798: double
0.9895: double
-0.61738: double
0.054169: double
0.48424: double
-0.35084: double
-0.27053: double
0.37829: double
0.11503: double
-0.39613: double
0.24266: double
0.39147: double
-0.075256: double
0.65093: double
-0.20822: double
-0.17456: double
0.53571: double
-0.16537: double
0.13582: double
-0.56016: double
0.016964: double
0.1277: double
0.94071: double
-0.22608: double
-0.021106: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 11873
to
{'0.0': Value(dtype='float64', id=None), '0.0.1': Value(dtype='float64', id=None), '0.0.2': Value(dtype='float64', id=None), '0.0.3': Value(dtype='float64', id=None), '0.0.4': Value(dtype='float64', id=None), '0.0.5': Value(dtype='float64', id=None), '0.0.6': Value(dtype='float64', id=None), '0.0.7': Value(dtype='float64', id=None), '0.0.8': Value(dtype='float64', id=None), '0.0.9': Value(dtype='float64', id=None), '0.0.10': Value(dtype='float64', id=None), '0.0.11': Value(dtype='float64', id=None), '0.0.12': Value(dtype='float64', id=None), '0.0.13': Value(dtype='float64', id=None), '0.0.14': Value(dtype='float64', id=None), '0.0.15': Value(dtype='float64', id=None), '0.0.16': Value(dtype='float64', id=None), '0.0.17': Value(dtype='float64', id=None), '0.0.18': Value(dtype='float64', id=None), '0.0.19': Value(dtype='float64', id=None), '0.0.20': Value(dtype='float64', id=None), '0.0.21': Value(dtype='float64', id=None), '0.0.22': Value(dtype='float64', id=None), '0.0.23': Value(dtype='float64', id=None), '0.0.24': Value(dtype='float64', id=None), '0.0.25': Value(dtype='float64', id=None), '0.0.26': Value(dtype='float64', id=None), '0.0.27': Value(dtype='float64', id=None), '0.0.28': Value(dtype='float64', id=None), '0.0.29': Value(dtype='float64', id=None), '0.0.30': Value(dtype='float64', id=None), '0.0.31': Value(dtype='float64', id=None), '0.0.32': Value(dtype='float64', id=None), '0.0.33': Value(dtype='float64', id=None), '0.0.34': Value(dtype='float64', id=None), '0.0.3
...
ne), '0.0.317': Value(dtype='float64', id=None), '0.0.318': Value(dtype='float64', id=None), '0.0.319': Value(dtype='float64', id=None), '0.0.320': Value(dtype='float64', id=None), '0.0.321': Value(dtype='float64', id=None), '0.0.322': Value(dtype='float64', id=None), '0.0.323': Value(dtype='float64', id=None), '0.0.324': Value(dtype='float64', id=None), '0.0.325': Value(dtype='float64', id=None), '0.0.326': Value(dtype='float64', id=None), '0.0.327': Value(dtype='float64', id=None), '0.0.328': Value(dtype='float64', id=None), '0.0.329': Value(dtype='float64', id=None), '0.0.330': Value(dtype='float64', id=None), '0.0.331': Value(dtype='float64', id=None), '0.0.332': Value(dtype='float64', id=None), '0.0.333': Value(dtype='float64', id=None), '0.0.334': Value(dtype='float64', id=None), '0.0.335': Value(dtype='float64', id=None), '0.0.336': Value(dtype='float64', id=None), '0.0.337': Value(dtype='float64', id=None), '0.0.338': Value(dtype='float64', id=None), '0.0.339': Value(dtype='float64', id=None), '0.0.340': Value(dtype='float64', id=None), '0.0.341': Value(dtype='float64', id=None), '0.0.342': Value(dtype='float64', id=None), '0.0.343': Value(dtype='float64', id=None), '0.0.344': Value(dtype='float64', id=None), '0.0.345': Value(dtype='float64', id=None), '0.0.346': Value(dtype='float64', id=None), '0.0.347': Value(dtype='float64', id=None), '0.0.348': Value(dtype='float64', id=None), '0.0.349': Value(dtype='float64', id=None), '0.0.350': Value(dtype='float64', id=None)}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1433, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 989, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1742, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1873, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 100 new columns ({'1.2582', '0.4187', '0.37829', '-0.091543', '0.48095', '0.090771', '0.39147', '0.67575', '-0.17456', '-0.35084', '0.48402', '0.10017', '0.041849', '0.1277', '-0.39926', '-0.11333', '-0.3253', '-0.075256', '0.17528', '0.1307', '0.26044', '-0.22439', '0.46444', '0.80842', '-0.20821', '-0.059798', '0.75348', '0.054169', '-0.037454', '0.68257', '0.18824', '0.22325', '-0.3336', '0.71263', '-0.071786', '-0.56016', '0.36488', '-0.55831', '0.015313', '0.48424', '-0.038519', '0.38433', '-0.11399', '0.67198', '-0.053462', '0.19716', '0.18809', '0.70502', '0.9895', '-0.036091', '-0.22608', '0.65093', '-0.014555', '-0.58722', '0.62255', '0.25586', '-0.53529', '0.53571', '-0.16484', '0.14877', '0.31399', '0.52522', '0.50097', '-0.44104', '-0.53573', '0.055773', '0.94071', '-0.34122', '-0.13286', '0.24266', '-0.16537', '0.016964', '-0.42795', '-0.20804', '-0.7607', '0.3222', '0.13582', '-0.54722', '0.034206', '0.50237', '-0.2539', '-0.24654', '-0.35431', '0.11503', '-0.021106', '-0.20822', '-0.61738', '-0.39613', '0.2286', '0.71104', '-0.17863', '0.55068', '0.30917', '-0.27053', '0.036634', '0.0080131', '-0.0072514', '0.4292', '-0.42664', '-0.52445'}) and 784 missing columns ({'0.0.130', '213.0.3', '0.0.1', '0.0.126', '216.0.2', '227.0.1', '211.0.4', '0.0.326', '0.0.85', '188.0.1', '181.0.1', '0.0.273', '0.0.349', '224.0.2', '0.0.87', '0.0.318', '167.0.1', '3.0.3', '215.0.6', '41.0', '228.0.6', '0.0.340', '1.0.7', '0.0.125', '234.0', '0.0.242', '0.0.54', '205.0.1', '3.0.2', '198.0.2', '0.0.306', '0.0.189', '185.0.1', '6.0.2', '163.0', '225.0.2', '205.0.3', '0.0.201', '218.0.7', '54.0', '0.0.44', '0.0.235', '0.0.22', '229.0.1', '0.0.25', '1.0.9', '240.0', '222.0.5', '154.0', '196.0.2', '0.0.231', '0.0.150', '180.0', '4.0.3', '0.0.90', '193.0.4', '232.0.2', '0.0.162', '0.0.285', '243.0', '219.0.4', '225.0', '234.0.2', '0.0.254', '230.0.1', '0.0.174', '240.0.1', '0.0.202', '0.0.56', '0.0.137', '216.0.3', '0.0.240', '221.0.8', '215.0.3', '195.0', '145.0', '176.0.3', '0.0.227', '0.0.274', '222.0.8', '183.0.2', '229.0', '69.0', '0.0.102', '218.0.3', '0.0.233', '175.0.1', '228.0.7', '234.0.1', '244.0', '209.0.5', '0.0.310', '0.0.259', '0.0.293', '44.0', '0.0.122', '2.0.1', '0.0.238', '0.0.219', '223.0.12', '0.0.195', '156.0.1', '224.0.5', '188.0.3', '0.0.199', '169.0', '0.0.88', '0.0.191', '2.0', '0.0.220', '209.0.1', '192.0.1', '0.0.290', '0.0.288', '212.0.4', '173.0', '0.0.31', '0.0.34', '0.0.192', '0.0.175', '44.0.1', '136.0', '0.0.140', '161.0', '0.0.325', '0.0.3', '0.0.75', '123.0.1', '219.0.3', '0.0.86', '220.0.4', '212.0.3', '0.0.104', '224.0.3', '0.0.243', '213.0.8', '0.0.329', '209.0.4', '130.0', '213.0.7', '191.0.2', '99.0', '0.0.284', '0.0.27'
...
', '218.0.9', '235.0', '223.0.11', '0.0.245', '0.0.42', '188.0.4', '218.0.5', '106.0', '167.0', '248.0', '0.0.18', '194.0.1', '0.0.106', '0.0.92', '127.0.1', '6.0.1', '0.0.298', '244.0.2', '0.0.127', '0.0.159', '219.0.2', '0.0.212', '211.0.3', '218.0.6', '189.0.1', '12.0', '170.0', '203.0.1', '0.0.139', '200.0.2', '198.0.1', '210.0.4', '0.0.99', '202.0.2', '0.0.107', '0.0.29', '215.0', '0.0.167', '200.0.1', '0.0.260', '122.0', '72.0.1', '0.0.16', '0.0.142', '0.0.71', '185.0.2', '0.0.67', '198.0.3', '221.0.6', '221.0.2', '73.0.1', '0.0.320', '206.0.2', '237.0', '0.0.272', '228.0.5', '176.0.1', '204.0.5', '0.0.196', '233.0.2', '0.0.113', '0.0.144', '207.0.1', '0.0.341', '0.0.148', '15.0', '57.0', '0.0.218', '208.0.3', '0.0.52', '0.0.147', '216.0.6', '221.0.9', '216.0.1', '212.0', '0.0.136', '211.0.5', '223.0.2', '0.0.53', '0.0.58', '0.0.70', '206.0', '1.0.5', '56.0', '249.0', '0.0.346', '0.0.239', '205.0.4', '0.0.24', '190.0', '221.0.4', '0.0.185', '146.0', '210.0.5', '0.0.118', '0.0.256', '0.0.297', '0.0.78', '0.0.63', '217.0.2', '220.0', '0.0.163', '0.0.209', '240.0.2', '0.0.48', '7.0', '206.0.3', '0.0.250', '0.0.38', '208.0.4', '0.0.223', '0.0.276', '0.0.13', '0.0.51', '224.0.1', '219.0', '64.0', '204.0.4', '0.0.315', '0.0.280', '0.0.312', '0.0.213', '0.0.149', '0.0.308', '0.0.69', '213.0.6', '202.0.1', '1.0.1', '134.0', '0.0.55', '217.0.5', '207.0', '0.0.7', '204.0.7', '0.0.116', '211.0', '194.0.2', '0.0.20', '0.0.184', '0.0.151', '239.0.1', '0.0.111', '229.0.5', '0.0.337'}).
This happened while the csv dataset builder was generating data using
hf://datasets/habedi/nearest-neighbors-datasets/glove-100-angular/train.csv (at revision 59a77df276545051fa0919910346655fd4fed3cf)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
0.0 float64 | 0.0.1 float64 | 0.0.2 float64 | 0.0.3 float64 | 0.0.4 float64 | 0.0.5 float64 | 0.0.6 float64 | 0.0.7 float64 | 0.0.8 float64 | 0.0.9 float64 | 0.0.10 float64 | 0.0.11 float64 | 0.0.12 float64 | 0.0.13 float64 | 0.0.14 float64 | 0.0.15 float64 | 0.0.16 float64 | 0.0.17 float64 | 0.0.18 float64 | 0.0.19 float64 | 0.0.20 float64 | 0.0.21 float64 | 0.0.22 float64 | 0.0.23 float64 | 0.0.24 float64 | 0.0.25 float64 | 0.0.26 float64 | 0.0.27 float64 | 0.0.28 float64 | 0.0.29 float64 | 0.0.30 float64 | 0.0.31 float64 | 0.0.32 float64 | 0.0.33 float64 | 0.0.34 float64 | 0.0.35 float64 | 0.0.36 float64 | 0.0.37 float64 | 0.0.38 float64 | 0.0.39 float64 | 0.0.40 float64 | 0.0.41 float64 | 0.0.42 float64 | 0.0.43 float64 | 0.0.44 float64 | 0.0.45 float64 | 0.0.46 float64 | 0.0.47 float64 | 0.0.48 float64 | 0.0.49 float64 | 0.0.50 float64 | 0.0.51 float64 | 0.0.52 float64 | 0.0.53 float64 | 0.0.54 float64 | 0.0.55 float64 | 0.0.56 float64 | 0.0.57 float64 | 0.0.58 float64 | 0.0.59 float64 | 0.0.60 float64 | 0.0.61 float64 | 0.0.62 float64 | 0.0.63 float64 | 0.0.64 float64 | 0.0.65 float64 | 0.0.66 float64 | 0.0.67 float64 | 0.0.68 float64 | 0.0.69 float64 | 0.0.70 float64 | 0.0.71 float64 | 0.0.72 float64 | 0.0.73 float64 | 0.0.74 float64 | 0.0.75 float64 | 0.0.76 float64 | 0.0.77 float64 | 0.0.78 float64 | 0.0.79 float64 | 0.0.80 float64 | 0.0.81 float64 | 0.0.82 float64 | 0.0.83 float64 | 0.0.84 float64 | 0.0.85 float64 | 0.0.86 float64 | 0.0.87 float64 | 0.0.88 float64 | 0.0.89 float64 | 0.0.90 float64 | 0.0.91 float64 | 0.0.92 float64 | 0.0.93 float64 | 0.0.94 float64 | 0.0.95 float64 | 1.0 float64 | 0.0.96 float64 | 0.0.97 float64 | 13.0 float64 | 73.0 float64 | 0.0.98 float64 | 0.0.99 float64 | 1.0.1 float64 | 4.0 float64 | 0.0.100 float64 | 0.0.101 float64 | 0.0.102 float64 | 0.0.103 float64 | 1.0.2 float64 | 1.0.3 float64 | 0.0.104 float64 | 0.0.105 float64 | 0.0.106 float64 | 0.0.107 float64 | 0.0.108 float64 | 0.0.109 float64 | 0.0.110 float64 | 0.0.111 float64 | 0.0.112 float64 | 0.0.113 float64 | 0.0.114 float64 | 0.0.115 float64 | 0.0.116 float64 | 3.0 float64 | 0.0.117 float64 | 36.0 float64 | 136.0 float64 | 127.0 float64 | 62.0 float64 | 54.0 float64 | 0.0.118 float64 | 0.0.119 float64 | 0.0.120 float64 | 1.0.4 float64 | 3.0.1 float64 | 4.0.1 float64 | 0.0.121 float64 | 0.0.122 float64 | 3.0.2 float64 | 0.0.123 float64 | 0.0.124 float64 | 0.0.125 float64 | 0.0.126 float64 | 0.0.127 float64 | 0.0.128 float64 | 0.0.129 float64 | 0.0.130 float64 | 0.0.131 float64 | 0.0.132 float64 | 0.0.133 float64 | 0.0.134 float64 | 6.0 float64 | 0.0.135 float64 | 102.0 float64 | 204.0 float64 | 176.0 float64 | 134.0 float64 | 144.0 float64 | 123.0 float64 | 23.0 float64 | 0.0.136 float64 | 0.0.137 float64 | 0.0.138 float64 | 0.0.139 float64 | 12.0 float64 | 10.0 float64 | 0.0.140 float64 | 0.0.141 float64 | 0.0.142 float64 | 0.0.143 float64 | 0.0.144 float64 | 0.0.145 float64 | 0.0.146 float64 | 0.0.147 float64 | 0.0.148 float64 | 0.0.149 float64 | 0.0.150 float64 | 0.0.151 float64 | 0.0.152 float64 | 0.0.153 float64 | 0.0.154 float64 | 155.0 float64 | 236.0 float64 | 207.0 float64 | 178.0 float64 | 107.0 float64 | 156.0 float64 | 161.0 float64 | 109.0 float64 | 64.0 float64 | 23.0.1 float64 | 77.0 float64 | 130.0 float64 | 72.0 float64 | 15.0 float64 | 0.0.155 float64 | 0.0.156 float64 | 0.0.157 float64 | 0.0.158 float64 | 0.0.159 float64 | 0.0.160 float64 | 0.0.161 float64 | 0.0.162 float64 | 0.0.163 float64 | 0.0.164 float64 | 0.0.165 float64 | 1.0.5 float64 | 0.0.166 float64 | 69.0 float64 | 207.0.1 float64 | 223.0 float64 | 218.0 float64 | 216.0 float64 | 216.0.1 float64 | 163.0 float64 | 127.0.1 float64 | 121.0 float64 | 122.0 float64 | 146.0 float64 | 141.0 float64 | 88.0 float64 | 172.0 float64 | 66.0 float64 | 0.0.167 float64 | 0.0.168 float64 | 0.0.169 float64 | 0.0.170 float64 | 0.0.171 float64 | 0.0.172 float64 | 0.0.173 float64 | 0.0.174 float64 | 0.0.175 float64 | 1.0.6 float64 | 1.0.7 float64 | 1.0.8 float64 | 0.0.176 float64 | 200.0 float64 | 232.0 float64 | 232.0.1 float64 | 233.0 float64 | 229.0 float64 | 223.0.1 float64 | 223.0.2 float64 | 215.0 float64 | 213.0 float64 | 164.0 float64 | 127.0.2 float64 | 123.0.1 float64 | 196.0 float64 | 229.0.1 float64 | 0.0.177 float64 | 0.0.178 float64 | 0.0.179 float64 | 0.0.180 float64 | 0.0.181 float64 | 0.0.182 float64 | 0.0.183 float64 | 0.0.184 float64 | 0.0.185 float64 | 0.0.186 float64 | 0.0.187 float64 | 0.0.188 float64 | 0.0.189 float64 | 0.0.190 float64 | 183.0 float64 | 225.0 float64 | 216.0.2 float64 | 223.0.3 float64 | 228.0 float64 | 235.0 float64 | 227.0 float64 | 224.0 float64 | 222.0 float64 | 224.0.1 float64 | 221.0 float64 | 223.0.4 float64 | 245.0 float64 | 173.0 float64 | 0.0.191 float64 | 0.0.192 float64 | 0.0.193 float64 | 0.0.194 float64 | 0.0.195 float64 | 0.0.196 float64 | 0.0.197 float64 | 0.0.198 float64 | 0.0.199 float64 | 0.0.200 float64 | 0.0.201 float64 | 0.0.202 float64 | 0.0.203 float64 | 0.0.204 float64 | 193.0 float64 | 228.0.1 float64 | 218.0.1 float64 | 213.0.1 float64 | 198.0 float64 | 180.0 float64 | 212.0 float64 | 210.0 float64 | 211.0 float64 | 213.0.2 float64 | 223.0.5 float64 | 220.0 float64 | 243.0 float64 | 202.0 float64 | 0.0.205 float64 | 0.0.206 float64 | 0.0.207 float64 | 0.0.208 float64 | 0.0.209 float64 | 0.0.210 float64 | 0.0.211 float64 | 0.0.212 float64 | 0.0.213 float64 | 0.0.214 float64 | 1.0.9 float64 | 3.0.3 float64 | 0.0.215 float64 | 12.0.1 float64 | 219.0 float64 | 220.0.1 float64 | 212.0.1 float64 | 218.0.2 float64 | 192.0 float64 | 169.0 float64 | 227.0.1 float64 | 208.0 float64 | 218.0.3 float64 | 224.0.2 float64 | 212.0.2 float64 | 226.0 float64 | 197.0 float64 | 209.0 float64 | 52.0 float64 | 0.0.216 float64 | 0.0.217 float64 | 0.0.218 float64 | 0.0.219 float64 | 0.0.220 float64 | 0.0.221 float64 | 0.0.222 float64 | 0.0.223 float64 | 0.0.224 float64 | 0.0.225 float64 | 6.0.1 float64 | 0.0.226 float64 | 99.0 float64 | 244.0 float64 | 222.0.1 float64 | 220.0.2 float64 | 218.0.4 float64 | 203.0 float64 | 198.0.1 float64 | 221.0.1 float64 | 215.0.1 float64 | 213.0.3 float64 | 222.0.2 float64 | 220.0.3 float64 | 245.0.1 float64 | 119.0 float64 | 167.0 float64 | 56.0 float64 | 0.0.227 float64 | 0.0.228 float64 | 0.0.229 float64 | 0.0.230 float64 | 0.0.231 float64 | 0.0.232 float64 | 0.0.233 float64 | 0.0.234 float64 | 0.0.235 float64 | 4.0.2 float64 | 0.0.236 float64 | 0.0.237 float64 | 55.0 float64 | 236.0.1 float64 | 228.0.2 float64 | 230.0 float64 | 228.0.3 float64 | 240.0 float64 | 232.0.2 float64 | 213.0.4 float64 | 218.0.5 float64 | 223.0.6 float64 | 234.0 float64 | 217.0 float64 | 217.0.1 float64 | 209.0.1 float64 | 92.0 float64 | 0.0.238 float64 | 0.0.239 float64 | 0.0.240 float64 | 1.0.10 float64 | 4.0.3 float64 | 6.0.2 float64 | 7.0 float64 | 2.0 float64 | 0.0.241 float64 | 0.0.242 float64 | 0.0.243 float64 | 0.0.244 float64 | 0.0.245 float64 | 237.0 float64 | 226.0.1 float64 | 217.0.2 float64 | 223.0.7 float64 | 222.0.3 float64 | 219.0.1 float64 | 222.0.4 float64 | 221.0.2 float64 | 216.0.3 float64 | 223.0.8 float64 | 229.0.2 float64 | 215.0.2 float64 | 218.0.6 float64 | 255.0 float64 | 77.0.1 float64 | 0.0.246 float64 | 0.0.247 float64 | 3.0.4 float64 | 0.0.248 float64 | 0.0.249 float64 | 0.0.250 float64 | 0.0.251 float64 | 0.0.252 float64 | 0.0.253 float64 | 0.0.254 float64 | 62.0.1 float64 | 145.0 float64 | 204.0.1 float64 | 228.0.4 float64 | 207.0.2 float64 | 213.0.5 float64 | 221.0.3 float64 | 218.0.7 float64 | 208.0.1 float64 | 211.0.1 float64 | 218.0.8 float64 | 224.0.3 float64 | 223.0.9 float64 | 219.0.2 float64 | 215.0.3 float64 | 224.0.4 float64 | 244.0.1 float64 | 159.0 float64 | 0.0.255 float64 | 0.0.256 float64 | 0.0.257 float64 | 0.0.258 float64 | 0.0.259 float64 | 18.0 float64 | 44.0 float64 | 82.0 float64 | 107.0.1 float64 | 189.0 float64 | 228.0.5 float64 | 220.0.4 float64 | 222.0.5 float64 | 217.0.3 float64 | 226.0.2 float64 | 200.0.1 float64 | 205.0 float64 | 211.0.2 float64 | 230.0.1 float64 | 224.0.5 float64 | 234.0.1 float64 | 176.0.1 float64 | 188.0 float64 | 250.0 float64 | 248.0 float64 | 233.0.1 float64 | 238.0 float64 | 215.0.4 float64 | 0.0.260 float64 | 0.0.261 float64 | 57.0 float64 | 187.0 float64 | 208.0.2 float64 | 224.0.6 float64 | 221.0.4 float64 | 224.0.7 float64 | 208.0.3 float64 | 204.0.2 float64 | 214.0 float64 | 208.0.4 float64 | 209.0.2 float64 | 200.0.2 float64 | 159.0.1 float64 | 245.0.2 float64 | 193.0.1 float64 | 206.0 float64 | 223.0.10 float64 | 255.0.1 float64 | 255.0.2 float64 | 221.0.5 float64 | 234.0.2 float64 | 221.0.6 float64 | 211.0.3 float64 | 220.0.5 float64 | 232.0.3 float64 | 246.0 float64 | 0.0.262 float64 | 3.0.5 float64 | 202.0.1 float64 | 228.0.6 float64 | 224.0.8 float64 | 221.0.7 float64 | 211.0.4 float64 | 211.0.5 float64 | 214.0.1 float64 | 205.0.1 float64 | 205.0.2 float64 | 205.0.3 float64 | 220.0.6 float64 | 240.0.1 float64 | 80.0 float64 | 150.0 float64 | 255.0.3 float64 | 229.0.3 float64 | 221.0.8 float64 | 188.0.1 float64 | 154.0 float64 | 191.0 float64 | 210.0.1 float64 | 204.0.3 float64 | 209.0.3 float64 | 222.0.6 float64 | 228.0.7 float64 | 225.0.1 float64 | 0.0.263 float64 | 98.0 float64 | 233.0.2 float64 | 198.0.2 float64 | 210.0.2 float64 | 222.0.7 float64 | 229.0.4 float64 | 229.0.5 float64 | 234.0.3 float64 | 249.0 float64 | 220.0.7 float64 | 194.0 float64 | 215.0.5 float64 | 217.0.4 float64 | 241.0 float64 | 65.0 float64 | 73.0.1 float64 | 106.0 float64 | 117.0 float64 | 168.0 float64 | 219.0.3 float64 | 221.0.9 float64 | 215.0.6 float64 | 217.0.5 float64 | 223.0.11 float64 | 223.0.12 float64 | 224.0.9 float64 | 229.0.6 float64 | 29.0 float64 | 75.0 float64 | 204.0.4 float64 | 212.0.3 float64 | 204.0.5 float64 | 193.0.2 float64 | 205.0.4 float64 | 211.0.6 float64 | 225.0.2 float64 | 216.0.4 float64 | 185.0 float64 | 197.0.1 float64 | 206.0.1 float64 | 198.0.3 float64 | 213.0.6 float64 | 240.0.2 float64 | 195.0 float64 | 227.0.2 float64 | 245.0.3 float64 | 239.0 float64 | 223.0.13 float64 | 218.0.9 float64 | 212.0.4 float64 | 209.0.4 float64 | 222.0.8 float64 | 220.0.8 float64 | 221.0.10 float64 | 230.0.2 float64 | 67.0 float64 | 48.0 float64 | 203.0.1 float64 | 183.0.1 float64 | 194.0.1 float64 | 213.0.7 float64 | 197.0.2 float64 | 185.0.1 float64 | 190.0 float64 | 194.0.2 float64 | 192.0.1 float64 | 202.0.2 float64 | 214.0.2 float64 | 219.0.4 float64 | 221.0.11 float64 | 220.0.9 float64 | 236.0.2 float64 | 225.0.3 float64 | 216.0.5 float64 | 199.0 float64 | 206.0.2 float64 | 186.0 float64 | 181.0 float64 | 177.0 float64 | 172.0.1 float64 | 181.0.1 float64 | 205.0.5 float64 | 206.0.3 float64 | 115.0 float64 | 0.0.264 float64 | 122.0.1 float64 | 219.0.5 float64 | 193.0.3 float64 | 179.0 float64 | 171.0 float64 | 183.0.2 float64 | 196.0.1 float64 | 204.0.6 float64 | 210.0.3 float64 | 213.0.8 float64 | 207.0.3 float64 | 211.0.7 float64 | 210.0.4 float64 | 200.0.3 float64 | 196.0.2 float64 | 194.0.3 float64 | 191.0.1 float64 | 195.0.1 float64 | 191.0.2 float64 | 198.0.4 float64 | 192.0.2 float64 | 176.0.2 float64 | 156.0.1 float64 | 167.0.1 float64 | 177.0.1 float64 | 210.0.5 float64 | 92.0.1 float64 | 0.0.265 float64 | 0.0.266 float64 | 74.0 float64 | 189.0.1 float64 | 212.0.5 float64 | 191.0.3 float64 | 175.0 float64 | 172.0.2 float64 | 175.0.1 float64 | 181.0.2 float64 | 185.0.2 float64 | 188.0.2 float64 | 189.0.2 float64 | 188.0.3 float64 | 193.0.4 float64 | 198.0.5 float64 | 204.0.7 float64 | 209.0.5 float64 | 210.0.6 float64 | 210.0.7 float64 | 211.0.8 float64 | 188.0.4 float64 | 188.0.5 float64 | 194.0.4 float64 | 192.0.3 float64 | 216.0.6 float64 | 170.0 float64 | 0.0.267 float64 | 2.0.1 float64 | 0.0.268 float64 | 0.0.269 float64 | 0.0.270 float64 | 66.0.1 float64 | 200.0.4 float64 | 222.0.9 float64 | 237.0.1 float64 | 239.0.1 float64 | 242.0 float64 | 246.0.1 float64 | 243.0.1 float64 | 244.0.2 float64 | 221.0.12 float64 | 220.0.10 float64 | 193.0.5 float64 | 191.0.4 float64 | 179.0.1 float64 | 182.0 float64 | 182.0.1 float64 | 181.0.3 float64 | 176.0.3 float64 | 166.0 float64 | 168.0.1 float64 | 99.0.1 float64 | 58.0 float64 | 0.0.271 float64 | 0.0.272 float64 | 0.0.273 float64 | 0.0.274 float64 | 0.0.275 float64 | 0.0.276 float64 | 0.0.277 float64 | 0.0.278 float64 | 0.0.279 float64 | 40.0 float64 | 61.0 float64 | 44.0.1 float64 | 72.0.1 float64 | 41.0 float64 | 35.0 float64 | 0.0.280 float64 | 0.0.281 float64 | 0.0.282 float64 | 0.0.283 float64 | 0.0.284 float64 | 0.0.285 float64 | 0.0.286 float64 | 0.0.287 float64 | 0.0.288 float64 | 0.0.289 float64 | 0.0.290 float64 | 0.0.291 float64 | 0.0.292 float64 | 0.0.293 float64 | 0.0.294 float64 | 0.0.295 float64 | 0.0.296 float64 | 0.0.297 float64 | 0.0.298 float64 | 0.0.299 float64 | 0.0.300 float64 | 0.0.301 float64 | 0.0.302 float64 | 0.0.303 float64 | 0.0.304 float64 | 0.0.305 float64 | 0.0.306 float64 | 0.0.307 float64 | 0.0.308 float64 | 0.0.309 float64 | 0.0.310 float64 | 0.0.311 float64 | 0.0.312 float64 | 0.0.313 float64 | 0.0.314 float64 | 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0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 111 | 93 | 85 | 27 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 190 | 244 | 248 | 255 | 255 | 155 | 202 | 212 | 150 | 9 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 213 | 227 | 233 | 227 | 243 | 246 | 255 | 250 | 255 | 146 | 0 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 6 | 244 | 233 | 236 | 244 | 232 | 214 | 248 | 242 | 251 | 33 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 72 | 255 | 231 | 239 | 244 | 237 | 226 | 255 | 252 | 207 | 44 | 24 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 125 | 231 | 215 | 253 | 252 | 234 | 246 | 254 | 239 | 128 | 114 | 63 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 189 | 230 | 198 | 185 | 219 | 218 | 255 | 206 | 255 | 133 | 31 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 224 | 241 | 255 | 236 | 237 | 223 | 191 | 219 | 166 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 61 | 245 | 202 | 220 | 237 | 220 | 242 | 199 | 255 | 178 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 153 | 247 | 205 | 202 | 212 | 207 | 225 | 190 | 255 | 93 | 141 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 222 | 228 | 233 | 243 | 248 | 242 | 242 | 209 | 255 | 0 | 90 | 177 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 46 | 255 | 221 | 235 | 235 | 213 | 248 | 220 | 206 | 255 | 225 | 28 | 255 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 116 | 250 | 215 | 227 | 231 | 226 | 255 | 195 | 216 | 250 | 255 | 159 | 255 | 160 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 7 | 226 | 202 | 241 | 247 | 241 | 228 | 255 | 175 | 229 | 252 | 241 | 255 | 224 | 224 | 76 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 131 | 255 | 210 | 212 | 251 | 252 | 229 | 255 | 175 | 227 | 254 | 239 | 245 | 230 | 223 | 149 | 90 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 227 | 238 | 238 | 225 | 205 | 242 | 246 | 223 | 187 | 221 | 254 | 238 | 255 | 233 | 156 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 77 | 255 | 239 | 248 | 252 | 237 | 195 | 201 | 232 | 190 | 205 | 252 | 235 | 255 | 199 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 207 | 223 | 186 | 215 | 223 | 255 | 255 | 217 | 229 | 254 | 251 | 244 | 240 | 255 | 207 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 53 | 254 | 234 | 239 | 222 | 190 | 187 | 200 | 250 | 237 | 195 | 251 | 250 | 237 | 253 | 192 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 4 | 2 | 0 | 0 | 190 | 247 | 233 | 231 | 250 | 254 | 247 | 208 | 208 | 231 | 226 | 207 | 246 | 235 | 248 | 175 | 0 | 0 | 0 | 0 | 0 | 1 | 6 | 0 | 0 | 0 | 0 | 117 | 245 | 184 | 242 | 238 | 235 | 238 | 244 | 233 | 255 | 255 | 99 | 155 | 255 | 233 | 252 | 154 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 33 | 141 | 255 | 197 | 208 | 184 | 234 | 246 | 240 | 243 | 231 | 255 | 77 | 0 | 172 | 255 | 231 | 253 | 139 | 0 | 1 | 0 | 0 | 0 | 0 | 84 | 243 | 246 | 255 | 254 | 240 | 241 | 190 | 229 | 198 | 243 | 244 | 238 | 255 | 80 | 0 | 0 | 176 | 255 | 231 | 252 | 113 | 0 | 3 | 0 | 0 | 0 | 25 | 223 | 239 | 230 | 231 | 231 | 234 | 241 | 247 | 191 | 225 | 230 | 252 | 255 | 173 | 0 | 0 | 0 | 170 | 255 | 228 | 251 | 97 | 0 | 4 | 0 | 0 | 0 | 103 | 241 | 217 | 228 | 230 | 238 | 241 | 238 | 248 | 244 | 216 | 172 | 223 | 255 | 0 | 0 | 7 | 0 | 159 | 255 | 234 | 251 | 74 | 0 | 4 | 0 | 0 | 0 | 9 | 188 | 229 | 242 | 241 | 243 | 240 | 236 | 238 | 241 | 251 | 237 | 253 | 64 | 0 | 0 | 3 | 0 | 154 | 255 | 229 | 255 | 62 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 28 | 123 | 199 | 249 | 255 | 255 | 255 | 255 | 255 | 245 | 72 | 0 | 0 | 0 | 3 | 0 | 117 | 255 | 245 | 220 | 19 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 38 | 72 | 89 | 71 | 37 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 64 | 42 | 24 | 0 | 0 | 0 | 0 | 0 |
0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 32 | 60 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 85 | 216 | 144 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 91 | 54 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 27 | 172 | 200 | 182 | 189 | 197 | 0 | 0 | 0 | 12 | 110 | 130 | 139 | 127 | 66 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 45 | 212 | 180 | 158 | 213 | 235 | 92 | 27 | 103 | 184 | 193 | 132 | 43 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | 0 | 0 | 194 | 222 | 163 | 163 | 182 | 239 | 211 | 164 | 69 | 25 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 55 | 244 | 242 | 229 | 200 | 216 | 63 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 24 | 117 | 190 | 0 | 0 | 0 | 0 | 0 | 157 | 230 | 226 | 202 | 206 | 148 | 0 | 0 | 3 | 18 | 30 | 39 | 65 | 48 | 1 | 2 | 1 | 1 | 0 | 144 | 197 | 184 | 206 | 150 | 0 | 0 | 0 | 0 | 5 | 94 | 157 | 189 | 203 | 243 | 177 | 108 | 136 | 149 | 154 | 150 | 145 | 108 | 0 | 0 | 0 | 0 | 0 | 101 | 235 | 197 | 171 | 230 | 30 | 0 | 0 | 0 | 0 | 47 | 200 | 206 | 148 | 154 | 109 | 75 | 54 | 61 | 61 | 57 | 75 | 54 | 6 | 16 | 1 | 0 | 0 | 0 | 190 | 126 | 0 | 200 | 101 | 0 | 29 | 85 | 119 | 127 | 154 | 101 | 90 | 97 | 66 | 118 | 105 | 121 | 91 | 94 | 122 | 48 | 41 | 112 | 113 | 115 | 106 | 94 | 197 | 206 | 114 | 194 | 255 | 119 | 123 | 118 | 79 | 86 | 55 | 108 | 74 | 74 | 81 | 57 | 103 | 54 | 82 | 86 | 83 | 46 | 29 | 72 | 34 | 74 | 59 | 83 | 100 | 63 | 96 | 83 | 114 | 64 | 70 | 63 | 100 | 69 | 100 | 91 | 81 | 87 | 92 | 94 | 87 | 115 | 68 | 70 | 96 | 24 | 20 | 99 | 90 | 104 | 86 | 103 | 82 | 91 | 48 | 78 | 64 | 109 | 92 | 79 | 74 | 95 | 108 | 91 | 88 | 95 | 97 | 75 | 79 | 100 | 92 | 96 | 97 | 14 | 6 | 97 | 75 | 91 | 79 | 90 | 63 | 94 | 90 | 91 | 100 | 72 | 75 | 83 | 81 | 79 | 55 | 73 | 48 | 88 | 81 | 83 | 68 | 61 | 69 | 39 | 79 | 2 | 0 | 82 | 115 | 109 | 109 | 123 | 78 | 95 | 92 | 91 | 105 | 90 | 110 | 117 | 130 | 132 | 137 | 141 | 142 | 128 | 126 | 151 | 180 | 170 | 163 | 162 | 114 | 0 | 0 | 0 | 2 | 39 | 63 | 85 | 112 | 101 | 109 | 105 | 90 | 94 | 77 | 59 | 47 | 43 | 19 | 2 | 0 | 0 | 0 | 3 | 25 | 20 | 27 | 24 | 9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
End of preview.
Nearest Neighbors Search Datasets
The datasets listed below are used in Hann library.
| Index | Dataset | Dimensions | Train Size | Test Size | Neighbors | Distance | Original Source |
|---|---|---|---|---|---|---|---|
| 1 | GloVe (25d) | 25 | 1,183,514 | 10,000 | 100 | Cosine | HDF5 (121MB) |
| 2 | GloVe (50d) | 50 | 1,183,514 | 10,000 | 100 | Cosine | HDF5 (235MB) |
| 3 | GloVe (100d) | 100 | 1,183,514 | 10,000 | 100 | Cosine | HDF5 (463MB) |
| 4 | GloVe (200d) | 200 | 1,183,514 | 10,000 | 100 | Cosine | HDF5 (918MB) |
| 5 | Last.fm | 65 | 292,385 | 50,000 | 100 | Cosine | HDF5 (135MB) |
| 6 | MNIST | 784 | 60,000 | 10,000 | 100 | Euclidean | HDF5 (217MB) |
| 7 | Fashion-MNIST | 784 | 60,000 | 10,000 | 100 | Euclidean | HDF5 (217MB) |
| 8 | SIFT | 128 | 1,000,000 | 10,000 | 100 | Euclidean | HDF5 (501MB) |
Acknowledgement
These are a subset of datasets from the ANN-Benchmarks project.
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