query
stringlengths
9
3.4k
document
stringlengths
9
87.4k
metadata
dict
negatives
listlengths
4
101
negative_scores
listlengths
4
101
document_score
stringlengths
3
10
document_rank
stringclasses
102 values
Testing {{...|indent}} with custom indentation level
def test_with_custom_indent(self): self.assertEqual(indent('foo', 3), ' foo')
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_with_default_indent(self):\n self.assertEqual(indent('foo'), ' foo')", "def test_adjust_indent():\n hr.Element.indent = 2\n\n body = hr.Body()\n body.append(hr.P(\"some text\"))\n html = hr.Html(body)\n\n file_contents = render_result(html)\n\n print(file_contents)\n lines...
[ "0.7285234", "0.68716925", "0.6601827", "0.6521513", "0.6454053", "0.6436792", "0.637126", "0.634604", "0.62667", "0.625941", "0.6186124", "0.61533314", "0.6131626", "0.6043399", "0.6043399", "0.602634", "0.59728074", "0.5963381", "0.59593403", "0.5902826", "0.58931136", "0...
0.7920645
0
Testing {{...|indent}} with multiple lines
def test_with_multiple_lines(self): self.assertEqual(indent('foo\nbar'), ' foo\n bar')
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_with_custom_indent(self):\n self.assertEqual(indent('foo', 3), ' foo')", "def test_multiple_indent():\n body = hr.Body()\n body.append(hr.P(\"some text\"))\n html = hr.Html(body)\n\n file_contents = render_result(html)\n\n print(file_contents)\n lines = file_contents.split(\"\...
[ "0.735322", "0.7073774", "0.69509804", "0.6840931", "0.68078333", "0.68071306", "0.67973256", "0.67146814", "0.66842926", "0.64460665", "0.63834816", "0.6257567", "0.6255706", "0.6243714", "0.6222274", "0.61781883", "0.6087206", "0.6076181", "0.60641384", "0.605803", "0.60136...
0.7795303
0
Testing {% querystring "update" %} basic usage
def test_update_basic_usage(self): self.assertEqual( self._render_tag(tag='{% querystring "update" "foo=bar" %}', query_str='foo=bar'), '?foo=bar')
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_update_with_querystring_key_overide(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"a=1\" \"a=2\" %}',\n query_str='foo=foo')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n...
[ "0.8160826", "0.81003916", "0.78848255", "0.781702", "0.7786558", "0.77261317", "0.7715116", "0.7595097", "0.7405113", "0.63803375", "0.6078759", "0.59671193", "0.5962284", "0.59361494", "0.59328645", "0.5919303", "0.5906413", "0.59014446", "0.58933955", "0.5768803", "0.57684...
0.84873694
0
Testing {% querystring "update" %} with an existing query
def test_update_with_tag_existing_query(self): rendered_result = self._render_tag( tag='{% querystring "update" "foo=bar" %}', query_str='a=1&b=2') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_update_with_existing_query_override(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"foo=bar\" %}',\n query_str='foo=foo&bar=baz')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]...
[ "0.8575229", "0.82114655", "0.8211125", "0.80703557", "0.79093665", "0.7821998", "0.7733886", "0.7647104", "0.7430527", "0.63636976", "0.62895095", "0.62777126", "0.622594", "0.62191993", "0.6099973", "0.60437036", "0.5997685", "0.59674245", "0.5927672", "0.590361", "0.589796...
0.8187381
3
Testing {% querystring "update" %} with an existing query that gets overridden
def test_update_with_existing_query_override(self): rendered_result = self._render_tag( tag='{% querystring "update" "foo=bar" %}', query_str='foo=foo&bar=baz') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_update_with_existing_query_with_two_args_override(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"foo=bar\" \"qux=baz\" %}',\n query_str='foo=foo&bar=bar&baz=baz&qux=qux')\n\n self.assertTrue(rendered_result.startswith('?'))\n self...
[ "0.82285905", "0.8130441", "0.80727655", "0.79774666", "0.78701615", "0.7824038", "0.7694648", "0.74788016", "0.72497344", "0.6282942", "0.6119359", "0.60488284", "0.6003275", "0.60010093", "0.5985671", "0.59831214", "0.5979202", "0.59778893", "0.59083915", "0.5906219", "0.58...
0.88072
0
Testing {% querystring "update" %} with two args that get overridden
def test_update_with_existing_query_with_two_args_override(self): rendered_result = self._render_tag( tag='{% querystring "update" "foo=bar" "qux=baz" %}', query_str='foo=foo&bar=bar&baz=baz&qux=qux') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(Quer...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_update_with_existing_query_override(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"foo=bar\" %}',\n query_str='foo=foo&bar=baz')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]...
[ "0.8393917", "0.81698006", "0.8157803", "0.7903674", "0.7751201", "0.772445", "0.76976013", "0.7611009", "0.7605545", "0.6213056", "0.6131876", "0.6074763", "0.6017298", "0.59298253", "0.5928301", "0.58698416", "0.58698416", "0.58597875", "0.5797873", "0.57940143", "0.578386"...
0.8527788
0
Testing {% querystring "update" %} with no value
def test_update_with_no_value(self): rendered_result = self._render_tag( tag='{% querystring "update" "foo" %}', query_str='') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('foo='))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_update_with_no_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"=foo\" %}',\n query_str='')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('=foo'))", "def test...
[ "0.83778876", "0.8344275", "0.79929906", "0.7838386", "0.77645916", "0.75217503", "0.7430781", "0.7206279", "0.6957165", "0.63444877", "0.61944747", "0.6171176", "0.6119739", "0.59485555", "0.5840339", "0.58194894", "0.5763195", "0.5723799", "0.5700123", "0.5652544", "0.56483...
0.8507903
0
Testing {% querystring "update" %} with multiple values
def test_update_with_multiple_values(self): rendered_result = self._render_tag( tag='{% querystring "update" "foo=bar=baz" %}', query_str='foo=foo') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_with_updating_multiple_values_of_a_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"a=1&a=2\" %}',\n query_str='foo=foo')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.83192694", "0.8115465", "0.7924868", "0.7861795", "0.7820674", "0.75683415", "0.74704516", "0.74522585", "0.7427821", "0.6365287", "0.62719387", "0.6235126", "0.6219152", "0.6127101", "0.6050058", "0.6045493", "0.6045493", "0.6033712", "0.59621257", "0.5841878", "0.5827072...
0.8562762
0
Testing {% querystring "update" %} with empty value
def test_update_with_empty_value(self): rendered_result = self._render_tag( tag='{% querystring "update" "foo=" %}', query_str='') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('foo='))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_update_with_no_value(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"foo\" %}',\n query_str='')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('foo='))", "def tes...
[ "0.85938555", "0.8381606", "0.7849045", "0.760292", "0.7579912", "0.73541963", "0.7286511", "0.7055417", "0.67807907", "0.6305068", "0.62017447", "0.61609757", "0.6049829", "0.587832", "0.57909924", "0.57764435", "0.5737658", "0.57255924", "0.5694696", "0.5607382", "0.5581019...
0.86261064
0
Testing {% querystring "update" %} with no key
def test_update_with_no_key(self): rendered_result = self._render_tag( tag='{% querystring "update" "=foo" %}', query_str='') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('=foo'))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_update_with_no_value(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"foo\" %}',\n query_str='')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('foo='))", "def tes...
[ "0.83767027", "0.8260205", "0.8205793", "0.8035107", "0.7845689", "0.7674357", "0.7564186", "0.7472058", "0.73783904", "0.6436952", "0.63122505", "0.6225137", "0.61859393", "0.612663", "0.6009522", "0.5991379", "0.5983234", "0.5963799", "0.5881154", "0.5877678", "0.58691776",...
0.8736628
0
Testing {% querystring "update" %} by updating multiple values of a key value gets overriden
def test_update_with_querystring_key_overide(self): rendered_result = self._render_tag( tag='{% querystring "update" "a=1" "a=2" %}', query_str='foo=foo') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_with_updating_multiple_values_of_a_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"a=1&a=2\" %}',\n query_str='foo=foo')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.8440153", "0.82962936", "0.7969206", "0.7880541", "0.7686545", "0.7478083", "0.74567306", "0.7453127", "0.73787194", "0.6426372", "0.6400556", "0.6357068", "0.63473326", "0.6334678", "0.6301991", "0.608709", "0.608709", "0.6033596", "0.59904945", "0.5971406", "0.5920102", ...
0.82806295
2
Testing {% querystring "update" %} by updating multiple values of a key value
def test_with_updating_multiple_values_of_a_key(self): rendered_result = self._render_tag( tag='{% querystring "update" "a=1&a=2" %}', query_str='foo=foo') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_update_with_multiple_values(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"update\" \"foo=bar=baz\" %}',\n query_str='foo=foo')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.84186804", "0.810681", "0.7557398", "0.7449745", "0.7355413", "0.7349155", "0.7247374", "0.7078936", "0.705895", "0.66061294", "0.64534163", "0.6402692", "0.6355075", "0.6283492", "0.6176012", "0.61271197", "0.5983697", "0.5941984", "0.59279925", "0.59279925", "0.5825642",...
0.8602916
0
Testing {% querystring "append" %} with appending on to an existing key
def test_append_with_basic_usage(self): rendered_result = self._render_tag( tag='{% querystring "append" "foo=baz" %}', query_str='foo=foo&bar=bar') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_append_with_new_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"append\" \"d=4\" %}',\n query_str='a=1&b=2&c=3')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.868256", "0.8091218", "0.7964661", "0.71309644", "0.70984197", "0.68421847", "0.6337886", "0.62352127", "0.61116505", "0.60739154", "0.6041855", "0.60166097", "0.5944315", "0.59102374", "0.5790828", "0.57822275", "0.5769435", "0.57542706", "0.57542706", "0.57223547", "0.57...
0.7863263
3
Testing {% querystring "append" %} with appending multiple values of a key
def test_append_with_multiple_values_and_same_key(self): rendered_result = self._render_tag( tag='{% querystring "append" "a=1&a=2&a=3" %}', query_str='a=0&&b=2&c=3') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_append_with_multiple_values_and_same_key_seperated(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"append\" \"a=1\" \"a=2\" \"a=3\" %}',\n query_str='a=0&&b=2&c=3')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(Query...
[ "0.8498463", "0.8341243", "0.76318073", "0.6942017", "0.6838789", "0.6705557", "0.65282977", "0.6202453", "0.6140832", "0.611182", "0.60828626", "0.59720445", "0.5939296", "0.5853891", "0.58489376", "0.58000696", "0.58000696", "0.56923723", "0.56226695", "0.5602836", "0.55750...
0.85816944
0
Testing {% querystring "append" %} with appending multiple values of a key fragment
def test_append_with_multiple_values_and_same_key_seperated(self): rendered_result = self._render_tag( tag='{% querystring "append" "a=1" "a=2" "a=3" %}', query_str='a=0&&b=2&c=3') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_resul...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_append_with_multiple_values_and_same_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"append\" \"a=1&a=2&a=3\" %}',\n query_str='a=0&&b=2&c=3')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_resu...
[ "0.86375725", "0.84450185", "0.781504", "0.68740207", "0.67290455", "0.66967934", "0.6629074", "0.62867236", "0.6149132", "0.61116165", "0.6078402", "0.6041519", "0.59463435", "0.5895472", "0.5895472", "0.58687717", "0.5769661", "0.5714091", "0.5696046", "0.56622285", "0.5643...
0.8574146
1
Testing {% querystring "append" %} with appending new keyvalue pair
def test_append_with_new_key(self): rendered_result = self._render_tag( tag='{% querystring "append" "d=4" %}', query_str='a=1&b=2&c=3') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_append_with_multiple_values_and_same_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"append\" \"a=1&a=2&a=3\" %}',\n query_str='a=0&&b=2&c=3')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_resu...
[ "0.8260465", "0.81529766", "0.8100246", "0.7122451", "0.70814383", "0.68417764", "0.65832335", "0.632912", "0.6328968", "0.63135624", "0.62243444", "0.6187808", "0.6101339", "0.6048709", "0.6048709", "0.5992231", "0.59557575", "0.59342337", "0.59126395", "0.5843746", "0.58304...
0.87845033
0
Testing {% querystring "remove" %} by removing a single instance of key
def test_remove_with_basic_usage(self): rendered_result = self._render_tag( tag='{% querystring "remove" "foo" %}', query_str='foo=foo&bar=bar') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('bar=bar'))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_remove_with_no_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"remove\" \"=foo\" %}',\n query_str='foo=foo&foo=bar&baz=baz&=foo')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.8608029", "0.85413694", "0.843683", "0.83284855", "0.8059885", "0.7788367", "0.7685585", "0.6994412", "0.66467637", "0.65057695", "0.64313275", "0.6383073", "0.6318103", "0.62087476", "0.6188468", "0.6135019", "0.60900307", "0.60900307", "0.6054148", "0.6018646", "0.597310...
0.8022075
5
Testing {% querystring "remove" %} by attempting to remove a nonexisting key
def test_remove_with_key_not_in_querystring(self): rendered_result = self._render_tag( tag='{% querystring "remove" "baz" %}', query_str='foo=foo&bar=bar') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_remove_with_no_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"remove\" \"=foo\" %}',\n query_str='foo=foo&foo=bar&baz=baz&=foo')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.84152013", "0.80939305", "0.79133326", "0.79089874", "0.7750738", "0.7373129", "0.7191346", "0.6528528", "0.6507897", "0.6199616", "0.61719894", "0.6120845", "0.6088209", "0.6058557", "0.60254574", "0.6016976", "0.5947428", "0.5919038", "0.58856094", "0.5815144", "0.577812...
0.83562726
1
Testing {% querystring "remove" %} by removing all instances of a key
def test_remove_with_key_appearing_multiple_times(self): rendered_result = self._render_tag( tag='{% querystring "remove" "foo" %}', query_str='foo=foo&foo=bar&bar=bar') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), Que...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_remove_with_no_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"remove\" \"=foo\" %}',\n query_str='foo=foo&foo=bar&baz=baz&=foo')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.85173905", "0.8459718", "0.8431988", "0.80031836", "0.7982969", "0.7942104", "0.7938666", "0.67572826", "0.6434704", "0.63995194", "0.62388736", "0.62070173", "0.6081511", "0.6012274", "0.5948167", "0.5887117", "0.586309", "0.58515227", "0.583285", "0.579642", "0.57885164"...
0.85131925
1
Testing {% querystring "remove" %} by removing a specific keyvalue pair
def test_remove_for_specific_key_value_pairs(self): rendered_result = self._render_tag( tag='{% querystring "remove" "a=4" %}', query_str='a=1&a=2&a=3&a=4') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_remove_with_key_not_in_querystring(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"remove\" \"baz\" %}',\n query_str='foo=foo&bar=bar')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.86211497", "0.86141086", "0.8397185", "0.8291769", "0.8162063", "0.81378734", "0.79093105", "0.65594715", "0.6130791", "0.60870177", "0.5993961", "0.56719804", "0.56274676", "0.5627308", "0.56103635", "0.56000274", "0.5595212", "0.5594768", "0.5589343", "0.55880225", "0.55...
0.8716594
0
Testing {% querystring "remove" %} by removing a value with no key
def test_remove_with_no_key(self): rendered_result = self._render_tag( tag='{% querystring "remove" "=foo" %}', query_str='foo=foo&foo=bar&baz=baz&=foo') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_remove_with_no_value(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"remove\" \"foo=\" %}',\n query_str='foo=foo&foo=bar&foo=&baz=baz')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]), Qu...
[ "0.8768574", "0.8608548", "0.85124594", "0.8144512", "0.80228597", "0.8015881", "0.7719858", "0.6615589", "0.64976746", "0.64727014", "0.6124275", "0.6086564", "0.5976051", "0.58541024", "0.56783885", "0.5660003", "0.5650449", "0.5639908", "0.5620342", "0.55923486", "0.555478...
0.8808378
0
Testing {% querystring "remove" %} by removing a key with no value
def test_remove_with_no_value(self): rendered_result = self._render_tag( tag='{% querystring "remove" "foo=" %}', query_str='foo=foo&foo=bar&foo=&baz=baz') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), QueryDict('baz=ba...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_remove_with_no_key(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"remove\" \"=foo\" %}',\n query_str='foo=foo&foo=bar&baz=baz&=foo')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n ...
[ "0.8876102", "0.86809886", "0.8464555", "0.8070043", "0.8045821", "0.7824446", "0.75509536", "0.66747534", "0.66039926", "0.649495", "0.6303622", "0.6141709", "0.6124753", "0.59449774", "0.5824463", "0.5803418", "0.57830054", "0.5782017", "0.57434434", "0.57386166", "0.566912...
0.8628873
2
Testing {% querystring "remove" %} by removing multiple keys and values
def test_remove_with_multiple_removes(self): rendered_result = self._render_tag( tag='{% querystring "remove" "foo" "bar" "baz=1" %}', query_str='foo=foo&bar=bar&foo=&baz=1&qux=qux') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_res...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_remove_for_specific_key_value_pairs(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"remove\" \"a=4\" %}',\n query_str='a=1&a=2&a=3&a=4')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n...
[ "0.87358123", "0.858387", "0.846932", "0.84642714", "0.840949", "0.8165784", "0.8064136", "0.6604116", "0.6400674", "0.6005364", "0.5994758", "0.5834086", "0.5800054", "0.57987905", "0.5753103", "0.5736332", "0.5731935", "0.5726603", "0.5711395", "0.56872743", "0.5683252", ...
0.84271014
4
Testing {% querystring "remove" %} by removing multiple specific keyvalue pairs
def test_remove_with_multiple_specific_values(self): rendered_result = self._render_tag( tag='{% querystring "remove" "foo=1" "foo=2" %}', query_str='foo=1&foo=2&foo=3') self.assertTrue(rendered_result.startswith('?')) self.assertEqual(QueryDict(rendered_result[1:]), Que...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_remove_for_specific_key_value_pairs(self):\n rendered_result = self._render_tag(\n tag='{% querystring \"remove\" \"a=4\" %}',\n query_str='a=1&a=2&a=3&a=4')\n\n self.assertTrue(rendered_result.startswith('?'))\n self.assertEqual(QueryDict(rendered_result[1:]),\n...
[ "0.8700103", "0.86342984", "0.84553623", "0.83464617", "0.8266275", "0.8107934", "0.80839235", "0.6472443", "0.6022613", "0.59204817", "0.5677038", "0.5628828", "0.562024", "0.5582078", "0.5559137", "0.55267173", "0.5524983", "0.55198497", "0.55075717", "0.55037594", "0.55020...
0.8674158
1
Returned a rendered template tag using a query string. This will render a ``querystring`` template using the provided template tag, with autoescaping turned off, and with the given query string as would be provided in a URL.
def _render_tag(self, tag, query_str): t = Template('{%% load djblets_utils %%}' '{%% autoescape off %%}%s{%% endautoescape %%}' % tag) request = HttpRequest() if query_str: request.GET = QueryDict(query_str) return t.render(Contex...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def template_string(template, **kwargs):\n\n temp = Template(template)\n return temp.render(**kwargs)", "def render_str(self, template, **params):\n return render_str(template, **params)", "def querystring(parser, token):\r\n bits = token.split_contents()\r\n tag = bits.pop(0)\r\n updates...
[ "0.5957292", "0.58817935", "0.57983536", "0.5778623", "0.57117337", "0.5596593", "0.55515957", "0.55368096", "0.5526971", "0.54989415", "0.54698455", "0.54629296", "0.5458646", "0.53809476", "0.5372585", "0.536746", "0.53206545", "0.5303815", "0.5260162", "0.5234884", "0.5216...
0.78619885
0
Adds a filter to the filters list, which is publicly accessible.
def add_filter(self, name, value, comparator='equals', case_sensitive=False): self.filters.append({'name': name, 'value': value, 'comparator': comparator, 'case_sensitive': case_sensitive, 'type': 'filter'}...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def add(self, new_filter: Filter) -> None:\r\n self.filters.append(new_filter)", "def add_filter(self, filter):\n self._filters.append(filter.as_dict())", "def add_filter(self, name: str, value: any):\n self.filters[name] = value", "def add_filter(self, filter_):\n assert has_pil,...
[ "0.8060301", "0.8001708", "0.7472604", "0.7350761", "0.73406386", "0.7131974", "0.7051238", "0.703273", "0.7029074", "0.68890387", "0.68645406", "0.684799", "0.6827727", "0.6825106", "0.67781705", "0.6726825", "0.67253965", "0.67034566", "0.6626885", "0.659624", "0.6581617", ...
0.6732986
15
Return a filter key and value if exact filter exists for name.
def get_exact_filter_by_name(self, name): for entry in self.filters: if (entry['type'] == 'filter' and entry['name'] == name and entry['comparator'] == 'equals'): return entry
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_filter(name):\n try:\n return FILTERS[name.upper()]\n except:\n msg = 'Unknown model of filter {}, options are {}'\n raise ValueError(msg.format(name, list(FILTERS.keys())))", "def manifest_filter(self, name):\n if not name:\n return self._data.index\n ...
[ "0.62365323", "0.61823237", "0.61162287", "0.61162287", "0.61162287", "0.61068785", "0.56777346", "0.55669785", "0.55378616", "0.553224", "0.55188286", "0.55173266", "0.5495687", "0.54905736", "0.54889935", "0.5462702", "0.5452204", "0.53600377", "0.5333365", "0.52472883", "0...
0.7875014
0
Set a limit to indicate the list should be truncated.
def set_limit(self, limit, truncated=False): self.limit = {'limit': limit, 'type': 'limit', 'truncated': truncated}
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def set_limit(self, limit):\n self.limit = limit\n self._prune()", "def limit(self, limit):\n self._limit = limit", "def limit(self, limit):\n raise NotImplementedError(\"This should have been implemented.\")", "def limit(self, limit):\n\n self._limit = limit", "def limit...
[ "0.75690305", "0.74136245", "0.7268453", "0.7227085", "0.7227085", "0.7227085", "0.7150918", "0.7135207", "0.70293975", "0.701623", "0.6900221", "0.68999344", "0.6751625", "0.6742927", "0.6742424", "0.66420597", "0.6582306", "0.64915013", "0.6447758", "0.6411383", "0.6300985"...
0.79677534
0
Builds and returns a QueryStrategy using a feature extractor and a base_df
def build_query_strategy(sent_df, col_names): # type: (DataFrame, ColumnNames) -> QueryStrategy init_extractor = SynStateALHeuristic.build_feature_extractor(sent_df, col_names) combined_features = init_extractor.transform(sent_df, col_names) return HintSVM(TextDataset(sent_df, col_names,...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def make_query_strategy(utility_measure: Callable, selector: Callable) -> Callable:\n def query_strategy(classifier: BaseEstimator, X: modALinput) -> Tuple:\n utility = utility_measure(classifier, X)\n query_idx = selector(utility)\n return query_idx, X[query_idx]\n\n return query_strate...
[ "0.5952889", "0.5775487", "0.55588496", "0.529119", "0.5259821", "0.5202929", "0.51925635", "0.5178014", "0.5163325", "0.5140859", "0.5063282", "0.50391376", "0.5026559", "0.49773774", "0.49665532", "0.49620157", "0.49497977", "0.49446902", "0.49109635", "0.4886161", "0.48770...
0.6853019
0
Parse a list of MAVEHGVS strings into Variant objects or error messages.
def parse_variant_strings( variants: Iterable[str], targetseq: Optional[str] = None, expected_prefix: Optional[str] = None, ) -> Tuple[List[Optional[Variant]], List[Optional[str]]]: if expected_prefix is not None and expected_prefix not in list("cgmnopr"): raise ValueError("invalid expected pref...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_values(self):\n self.assertEqual(\"test\", grammar._VALUE.parseString(\"test\")[0])\n self.assertEqual(\"test(*)\", grammar._VALUE.parseString(\"test(*)\")[0])\n self.assertEqual(\"test(123)\", grammar._VALUE.parseString(\"test(123)\")[0])\n self.assertEqual(\"123\", grammar._V...
[ "0.55300385", "0.54785925", "0.5428499", "0.54011434", "0.53826064", "0.5340443", "0.5202114", "0.5156769", "0.51507455", "0.5124695", "0.5124507", "0.5102875", "0.5073347", "0.5016438", "0.49852303", "0.49693668", "0.4961004", "0.49565905", "0.495055", "0.49437034", "0.49418...
0.6759886
0
Generate a batch of binary masks for data.
def _generate_masks(self, data, batch_size): height, width = data.shape[2], data.shape[3] mask_size = (self._down_sample_size, self._down_sample_size) up_size = (height + mask_size[0], width + mask_size[1]) mask = np.random.random((batch_size, 1) + mask_size) < self._mask_probability ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def bits():\n for d in data:\n for i in [5, 4, 3, 2, 1, 0]:\n yield (d >> i) & 1", "def test_get_mask(self):\n\n spine_data_loader = SpineDataLoader(dirpath_data=self.dirpath,\n batch_size=4)\n\n for idx in range(4):\n ...
[ "0.65286094", "0.64621294", "0.63746774", "0.62852716", "0.6263692", "0.6249675", "0.6237803", "0.62282276", "0.6218766", "0.62182695", "0.6199647", "0.6149319", "0.6104692", "0.6101146", "0.60662824", "0.6061052", "0.60520583", "0.6050134", "0.6042464", "0.60416996", "0.6024...
0.7697249
0
Generates attribution maps for inputs.
def __call__(self, inputs, targets): self._verify_data(inputs, targets) height, width = inputs.shape[2], inputs.shape[3] if self._num_classes is None: self._num_classes = self.network(inputs).shape[1] # Due to the unsupported Op of slice assignment, we use numpy array here ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _mappings(self, inputs):\n return self.mapbias + tensor.dot(\n self._factorsX(inputs) * self._factorsY(inputs), self.whf_in.T)", "def _makeimap(self):\n self.map_[\"source\"] = \"nasa\"\n self.map_[\"instrument\"] = \"goes\"\n self.map_[\"physobs\"] = \"irradiance\"\n ...
[ "0.61699957", "0.60260445", "0.60112804", "0.5939966", "0.580691", "0.5781744", "0.575463", "0.57534486", "0.5744748", "0.57132673", "0.562912", "0.56262803", "0.55839884", "0.55180967", "0.5485834", "0.5476611", "0.54653805", "0.54362833", "0.54144365", "0.5332345", "0.53315...
0.0
-1
Verify the validity of the parsed inputs.
def _verify_data(inputs, targets): check_value_type('inputs', inputs, Tensor) if len(inputs.shape) != 4: raise ValueError(f'Argument inputs must be 4D Tensor, but got {len(inputs.shape)}D Tensor.') check_value_type('targets', targets, (Tensor, int, tuple, list)) if isinstance...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def check_all_user_inputs_valid(self):\n self.check_RNN_layers_valid()\n self.check_activations_valid()\n self.check_embedding_dimensions_valid()\n self.check_initialiser_valid()\n self.check_y_range_values_valid()\n self.check_return_final_seq_only_valid()", "def isInpu...
[ "0.75899994", "0.74123174", "0.7121421", "0.70710063", "0.703926", "0.7032707", "0.69892865", "0.68170273", "0.68090117", "0.67556083", "0.674802", "0.67474055", "0.67446667", "0.67359823", "0.6695144", "0.665351", "0.6626515", "0.6617128", "0.65915847", "0.6570732", "0.65447...
0.0
-1
To unify targets to be 2D numpy.ndarray.
def _unify_targets(inputs, targets): if isinstance(targets, int): return np.array([[targets] for _ in inputs]).astype(np.int) if isinstance(targets, Tensor): if not targets.shape: return np.array([[targets.asnumpy()] for _ in inputs]).astype(np.int) if...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def targets(self) -> Optional[jnp.ndarray]:\n pass", "def target_array(self):\n target_dtype = np.dtype([('targetID', np.int64),\n ('x', np.float64),\n ('y', np.float64),\n ('z', np.float64),\n ...
[ "0.6756555", "0.61209065", "0.6060304", "0.60284156", "0.6013608", "0.596247", "0.58169913", "0.58032334", "0.5798457", "0.5793701", "0.5786053", "0.57662976", "0.5763996", "0.5749886", "0.570804", "0.57043284", "0.56917435", "0.56873614", "0.56873614", "0.56604946", "0.56528...
0.6536828
1
Compiles a message that can be posted to Slack after a call has been made
def compile_slack_phone_message(phone_from, phone_to, status, location): call_from_user = _query_user(phone_from) call_from = _format_caller(call_from_user, phone_from) call_to_user = _query_user(phone_to) call_to = _format_caller(call_to_user, phone_to) location_str = list(filter(lambda x: x[0] ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def generate_message(ctx, question, answer):\n return preamble.format(channel=rules_channel(ctx).id) + question + answer", "def message(**payload):\n web_client = payload[\"web_client\"]\n\n # Getting information from the response\n data = payload[\"data\"]\n channel_id = data.get(\"channel\")\n ...
[ "0.6430378", "0.61646515", "0.61048555", "0.6087194", "0.60472983", "0.6007284", "0.6002899", "0.5978185", "0.5974844", "0.5949206", "0.59122825", "0.57997376", "0.57194704", "0.5707758", "0.56715614", "0.5656784", "0.5643831", "0.5628419", "0.56189126", "0.5614972", "0.55933...
0.6493337
0
Compile a message that can be posted to Slack after a SMS has been received
def compile_slack_sms_message(_sms_from, message): sms_from_user = _query_user(_sms_from) sms_from = _format_caller(sms_from_user, _sms_from) pretext = "Nytt SMS från %s" % (sms_from, ) fallback = "%s \n\"%s\"" % (pretext, message) return { 'attachments': [ { 'p...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def compile_slack_phone_message(phone_from, phone_to, status, location):\n\n call_from_user = _query_user(phone_from)\n call_from = _format_caller(call_from_user, phone_from)\n\n call_to_user = _query_user(phone_to)\n call_to = _format_caller(call_to_user, phone_to)\n\n location_str = list(filter(la...
[ "0.67178714", "0.6370651", "0.6323726", "0.63050747", "0.6260869", "0.62185776", "0.619205", "0.6175053", "0.6171278", "0.6103282", "0.6072457", "0.60609436", "0.60509944", "0.60441846", "0.60350686", "0.6020911", "0.6018892", "0.5999021", "0.5975836", "0.59454054", "0.593202...
0.71974516
0
Retrieves first name, last name and groups corresponding to a phone number from the database, if it exists. If multiple users have the same number, none will be queried
def _query_user(phone): if not is_valid_phone_number(phone): return None try: user = Profile.objects.get(mobile_phone=_remove_area_code(phone)).user return { 'first_name': user.first_name, 'last_name': user.last_name, 'groups': [group.name if group.n...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_user(conn ,phone_number: str) -> Tuple[str, List[str], str]:\n with conn.cursor() as cur:\n\n # Get user info from db.\n cur.execute(\"SELECT * FROM users WHERE phone_number = %s\", (phone_number,))\n usr = cur.fetchone()\n if usr is None:\n return None\n re...
[ "0.6712627", "0.6684366", "0.6540897", "0.6530467", "0.64618504", "0.64562565", "0.6306256", "0.62955487", "0.6291792", "0.62606305", "0.6074895", "0.6006919", "0.5898004", "0.57387257", "0.5648351", "0.5647391", "0.5595597", "0.5593799", "0.55526763", "0.549593", "0.5446735"...
0.75981003
0
Formats caller information into a readable string
def _format_caller(call_user, phone): # The phone number is private or not provided if not phone: return 'dolt nummer' if is_valid_phone_number(phone): # Set the phone number as a clickable link caller = '<tel:%s|%s>' % (phone, phone) else: caller = phone if call_us...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _print_caller(self):\n import traceback\n print '\\n'.join(['%s:%d %s'%(f,l,c) for f,l,m,c in traceback.extract_stack()])", "def format_call(func, args, kwargs, object_name=\"Memory\"):\r\n path, signature = format_signature(func, *args, **kwargs)\r\n msg = '%s\\n[%s] Calling %s...\\n%s' ...
[ "0.7178175", "0.6633771", "0.6473987", "0.6436923", "0.63126194", "0.6247994", "0.616689", "0.61521333", "0.6140672", "0.6119034", "0.60761124", "0.6044592", "0.60336864", "0.6026234", "0.5985547", "0.5975166", "0.594615", "0.5940507", "0.59292597", "0.59132135", "0.5875647",...
0.739633
0
Removes the area code (+46) from the given phone number and replaces it with 0
def _remove_area_code(phone): if not phone.startswith('+46'): return phone else: return '0' + phone[3:]
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def clean_phone(number):\n numberlist = re.findall(\"\\d\",number)\n new_number = \"\".join(numberlist)\n if len(new_number) == 8:\n \tnew_number = \"010\" + new_number\n\tnew_number = new_number[-11:]\n\tif new_number.startswith('1'):\n\t\tnew_number = \"+86-\" + new_number\n\telse:\n\t\tnew_number = ...
[ "0.7460669", "0.72332627", "0.69025934", "0.6782024", "0.67445296", "0.65414447", "0.6436624", "0.64058983", "0.63791174", "0.6344951", "0.6307682", "0.62712353", "0.62262017", "0.6223244", "0.61936754", "0.618883", "0.61685634", "0.61436635", "0.61339825", "0.6082766", "0.60...
0.87382734
0
Update all_dig and critic networks from experience
def train_models(self, states, actions, rewards): # Compute discounted rewards and Advantage (TD. Error) discounted_rewards = self.discount(rewards) state_values = self.critic.predict(np.array(states)) advantages = discounted_rewards - np.reshape(state_values, len(state_values)) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def update_networks(self, agent, force_hard=False):\n\n if self.update_type == \"soft\" and not force_hard:\n self._soft_update(agent.actor, agent.actor_target)\n self._soft_update(agent.critic, agent.critic_target)\n elif self.t_step % self.C == 0 or force_hard:\n se...
[ "0.5987986", "0.59774065", "0.5852359", "0.5723496", "0.5722127", "0.5659423", "0.56003433", "0.55656457", "0.5533941", "0.55081475", "0.5478906", "0.54221094", "0.5345253", "0.53410554", "0.5320656", "0.5314036", "0.5308348", "0.5307876", "0.5292902", "0.5254924", "0.5253444...
0.0
-1
Perform one epoch of training
def fit(self, inp, targ): self.model.fit(inp, targ, epochs=1, verbose=0)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def train_one_epoch(self):\n raise NotImplementedError", "def train_one_epoch(self):\n\t\tself.model.train()\n\t\ttrain_loss = 0\n\n\t\tfor batch_idx, data in enumerate(self.data_loader.train_loader):\n\t\t\tInput = data[0].float().to(self.device)\n\t\t\tOutput = data[1].float().to(self.device)\n\n\t\t\ts...
[ "0.8600666", "0.81979114", "0.789333", "0.7769742", "0.7769742", "0.7769742", "0.7769742", "0.77149934", "0.7679192", "0.76494", "0.76294416", "0.762177", "0.76099306", "0.7557603", "0.75575155", "0.7533645", "0.75261545", "0.74891675", "0.7481246", "0.7457709", "0.7414106", ...
0.0
-1
Assemble Actor network to predict probability of each action
def addHead(self, network): x = Dense(128, activation='relu')(network.output) out = Dense(self.action_dim, activation='softmax')(x) return Model(network.input, out)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __init__(self, agents_count, state_size, action_size, random_seed, buffer_size, batch_size, gamma, fc1_units, fc2_units, noise, lr_actor, lr_critic):\n\n self.agents_count = agents_count\n self.state_size = state_size\n self.action_size = action_size\n self.seed = random.seed(random...
[ "0.675407", "0.6629846", "0.65038335", "0.6496285", "0.64330333", "0.63712305", "0.6325671", "0.62510425", "0.6241472", "0.6240361", "0.6224863", "0.6187869", "0.61779225", "0.6160453", "0.614577", "0.61370224", "0.6131205", "0.61091536", "0.6085751", "0.6077727", "0.6053825"...
0.0
-1
Scalar Value Tensorflow Summary
def tfSummary(self, tag, val): return tf.Summary(value=[tf.Summary.Value(tag=tag, simple_value=val)])
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def make_scalar_summary(tag: Text, value: float) -> tf.summary.Summary:\n return tf.summary.Summary(\n value=[tf.summary.Summary.Value(tag=tag, simple_value=value)])", "def scalar_summary(self, tag, value, step):\n with self.writer.as_default():\n tf.summary.scalar(name=tag, data=value, s...
[ "0.7899163", "0.78066623", "0.78044254", "0.77813464", "0.77813464", "0.77813464", "0.7400692", "0.72948694", "0.7209247", "0.720071", "0.7129272", "0.6975382", "0.69615805", "0.69615805", "0.69285697", "0.6787779", "0.67800814", "0.67556655", "0.6746168", "0.6720047", "0.668...
0.73794854
7
als het regtype wijzigt moeten ook de naam ervan, de player, de editor en het filepad en de url aangepast worden (door het item op te halen)
def type(self, value): self._type_id = value data = RegType(value) self.regtype = data.typenaam ## self.player = data.playernaam ## self.editor = data.readernaam self.pad = os.path.join(data.padnaam, self._file) self.url = '/'.join((data.htmlpadnaam, self._...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def on_ur_choose_ok_btn_clicked(self):\n ur_type = self.ur_choose_box.currentText()\n self.ur.set_UR_ROBOT(ur_type)\n self.set_ur_info_txt(\"set UR type: \" + ur_type )", "def add_player(self):\n title = \"Bienvenue dans le gestionnaire de tournois d'échec.\\nAjout d'un joueur\"\n ...
[ "0.5468898", "0.5372713", "0.5341861", "0.5332574", "0.5239701", "0.5222167", "0.51035255", "0.50887513", "0.5044618", "0.49934238", "0.49651843", "0.49612513", "0.4917673", "0.48918155", "0.48897576", "0.48815265", "0.48449853", "0.48328492", "0.48286667", "0.48030823", "0.4...
0.65343946
0
als het songid wijzigt moet ook de titel aangepast worden (door de song te raadplegen)
def song(self, value): self._song_id = value data = Song(value) self.songtitel = data.songtitel if data.found else ""
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_title():", "def media_title(self):\n return self.coordinator.data.nowplaying[self.zone.SourceID].CurrSong.Title", "def construct_metadata(song):\n print(song) #temp", "def tv_tropes_id(title):\n pass", "def get_title_by_id(id):\n\n # your code", "def get_title_song(mess_chat_id):\...
[ "0.666082", "0.64697766", "0.6342941", "0.63207954", "0.62568146", "0.6199513", "0.618698", "0.6174337", "0.61728776", "0.61343974", "0.6076811", "0.60717183", "0.6051481", "0.6007897", "0.5971578", "0.59425765", "0.5912056", "0.58918613", "0.58918613", "0.58787864", "0.58745...
0.70082396
0
als de filenaam wijzigt moeten ook het filepad en de url aangepast worden
def file(self, value): self._file = value data = RegType(self._type_id) self.pad = os.path.join(os.path.abspath(data.padnaam), value) self.url = '/'.join((data.htmlpadnaam.split('/')[0], value))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _file_url(self, fid):\n base = self.tq.threatq_host + '/files/'\n return base + str(fid) + '/details'", "def dod():\n file = requests.get(\"https://www.bewakoof.com/design-of-the-day\")\n soup = bs4.BeautifulSoup(file.text, \"lxml\")\n # print(soup)\n\n linkList = soup.select(\"a[cl...
[ "0.5994933", "0.58208025", "0.58060807", "0.5795565", "0.57798964", "0.57758725", "0.57498974", "0.5661795", "0.5653526", "0.55924445", "0.5558127", "0.5550978", "0.55254954", "0.5517677", "0.551227", "0.5458117", "0.54356974", "0.54323816", "0.542589", "0.53811246", "0.53685...
0.5779342
5
Import the `datafile` Excel sheet to a CSV representation stored within the database that can be further processed without large filesystem operations. This also stores the original file's hash in order to skip importing unchanged data. Returns boolean (whether file was imported).
def import_datafile(db, infile): res = stat(infile) mtime = datetime.utcfromtimestamp(res.st_mtime) hash = md5hash(infile) data_file = db.model.data_file # Should maybe make sure error is not set rec = db.get(data_file, hash) # We are done if we've already imported if rec is not None:...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def insert_data_from_file(self, filename):\n self.get_cursor()\n ct = len([True for c in self.table.columns if c[1][0][:3] == \"ct-\"]) != 0\n if (([self.table.cleanup.function, self.table.delimiter,\n self.table.header_rows] == [no_cleanup, \",\", 1])\n and not self.ta...
[ "0.55844575", "0.54056644", "0.53893214", "0.53867716", "0.5307293", "0.5246726", "0.5222508", "0.5163896", "0.5116722", "0.509766", "0.509731", "0.5044055", "0.5019669", "0.4979874", "0.49676162", "0.49665996", "0.4965659", "0.49415362", "0.49070588", "0.487215", "0.48677886...
0.6357831
0
Check for errors key in data
def render(self, data, media_type=None, renderer_context=None): errors = data.get('errors', None) if errors: """ We will let the default JSONRenderer handle rendering errors. """ return super(NotificationJSONRenderer, self).render(data) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def check_errors(self, data):\n for entry in data:\n if entry.find('ERROR') != -1:\n return entry\n return False", "def HasErrors(self):\n for name in self._GetStreamNames():\n if name.startswith('error_data.'):\n return True\n\n return False", "def che...
[ "0.7919017", "0.69542503", "0.6922763", "0.6920671", "0.66628516", "0.6653711", "0.653247", "0.64985657", "0.64975333", "0.64232266", "0.6421302", "0.6382952", "0.6375637", "0.63663316", "0.63554454", "0.6325752", "0.63249743", "0.6294488", "0.6289791", "0.6270901", "0.622206...
0.0
-1
Decorator for class functions to catch errors and display a dialog box for a success or error. Checks if method is a bound method in order to properly handle parents for dialog box.
def errorCheck(success_text=None, error_text="Error!",logging=True,show_traceback=False,skip=False): def decorator(func): @functools.wraps(func) def wrapper(*args, **kwargs): if inspect.ismethod(func): self = args[0] else: self = None ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def error_handler(call_on_errors):\n assert callable(call_on_errors)\n def entangle(method):\n @functools.wraps(method)\n def wrapper(self, *args, **kwargs):\n try:\n return method(self, *args, **kwargs)\n except InputInvalidException:\n retur...
[ "0.60215515", "0.5855564", "0.58513117", "0.5837953", "0.57659924", "0.5627547", "0.5543959", "0.5534907", "0.5445549", "0.54414475", "0.5429948", "0.53747135", "0.53649336", "0.53635114", "0.53361785", "0.53337044", "0.5322622", "0.53081447", "0.52866286", "0.51993626", "0.5...
0.61921567
0
Set the hybrid weights and normalize.
def set_weights(self, weights): self._weights = weights self.normalize_weights() ########################added #self.get_weights()
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def normalize_weights(self):\n total_weight = sum(self.weights)\n self.norm_weights = self.weights / float(total_weight)", "def init_weights(self) -> None:\n nn.init.kaiming_normal_(self._U)\n nn.init.kaiming_normal_(self._W)\n nn.init.kaiming_normal_(self._V)\n\n nn.ini...
[ "0.72134966", "0.6985224", "0.69822764", "0.6975164", "0.6933884", "0.6900587", "0.6896751", "0.6887905", "0.68712306", "0.6845061", "0.68007916", "0.6796026", "0.6750444", "0.67392725", "0.6735572", "0.6713388", "0.6695148", "0.6688466", "0.6679644", "0.6679555", "0.66673744...
0.68913823
7
Normalize weight vector. Negative weights set to zero, and whole vector sums to 1.0.
def normalize_weights(self): # Set negative weights to zero # Normalize to sum to one. self.new_weight=[] for i in self._weights: if any(i < 0 for i in self._weights): self.new_weight = [0,1] elif all(i == 0 for i in self._weig...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def normalize_weights(self):\n total_weight = sum(self.weights)\n self.norm_weights = self.weights / float(total_weight)", "def normalize(self, weights):\n tot = sum(weights)\n newW = [-1] * self.numParticles\n for i in range(len(weights)):\n newW[i] = weights[i] / t...
[ "0.81079364", "0.789293", "0.7727452", "0.77199495", "0.76464504", "0.7522993", "0.7492114", "0.74709684", "0.7452147", "0.74076146", "0.73972994", "0.7382311", "0.72835106", "0.7223835", "0.72054964", "0.71810776", "0.7152016", "0.7151621", "0.71274734", "0.7124058", "0.7097...
0.8004195
1
Fitting procedure for weighted hybrid.
def fit(self, trainset): # Set the trainset instance variable self.trainset = trainset # Fit all of the components using the trainset. for comp in self._components: comp.fit(self.trainset) # Create arrays for call to LinearRegression function ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def fit(self, X, y, sample_weight, **kwargs):\n pass", "def fit(self, X, y, sample_weight=...):\n ...", "def partial_fit(self, X, y, sample_weight=...):\n ...", "def fit(self, X, y=..., sample_weight=...):\n ...", "def fit(self, X, y, sample_weight=..., **fit_params):\n ....
[ "0.66374683", "0.66348845", "0.6526766", "0.65023494", "0.64412016", "0.6400339", "0.6369283", "0.6312477", "0.6266756", "0.61228245", "0.61060977", "0.60862863", "0.60851836", "0.60767686", "0.60306305", "0.6029443", "0.59583247", "0.5951319", "0.59436023", "0.5941542", "0.5...
0.5765423
36
Predict a rating using the hybrid.
def predict(self, uid, iid, r_ui=None, clip=True, verbose=False): # Iterate over the components and build up an aggregate score using the weights. # Clip the estimate into [lower_bound, higher_bound] if clip parameter is True. See algo_base.py. # Create a new Prediction object. Retain any Predi...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def predict(self, review):\n raise NotImplementedError", "def predict_rating(self, uid, iid):\n raise NotImplementedError", "def predict(self):\n input_item_vector = self.item_embeddings(self.input_item)\n input_user_vector = self.user_embeddings(self.input_user)\n input_item...
[ "0.7144902", "0.6990728", "0.68439126", "0.65286076", "0.65247154", "0.6503644", "0.6481975", "0.64237213", "0.6412544", "0.6336438", "0.6310404", "0.62797916", "0.62339693", "0.62032557", "0.61923355", "0.6188533", "0.6164036", "0.611857", "0.6106398", "0.6069138", "0.605971...
0.5475563
84
Returns the path where the .NET2 Framework SDK is installed
def _getNETSDKPath(): try: dotNETSDK_root_key = win32api.RegOpenKeyEx(win32con.HKEY_LOCAL_MACHINE, 'SOFTWARE\\Microsoft\\Microsoft SDKs\\.NETFramework\\v2.0', 0, win32con.KEY_READ) found = False i = 0 try: try: while not found: name, obj, ntype = win32api.RegEnumValue(d...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_windows_sdk_path():\n try:\n import _winreg as winreg\n except ImportError:\n import winreg\n sub_key = r\"Software\\Microsoft\\Microsoft SDKs\\Windows\"\n with winreg.OpenKey(winreg.HKEY_LOCAL_MACHINE, sub_key) as key:\n name = \"CurrentInstallFolder\"\n return winr...
[ "0.6635156", "0.6456769", "0.62163496", "0.614617", "0.602612", "0.5883742", "0.5740016", "0.5641935", "0.5618597", "0.5578395", "0.556722", "0.55422294", "0.5538291", "0.5534905", "0.5458584", "0.54463655", "0.5429792", "0.538674", "0.53390664", "0.53347325", "0.5324747", ...
0.7449213
0
Action to build a Interop Assembly
def TLBImpGenerator( target , source , env , for_program = 0 , for_signature = 0 ): src = source[0].children() assert len(src) >= 1, "[.NET] TLBIMP: At least one source is needed...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def build():", "def build(_):", "def build(self):\n env = ConfigureEnvironment(self.deps_cpp_info, self.settings)\n\n set_path_command = \"\"\n # Download nasm as build tool. This should go to source()\n if self.options.SSE == True:\n if self.settings.os == \"Linux\":\n ...
[ "0.6061387", "0.6016387", "0.5995655", "0.5988128", "0.5966147", "0.5926007", "0.59072775", "0.5879068", "0.5803612", "0.5770824", "0.5762788", "0.5761971", "0.5617735", "0.56099427", "0.55962396", "0.55935985", "0.5550624", "0.5541919", "0.55388945", "0.5502842", "0.54805297...
0.0
-1
Add Builders and construction variables for tlbimp to an Environment.
def generate(env): if not exists(env): return 0; TLBImpBuilder = env.Builder( action = SCons.Action.Action( TLBImpGenerator , generator = 1 #, ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def SetupEnvironment(self):\n pass", "def initialize():\n environment = Environment()\n environment.setup()", "def _setup_environment_vars(self, opts):\n # Check that these directories actually exist\n assert os.path.isdir(opts.movie_advisor_home)\n\n #if not 'install-bento' in se...
[ "0.5895225", "0.558601", "0.55457306", "0.5461047", "0.54333425", "0.54333425", "0.53935885", "0.53727806", "0.53727806", "0.53727806", "0.53727806", "0.53727806", "0.53727806", "0.53605133", "0.5341112", "0.5335485", "0.53309304", "0.52924156", "0.5265965", "0.5261049", "0.5...
0.69298697
0
Causes server overload and getting html file instead of xml.
def replicate_no_data_error(): for i in range(10): content = ust.get_web_xml(2002) print(content[:100]) if not content.startswith("<?xml"): ust.save_local_xml("error", content)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def xml():\n response = make_response(render_template(\"sample.xml\"))\n response.headers[\"Content-Type\"] = \"application/xml\"\n return response", "def resp_html(s):\n legal_files = [\"/js/lib/underscore-min.js\", \"/js/app.js\", \"/favicon.ico\",\n \"/js/functions.js\",\n \"...
[ "0.65271014", "0.64710474", "0.6452593", "0.6315595", "0.6288682", "0.62656873", "0.6259988", "0.62425387", "0.6197703", "0.60214233", "0.6019729", "0.600078", "0.5949993", "0.5936287", "0.5930039", "0.5928301", "0.59137774", "0.5906879", "0.58964616", "0.5866265", "0.5851666...
0.0
-1
Read data from csv file
def read_data_from_csv(filename): df = pd.read_csv(filename) return df
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def read_csv():", "def get_data(self, csv_file):\n pass", "def read_csv_file(self):\n pass", "def _read_csv(self):\n self.function_name = '_read_csv'\n with open(os.path.join(self.task.downloads, self.csv_name)) as csv_file:\n reader = csv.reader(csv_file, dialect='exce...
[ "0.87807596", "0.8258365", "0.82231927", "0.80518544", "0.7741182", "0.77344966", "0.7645234", "0.7630955", "0.76301515", "0.7603399", "0.75338715", "0.75046605", "0.73751795", "0.73479563", "0.73407483", "0.73322785", "0.7243711", "0.7212572", "0.7189876", "0.71840364", "0.7...
0.0
-1
Compare two categorical histograms and return a overlap score based on RMSE b1 bin edges of hist 1 b2 bin edges of hist 2 h1 histogram values of hist 1 h2 histogram values of hist 2 Return rmsebased overlap score
def _compare_cat_hist(b1, b2, h1, h2): cbe = list(set(b1) | set(b2)) total = len(cbe) rmse = 0.0 if sum(h1) == 0 or sum(h2) == 0: return 0.0 for index in range(total): sh1 = 0.0 sh2 = 0.0 try: sh1 = float(h1[b1.index(cbe[index])]) except Excepti...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _compare_cont_hist(b1, b2, h1, h2):\n\n b1 = copy.deepcopy(b1)\n h1 = copy.deepcopy(h1)\n b2 = copy.deepcopy(b2)\n h2 = copy.deepcopy(h2)\n\n bd1 = [float(x) for x in b1]\n bd2 = [float(x) for x in b2]\n\n inf = float('inf')\n\n if bd1[0] == -inf:\n del bd1[0]\n del h1[0]\...
[ "0.6879068", "0.61870795", "0.6087511", "0.60521746", "0.60158235", "0.59968966", "0.59831977", "0.5939749", "0.5920067", "0.5869332", "0.57640076", "0.5714345", "0.568633", "0.56607807", "0.56391025", "0.5636864", "0.56339514", "0.56069434", "0.56041086", "0.5590879", "0.552...
0.75110346
0
Compare two continuous histograms and return a overlap score based on RMSE b1 bin edges of hist 1 b2 bin edges of hist 2 h1 histogram values of hist 1 h2 histogram values of hist 2 Return rmsebased overlap score
def _compare_cont_hist(b1, b2, h1, h2): b1 = copy.deepcopy(b1) h1 = copy.deepcopy(h1) b2 = copy.deepcopy(b2) h2 = copy.deepcopy(h2) bd1 = [float(x) for x in b1] bd2 = [float(x) for x in b2] inf = float('inf') if bd1[0] == -inf: del bd1[0] del h1[0] if bd1[-1] == i...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _compare_cat_hist(b1, b2, h1, h2):\n cbe = list(set(b1) | set(b2))\n\n total = len(cbe)\n rmse = 0.0\n\n if sum(h1) == 0 or sum(h2) == 0:\n return 0.0\n\n for index in range(total):\n sh1 = 0.0\n sh2 = 0.0\n try:\n sh1 = float(h1[b1.index(cbe[index])])\n ...
[ "0.71725196", "0.65142816", "0.62537", "0.6232226", "0.6183617", "0.61671734", "0.61571336", "0.60737", "0.60692286", "0.5956013", "0.59121007", "0.5891987", "0.58791715", "0.5832929", "0.5825022", "0.5769532", "0.5756441", "0.5727408", "0.5718197", "0.56934917", "0.5683721",...
0.72700787
0
Evaluates base attribute value of person based on features, age, gender, etc.
def attribute(self, attribute): value = 3 if self.age == "child": value -= 1 if attribute == "physique" or attribute == "phy": if self.age == "adult": value += 1 if self.gender == "male": value += 1 elif self.gender ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def feature_extraction(_data):\n # Find the digits in the given string Example - data='18-20' digits = '1820'\n digits = str(''.join(c for c in _data if c.isdigit()))\n # calculate the length of the string\n len_digits = len(digits)\n # splitting digits in to values example - digits = '1820' ages = ...
[ "0.5633302", "0.56305707", "0.5630525", "0.55070186", "0.5478344", "0.54693484", "0.54613477", "0.54573274", "0.5453816", "0.54529595", "0.5422918", "0.5317995", "0.53045845", "0.52880687", "0.5277273", "0.527475", "0.52594405", "0.52482104", "0.5226759", "0.5215583", "0.5201...
0.6449075
0
Setup hook for test functions
def set_random_seed(): np.random.seed(42)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def startTestHook(self):", "def before_test(self, func, *args, **kwargs):\n pass", "def _setup_hook(add_print=False, unit_test=False):\n # we can check many things, needed module\n # any others things before unit tests are started\n if add_print: # pragma: no cover\n print(\"Success: _s...
[ "0.8313271", "0.81225944", "0.7666217", "0.76513565", "0.7531333", "0.7241329", "0.7240884", "0.7231332", "0.71408564", "0.71379", "0.71314776", "0.71314776", "0.71314776", "0.71100473", "0.71100473", "0.71100473", "0.71100473", "0.71100473", "0.7108671", "0.7104875", "0.7104...
0.0
-1
Permute the rows of _X_ to minimize error with Y X numpy.array input matrix Y numpy.array comparison matrix numpy.array X with permuted rows
def match_rows(X, Y): n, d = X.shape n_, d_ = Y.shape assert n == n_ and d == d_ # Create a weight matrix to compare the two W = zeros((n, n)) for i, j in it.product(xrange(n), xrange(n)): # Cost of 'assigning' j to i. W[i, j] = norm(X[j] - Y[i]) matching = Munkres().comput...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def minimization(X, Y, r, L, max_iter):\r\n # Define the number of data points and dimension\r\n N = float(X.shape[0])\r\n d = X.shape[1]\r\n # Calculate the number of classes, q\r\n classes, q = [], 0\r\n for i in Y:\r\n if i not in classes:\r\n classes.append(i)\r\n ...
[ "0.6005412", "0.57721156", "0.5615864", "0.5493835", "0.5483619", "0.5443258", "0.5408048", "0.54043865", "0.5401539", "0.53839874", "0.5312149", "0.52332896", "0.5208205", "0.5194843", "0.51693314", "0.51317805", "0.5129201", "0.5125701", "0.51203144", "0.5120278", "0.509220...
0.55736876
3
Permute the columns of _X_ to minimize error with Y X numpy.array input matrix Y numpy.array comparison matrix numpy.array X with permuted columns
def match_columns(X, Y): return match_rows(X.T, Y.T).T
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def minimization(X, Y, r, L, max_iter):\r\n # Define the number of data points and dimension\r\n N = float(X.shape[0])\r\n d = X.shape[1]\r\n # Calculate the number of classes, q\r\n classes, q = [], 0\r\n for i in Y:\r\n if i not in classes:\r\n classes.append(i)\r\n ...
[ "0.57728726", "0.5513668", "0.5505098", "0.55045456", "0.54993117", "0.5482494", "0.5446407", "0.5415227", "0.5190842", "0.51712316", "0.5169295", "0.51312906", "0.5128692", "0.51256347", "0.5114543", "0.5111518", "0.5092772", "0.50871706", "0.5069459", "0.5014843", "0.500823...
0.0
-1
Permute the rows of _X_ to minimize error with Y, ignoring signs of the columns X numpy.array input matrix Y numpy.array comparison matrix numpy.array X with permuted rows
def match_rows_sign(X, Y): n, d = X.shape n_, d_ = Y.shape assert n == n_ and d == d_ # Create a weight matrix to compare the two W = zeros((n, n)) for i, j in it.product(xrange(n), xrange(n)): # Cost of 'assigning' j to i. W[i, j] = min(norm(X[j] - Y[i]), norm(X[j] + Y[i]) ) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def match_rows(X, Y):\n n, d = X.shape\n n_, d_ = Y.shape\n assert n == n_ and d == d_\n\n # Create a weight matrix to compare the two\n W = zeros((n, n))\n for i, j in it.product(xrange(n), xrange(n)):\n # Cost of 'assigning' j to i.\n W[i, j] = norm(X[j] - Y[i])\n\n matching = ...
[ "0.5769132", "0.5621818", "0.5586707", "0.54072565", "0.5397549", "0.5358403", "0.5319366", "0.5261717", "0.5255339", "0.52463937", "0.5175605", "0.5172019", "0.516699", "0.5155215", "0.51417834", "0.5139273", "0.5113438", "0.5082017", "0.5073561", "0.5043652", "0.502834", ...
0.6251233
0
Permute the columns of _X_ to minimize error with Y X numpy.array input matrix Y numpy.array comparison matrix numpy.array X with permuted columns
def match_columns_sign(X, Y): return match_rows_sign(X.T, Y.T).T
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def minimization(X, Y, r, L, max_iter):\r\n # Define the number of data points and dimension\r\n N = float(X.shape[0])\r\n d = X.shape[1]\r\n # Calculate the number of classes, q\r\n classes, q = [], 0\r\n for i in Y:\r\n if i not in classes:\r\n classes.append(i)\r\n ...
[ "0.5772518", "0.5513114", "0.55048376", "0.5504506", "0.5497998", "0.5483119", "0.5446532", "0.54128504", "0.51880956", "0.5170174", "0.5169824", "0.51304454", "0.51295435", "0.51251924", "0.511645", "0.5111009", "0.50921506", "0.5091046", "0.5069536", "0.50172967", "0.500797...
0.0
-1
Symmeterize X by taking the average over index swaps
def symmetrize(X): def to_einstr(ls): return "".join([chr(ord('a') + l) for l in ls]) D, R = X.shape[0], len(X.shape) X_ = np.zeros(X.shape) for new_order in it.permutations(xrange(R)): # Permute axes with einsum X_ += np.einsum('%s->%s'%(to_einstr(range(R)), to_einstr(new_orde...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def make_htc_proxy_X(X: np.ndarray):\n return np.array([[np.sum(x[:,2]) / np.sum(x[:,0] - x[:,1])] for x in X])", "def spmv (n, A, x):\n y = dense_vector (n)\n for (i, A_i) in A.items ():\n s = 0\n for (j, a_ij) in A_i.items ():\n s += a_ij * x[j]\n y[i] = s\n return y...
[ "0.5607386", "0.553855", "0.5428017", "0.54226446", "0.5391692", "0.52491593", "0.5243481", "0.52385086", "0.52290297", "0.52167934", "0.51987404", "0.518546", "0.5184872", "0.5184176", "0.51819617", "0.51787955", "0.51733136", "0.51629096", "0.51586044", "0.5153181", "0.5135...
0.54973876
2
Best k rank approximation of X
def approxk( X, k ): U, D, Vt = svdk( X, k ) return U.dot( diag( D ) ).dot( Vt )
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_k_rank_approximate(corpus):\n return", "def k_rank_approximate(doc_matrix, k):\n return []", "def find_best_k(X_train, y_train, X_test, y_test, min_k=1, max_k=25):\n best_k = 0\n best_score = 0.0\n for k in range(min_k, max_k+1, 2):\n knn = KNeighborsClassifier(n_neighbors=k)\n ...
[ "0.7249297", "0.6936327", "0.6904994", "0.6861161", "0.6829809", "0.6763813", "0.6700686", "0.6638918", "0.66321963", "0.6611971", "0.66007423", "0.6570125", "0.65181285", "0.6470639", "0.6424994", "0.6337252", "0.63190573", "0.6308365", "0.6288242", "0.62855273", "0.6273842"...
0.0
-1
Return the matrix W that whitens A, i.e. W^T A W = I. Assumes A is krank
def get_whitener( A, k ): U, D, _ = svdk(A, k) Ds = sqrt(D) Di = 1./Ds return U.dot(diag(Di)), U.dot(diag(Ds))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_whitener( A, k ):\n\n assert( mrank( A ) == k )\n # Verify PSD\n e = eigvals( A )[:k].real\n if not (e >= 0).all():\n print \"Warning: Not PSD\"\n print e\n\n # If A is PSD\n U, _, _ = svdk( A, k )\n A2 = cholesky( U.T.dot( A ).dot( U ) )\n W, Wt = U.dot( pinv( A2 ) ), U.d...
[ "0.6679822", "0.6614693", "0.60433185", "0.59283435", "0.58992916", "0.58858484", "0.58538705", "0.5814695", "0.58103746", "0.57965314", "0.57956904", "0.5754126", "0.569407", "0.5680597", "0.56494105", "0.56493133", "0.56258327", "0.56196856", "0.559079", "0.55781454", "0.55...
0.6306559
2
Mark this gateway as activated once all hosts are present
def activate(self): super().activate() self._change_lease_time(self.runner.config.get("dhcp_lease_time")) self._scan_finalize()
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def activate(self):\n if not self.is_active:\n self.is_active = True\n self.activated_at = datetime.datetime.utcnow()\n import messaging # avoid circular import\n messaging.send_activated_emails(self)\n self.save()", "def will_activate(self):\n pass", "...
[ "0.649022", "0.62420017", "0.62247014", "0.62229514", "0.61747885", "0.60823977", "0.5991591", "0.59835577", "0.59835577", "0.5937712", "0.59232575", "0.5872324", "0.5801475", "0.57522196", "0.56951773", "0.5690092", "0.56435835", "0.56178784", "0.5608467", "0.5608442", "0.56...
0.6164519
5
Generic function for executing scripts on gateway
def execute_script(self, action, *args): self.host.cmd(('./%s' + len(args) * ' %s') % (action, *args))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def execute():", "def run_script(self):\n pass", "def _remoteScript(self, source_script):", "def execute(self, task, script, **kwargs):\n locals().update(kwargs)\n exec(script)", "def script(self):", "def run_script(self, params, config_no):\n raise NotImplementedError()", "...
[ "0.70167863", "0.6843984", "0.66409093", "0.6636534", "0.66298825", "0.6618063", "0.65771", "0.6514994", "0.6483146", "0.6415274", "0.6374572", "0.6347435", "0.6345401", "0.62912744", "0.62529486", "0.62529486", "0.62529486", "0.62529486", "0.6233822", "0.6232493", "0.6229819...
0.6316474
13
Requests a new ip for the device
def request_new_ip(self, mac): self.execute_script('new_ip', mac)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def purchase_ip(self, debug=False):\n json_scheme = self.gen_def_json_scheme('SetPurchaseIpAddress')\n json_obj = self.call_method_post(method='SetPurchaseIpAddress', json_scheme=json_scheme, debug=debug)\n try:\n ip = Ip()\n ip.ip_addr = json_obj['Value']['Value']\n ...
[ "0.64938843", "0.64519453", "0.6445656", "0.64216715", "0.62110883", "0.6202511", "0.61524814", "0.60697424", "0.6024123", "0.59993225", "0.5996937", "0.59684306", "0.5909357", "0.58596236", "0.583989", "0.58333933", "0.58167917", "0.58154434", "0.5806481", "0.579902", "0.572...
0.7935096
0
Change dhcp response time for device mac
def change_dhcp_response_time(self, mac, time): self.execute_script('change_dhcp_response_time', mac, time)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def stop_dhcp_response(self, mac):\n self.change_dhcp_response_time(mac, -1)", "def dhcp(self, dhcp):\n\n self._dhcp = dhcp", "def dhcp_utilization(self, dhcp_utilization):\n\n self._dhcp_utilization = dhcp_utilization", "def dhcp_callback(self, state, target_mac=None, target_ip=None, ex...
[ "0.68185097", "0.61317915", "0.60052145", "0.5939863", "0.5885967", "0.5811359", "0.58035165", "0.57373697", "0.5646604", "0.5625567", "0.55827594", "0.55206007", "0.5491405", "0.5381941", "0.53621304", "0.5349844", "0.53050417", "0.52728444", "0.52219707", "0.5220993", "0.52...
0.82894284
0
Stops DHCP response for the device
def stop_dhcp_response(self, mac): self.change_dhcp_response_time(mac, -1)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def stop(self):\n\n if not self._dhcp_client_ctrl is None:\n self._dhcp_client_ctrl.exit()\n if not self._slave_dhcp_process is None:\n self._slave_dhcp_process.kill()\n logger.debug('DHCP client stopped on ' + self._ifname)\n \n self._new_lease_event.cl...
[ "0.7163312", "0.631082", "0.6104016", "0.60529596", "0.60524225", "0.6041364", "0.6019232", "0.5972226", "0.592156", "0.5913132", "0.590311", "0.5891132", "0.589016", "0.58840525", "0.58741194", "0.58572394", "0.582723", "0.5790126", "0.57324284", "0.5713386", "0.5644416", ...
0.8130744
0
Change dhcp range for devices
def change_dhcp_range(self, start, end, prefix_length): self.execute_script('change_dhcp_range', start, end, prefix_length)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_dhcp_range(options, index):\n second_octet = 160 + index\n return \"192.%s.1.2-192.%s.255.254\" % (second_octet, second_octet)", "def dhcp(self, dhcp):\n\n self._dhcp = dhcp", "def configureDHCP():\n dhcpStart = config.get(\"hotspot\", \"dhcpstart\")\n dhcpEnd = config.get(\"hotspot\...
[ "0.6616823", "0.6526532", "0.64205295", "0.61709964", "0.5981741", "0.581122", "0.57029295", "0.56834716", "0.565777", "0.5584433", "0.54814816", "0.5453117", "0.5443394", "0.5424341", "0.5423709", "0.535401", "0.53214043", "0.5315264", "0.5313163", "0.53032523", "0.5302614",...
0.82061505
0
Converts a single track record into m3u format. Need the normalization to fix the way Apple handles e.g. combining diacriticals.
def to_m3u_track(record: Dict[str, str]) -> str: location = normalize(unquote(record.get("Location"))) # m3u duration in seconds, not ms duration = int(record.get("Total Time")) // 1000 name = normalize(unquote(record.get("Name"))) artist = normalize(unquote( record.get("Artist") or ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def to_m3u_list(list_name: str, tracks: List[str]) -> str:\n\n return M3U_TEMPLATE.format(name=list_name, tracks=\"\\n\".join(tracks))", "def encodeMP3(self, wavf: str, dstf: str, cover: str, meta: TrackMeta) -> None:\n FNULL = open(os.devnull, 'w')\n subprocess.call(['lame', '-V2', wavf, dstf],...
[ "0.62157995", "0.5663488", "0.5658642", "0.5651403", "0.56042594", "0.55793566", "0.5382664", "0.53761894", "0.5295393", "0.52780515", "0.52740747", "0.517685", "0.5173452", "0.51395833", "0.50221336", "0.5019546", "0.501397", "0.50028616", "0.49967933", "0.49772906", "0.4931...
0.75607246
0
Converts a list of serialized m3u tracks into a playlist.
def to_m3u_list(list_name: str, tracks: List[str]) -> str: return M3U_TEMPLATE.format(name=list_name, tracks="\n".join(tracks))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def playlist(self):\n def iconv(s):\n encoding = self.options[\"id3_encoding\"]\n try:\n if encoding:\n return s.encode('latin1').decode(encoding).encode('utf-8')\n else:\n return s.encode('latin1')\n except...
[ "0.6630314", "0.63010496", "0.6148589", "0.59600264", "0.59457934", "0.5888346", "0.5799984", "0.5784354", "0.5780846", "0.5779549", "0.577538", "0.5754062", "0.5745098", "0.57270885", "0.56991816", "0.5638412", "0.5616215", "0.56141627", "0.55851924", "0.55660707", "0.554203...
0.7024213
0
get the value of property _Chassis
def Chassis(self): return self._Chassis
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def getCharger(self):\r\n if hasattr(self, \"charger\"):\r\n return self.charger\r\n else:\r\n return None", "def value(self):\r\n return self.__cargo", "def test_get_chassis(self):\n resp = self.chassis_client.get_chassis(self.chassis.uuid)\n self.a...
[ "0.66945535", "0.62844557", "0.62731075", "0.6223003", "0.6214095", "0.59638584", "0.5955606", "0.59384584", "0.5914062", "0.5908595", "0.59032404", "0.5835006", "0.5728211", "0.5713444", "0.5685319", "0.5671768", "0.55380136", "0.55307096", "0.5527974", "0.5512052", "0.55114...
0.7827342
0
get the value of property _Option
def Option(self): return self._Option
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_option(self, option):\n\t\treturn self.options[option]", "def get(self, option):\n return get(self.name, option)", "def get_option_value(self, key):\n\n # Check the key.\n self.__assert_option(key)\n\n # Get and return the value.\n return self.__opt[key]", "def Op...
[ "0.8096682", "0.77774155", "0.7556324", "0.75427765", "0.7527482", "0.75237143", "0.7513979", "0.7381633", "0.73176533", "0.7317182", "0.7203971", "0.7119419", "0.7093766", "0.708832", "0.70553225", "0.70469296", "0.7028326", "0.70008403", "0.6975328", "0.6930424", "0.6911173...
0.7837177
1
get the value of property _InfoList
def InfoList(self): return self._InfoList
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def getList(self):\n\treturn self.list", "def get_property_list(self,filtr):\n\n\n return self.dp.get_property_list(filtr)", "def getList(self):\n return self.list_", "def getList(self):\n return self.list", "def getList(self):\n return self.value if not self.isInteger() else No...
[ "0.7216128", "0.7161023", "0.6889498", "0.6854613", "0.6703801", "0.67022556", "0.67022556", "0.6509974", "0.6453372", "0.6416943", "0.6414781", "0.6305439", "0.63044363", "0.62954116", "0.62520516", "0.6248864", "0.6243234", "0.6201517", "0.6139812", "0.6132445", "0.6105383"...
0.6851435
4
Draws a Run the test button on the page for a user.
def Button(request): params = { 'mimetype': 'text/javascript', 'fn': request.GET.get('fn', '_bRunTest'), 'btn_text': request.GET.get('btn_text', 'Run the test'), 'cb_text': request.GET.get('cb_text', 'and send my results to Browserscope (anonymously)'), } return util.Render(request, 'user_...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def make_run_button(self):\n\n run_button = Button(\n self.master, text=\"Run\", command=self.run_simulator)\n run_button.grid(row=6, column=1)\n\n return run_button", "def click_button(self):\n self.q(css='div#fixture button').first.click()", "def trigger_output(self):\n...
[ "0.63031554", "0.62987185", "0.61791044", "0.61791044", "0.6075906", "0.6057968", "0.6029089", "0.5979514", "0.58742535", "0.58558655", "0.5851815", "0.57412475", "0.57368124", "0.5710455", "0.5707332", "0.5673687", "0.5670039", "0.56390357", "0.56390077", "0.56256866", "0.56...
0.73393345
0
The User Test results table.
def Table(request, key): test = models.user_test.Test.get_mem(key) if not test: msg = 'No test was found with test_key %s.' % key return http.HttpResponseServerError(msg) params = { 'hide_nav': True, 'hide_footer': True, 'test': test, } return util.GetResults(request, 'user_test_table.ht...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def tabulate(self):\n for test_name, test in self.test_types.items():\n for ivs_name, ivs in self.ivs.items():\n if self.verbose:\n print(\"{0}: {1}\".format(ivs_name, test_name))\n tree = test(ivs)\n if not tree:\n ...
[ "0.6465429", "0.6332018", "0.61088157", "0.6088008", "0.60849124", "0.6081138", "0.6075006", "0.60503936", "0.5999182", "0.5970002", "0.59527034", "0.58797216", "0.5863282", "0.5861191", "0.5843431", "0.5839", "0.5837997", "0.58336246", "0.5829603", "0.5828688", "0.58000195",...
0.728486
0
Shows a table of user tests.
def Index(request): output = request.GET.get('o') if output == 'gviz_table_data': return http.HttpResponse(FormatUserTestsAsGviz(request)) else: params = { 'height': '400px', 'width': 'auto', 'page_size': 20 } return util.Render(request, 'user_tests_index.html', params)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def Table(request, key):\n test = models.user_test.Test.get_mem(key)\n if not test:\n msg = 'No test was found with test_key %s.' % key\n return http.HttpResponseServerError(msg)\n\n params = {\n 'hide_nav': True,\n 'hide_footer': True,\n 'test': test,\n }\n\n return util.GetResults(request, 'u...
[ "0.80550736", "0.68134075", "0.65610844", "0.653583", "0.65127206", "0.64837927", "0.6434516", "0.6418729", "0.63465595", "0.6317583", "0.6233405", "0.62315404", "0.6187078", "0.61481196", "0.6106531", "0.60995424", "0.6074437", "0.6015064", "0.6000204", "0.59634566", "0.5962...
0.7154061
1
Sends an API request to run one's test page on WebPagetest.org.
def WebPagetest(request, key): test = models.user_test.Test.get_mem(key) if not test: msg = 'No test was found with test_key %s.' % key return http.HttpResponseServerError(msg) current_user = users.get_current_user() if (test.user.key().name() != current_user.user_id() and not users.is_current_...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_doGet(self) -> None:\n\n status_code = apicall.doGet(URL, self._browserheader)\n print(\"in do get:\", status_code)\n assert status_code == API_SUCCESS", "def test(base_url='http://localhost:8000/'):\n with env.cd(settings.PROJECT_PATH):\n # env.run('python rnacentral/apiv...
[ "0.6589668", "0.6506418", "0.63798094", "0.62275237", "0.61125684", "0.61125684", "0.60818094", "0.60572374", "0.5959362", "0.5956013", "0.5955665", "0.59321207", "0.59309137", "0.5920353", "0.5911625", "0.59028727", "0.5895274", "0.589447", "0.5891544", "0.58683366", "0.5864...
0.6819468
0
Power iteration method to find the aproximation to the greatest (in absulute value) eigenvalue of A. Done in a hurry, expect bugs JGC.
def Power(A:np.array ,x=None,N=25,tol=1e-10) -> (float,np.array): if x is None: x = np.random.rand(A.shape[0]) for i in range(N): x_new = np.dot(A,x) x_new_norm = np.linalg.norm(x_new ,np.inf) if np.allclose(x, (x_new/x_new_norm), rtol=tol): print('Itr:', i) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def power_method(A, x, maxit):\n\teigenvalue=0.0\n\ttolerence = 1 * pow(10,-9)\n\tfor i in xrange(maxit):\n\t\toldx = x \n\t\ty = A*x\n\t\tx=y/np.linalg.norm(y)\n\t\toldeigenvalue = eigenvalue \n\t\teigenvalue = np.linalg.norm(A*x) \n\t\tif abs(eigenvalue - oldeigenvalue) < tolerence:\n\t\t\tbreak\n\tif i==maxit:\...
[ "0.7566861", "0.73448116", "0.7048662", "0.6752419", "0.65466344", "0.6451325", "0.6369588", "0.6356069", "0.63117385", "0.62962544", "0.6187652", "0.6078297", "0.60739726", "0.607197", "0.6038648", "0.6006909", "0.5948508", "0.59455055", "0.59452844", "0.59440184", "0.593328...
0.6182314
11
Inverse Power iteration method to find the aproximation to the greatest (in absulute value) eigenvalue of A. Done in a hurry, expect bugs JGC.
def InversePower(A,x=None,N=25,tol=1e-10) -> (float,np.array): if x is None: x = np.random.rand(A.shape[0]) micra = np.dot(x, np.dot(A,x)) / np.dot(x,x) for i in range(N): try: x_new = np.linalg.solve((A - micra * np.eye(A.shape[0])),x) except np.li...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def power_method(A, x, maxit):\n\teigenvalue=0.0\n\ttolerence = 1 * pow(10,-9)\n\tfor i in xrange(maxit):\n\t\toldx = x \n\t\ty = A*x\n\t\tx=y/np.linalg.norm(y)\n\t\toldeigenvalue = eigenvalue \n\t\teigenvalue = np.linalg.norm(A*x) \n\t\tif abs(eigenvalue - oldeigenvalue) < tolerence:\n\t\t\tbreak\n\tif i==maxit:\...
[ "0.72239393", "0.70839024", "0.7030267", "0.69525474", "0.6750316", "0.66513926", "0.6485864", "0.645784", "0.64403886", "0.63530344", "0.63164145", "0.6248044", "0.62433696", "0.6153581", "0.61078584", "0.6105183", "0.60567576", "0.6048559", "0.6033499", "0.6026995", "0.6013...
0.69095784
4
Return download file async task ID.
def download_report(request): params = request.query_params provider_uuid = params.get("provider_uuid") bill_date = params.get("bill_date") async_download_result = check_report_updates.delay(provider_uuid=provider_uuid, bill_date=bill_date) return Response({"Download Request Task ID": str(async_down...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def download_id(self):\n return self._download_id", "def filename(self):\n return TaskInfo._filename(self.id)", "def task_id(self):\n return self._task_id", "def task_id(self):\n return self._task_id", "def task_id(self):\n return self._task_id", "def task_id(self):\n ...
[ "0.70306355", "0.62711185", "0.619602", "0.619602", "0.619602", "0.619602", "0.6151306", "0.61128384", "0.6065525", "0.60156596", "0.59504765", "0.5933045", "0.59033257", "0.58997345", "0.5861137", "0.58508414", "0.5824823", "0.58083946", "0.57929415", "0.5732542", "0.5555419...
0.51462674
51
return a list of usernames
def getUserIds(self): raise BorkedGetUserIds
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def fetch_usernames(self, users):\n user_list = []\n for user in users:\n user_list.append(user.username)\n return user_list", "def get_usernames(self) -> list:\n db_list = list(self.cursor.execute('SELECT * FROM sqlite_master'))\n users = [db_list[i][1] for i in ran...
[ "0.8254673", "0.8202673", "0.81975025", "0.81699914", "0.80344695", "0.7973188", "0.7936999", "0.7750456", "0.7707939", "0.7660891", "0.75287163", "0.7509422", "0.74992317", "0.7474466", "0.7430047", "0.7430047", "0.7404997", "0.7355548", "0.7321912", "0.72478724", "0.7229752...
0.0
-1
return a dictionary for the specified user
def getUserDetails(self,name): raise BorkedGetUserDetails
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_user(self, username):\n return {}", "def get_user_info_by_id(self, user_id: int) -> dict:", "def get_user_info_by_name(self, username: str) -> dict:", "def user2dict(self):\n d = {}\n d['username'] = self.username\n d['level'] = self.level\n d['name'] = self.name\n ...
[ "0.77568305", "0.768912", "0.7298596", "0.717455", "0.71563697", "0.70042753", "0.69673264", "0.69673264", "0.6947405", "0.69409996", "0.6886997", "0.687795", "0.686486", "0.68484455", "0.682049", "0.68039507", "0.67354625", "0.6730009", "0.67261606", "0.6707358", "0.6679629"...
0.0
-1
delete the specified user
def deleteUser(self,name): raise BorkedDeleteUser
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def delete_user():", "def delete_user():\n #TODO user delete\n pass", "def delete_user(id):\n pass", "def delete_user(self, user):\n self.delete(user)", "def delete_user():\r\n raise NotImplementedError()", "def delete_user(self, user):\n self.execute(TABELLE['id_users'][\"delete...
[ "0.9422259", "0.91196513", "0.87678933", "0.8663568", "0.8519105", "0.8378481", "0.8353873", "0.8314292", "0.83081704", "0.82458866", "0.8182655", "0.8174193", "0.8103117", "0.8050191", "0.8003963", "0.79871947", "0.79807925", "0.79807925", "0.79807925", "0.7939734", "0.79331...
0.8174213
11
Sync all foreign models in instance to data using their class object and manager name. More info
def _sync_foreign_model(self, instance, data, cls, manager_name): # Remove all foreign instances that are not featured in data data_ids = [item["id"] for item in data if "id" in item] for existing_foreigns in getattr(instance, manager_name).all(): if existing_foreigns.id not in data_...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _save_reverse_relations(self, related_objects, instance):\n for field, related_field, data, kwargs in related_objects:\n # inject the PK from the instance\n if isinstance(field, serializers.ListSerializer):\n for obj in data:\n obj[related_field.na...
[ "0.59193707", "0.5779825", "0.5765561", "0.57606983", "0.5748647", "0.5728996", "0.57228345", "0.5692319", "0.5648922", "0.56440777", "0.5624399", "0.5589713", "0.55817914", "0.5570557", "0.5563866", "0.55505055", "0.5546178", "0.55017614", "0.54824173", "0.5429872", "0.54124...
0.7405185
0
Call the hello function on the server and return the result.
def hello(self): return self.get('hello')
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def hello():\n retrun", "async def hello(self) -> httpx.Response:\n return await self._client.get(\"/hello\")", "def clientHello():\r\n status = \"100 Hello\"\r\n return status", "def clientHello():\r\n status = \"100 Hello message\"\r\n return status", "def hello():\r\n return 'He...
[ "0.74273133", "0.7312451", "0.7237536", "0.72003543", "0.71573603", "0.7133468", "0.7133468", "0.7133468", "0.7123103", "0.70987844", "0.7085487", "0.7065409", "0.7045549", "0.7045549", "0.70365185", "0.7003258", "0.7003258", "0.68275064", "0.68275064", "0.66725296", "0.66226...
0.6134317
44
Get user's own data
def get_user(self): return self.get('users/self')
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_user_data(self):\n return self.user_data", "def get_user_data(self):\n return self.user_data", "def GetUserData(self):\r\n\r\n return self.user_data", "def get_users_info(): \n \n data = user_obj.get_users_info()\n return data", "def user_data(self, token, *args, **kwa...
[ "0.7346554", "0.7346554", "0.702832", "0.70269656", "0.70178115", "0.6979298", "0.69016683", "0.6887115", "0.6877017", "0.68511564", "0.68483585", "0.6790905", "0.6789756", "0.676934", "0.676698", "0.67207366", "0.6701845", "0.66807324", "0.66678333", "0.6646441", "0.66367084...
0.67037046
16
Add a new user.
def add_user(self, userdict): return self.post('users', userdict)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def add_user(self, user: User):\n raise NotImplementedError", "def add_new_user(self, user):\n # print(\"Saving new user\")\n self.execute(TABELLE['id_users']['insert']['complete_user'],\n (user['id'], False, False, True, False, False))\n\n self.execute(TABELLE['us...
[ "0.8224321", "0.8097242", "0.80321217", "0.8012731", "0.7990591", "0.79581565", "0.79572564", "0.79328835", "0.78550637", "0.7787805", "0.77765447", "0.76982147", "0.7690718", "0.7666681", "0.7645647", "0.76305556", "0.76225454", "0.76181775", "0.76167804", "0.7580023", "0.75...
0.79476553
7
Verify user's email address.
def verify_user(self, tokendict): return self.post('verify', tokendict)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def validate_email(self, data):\n user = account_models.User.objects.filter(username__iexact=data, is_active=True)\n if user:\n return data\n raise serializers.ValidationError(\"Email address not verified for any user account\")", "def verify_email(uid, token):\n return True", ...
[ "0.734524", "0.7308401", "0.72833353", "0.72769487", "0.7266312", "0.7222744", "0.719752", "0.7192644", "0.7154709", "0.71305126", "0.7122744", "0.7122744", "0.711976", "0.7107592", "0.70995986", "0.70905817", "0.7075895", "0.7061155", "0.7052013", "0.70404774", "0.70316905",...
0.0
-1
(Re)send a verification email containing the mail token.
def resend_email(self, userdict): return self.post('resend', userdict)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __send_verification(self, email):\r\n user = User.getUser(email.lower())\r\n if user is None or user.verified:\r\n self.set_error(constants.STATUS_BAD_REQUEST, message=None, url=\"/\")\r\n return\r\n user.verificationCode = b64encode(CryptoUtil.get_verify_code(), \"*$...
[ "0.7737019", "0.7492372", "0.7328165", "0.7180312", "0.7097058", "0.70648235", "0.699281", "0.6959131", "0.6955948", "0.6953977", "0.689043", "0.6873777", "0.6870506", "0.68568414", "0.68528575", "0.68398434", "0.68350035", "0.6789642", "0.67644554", "0.67504543", "0.66960806...
0.0
-1
User login procedure. Gets a token issued by a service. The client has to pass the token received to the set_token() operation in order to be able to use operations which require a token (e.g. get_user() ).
def login(self, username, password): cred = {"email": username, "passwd": password} return self.post("login", cred)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def login_user(self):\n response = self.client.post(self.login_url, self.login_data, format='json')\n return response.data['token']", "def login(self, *, app, user):\n method = 'POST'\n path = self.path('login')\n app = extract_id(app)\n user = extract_name(user)\n ...
[ "0.770769", "0.735973", "0.7307667", "0.72087055", "0.7181291", "0.71156687", "0.71017563", "0.70751464", "0.7044662", "0.7014622", "0.6947498", "0.69207746", "0.6918182", "0.69174045", "0.69112045", "0.68798155", "0.6841188", "0.6787755", "0.67774296", "0.6752684", "0.67461"...
0.0
-1
Request user password change.
def change_password(self, password, newpassword): cred = {"newpasswd": newpassword, "passwd": password} return self.put("passwd", cred)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def change_password(change_account):\n change_data(change_account, changed_data='password')", "def view_update_user(self, user, new_pw, old_pw):\r\n user.realm._checker.passwd(user.userID, new_pw, old_pw)", "def request_password_reset():", "def requestPasswordChange(self):\n dialog = None\n\...
[ "0.76112264", "0.7605729", "0.7554295", "0.75465333", "0.7543091", "0.74020135", "0.7393788", "0.7372257", "0.7335482", "0.73278356", "0.7280589", "0.72770023", "0.72231317", "0.721314", "0.7179302", "0.71641296", "0.71582377", "0.71451783", "0.7142202", "0.71153325", "0.7103...
0.70136094
28
main function for wiz phylogeny
def run(args): logger = logging.getLogger(__name__) try: # create output dir util.create_dir(args.output) # download assemblies args.clade = "nostocales" download_output_dir = f"{args.output}/assemblies" util.create_dir(download_output_dir) a = download....
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def main():\n \n # 1. Learn a decision tree from the data in training.txt\n print \"--Building trees--\"\n train_examples = read_file('training.txt')\n print(train_examples)\n attrs = range(len(train_examples[0])-1)\n rand_tree = decision_tree_learning(train_examples, attrs, use_gain=False)\n ...
[ "0.6261205", "0.6163411", "0.5972955", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", "0.59479934", ...
0.5846969
26
Compute the accuracy of given data and labels
def compute_acc(model, data, labels, num_samples=None, batch_size=100): N = data.shape[0] if num_samples is not None and N > num_samples: indices = np.random.choice(N, num_samples) N = num_samples data = data[indices] labels = labels[indices] num_batches = N // batch_size if N % batch_size != 0: num_batc...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def accuracy(preds, labels):\n correct = preds == labels\n return correct.sum().float() / correct.shape[0]", "def accuracy(labels, preds):\n\tassert labels.shape[0]==preds.shape[0]\n\treturn np.sum(preds==labels)/float(labels.shape[0])", "def accuracy(predictions, labels):\n return (100.0 * np...
[ "0.8793522", "0.85333335", "0.85008204", "0.8346891", "0.82966006", "0.82930976", "0.8210196", "0.8203935", "0.815255", "0.81517625", "0.81436825", "0.81409377", "0.813725", "0.813725", "0.813651", "0.81324446", "0.80092573", "0.7987069", "0.7965384", "0.79520595", "0.7923528...
0.0
-1