query
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3.4k
document
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87.4k
metadata
dict
negatives
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negative_scores
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document_score
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3
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document_rank
stringclasses
102 values
Similar to forward but only return features.
def extract_features(self, *args, **kwargs): return self(*args, **kwargs)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def forward(self, x):\n x = self.features(x)\n return x", "def forward(self, x):\n out = self.features(x)\n out = out.view(out.size(0), -1)\n out = self.classifier(out)\n return out", "def feature_forward(self, x):\n raise NotImplementedError", "def forward(se...
[ "0.7967969", "0.760584", "0.75046647", "0.7499447", "0.7416259", "0.7416259", "0.72424513", "0.72424513", "0.7232947", "0.70870334", "0.70865875", "0.707531", "0.7027191", "0.70249134", "0.7017389", "0.6983659", "0.69587934", "0.69422793", "0.69087887", "0.6823038", "0.680118...
0.649879
46
Maximum length supported by the model.
def max_positions(self): return None
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _max_length(self):\n return self.__max_length", "def max_length(self):\n\t\treturn self._max_length", "def max_length(self) -> int | None:\n return self._underlying.max_length", "def _model_string_maxlen():\n # hardcoded for convenience. Could be dynamically set in future.\n # the cur...
[ "0.8716974", "0.86320573", "0.8242798", "0.79171175", "0.7627716", "0.74776286", "0.7413979", "0.73472226", "0.7304889", "0.729216", "0.72890776", "0.72890776", "0.72890776", "0.72890776", "0.7246226", "0.723191", "0.72292733", "0.7178601", "0.7171193", "0.7171193", "0.717119...
0.0
-1
Copies parameters and buffers from state_dict into this module and its descendants.
def load_state_dict( self, state_dict, strict=True, model_cfg: Optional[DictConfig] = None, args: Optional[Namespace] = None, ): if model_cfg is None and args is not None: logger.warn("using 'args' is deprecated, please update your code to use dataclass c...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def load_module_state_dict(model, state_dict):\n import warnings\n from torch.nn import Parameter\n\n own_state = model.state_dict()\n for name, param in state_dict.items():\n if name not in own_state:\n warnings.warn('Skipping unexpected key \"{}\" in state_dict'.format(name))\n continue\n if ...
[ "0.66583735", "0.664117", "0.65838176", "0.6577891", "0.6577891", "0.6577891", "0.6568233", "0.65149814", "0.6509628", "0.6479878", "0.6430346", "0.63857406", "0.63502574", "0.6313521", "0.6310964", "0.6254895", "0.6218633", "0.6208851", "0.6208851", "0.61912245", "0.6176404"...
0.0
-1
Upgrade old state dicts to work with newer code.
def upgrade_state_dict(self, state_dict): self.upgrade_state_dict_named(state_dict, "")
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def upgrade_state_dict(self, state_dict):\n return state_dict", "def upgrade_state_dict(self, state_dict):\n return state_dict", "def upgrade_state_dict_named(self, state_dict, name):\n return state_dict", "def upgrade_state_dict_named(self, state_dict, name):\n if isinstance(self...
[ "0.7917492", "0.7917492", "0.69369066", "0.69109243", "0.6885705", "0.6883396", "0.67861336", "0.6740603", "0.6673967", "0.65714043", "0.64959174", "0.64618635", "0.643894", "0.6428928", "0.64061856", "0.63866913", "0.6349402", "0.6349402", "0.6349402", "0.63482106", "0.63454...
0.7855617
2
Upgrade old state dicts to work with newer code.
def upgrade_state_dict_named(self, state_dict, name): assert state_dict is not None def do_upgrade(m, prefix): if len(prefix) > 0: prefix += "." for n, c in m.named_children(): name = prefix + n if hasattr(c, "upgrade_state_dict_n...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def upgrade_state_dict(self, state_dict):\n return state_dict", "def upgrade_state_dict(self, state_dict):\n return state_dict", "def upgrade_state_dict(self, state_dict):\n self.upgrade_state_dict_named(state_dict, \"\")", "def upgrade_state_dict_named(self, state_dict, name):\n ...
[ "0.7917492", "0.7917492", "0.7855617", "0.69369066", "0.69109243", "0.6885705", "0.6883396", "0.67861336", "0.6673967", "0.65714043", "0.64959174", "0.64618635", "0.643894", "0.6428928", "0.64061856", "0.63866913", "0.6349402", "0.6349402", "0.6349402", "0.63482106", "0.63454...
0.6740603
8
State from trainer to pass along to model at every update.
def set_num_updates(self, num_updates): def _apply(m): if hasattr(m, "set_num_updates") and m != self: m.set_num_updates(num_updates) self.apply(_apply)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def update(self):\r\n\r\n self.target.load_state_dict(self.model.state_dict())\r\n self.target.eval()", "def _update_state(self) -> None:\n raise NotImplementedError(\"\")", "def update(self):\n self._state = 23", "def __setstate__(self, state):\n for i, j in state.items():...
[ "0.68623984", "0.65738016", "0.6389785", "0.63878083", "0.63878083", "0.6346078", "0.632304", "0.6316907", "0.63158894", "0.62950206", "0.62747675", "0.6228687", "0.62115747", "0.61572886", "0.6151144", "0.61358815", "0.6128747", "0.6117633", "0.61115754", "0.60902363", "0.60...
0.0
-1
Prepare model for inference.
def prepare_for_inference_(self, cfg: DictConfig): kwargs = {} kwargs["beamable_mm_beam_size"] = ( None if getattr(cfg.generation, "no_beamable_mm", False) else getattr(cfg.generation, "beam", 5) ) kwargs["need_attn"] = getattr(cfg.generation, "print_a...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def prepare_model(self, **kwargs):\n pass", "def _prepare_models(self):\n if self.freeze_layers is not None:\n self._set_freeze_layers()\n self._load_weight_if_possible()\n print(self.keras_model.summary())\n self.show_configuration()", "def _prepare_model(model):\...
[ "0.7220497", "0.6995641", "0.6750298", "0.6719687", "0.6642642", "0.6581851", "0.655064", "0.65285116", "0.6469628", "0.64194965", "0.6413053", "0.63571155", "0.6351184", "0.6339454", "0.6268862", "0.621674", "0.62010896", "0.6197796", "0.6182808", "0.6175428", "0.6143744", ...
0.6142857
21
Legacy entry point to optimize model for faster generation. Prefer prepare_for_inference_.
def make_generation_fast_(self, **kwargs): if self._is_generation_fast: return # only apply once self._is_generation_fast = True # remove weight norm from all modules in the network def apply_remove_weight_norm(module): try: nn.utils.remove_weigh...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def prepare_for_inference_(self, cfg: DictConfig):\n kwargs = {}\n kwargs[\"beamable_mm_beam_size\"] = (\n None\n if getattr(cfg.generation, \"no_beamable_mm\", False)\n else getattr(cfg.generation, \"beam\", 5)\n )\n kwargs[\"need_attn\"] = getattr(cfg....
[ "0.66357416", "0.6435547", "0.6425532", "0.6382971", "0.63047934", "0.62150675", "0.61909837", "0.618501", "0.61801004", "0.61703575", "0.61690223", "0.6104167", "0.61037815", "0.60797703", "0.60760516", "0.60611004", "0.6051706", "0.6045596", "0.60392493", "0.60384226", "0.6...
0.5612692
86
Make model exportable via ONNX trace.
def prepare_for_onnx_export_(self, **kwargs): seen = set() def apply_prepare_for_onnx_export_(module): if ( module != self and hasattr(module, "prepare_for_onnx_export_") and module not in seen ): seen.add(module) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def save_model_trace(output_path: str, model, trace):\n with open(output_path, \"wb\") as buff:\n pickle.dump({\"model\": model, \"trace\": trace}, buff)", "def _export_model(\n self,\n precision: ModelPrecision = ModelPrecision.FP32,\n export_format: ExportType = ExportType.ONNX,\...
[ "0.6291736", "0.6285025", "0.6270091", "0.6234275", "0.62069386", "0.619961", "0.6187291", "0.61252743", "0.60836756", "0.60751414", "0.6073943", "0.6032004", "0.60168093", "0.6003671", "0.59661406", "0.59655607", "0.5894144", "0.58792424", "0.5831612", "0.5830936", "0.581422...
0.5111722
86
Run the forward pass for an encoderdecoder model. First feed a batch of source tokens through the encoder. Then, feed the encoder output and previous decoder outputs (i.e., teacher forcing) to
def forward(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) decoder_out = self.decoder( prev_output_tokens, encoder_out=encoder_out, **kwargs ) return decoder_out
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def forward(self, inputs_encoder, inputs_decoder):\n states_encoder = self.encoder(inputs_encoder)\n outputs_decoder, states_decoder = self.decoder(inputs_decoder, states_encoder)\n return outputs_decoder, states_decoder", "def forward(self, input_token, target_token, timestep, *inputs):\n ...
[ "0.701429", "0.69124043", "0.6851775", "0.67677385", "0.6747971", "0.66699445", "0.6630951", "0.66303766", "0.66230434", "0.6597526", "0.6532557", "0.64746654", "0.6471793", "0.64453846", "0.6425172", "0.6398952", "0.63581455", "0.63262665", "0.6285209", "0.62551093", "0.6229...
0.72772044
0
Similar to forward but only return features.
def extract_features(self, src_tokens, src_lengths, prev_output_tokens, **kwargs): encoder_out = self.encoder(src_tokens, src_lengths=src_lengths, **kwargs) features = self.decoder.extract_features( prev_output_tokens, encoder_out=encoder_out, **kwargs ) return features
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def forward(self, x):\n x = self.features(x)\n return x", "def forward(self, x):\n out = self.features(x)\n out = out.view(out.size(0), -1)\n out = self.classifier(out)\n return out", "def feature_forward(self, x):\n raise NotImplementedError", "def forward(se...
[ "0.79697466", "0.76076716", "0.75065845", "0.7501061", "0.74177617", "0.74177617", "0.7244547", "0.7244547", "0.72351825", "0.7089535", "0.7087904", "0.707691", "0.70283806", "0.7025825", "0.70186174", "0.69859", "0.6958993", "0.69443464", "0.6910774", "0.6824163", "0.6802641...
0.0
-1
Project features to the default output size (typically vocabulary size).
def output_layer(self, features, **kwargs): return self.decoder.output_layer(features, **kwargs)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def output_layer(self, features):\n if self.adaptive_softmax is None:\n # project back to size of vocabulary\n return self.output_projection(features)\n else:\n return features", "def output_layer(self, features):\n if self.adaptive_softmax is None:\n ...
[ "0.6638207", "0.6638207", "0.6577222", "0.6140908", "0.5902171", "0.57811534", "0.57029545", "0.56978285", "0.5550671", "0.5508458", "0.5488962", "0.5481165", "0.5439393", "0.5435829", "0.5433342", "0.5406714", "0.5345856", "0.53426707", "0.5334511", "0.53146225", "0.5297098"...
0.0
-1
Maximum length supported by the model.
def max_positions(self): return (self.encoder.max_positions(), self.decoder.max_positions())
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _max_length(self):\n return self.__max_length", "def max_length(self):\n\t\treturn self._max_length", "def max_length(self) -> int | None:\n return self._underlying.max_length", "def _model_string_maxlen():\n # hardcoded for convenience. Could be dynamically set in future.\n # the cur...
[ "0.8716974", "0.86320573", "0.8242798", "0.79171175", "0.7627716", "0.74776286", "0.7413979", "0.73472226", "0.7304889", "0.729216", "0.72890776", "0.72890776", "0.72890776", "0.72890776", "0.7246226", "0.723191", "0.72292733", "0.7178601", "0.7171193", "0.7171193", "0.717119...
0.0
-1
Maximum length supported by the decoder.
def max_decoder_positions(self): return self.decoder.max_positions()
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def max_length(self):\n\t\treturn self._max_length", "def max_length(self) -> int | None:\n return self._underlying.max_length", "def _max_length(self):\n return self.__max_length", "def len_max(self):\n return 16 + 16 + 8 + 8 + Tools.bin_to_dec(self.get_data_size()) + Tools.bin_to_dec(s...
[ "0.82367796", "0.8202278", "0.81383824", "0.7740065", "0.74943036", "0.7392791", "0.7387785", "0.73560953", "0.73500305", "0.7269678", "0.7269678", "0.7269678", "0.7269678", "0.7152936", "0.7132618", "0.70659894", "0.70256686", "0.69994396", "0.69542176", "0.68982536", "0.683...
0.0
-1
Helper function to build shared embeddings for a set of languages after checking that all dicts corresponding to those languages are equivalent.
def build_shared_embeddings( dicts: Dict[str, Dictionary], langs: List[str], embed_dim: int, build_embedding: callable, pretrained_embed_path: Optional[str] = None, ): shared_dict = dicts[langs[0]] if any(dicts[lang] != shared_dict for lang in langs): ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def common_languages(programmers: dict):\n lang_sets = [set(languages) for languages in programmers.values()]\n return set.intersection(*lang_sets)", "def build_embeddings(opt, word_dict, for_encoder='src'):\n if for_encoder=='src':\n embedding_dim = opt.src_word_vec_size #512\n elif for_encoder=='t...
[ "0.61849177", "0.60261357", "0.58251303", "0.58241105", "0.5754996", "0.5612742", "0.55997604", "0.55655533", "0.5560588", "0.55467874", "0.5529211", "0.55203277", "0.53863925", "0.53837025", "0.53825444", "0.535034", "0.5340224", "0.530569", "0.5258829", "0.5242967", "0.5226...
0.7487093
0
Maximum length supported by the model.
def max_positions(self): return { key: ( self.models[key].encoder.max_positions(), self.models[key].decoder.max_positions(), ) for key in self.keys }
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _max_length(self):\n return self.__max_length", "def max_length(self):\n\t\treturn self._max_length", "def max_length(self) -> int | None:\n return self._underlying.max_length", "def _model_string_maxlen():\n # hardcoded for convenience. Could be dynamically set in future.\n # the cur...
[ "0.8716974", "0.86320573", "0.8242798", "0.79171175", "0.7627716", "0.74776286", "0.7413979", "0.73472226", "0.7304889", "0.729216", "0.72890776", "0.72890776", "0.72890776", "0.72890776", "0.7246226", "0.723191", "0.72292733", "0.7178601", "0.7171193", "0.7171193", "0.717119...
0.0
-1
Maximum length supported by the decoder.
def max_decoder_positions(self): return min(model.decoder.max_positions() for model in self.models.values())
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def max_length(self):\n\t\treturn self._max_length", "def max_length(self) -> int | None:\n return self._underlying.max_length", "def _max_length(self):\n return self.__max_length", "def len_max(self):\n return 16 + 16 + 8 + 8 + Tools.bin_to_dec(self.get_data_size()) + Tools.bin_to_dec(s...
[ "0.82367796", "0.8202278", "0.81383824", "0.7740065", "0.74943036", "0.7392791", "0.7387785", "0.73560953", "0.73500305", "0.7269678", "0.7269678", "0.7269678", "0.7269678", "0.7152936", "0.7132618", "0.70659894", "0.70256686", "0.69994396", "0.69542176", "0.68982536", "0.683...
0.0
-1
Copies parameters and buffers from state_dict into this module and its descendants.
def load_state_dict( self, state_dict, strict=True, model_cfg=None, args: Optional[Namespace] = None, ): if model_cfg is None and args is not None: logger.warn("using 'args' is deprecated, please update your code to use dataclass config") mode...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def load_module_state_dict(model, state_dict):\n import warnings\n from torch.nn import Parameter\n\n own_state = model.state_dict()\n for name, param in state_dict.items():\n if name not in own_state:\n warnings.warn('Skipping unexpected key \"{}\" in state_dict'.format(name))\n continue\n if ...
[ "0.6657184", "0.6639518", "0.6582654", "0.65766114", "0.65766114", "0.65766114", "0.65670323", "0.6513895", "0.6508519", "0.6479238", "0.6429001", "0.6384255", "0.6348956", "0.63133305", "0.6309515", "0.6254098", "0.6217675", "0.6207956", "0.6207956", "0.618914", "0.6175665",...
0.0
-1
Run the forward pass for a decoderonly model. Feeds a batch of tokens through the decoder to predict the next tokens.
def forward(self, src_tokens, **kwargs): return self.decoder(src_tokens, **kwargs)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def forward(self, input_token, target_token, timestep, *inputs):\n log_probs_per_model = []\n state_outputs = []\n next_state_input = len(self.models)\n vocab_reduction_module = self.models[0].decoder.vocab_reduction_module\n if vocab_reduction_module is not None:\n po...
[ "0.6603129", "0.6492484", "0.63717306", "0.63408744", "0.6279637", "0.6271893", "0.6234917", "0.6129099", "0.611657", "0.6088647", "0.6083846", "0.6076934", "0.60715055", "0.6046105", "0.60413575", "0.6034035", "0.6028133", "0.6008129", "0.6004112", "0.6004112", "0.59878695",...
0.66097987
0
Similar to forward but only return features.
def extract_features(self, src_tokens, **kwargs): return self.decoder.extract_features(src_tokens, **kwargs)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def forward(self, x):\n x = self.features(x)\n return x", "def forward(self, x):\n out = self.features(x)\n out = out.view(out.size(0), -1)\n out = self.classifier(out)\n return out", "def feature_forward(self, x):\n raise NotImplementedError", "def forward(se...
[ "0.7967969", "0.760584", "0.75046647", "0.7499447", "0.7416259", "0.7416259", "0.72424513", "0.72424513", "0.7232947", "0.70870334", "0.70865875", "0.707531", "0.7027191", "0.70249134", "0.7017389", "0.6983659", "0.69587934", "0.69422793", "0.69087887", "0.6823038", "0.680118...
0.0
-1
Project features to the default output size (typically vocabulary size).
def output_layer(self, features, **kwargs): return self.decoder.output_layer(features, **kwargs)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def output_layer(self, features):\n if self.adaptive_softmax is None:\n # project back to size of vocabulary\n return self.output_projection(features)\n else:\n return features", "def output_layer(self, features):\n if self.adaptive_softmax is None:\n ...
[ "0.6638207", "0.6638207", "0.6577222", "0.6140908", "0.5902171", "0.57811534", "0.57029545", "0.56978285", "0.5550671", "0.5508458", "0.5488962", "0.5481165", "0.5439393", "0.5435829", "0.5433342", "0.5406714", "0.5345856", "0.53426707", "0.5334511", "0.53146225", "0.5297098"...
0.0
-1
Maximum length supported by the model.
def max_positions(self): return self.decoder.max_positions()
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _max_length(self):\n return self.__max_length", "def max_length(self):\n\t\treturn self._max_length", "def max_length(self) -> int | None:\n return self._underlying.max_length", "def _model_string_maxlen():\n # hardcoded for convenience. Could be dynamically set in future.\n # the cur...
[ "0.8716974", "0.86320573", "0.8242798", "0.79171175", "0.7627716", "0.74776286", "0.7413979", "0.73472226", "0.7304889", "0.729216", "0.72890776", "0.72890776", "0.72890776", "0.72890776", "0.7246226", "0.723191", "0.72292733", "0.7178601", "0.7171193", "0.7171193", "0.717119...
0.0
-1
Maximum length supported by the decoder.
def max_decoder_positions(self): return self.decoder.max_positions()
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def max_length(self):\n\t\treturn self._max_length", "def max_length(self) -> int | None:\n return self._underlying.max_length", "def _max_length(self):\n return self.__max_length", "def len_max(self):\n return 16 + 16 + 8 + 8 + Tools.bin_to_dec(self.get_data_size()) + Tools.bin_to_dec(s...
[ "0.8236343", "0.8202248", "0.8137818", "0.7740491", "0.74942285", "0.73933655", "0.73868734", "0.73562765", "0.73506683", "0.7270788", "0.7270788", "0.7270788", "0.7270788", "0.71526325", "0.71336365", "0.7065415", "0.70252305", "0.6999362", "0.69545406", "0.6898572", "0.6831...
0.0
-1
Run the forward pass for a encoderonly model. Feeds a batch of tokens through the encoder to generate features.
def forward(self, src_tokens, src_lengths, **kwargs): return self.encoder(src_tokens, src_lengths, **kwargs)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def forward(self, *args, mode=\"train\", **kwargs):\n raise NotImplementedError", "def forward_train(self, *args, **kwargs):\n pass", "def on_iter_forward(self, runner):\n # unpack features into features and targets\n *features, target = runner.batch\n # Forward features\n ...
[ "0.6728241", "0.6650468", "0.6460877", "0.6409032", "0.64004755", "0.6309681", "0.6290481", "0.6290481", "0.62198967", "0.6101901", "0.60935724", "0.60271454", "0.60040045", "0.60037327", "0.5998024", "0.59907097", "0.595687", "0.59533715", "0.59453046", "0.592361", "0.589824...
0.59850585
16
Get normalized probabilities (or log probs) from a net's output.
def get_normalized_probs(self, net_output, log_probs, sample=None): encoder_out = net_output["encoder_out"] if torch.is_tensor(encoder_out): logits = encoder_out.float() if log_probs: return F.log_softmax(logits, dim=-1) else: return F....
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_normalized_probs(\n self,\n net_output: Tuple[Tensor, Optional[Dict[str, List[Optional[Tensor]]]]],\n log_probs: bool,\n sample: Optional[Dict[str, Tensor]] = None,\n ):\n return self.get_normalized_probs_scriptable(net_output, log_probs, sample)", "def get_normalize...
[ "0.786489", "0.786489", "0.76229286", "0.74452895", "0.74319875", "0.7307105", "0.7222653", "0.6552553", "0.61393046", "0.61393046", "0.6104034", "0.60671765", "0.60171", "0.59450716", "0.59266907", "0.5864929", "0.58568776", "0.5764361", "0.576258", "0.57360137", "0.5730258"...
0.7652318
2
Maximum length supported by the model.
def max_positions(self): return self.encoder.max_positions()
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _max_length(self):\n return self.__max_length", "def max_length(self):\n\t\treturn self._max_length", "def max_length(self) -> int | None:\n return self._underlying.max_length", "def _model_string_maxlen():\n # hardcoded for convenience. Could be dynamically set in future.\n # the cur...
[ "0.8716974", "0.86320573", "0.8242798", "0.79171175", "0.7627716", "0.74776286", "0.7413979", "0.73472226", "0.7304889", "0.729216", "0.72890776", "0.72890776", "0.72890776", "0.72890776", "0.7246226", "0.723191", "0.72292733", "0.7178601", "0.7171193", "0.7171193", "0.717119...
0.0
-1
matches db in other models
def check_db(self): if self.db == 'user': db = USERS_LIST return db elif self.db == 'questions': db = QUESTIONS_LIST return db elif self.db == 'meetups': db = MEETUPS_LIST return db elif self.db == 'rsvp': ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_same_models(self):\n\t\t\n\t\t# TODO: finish\n\t\tpass", "def test_db_models_correspondance(self):\n\n # test that nb models = nb tables in database\n self.assertEqual(\n len(self.mb_model_list),\n len(self.db_table_list)\n )\n\n # test that all db table...
[ "0.6341742", "0.6210895", "0.6144687", "0.6064236", "0.6064236", "0.6064236", "0.6064236", "0.6064236", "0.59456795", "0.58195233", "0.5759502", "0.57371247", "0.5600448", "0.5599722", "0.5585323", "0.5575498", "0.5554543", "0.5548634", "0.55253047", "0.54952055", "0.5494057"...
0.52401924
49
checks for specified items in db
def search_db(self, key, item): db = self.check_db() data = [record for record in db if record[key] == item] if data: return data[0] else: return False
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def check_item(self, item, key, db):\n data = [record for record in db if record[key] == item]\n return data", "def _do_check(self):\n try:\n #breakpoint()\n ApplicationsItem.objects.exists()\n #print (\"Checking\")\n return True\n\n except ...
[ "0.7209519", "0.66955376", "0.64641505", "0.63738173", "0.62730956", "0.62207234", "0.62193686", "0.621744", "0.6208144", "0.6127033", "0.60449225", "0.60387725", "0.6019061", "0.60156083", "0.59945893", "0.59671897", "0.59341395", "0.59341395", "0.59079635", "0.59011936", "0...
0.57796776
27
checks for data in dictionaries
def check_item(self, item, key, db): data = [record for record in db if record[key] == item] return data
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def check_for_dict(check):", "def assertContainsDict(self, dictionary, data):\n for key in dictionary:\n self.assertTrue(key in data, msg=\"Data doesn't have key '{}'\".format(key))\n value = dictionary[key]\n value2 = data[key]\n self.assertEqual(value, value2,\n msg...
[ "0.7747151", "0.6971249", "0.68852633", "0.67550725", "0.66045713", "0.6558467", "0.65399414", "0.6536623", "0.65336466", "0.64672124", "0.64451367", "0.6438732", "0.6414698", "0.6408215", "0.6394478", "0.63816035", "0.6354253", "0.6320167", "0.6312501", "0.62963307", "0.6282...
0.0
-1
method appends data to relevant lists
def save_data(self, new): db = self.check_db() db.append(new) return db
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def append(self, data):\n self.data_list.append(data)", "def append(self, data):\n # Check to see if main_list is full\n if self.num_elements == len(self.main_list):\n # Increase size of main_list\n self._expand_main_list()\n\n # Add element to mains_list\n ...
[ "0.7349716", "0.70946217", "0.676391", "0.6717995", "0.662144", "0.6402497", "0.6395965", "0.6313758", "0.6230241", "0.616947", "0.6059787", "0.6051917", "0.6046969", "0.6022753", "0.60204107", "0.60145926", "0.60092086", "0.5984431", "0.59686613", "0.5963892", "0.5936058", ...
0.0
-1
append questions to meetups
def questions_meetups(cls): for meetup in MEETUPS_LIST: for question in QUESTIONS_LIST: if meetup["meetup_id"] == question["meetup"]: meetups = MEETUPS_LIST.append(question) return meetups
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def add_questions(self, questions):\n for question in questions:\n self.questions.append(question)", "def addQuestion(self):\n self.questions.append(Question(self))", "def test_ask_question_meetup(self):\n res = self.client().post(\n '/api/v2/auth/login',\n ...
[ "0.6729881", "0.58960307", "0.58123344", "0.5767761", "0.56640637", "0.56520694", "0.5624104", "0.56080323", "0.558679", "0.5563679", "0.54919684", "0.54856396", "0.5463428", "0.54592943", "0.54343736", "0.5408007", "0.5386879", "0.5353735", "0.5335016", "0.53313965", "0.5325...
0.6592625
1
Merge given lists of items, each assumed to already be in sorted order, and return a new list containing all items in sorted order.
def merge(items1, items2): # TODO: Repeat until one list is empty # TODO: Find minimum item in both lists and append it to new list # TODO: Append remaining items in non-empty list to new list sorted_list = [] while len(items1) > 0 and len(items2) > 0: if items1[0] > items2[0]: s...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def merge_sort(items):\n # TODO: Check if list is so small it's already sorted (base case)\n # TODO: Split items list into approximately equal halves\n # TODO: Sort each half by recursively calling merge sort\n # TODO: Merge sorted halves into one list in sorted order\n if len(items) > 1:\n p...
[ "0.721071", "0.7078392", "0.69871604", "0.6984387", "0.6846355", "0.6845632", "0.6842484", "0.68040794", "0.6802449", "0.67312914", "0.6691758", "0.6666847", "0.6653178", "0.66418296", "0.66312414", "0.6543016", "0.6506305", "0.6490836", "0.64799863", "0.63232726", "0.6314037...
0.6188329
35
Sort given items by splitting list into two approximately equal halves, sorting each with an iterative sorting algorithm, and merging results into a list in sorted order.
def split_sort_merge(items): # TODO: Split items list into approximately equal halves pivot = len(items) // 2 # TODO: Sort each half using any other sorting algorithm # sort first half in-place (insertion sort) left = insertion_sort(items[:pivot]) right = insertion_sort(items[pivot:]) # TOD...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def merge_sort(items):\r\n # TODO: Check if list is so small it's already sorted (base case)\r\n # TODO: Split items list into approximately equal halves\r\n # TODO: Sort each half by recursively calling merge sort\r\n # TODO: Merge sorted halves into one list in sorted order\r", "def split_sort_merg...
[ "0.8726051", "0.81528765", "0.80984515", "0.8059322", "0.7023216", "0.70154995", "0.6990501", "0.6860262", "0.68393856", "0.6779179", "0.67722803", "0.676385", "0.67394567", "0.6680889", "0.66504735", "0.66409963", "0.66250134", "0.6570565", "0.65390325", "0.65087175", "0.649...
0.8257935
1
Sort given items by splitting list into two approximately equal halves, sorting each recursively, and merging results into a list in sorted order.
def merge_sort(items): # TODO: Check if list is so small it's already sorted (base case) # TODO: Split items list into approximately equal halves # TODO: Sort each half by recursively calling merge sort # TODO: Merge sorted halves into one list in sorted order if len(items) > 1: pivot = len(...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def merge_sort(items):\r\n # TODO: Check if list is so small it's already sorted (base case)\r\n # TODO: Split items list into approximately equal halves\r\n # TODO: Sort each half by recursively calling merge sort\r\n # TODO: Merge sorted halves into one list in sorted order\r", "def split_sort_merg...
[ "0.8773151", "0.8188119", "0.81388664", "0.798833", "0.7131648", "0.7076493", "0.70207715", "0.69530886", "0.69216335", "0.68825495", "0.6867648", "0.67908096", "0.6790778", "0.6765791", "0.6753519", "0.6604599", "0.6551974", "0.6479334", "0.6464495", "0.6449473", "0.6443957"...
0.8186886
2
Returns the pivot index; medium of three values.
def get_pivot(items, low, high): mid = low + (high - low) // 2 pivot = high if items[low] < items[mid]: if items[mid] < items[high]: pivot = mid elif items[low] < items[high]: pivot = low return pivot
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def choose_pivot(self,number_list):\n\t\tpivot_index = int(len(number_list)/2)\n\t\tpivot_number = number_list[pivot_index]\n\n\t\treturn pivot_number", "def find_pivot_idx(arr: List[int]) -> int:\n\n def _find_pivot_idx_rec(arr: List[int], low: int, high: int):\n # base cases for recussion\n if...
[ "0.65215987", "0.64629585", "0.64192975", "0.62487525", "0.58603865", "0.57840645", "0.5714783", "0.56102514", "0.55514604", "0.55514604", "0.54942656", "0.5458819", "0.54373527", "0.5427911", "0.54098815", "0.54095817", "0.53836995", "0.5312268", "0.5308989", "0.5286883", "0...
0.6246256
4
Return index `p` after inplace partitioning given items in range `[low...high]` by choosing a pivot;
def partition(items, low, high): pivot = get_pivot(items, low, high) pivot_value = items[pivot] items[pivot], items[low] = items[low], items[pivot] border = low for i in range(low, high + 1): if items[i] < pivot_value: border += 1 items[i], items[border] = items[bor...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def partition(items, low, high):\r\n # TODO: Choose a pivot any way and document your method in docstring above\r\n # TODO: Loop through all items in range [low...high]\r\n # TODO: Move items less than pivot into front of range [low...p-1]\r\n # TODO: Move items greater than pivot into back of range [p...
[ "0.822207", "0.76429975", "0.73392904", "0.72657746", "0.72602177", "0.7255673", "0.71701676", "0.71040815", "0.70656335", "0.70323384", "0.6998994", "0.6968002", "0.6927823", "0.68875295", "0.6801938", "0.6789494", "0.6785587", "0.6759109", "0.6724285", "0.66690946", "0.6632...
0.6819066
14
Sort given items in place by partitioning items in range `[low...high]` around a pivot item and recursively sorting each remaining sublist range.
def quick_sort(items, low=None, high=None): # TODO: Check if high and low range bounds have default values (not given) if low == None and high == None: low = 0 high = len(items) - 1 # TODO: Check if list or range is so small it's already sorted (base case) if low < high: # TODO:...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def quick_sort(items, low=None, high=None):\r\n # TODO: Check if high and low range bounds have default values (not given)\r\n # TODO: Check if list or range is so small it's already sorted (base case)\r\n # TODO: Partition items in-place around a pivot and get index of pivot\r\n # TODO: Sort each subl...
[ "0.8534162", "0.83972293", "0.8130411", "0.8083101", "0.77443224", "0.73497665", "0.73414725", "0.7310896", "0.73063093", "0.72519565", "0.7236841", "0.722917", "0.71980727", "0.71330124", "0.71062857", "0.69406766", "0.6931097", "0.69225633", "0.6897201", "0.6845998", "0.683...
0.83508146
2
Wrapper because of direct method passing as parameter for function fields
def _amount_all_wrapper(self, cr, uid, ids, field_name, arg, context=None): return self._amount_all(cr, uid, ids, field_name, arg, context=context)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def wrapper(*args):", "def _wrapper(func, args):\n return func(*args)", "def dummy_fn(self, *args, **kwargs):", "def __call__(self, func):\n # Set or extend the function's \"custom_fields\" attribute\n func.required_fields = getattr(func, \"required_fields\", {})\n func.required_f...
[ "0.6877823", "0.6630563", "0.65944004", "0.6579329", "0.6438432", "0.6354632", "0.6340434", "0.63361", "0.63281965", "0.6325962", "0.6294449", "0.62756634", "0.62756634", "0.62608796", "0.62442553", "0.62360185", "0.6232976", "0.6138593", "0.61032456", "0.6087355", "0.6087275...
0.0
-1
Prepare the dict of values to create the new invoice for a sales order. This method may be overridden to implement custom invoice generation (making sure to call super() to establish a clean extension chain).
def _prepare_invoice(self, cr, uid, order, lines, context=None): if context is None: context = {} journal_id = self.pool['account.invoice'].default_get(cr, uid, ['journal_id'], context=context)['journal_id'] if not journal_id: raise osv.except_osv(_('Error!'), ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _prepare_invoice(self, cr, uid, order, lines, context=None):\n invoice_vals = super(my_sale_order, self)._prepare_invoice(cr, uid, order,\n lines, context)\n\n invoice_vals.update({\n 'partner_shipping_id': order.partner_sh...
[ "0.7572903", "0.7399539", "0.7326662", "0.7299961", "0.7298513", "0.6951028", "0.67825764", "0.672891", "0.66003096", "0.6537039", "0.6524015", "0.6505327", "0.6362113", "0.62868905", "0.6002067", "0.59419024", "0.58609736", "0.5800843", "0.5795285", "0.5761803", "0.5745461",...
0.6968259
5
Test case for retrieve_iso20022_account_statement
def test_retrieve_iso20022_account_statement(self): pass
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_retrieve_iso20022_account_statement_ids(self):\n pass", "def test_client_bank_account_retrieve(self):\n pass", "def test_lookup_account(self):\n pass", "def test_duo_account_get(self):\n pass", "def test_get_account(self):\n account = Account(self.client, \"suppo...
[ "0.7644585", "0.7231425", "0.6812529", "0.67593855", "0.6592105", "0.6502707", "0.6176865", "0.6147158", "0.61404335", "0.60323703", "0.59900594", "0.5960902", "0.5945615", "0.5922146", "0.59219354", "0.5869125", "0.58601254", "0.5782012", "0.5768315", "0.5767688", "0.5763573...
0.9354298
0
Test case for retrieve_iso20022_account_statement_ids
def test_retrieve_iso20022_account_statement_ids(self): pass
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_retrieve_iso20022_account_statement(self):\n pass", "def get_account_ids(response):\n return [account['Id'] for account in response[0]]", "def _get_account_ids_for_payment(cls, batch_type) -> List[int]:\n # CREDIT : Distribution code against fee schedule\n # DEBIT : Distributio...
[ "0.6812683", "0.62837356", "0.58088756", "0.54739934", "0.54076695", "0.54023266", "0.5306752", "0.5292501", "0.5267886", "0.519998", "0.5199648", "0.51961565", "0.5137261", "0.5128088", "0.5126967", "0.5121954", "0.51077014", "0.5105441", "0.5099286", "0.50584334", "0.502265...
0.9331227
0
Test case for retrieve_iso20022_payment_instruction
def test_retrieve_iso20022_payment_instruction(self): pass
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_retrieve_iso20022_payment_instruction_status_report(self):\n pass", "def test_submit_iso20022_payment_instruction(self):\n pass", "def test_get_pay_in_details(self):\n pass", "def test_retrieve_iso20022_account_statement(self):\n pass", "def test_get_nveto_pmt_item(self...
[ "0.7929505", "0.77909356", "0.675292", "0.6300968", "0.5816299", "0.5814966", "0.5679115", "0.5636617", "0.5615561", "0.5567673", "0.5559224", "0.54956764", "0.5464819", "0.5416394", "0.53895956", "0.53485143", "0.5336239", "0.53228277", "0.5236428", "0.5206498", "0.5196977",...
0.9272758
0
Test case for retrieve_iso20022_payment_instruction_status_report
def test_retrieve_iso20022_payment_instruction_status_report(self): pass
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_retrieve_iso20022_payment_instruction(self):\n pass", "def test_submit_iso20022_payment_instruction(self):\n pass", "def test_get_pay_in_details(self):\n pass", "def payment_info_and_status(report):\n\n order_data = open(report)\n for line in order_data:\n order = l...
[ "0.7529059", "0.62428045", "0.6210108", "0.61702275", "0.59611535", "0.59611535", "0.5566417", "0.5521743", "0.54387635", "0.5430647", "0.5417798", "0.5384927", "0.53397685", "0.53175116", "0.5312262", "0.52929735", "0.5252589", "0.51756126", "0.5153839", "0.51465034", "0.514...
0.94082433
0
Test case for submit_iso20022_payment_instruction
def test_submit_iso20022_payment_instruction(self): pass
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def test_retrieve_iso20022_payment_instruction(self):\n pass", "def test_retrieve_iso20022_payment_instruction_status_report(self):\n pass", "def awaiting_payment(self):", "def test_authorize_pending_payment(self):\n pass", "def test_cancel_pending_payment(self):\n pass", "def...
[ "0.80150306", "0.69385153", "0.6408621", "0.6376836", "0.6285447", "0.62363833", "0.62204283", "0.6191187", "0.6069737", "0.6022646", "0.599725", "0.5964021", "0.59160477", "0.5867808", "0.5816957", "0.5812033", "0.57907003", "0.5760644", "0.574709", "0.57147986", "0.56779367...
0.93557435
0
Load the federalist papers as a tokenized list of strings, one for each eassay
def load_federalist_corpus(filename): with open(filename, "rt") as f: data = f.read() papers = data.split("FEDERALIST") # all start with "To the people of the State of New York:" (sometimes . instead of :) # all end with PUBLIUS (or no end at all) locations = [(i, [-1] + [m.end() + 1 for m ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def load_primers(tsv_filename):\n answer = []\n with open(tsv_filename) as handle:\n for line in handle:\n if line.startswith(\"#\"):\n continue\n parts = line.rstrip(\"\\n\").split(\"\\t\")\n if len(parts) == 2:\n left, right = parts\n ...
[ "0.58227915", "0.5724984", "0.56780964", "0.56092507", "0.5532302", "0.5484157", "0.53883326", "0.5370804", "0.53507763", "0.5328658", "0.5301289", "0.5288816", "0.52872497", "0.5275812", "0.52693164", "0.5240785", "0.5199876", "0.5166234", "0.5156385", "0.51557773", "0.51299...
0.6521002
0
Create TFIDF matrix. This function creates a TFIDF matrix from the docs input.
def tfidf(docs): vocab = {} df = {} regex = re.compile("\s+") count = 0 for doc in docs: terms = re.split(regex, doc) for term in set(terms): if len(term) > 0: if term not in vocab: vocab[term] = count # (index, df) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def tfidf(self):\n matrix = numpy.zeros(self.shape)\n # the number of words in a document\n words_per_doc = numpy.asarray(self.sum(axis=1), dtype=float)\n # the number of documents in which a word is attested.\n word_frequencies = numpy.asarray(numpy.sum(self > 0, axis=0), dtype=...
[ "0.6873089", "0.66823035", "0.6493619", "0.6493619", "0.6493619", "0.64821815", "0.64000684", "0.6270314", "0.6241418", "0.6232862", "0.6105327", "0.6093112", "0.6044048", "0.6034128", "0.5971325", "0.5963278", "0.59623235", "0.5951346", "0.5939513", "0.5932343", "0.59086144"...
0.6719997
1
Return a matrix of cosine similarities.
def cosine_similarity(X): matrix = X.dot(X.transpose()).todense() mat_len = len(matrix) norms = [0] * mat_len for i in range(0, mat_len): norms[i] = 1.0 / np.sqrt(matrix.item((i, i))) norm_mat = np.matrix(norms) return np.multiply(norm_mat.transpose().dot(norm_mat), matrix)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def cosineDistanceMatrix():\n\n\tmatrix = movieMatrix()\n\tsimilarity = np.dot(matrix, matrix.T)\n\tsquareMag = np.diag(similarity)\n\tinvSquareMag = 1/squareMag\n\tinvSquareMag[np.isinf(invSquareMag)]=0\n\tinvMag = np.sqrt(invSquareMag)\n\tcosine = similarity * invMag\n\tcosine = cosine.T * invMag\n\treturn cosin...
[ "0.8285621", "0.78920585", "0.7579562", "0.74811274", "0.743485", "0.7388749", "0.73220444", "0.71874624", "0.71846974", "0.7142988", "0.70933944", "0.7079397", "0.7016768", "0.69719607", "0.69497156", "0.69476235", "0.6929229", "0.69159657", "0.6877522", "0.68710095", "0.684...
0.8017912
1
Initialize an ngram language model.
def __init__(self, docs, n): self.n = n self.dict = {} self.vocab = set() self.sum_index = "*sum*" regex = re.compile("\s+") count = 0 for doc in docs: terms = re.split(regex, doc) for term in terms: if term not in self.voca...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def initialize_module():\n global ngram_model\n ngram_model = load_model()", "def __init__(self,lm=None):\n self.error_types=('substitution','transposition','insertion','deletion')\n self.error_distribution = {t:defaultdict(float) for t in self.error_types}\n \n #create Language...
[ "0.7703326", "0.7314437", "0.6971028", "0.68830884", "0.66832644", "0.65838814", "0.65838814", "0.65757096", "0.6545955", "0.6534452", "0.65227824", "0.6454586", "0.6412272", "0.6405899", "0.6397114", "0.6393375", "0.63846684", "0.6321946", "0.6315248", "0.62733215", "0.62515...
0.0
-1
Evaluate perplexity of model on some text.
def perplexity(self, text, alpha=1e-3): regex = re.compile("\s+") terms = re.split(regex, text) n = self.n D = self.D logp_sum = 0.0 for term in terms: if term not in self.vocab: D += 1 for i in range(0, len(terms) - n + 1): ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def eval_text(self, text):\n # Pre-process sentence given\n sents = text.split('\\n')\n words = []\n for sent in sents:\n words.extend(list(sent))\n\n for idx, word in enumerate(words):\n if (word, ) not in self.uni_dist:\n words[idx] = TOKENS...
[ "0.80092084", "0.7292834", "0.70828134", "0.70655626", "0.70471746", "0.67445874", "0.6689637", "0.6630751", "0.6520073", "0.64775157", "0.6447067", "0.6447067", "0.6444355", "0.6362294", "0.6353481", "0.6342418", "0.6300517", "0.6281196", "0.6277169", "0.62741715", "0.610413...
0.6894882
5
Generate a random sample of k words.
def sample(self, k): result = "" current = self.gen_beginning() for i in range(0, k): result += current[0] + " " t = tuple(current) if t in self.dict: c_sum = self.dict[t][self.sum_index] rand = random.randint(0, c_sum) ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def sample(words, n=10) -> str:\n return [random.choice(words) for _ in range(n)]", "def generate(self, words=5):\n seed()\n return sorted(self.keywords, key=lambda *args: random())[0:words]", "def initialize_k_mediods(data, k):\n return random.sample(range(len(data)), k)", "def sample(da...
[ "0.72455376", "0.70879835", "0.693704", "0.6925264", "0.6848805", "0.6814129", "0.67589587", "0.6738232", "0.6705459", "0.6673047", "0.65499115", "0.6505917", "0.64209366", "0.64151245", "0.641502", "0.6339997", "0.6326541", "0.62663114", "0.6211554", "0.62094414", "0.6188869...
0.7714014
0
Score is percentage of first relevant item in list that occur at rank k or lower. First element is 'rank 1'. Relevance is binary (nonzero is relevant).
def hit_rate_at_k(rs, k): if k < 1 or k > len(rs[0]): raise ValueError('k value must be >=1 and < Max Rank') hits = 0 for r in rs: if np.sum(r[:k]) > 0: hits += 1 return hits / len(rs)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def ranking_precision_score(y_true, y_score, k=10):\n unique_y = np.unique(y_true)\n\n if len(unique_y) > 2:\n raise ValueError(\"Only supported for two relevance levels.\")\n\n n_relevant = 0\n n_pos = 0\n for relevance_score in y_true:\n if relevance_score == 1:\n n_pos +=...
[ "0.69463134", "0.6472632", "0.62562585", "0.61396897", "0.6135696", "0.60684866", "0.6028253", "0.6027478", "0.6008297", "0.5992598", "0.5989352", "0.59800893", "0.59766746", "0.59675956", "0.5918821", "0.5904292", "0.59037226", "0.58975285", "0.58792955", "0.58758146", "0.58...
0.60977244
5
Score is mean rank of the first relevant item in list First element is 'rank 1'. Relevance is binary (nonzero is relevant).
def mean_rank(rs): _rs = [] for r in rs: ids = np.asarray(r).nonzero()[0] if len(ids) == 0: _rs.append(0) else: _rs.append(ids[0] + 1) return np.mean(_rs)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def score(self,*val):\n if len(val):\n self._score = val[0]\n self.evaluated = 1\n else: self.evaluate()\n return self._score", "def relevance_ranking(data, ranked_list, gamma=0.5, stop_prob=0.7):\n total_relevance = 0\n for query in ranked_list:\n exposu...
[ "0.6466421", "0.6462538", "0.6315955", "0.62352824", "0.6227623", "0.6222124", "0.6193854", "0.61890227", "0.61771", "0.6152061", "0.60592294", "0.6055092", "0.6036504", "0.6008186", "0.59818774", "0.5965349", "0.596181", "0.5955918", "0.5945452", "0.5939978", "0.59156084", ...
0.6088967
10
Score is reciprocal of the rank of the first relevant item First element is 'rank 1'. Relevance is binary (nonzero is relevant).
def mean_reciprocal_rank(rs): rs = (np.asarray(r).nonzero()[0] for r in rs) return np.mean([1. / (r[0] + 1) if r.size else 0. for r in rs])
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def recip_rank(recs, truth):\n good = recs['item'].isin(truth.index)\n npz, = np.nonzero(good)\n if len(npz):\n return 1.0 / (npz[0] + 1.0)\n else:\n return 0.0", "def calculate_item_relevance_scores(self, user_similarity_profile):\r\n scores = user_similarity_profile.dot(self.ra...
[ "0.65378183", "0.651771", "0.6467377", "0.6389885", "0.6330632", "0.6313076", "0.6265125", "0.62329", "0.6205835", "0.6196562", "0.6186391", "0.6167026", "0.61501414", "0.61466914", "0.61421686", "0.6131228", "0.61183125", "0.6111582", "0.6069214", "0.6068116", "0.6040775", ...
0.5956831
28
Score is precision after all relevant documents have been retrieved Relevance is binary (nonzero is relevant).
def r_precision(r): r = np.asarray(r) != 0 z = r.nonzero()[0] if not z.size: return 0. return np.mean(r[:z[-1] + 1])
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def score(self, index, query, doc_id):\n return 1", "def scoring(self):\n pass", "def score(self):", "def score(self, doc, c):\n # >>> YOUR ANSWER HERE\n # the inner loop in the TEST NAIVE BAYES, sum up the logprior of the class and all words' loglikelihood\n sum = self.log...
[ "0.7592948", "0.7098235", "0.68290615", "0.6801507", "0.6624223", "0.6606512", "0.66021997", "0.6587521", "0.6507607", "0.6479378", "0.6477939", "0.6475813", "0.6468395", "0.6463468", "0.64520186", "0.6447075", "0.64418066", "0.6423969", "0.6407707", "0.64054066", "0.63903534...
0.0
-1
Score is precision @ k Relevance is binary (nonzero is relevant).
def precision_at_k(r, k = None): assert k is None or k >= 1 r = np.asarray(r)[:k] != 0 if r.size != k and k is not None: raise ValueError('Relevance score length < k') return np.mean(r)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def precision_at_k(r, k):\n assert k >= 1\n r = np.asarray(r)[:k] != 0\n if r.size != k:\n raise ValueError('Relevance score length < k')\n return np.mean(r)", "def ranking_precision_score(y_true, y_score, k=10):\n unique_y = np.unique(y_true)\n\n if len(unique_y) > 2:\n raise ValueErr...
[ "0.7579704", "0.75730854", "0.75206596", "0.7273513", "0.70572793", "0.6807414", "0.6794766", "0.67622024", "0.6718873", "0.66709834", "0.666568", "0.66625845", "0.66458786", "0.65949607", "0.6592178", "0.65699744", "0.65699744", "0.65699744", "0.65677255", "0.65368253", "0.6...
0.73947746
3
Score is recall after all relevant documents have been retrieved Relevance is binary (nonzero is relevant).
def recall_at_k(r, max_rel, k = None): assert k is None or k >= 1 r = r[:k] r = np.asarray(r) != 0 if np.sum(r) > max_rel: raise ValueError('Number of relevant documents retrieved > max_rel') return np.sum(r) / max_rel
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def score(self, index, query, doc_id):\n return 1", "def scoring(self):\n pass", "def score(self):", "def score(self, doc, c):\n # >>> YOUR ANSWER HERE\n # the inner loop in the TEST NAIVE BAYES, sum up the logprior of the class and all words' loglikelihood\n sum = self.log...
[ "0.77042514", "0.70804054", "0.6825153", "0.6698889", "0.65968883", "0.65834653", "0.65075636", "0.6494543", "0.6459717", "0.6410981", "0.6352758", "0.63505363", "0.63395846", "0.6330539", "0.6262381", "0.62484777", "0.62484777", "0.62329364", "0.6213522", "0.6205233", "0.620...
0.0
-1
Score is harmonic mean of precision and recall Relevance is binary (nonzero is relevant).
def f1_score_at_k(r, max_rel, k = None): p = precision_at_k(r, k) r = recall_at_k(r, max_rel, k) return 2 * p * r / (p + r)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def scoring(estimator, features_test, labels_test):\n pred = estimator.predict(features_test)\n p = metrics.precision_score(labels_test, pred, average='micro')\n r = metrics.recall_score(labels_test, pred, average='micro')\n if p > 0.3 and r > 0.3:\n return metrics.f1_score(labels_test, pred, av...
[ "0.70694923", "0.69527805", "0.6776279", "0.6741725", "0.6705837", "0.66831845", "0.66708565", "0.6602802", "0.65940034", "0.65889955", "0.65657234", "0.6541111", "0.6517365", "0.6513724", "0.6502784", "0.6447551", "0.64293504", "0.64244366", "0.64214677", "0.6412047", "0.639...
0.0
-1
Score is average precision (area under PR curve) Relevance is binary (nonzero is relevant).
def average_precision(r): r = np.asarray(r) != 0 out = [precision_at_k(r, k + 1) for k in range(r.size) if r[k]] if not out: return 0. return np.mean(out)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def calc_score(score):\n if not score:\n return 0\n dbot_score = 1\n if score >= 95:\n dbot_score = 3\n elif score >= 75:\n dbot_score = 2\n return dbot_score", "def _score_to_decision(self, score):", "def scoring(estimator, features_test, labels_test):\n pred = estimator...
[ "0.6821534", "0.6812478", "0.67836845", "0.672481", "0.67069656", "0.66571444", "0.65934145", "0.6568065", "0.6524064", "0.6514728", "0.64933944", "0.64831716", "0.64719576", "0.6461697", "0.6461697", "0.64496106", "0.6446607", "0.6385337", "0.63759947", "0.6358375", "0.63334...
0.0
-1
Score is mean average precision Relevance is binary (nonzero is relevant).
def mean_average_precision(rs): return np.mean([average_precision(r) for r in rs])
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def score(self):", "def score(title, min_votes=0, precision=1):\n scores = []\n if imdb and _imdb_enabled and app.config.getboolean(\"service_imdb\", \"enabled\"):\n scores.append(_imdb_score(title, min_votes=min_votes))\n #if tmdb and _tmdb_enabled and app.config.getboolean(\"service_themoviedb\...
[ "0.687467", "0.6800429", "0.675096", "0.67286605", "0.6719268", "0.66333705", "0.6626313", "0.66048586", "0.6598404", "0.6587723", "0.65846354", "0.6582585", "0.6577583", "0.6575322", "0.65628225", "0.65511286", "0.652377", "0.6522324", "0.65179324", "0.65176654", "0.6493894"...
0.0
-1
Score is discounted cumulative gain (dcg) Relevance is positive real values. Can use binary as the previous methods. Example from
def dcg_at_k(r, k, method=0): r = np.asfarray(r)[:k] if r.size: if method == 0: return r[0] + np.sum(r[1:] / np.log2(np.arange(2, r.size + 1))) elif method == 1: return np.sum(r / np.log2(np.arange(2, r.size + 2))) else: raise ValueError('method must b...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _dcg(scores, discount=np.log2):\n scores = np.nan_to_num(scores)\n ranks = np.arange(1, len(scores) + 1)\n disc = discount(ranks)\n np.maximum(disc, 1, out=disc)\n np.reciprocal(disc, out=disc)\n return np.dot(scores, disc)", "def dcg(relevances, rank=10):\n relevances = np.asarray(relev...
[ "0.6852342", "0.67582947", "0.66583407", "0.66019785", "0.65993136", "0.65316784", "0.6455386", "0.63087153", "0.62610835", "0.6168453", "0.61438054", "0.61242175", "0.6114647", "0.6109967", "0.6108396", "0.60803115", "0.6071427", "0.60680217", "0.60537493", "0.6034375", "0.6...
0.0
-1
Score is normalized discounted cumulative gain (ndcg) Relevance is positive real values. Can use binary as the previous methods. Example from
def ndcg_at_k(r, k, method=0): dcg_max = dcg_at_k(sorted(r, reverse=True), k, method) if not dcg_max: return 0. return dcg_at_k(r, k, method) / dcg_max
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def ndcg_score(y_true, y_score, k=5, gains=\"exponential\"):\n best = dcg_score(y_true, y_true, k, gains)\n actual = dcg_score(y_true, y_score, k, gains)\n return actual / best", "def dcg(relevances, rank=10):\n relevances = np.asarray(relevances)[:rank]\n n_relevances = len(relevances)\n if n_...
[ "0.6957888", "0.6815953", "0.6708947", "0.66132414", "0.6586523", "0.65334326", "0.64976776", "0.64840215", "0.63853425", "0.63208604", "0.6289914", "0.62134665", "0.62114406", "0.6167021", "0.6139633", "0.6125743", "0.6110018", "0.60895586", "0.6078114", "0.6026861", "0.5994...
0.0
-1
Shift the colours to associate a value standing anywhere in the new cmap (relatively to the two extremes start & stop or min & max) with whichever value / colour of the input cmap (by default the midpoint). If the input cmap is divergent, this will be white by default. The locpoint value cannot be the min or max (start...
def shift_cmap(cmap, start=0., locpoint=0.5, stop=1.0, name='centered'): # declare a colour + transparency dictionary cdict={'red':[], 'green':[], 'blue':[], 'alpha':[]} # regular index to compute the colors RegInd = np.linspace(start, stop, cmap.N) # shifted index to match what the data ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name=\"shiftedcmap\"):\n cdict = {\"red\": [], \"green\": [], \"blue\": [], \"alpha\": []}\n\n # regular index to compute the colors\n reg_index = np.linspace(start, stop, 257)\n\n # shifted index to match the data\n shift_index = np.hstack(...
[ "0.7426894", "0.71807843", "0.6564476", "0.6303851", "0.6023876", "0.59686595", "0.59551555", "0.58223695", "0.5817288", "0.5612482", "0.5571391", "0.55472046", "0.5510123", "0.5468494", "0.5433831", "0.541819", "0.5417777", "0.536488", "0.532748", "0.5306837", "0.53066117", ...
0.78395176
0
Solve the 2D Poisson equation on a uniform grid with isotropic spacing (dx=dy) using the Point Jacobi method. REFERENCE P. Moin (2010), Section 5.10.2
def pjacobi_poissoneq(RHS, phi0, tol=1e-2, max_iter=1e3): M, N = phi0.shape phi = phi0.copy() phip = phi + np.random.random(phi.shape) k=0 while np.abs(phip-phi).max()>tol and k<=max_iter: print((np.abs(phip-phi).max(),tol)) phi = phip for j in range(1, M-1): for...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def prob2():\n x, i, j = sy.symbols('x, i, j')\n expr = sy.product(sy.summation(j*(sy.sin(x) + sy.cos(x)), (j, i, 5)), (i, 1, 5))\n return sy.simplify(expr)", "def _solve2D(self, simu=None):\n ghosts_v = self.output_field.topology.ghosts()\n ghosts_w = self.input_field.topology.ghosts()\n ...
[ "0.5994", "0.5820883", "0.57296526", "0.5440243", "0.54124415", "0.5392034", "0.53877366", "0.5370115", "0.5332657", "0.5315776", "0.52889276", "0.52889276", "0.5241523", "0.52312505", "0.52216476", "0.5212408", "0.52116287", "0.5205877", "0.5196524", "0.51758933", "0.5171616...
0.53672427
8
Solve an elliptic PDE with the GaussSeidel method.
def gaussseidel_poissoneq(A, x0): return 1
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def Gauss_Seidel_Solve(A,b,tol=1.0e-6,max_iterations=100,LOUD=False):\n [Nrow, Ncol] = A.shape\n assert Nrow == Ncol\n N = Nrow\n converged = False\n iteration = 1\n x = np.random.rand(N) #random initial guess \n x_new = np.zeros(N)\n while not(converged):\n x = x_new.copy() #replace...
[ "0.6432076", "0.63487226", "0.60870856", "0.6001174", "0.589638", "0.5896034", "0.5860473", "0.5841443", "0.5816136", "0.581226", "0.57875973", "0.5765089", "0.5763309", "0.57576144", "0.57526207", "0.57224756", "0.56512624", "0.56261045", "0.5540731", "0.5519879", "0.5495409...
0.60497034
3
Power iteration Algorithm for largest eigenvalue
def power_iteration(A, num_simulations): b_k = np.random.rand(A.shape[1]) for _ in range(num_simulations): # calculate the matrix-by-vector product Ab b_k1 = A.dot(b_k) # calculate the norm b_k1_norm = LA.norm(b_k1) # re normalize the vector b_k = b_k1 / b_k1_n...
{ "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:\...
[ "0.70080876", "0.6993816", "0.6931118", "0.6889238", "0.68778664", "0.673645", "0.66521704", "0.6639608", "0.65055937", "0.6420724", "0.6238939", "0.6211664", "0.6209953", "0.6197342", "0.6181818", "0.6136207", "0.612645", "0.61106384", "0.60200745", "0.60172164", "0.6008851"...
0.0
-1
use the approximated eigenvector returned by Power Iteration method to compute the largest eigenvalue of the matrix A
def compute_largest_eigenvalue(A, num_simulations): b_k = power_iteration(A, num_simulations) return b_k.dot(A).dot(b_k) / (b_k.dot(b_k))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def check(mat, otp):\n prd = mat*otp\n eigval = prd[0]/otp[0]\n print 'computed eigenvalue :' , eigval\n [eigs, vecs] = np.linalg.eig(mat)\n abseigs = list(abs(eigs))\n ind = abseigs.index(max(abseigs))\n print ' largest eigenvalue :', eigs[ind]", "def calculate_biggest_eigenvalue(cls, covar...
[ "0.7686338", "0.75658166", "0.7420887", "0.7406523", "0.7244843", "0.6996089", "0.6981068", "0.6972226", "0.6875169", "0.68482476", "0.6837701", "0.68196994", "0.68079334", "0.6799966", "0.6798281", "0.67714775", "0.6687908", "0.6684467", "0.66705984", "0.6626259", "0.6624495...
0.7842253
0
Saves a generated sample from the test set
def sample_images(batches_done): imgs = next(iter(val_dataloader)) G_AB.eval() G_BA.eval() real_A = Variable(imgs["A"].type(Tensor)) fake_B = G_AB(real_A) real_B = Variable(imgs["B"].type(Tensor)) fake_A = G_BA(real_B) # Arrange images along x-axis real_A = make_grid(real_A[:3,:,:,:]...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def gen_sample_report():\n sample_report().save()", "def save_sample_dict(self):\n with open(self._sample_dict_path, 'w+') as fp:\n pickle.dump(self.sample_dict, fp)", "def save(self):\n with open(\"samples.txt\", \"a\") as f:\n f.write(str(self) + \"\\n\")", "def save_...
[ "0.696222", "0.67525", "0.6740261", "0.6466196", "0.6399194", "0.6381273", "0.62999547", "0.62947327", "0.6278773", "0.62547344", "0.6247668", "0.62058157", "0.61862093", "0.6174158", "0.6138744", "0.61361045", "0.6130006", "0.6045542", "0.60450554", "0.60444295", "0.60188967...
0.0
-1
Executes the action using the given actors.
def execute(self, cast): paddles = cast["paddle"] bricks = cast["brick"] ball = cast["ball"][0] score = cast["score"][0] # breaks the bricks the ball runs into for brick in bricks: if ball.get_position().equals(brick.get_position()):...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def execute_actions(self, actions):\n execute_actions(self.board, self.agent_locs, actions)", "def execute_action_sequence(actions):\n for action in actions:\n action.execute()\n rospy.logdebug( \"Action sequence finished\")\n return", "def _advance_by_action(game, agents, action):\n ...
[ "0.7169595", "0.67209524", "0.62902516", "0.627168", "0.6250816", "0.6250816", "0.6233038", "0.6233038", "0.61962575", "0.6146749", "0.61296886", "0.6109823", "0.6078985", "0.60676247", "0.60651207", "0.60203123", "0.5983621", "0.5932604", "0.58554614", "0.58333886", "0.58330...
0.0
-1
Calculates total memory occupied by tag.
def size(self): if self._buffer is not None: length = SIZEOF_TAGHEADER if self._header.value_type == b'B': # TODO make sure this is right, need data that uses B to verify length += SIZEOF_UINT32 + (len(self._buffer)) elif self._header.value_typ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_total_memory_size(self):\n memory = 0\n for i in range(4):\n for j in range(4):\n memory += self.system.operator[i, j].memory\n return memory", "def estimated_lookup_memory(self):\n return 60 * len(self.docvecs.offset2doctag) + 140 * len(self.docvecs....
[ "0.6964374", "0.6698501", "0.65641403", "0.647925", "0.64773256", "0.64645046", "0.6431399", "0.6416539", "0.63977325", "0.63855255", "0.63691133", "0.6357961", "0.6333625", "0.6313083", "0.62849903", "0.62525266", "0.62389946", "0.620012", "0.61522454", "0.6151238", "0.61475...
0.56251746
87
Length of data stored by tag.
def __len__(self): if self._buffer is not None: if self._header.value_type in b'ZBH': return len(self._buffer) else: return 1 else: return 0
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def Length(data):\n return len(data)", "def size(self):\n if self._buffer is not None:\n length = SIZEOF_TAGHEADER\n if self._header.value_type == b'B':\n # TODO make sure this is right, need data that uses B to verify\n length += SIZEOF_UINT32 + (len...
[ "0.736296", "0.71680456", "0.7121816", "0.70960975", "0.7015388", "0.70117235", "0.7010031", "0.7010031", "0.7010031", "0.70061755", "0.6973364", "0.69203866", "0.69203866", "0.69203866", "0.6889017", "0.6884412", "0.68721616", "0.68721616", "0.6864763", "0.68622303", "0.6829...
0.6341916
99
Load from SAM formatted tag.
def from_sam(column): tag = Tag.__new__(Tag) header = TagHeader() tag._header = header header.tag, value_type, tag._buffer = column.split(b':', 2) header.value_type = int(value_type) return tag
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _readtag(self):\n tag = Tag()\n tag.tag = self.reader.readint(1)\n tag.len = self.reader.readint(2)\n\n if tag.len > 0:\n tag.data = self.reader.read(tag.len)\n return tag", "def loads(cls, raw: bytes) -> 'Tag':\n meta = json.loads(raw.decode('utf-8'))\n ...
[ "0.5769399", "0.57009256", "0.54815173", "0.541181", "0.52811766", "0.5242441", "0.5222803", "0.5183103", "0.5168342", "0.51363677", "0.5094421", "0.50843364", "0.50767493", "0.5076378", "0.50727826", "0.50671816", "0.50626606", "0.5058337", "0.5029517", "0.5023263", "0.50008...
0.59755653
0
Convert to string representation of Tag. See __bytes__() to convert to SAM format.
def __repr__(self): return "{}:{}:{}".format(self._header.tag.decode('ASCII'), self._header.value_type.decode('ASCII') if self._header.value_type in b'AifZHB' else 'i', _to_str(self._buffer))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __str__(self):\n return str(self.tag)", "def serialize(value):\n if isinstance(value, exifread.classes.IfdTag):\n return value.printable\n else:\n return str(value)", "def dump(self, tag):\n length = tag.length\n result = self._read_bytes(0, length)\...
[ "0.71483946", "0.68341696", "0.6832286", "0.6536208", "0.64951533", "0.6490092", "0.6431469", "0.63714004", "0.6358486", "0.6336265", "0.62896115", "0.6246241", "0.62305814", "0.62295955", "0.62253994", "0.6214808", "0.6121835", "0.6118026", "0.60675", "0.6050999", "0.6043127...
0.59634566
25
Convert tag to SAM formatted bytes.
def __bytes__(self): if self._header.value_type in b'ZH': value = self._buffer[:-1] # Omit the trailing Null elif self._header.value_type in b'AB': value = self._buffer else: value = str(self._buffer.value).encode('ASCII') return self._header.tag + b'...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def dump(self, tag):\n length = tag.length\n result = self._read_bytes(0, length)\n self._buffer = self._buffer[length:]\n self._resetTag()\n return tag.raw + result", "def try_tag_to_string(tag_data):\n if not isinstance(tag_data, array.array):\n return tag_data\n\n ...
[ "0.63688046", "0.6034328", "0.5500381", "0.5474561", "0.53866506", "0.53325385", "0.5233147", "0.5149721", "0.51401734", "0.51223713", "0.5118963", "0.50812024", "0.5053599", "0.5010087", "0.48974398", "0.48746136", "0.48496717", "0.48460436", "0.48277447", "0.4824602", "0.47...
0.54308015
4
Convert to a BAM formatted bytes representation of the tag.
def pack(self): # TODO Avoid copying data return bytearray(self._header) + bytearray(self._buffer)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def to_bytes(self) -> bytes:", "def get_binary(self):\n data = bytes()\n\n for tag in self._tags:\n value = 0\n if tag in self.fields.keys():\n value = self.fields[tag]\n try:\n data += struct.pack(\"<I\", value)\n except str...
[ "0.65274245", "0.64196163", "0.6401471", "0.6303315", "0.6299956", "0.626853", "0.62506604", "0.62035865", "0.615007", "0.612048", "0.61170906", "0.60998684", "0.602263", "0.6001303", "0.5981385", "0.58501893", "0.5847806", "0.5839017", "0.5839017", "0.5839017", "0.5839017", ...
0.0
-1
Duplicate the tag instance and underlying buffer.
def copy(self): new = Tag.__new__(Tag) new._header = TagHeader.from_buffer_copy(self._header) new._buffer = bytearray(self._buffer) return new
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def clone(self):\r\n cp = self.__class__(self.type, self.data, self.name)\r\n cp.tag = copy(self.tag)\r\n return cp", "def clone(self):\r\n #return copy(self)\r\n cp = self.__class__(self.type, None, None, self.name)\r\n cp.tag = copy(self.tag)\r\n return cp", "...
[ "0.7311448", "0.71100277", "0.6665762", "0.6128848", "0.60210073", "0.59999156", "0.59991366", "0.5981431", "0.59087753", "0.5877438", "0.58530945", "0.5850476", "0.58475935", "0.584531", "0.58307993", "0.58307993", "0.58307993", "0.58262277", "0.58262277", "0.58262277", "0.5...
0.79683113
0
Return True if VM has already been registered.
def is_registered(self, thevm): return self.is_registered_vm_ref(thevm.get_id())
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def is_registered(self):\n return self._is_registered", "def is_registered(self) -> bool:\n from arkouda.util import is_registered\n\n if self.registered_name is None:\n return False\n return is_registered(self.registered_name)", "def is_registered(self):\n if self...
[ "0.69196904", "0.68907464", "0.6320427", "0.628127", "0.622022", "0.61987096", "0.6179819", "0.61126006", "0.60980445", "0.6064852", "0.6058638", "0.60367787", "0.60367787", "0.59639454", "0.59211785", "0.5887907", "0.5887252", "0.58583194", "0.58470297", "0.5841361", "0.5797...
0.8016748
0
This function is for processing a vmrecord and determining the course of action that should be taken.
def process_vmrecord(self, vmref, vmrecord): is_monitored = self.is_registered_vm_ref(vmref) should_monitor = self._should_monitor(vmrecord) if not is_monitored and should_monitor: self.start_monitoring(vmref) elif is_monitored and not should_monitor: self.stop_mo...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def process_record(self, record):\n raise NotImplementedError('Process record needs to be customized')", "def step(self):\n\n #make a dictionary of rules and which are active\n binary = self.int_to_8_bit_binary(self.rule_nbr)\n binary_str = ''.join(binary)\n\n active_rules = di...
[ "0.5164569", "0.49395716", "0.4700608", "0.46963623", "0.4691204", "0.46837854", "0.46780255", "0.46223032", "0.4606696", "0.45727763", "0.44958025", "0.44768083", "0.4473273", "0.44693777", "0.44464642", "0.44464642", "0.44457793", "0.44338378", "0.44290945", "0.44153652", "...
0.5090451
1
Tidy TLS secrets after vmdestroy
def process_vm_del(self, vm_ref): if vm_ref in self.tls_secret_cache: for key in tls_secret.XSCONTAINER_TLS_KEYS: if key in self.tls_secret_cache[vm_ref]: secret_uuid = self.tls_secret_cache[vm_ref][key] session = self.host.get_session() ...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _clear_secret_token_map():\n global _secret_token_map\n _secret_token_map = None", "def terraform_destroy():\n return subprocess.call([\n \"terraform\",\n \"destroy\",\n \"-var-file=terraform/aws/security.tfvars\",\n \"terraform/aws\"\n ])", "def prepare_secrets(c, r...
[ "0.5807785", "0.5727637", "0.5725778", "0.55358976", "0.5488105", "0.5402988", "0.5384859", "0.52961004", "0.5294215", "0.5262412", "0.5256724", "0.5233984", "0.51561475", "0.50737864", "0.5048128", "0.5044511", "0.5015237", "0.5011312", "0.5009923", "0.497746", "0.49721035",...
0.63315004
0
This function handles SIGTERM and SIGINT. It does by tearing down the monitoring. We need to do this as we don't want threads to be hanging around, after the docker_monitor has quit.
def interrupt_handler(signum, frame): if DOCKER_MONITOR: util.log.warning("Signal %d received - Tearing down monitoring" % (signum)) DOCKER_MONITOR.tear_down_all() sys.exit(0)
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __sigint_handler(signal, frame):\n logging.debug(\"SIGINT or SIGTERM catched\")\n logging.debug(\"Raise t_stop_event\")\n t_stop_event.set() # Set stop flag to true for all launched threads\n logging.info(\"Stopping daemons...\")\n sleep(1)", "def signal_handler(signum, frame):\n s...
[ "0.68981844", "0.6877391", "0.6854961", "0.6788198", "0.6648828", "0.65698", "0.65544385", "0.6520777", "0.6460851", "0.6307506", "0.62880915", "0.62261784", "0.6216414", "0.62163615", "0.61946", "0.6181283", "0.61717325", "0.61657053", "0.6160173", "0.6152518", "0.6125579", ...
0.67288566
4
Returns a string representation of the given Bouquet.
def bouquet_to_string(bouquet: Bouquet) -> str: flowers = sorted(bouquet.flowers.items()) flower_quantities = (f"{count}{flower.species}" for flower, count in flowers) return "".join(chain(bouquet.name, bouquet.size, flower_quantities))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def __str__(self):\n tag = []\n for key in self.tags:\n if key == 'label':\n self.type = self.tags[key]\n else:\n try:\n tag.append(\"%s=%0.3f\" % (str(key), self.tags[key]))\n except TypeError:\n ...
[ "0.6125326", "0.6092544", "0.60388684", "0.6008322", "0.6004144", "0.5972039", "0.5931697", "0.5896468", "0.5836418", "0.5798204", "0.5778516", "0.5772454", "0.57675993", "0.57460415", "0.57389605", "0.5727558", "0.5709913", "0.5687112", "0.567919", "0.56786853", "0.56669056"...
0.8395375
0
Returns an approximation of the design's complexity to create.
def design_complexity(design: Design) -> int: diversity = 3 * len(design.required) abundance = 2 * sum(design.required.values()) return diversity + abundance + design.additional
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def complexity(self):\n raise NotImplementedError()", "def complexity(self) -> str:\n return pulumi.get(self, \"complexity\")", "def time_complexities():\n return \"Best Case: O(n), Average Case: O(n), Worst Case: O(n)\"", "def _calculate_complexity(workflow):\n complexity = estimate_comp...
[ "0.8077096", "0.6961893", "0.6919179", "0.6726002", "0.6631515", "0.66048616", "0.6532741", "0.64464813", "0.6343992", "0.63089466", "0.62893844", "0.61888796", "0.6182711", "0.61719126", "0.6056263", "0.60038203", "0.59974915", "0.5947808", "0.5912837", "0.5826208", "0.57910...
0.7889661
1
Returns a dict of flowers and amount required across all designs.
def flower_demand(designs: Iterable[Design]) -> FlowerCounter: elements = (design.required.elements() for design in designs) return Counter(chain.from_iterable(elements))
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _evaluate(self, design: Design) -> Dict[str, float]:\n state_dict = dict()\n for i, key in enumerate(self.params_vec.keys()):\n state_dict[key] = self.params_vec[key][design[i]]\n results = dict()\n for netlist_name, netlist_module in self.netlist_module_dict.items():\n ...
[ "0.5650389", "0.55767566", "0.55356234", "0.5211133", "0.5200495", "0.51428133", "0.51294917", "0.5111226", "0.50917345", "0.50691676", "0.5068217", "0.5063549", "0.5061189", "0.50606686", "0.5054326", "0.5049696", "0.50316507", "0.5015505", "0.50010157", "0.4985385", "0.4977...
0.67191625
0
Yields lines from the given filepointer until an empty line is hit.
def read_lines(fp: TextIO) -> Iterator[str]: while line := fp.readline().strip(): yield line
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def file_generator(fhandle):\n for line in fhandle:\n yield line.strip()", "def read_on(reader, f):\n while True:\n try:\n line = reader(f)\n except StopIteration:\n break\n\n if line is not None:\n yield line", "def file_reading_iterator_raw(f...
[ "0.7242552", "0.71367556", "0.71172196", "0.69629115", "0.68658143", "0.68133694", "0.67623186", "0.67088294", "0.6681538", "0.6673487", "0.6588528", "0.6549278", "0.6515804", "0.65141517", "0.64825076", "0.6482102", "0.6398926", "0.6333425", "0.6327357", "0.63264656", "0.631...
0.6880809
4
Decorator to filter printed parsed messages and for prepending origin. func should be a handler function to parse byte arrays.
def format(func: Callable[[bytes], Tuple[bytes, str]]) -> \ Callable[[Iterable, Mapping[str, Any]], bytes]: @wraps(func) def wrapper(*args, **kwargs): origin = kwargs.pop('origin', helpers.ConnectionType.CLIENT) direction = DIRECTION_FORMAT.get(origin, '???') data, message = func...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def stringfilter(func):\n @wraps(func)\n def _dec(*args, **kwargs):\n if args:\n args = list(args)\n args[0] = str(args[0])\n return func(*args, **kwargs)\n\n return _dec", "def preprocess_func(cls, func):\n return func", "def preprocess_func(cls, func):\n ...
[ "0.60364217", "0.59651476", "0.5833045", "0.53841347", "0.52038354", "0.5055278", "0.5047875", "0.4971399", "0.49548736", "0.4936261", "0.49300504", "0.49008164", "0.48955798", "0.4811547", "0.48098522", "0.47704333", "0.47341463", "0.47156164", "0.47128054", "0.47027594", "0...
0.54019004
3
NOOP handler. Do nothing.
def handle_ack(data: bytes) -> Tuple[bytes, str]: return data, ''
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def noop():", "def noop(*args, **kwargs):\n pass", "def __noop(self, *args, **kwargs):\n return None", "def nothing():\n pass", "def command_noop(self, arg):\n if arg:\n raise errors.BadArguments('NOOP')\n self.write_ok()", "def nop(*args, **kwargs):\n pass", "def r...
[ "0.8270344", "0.77878445", "0.76386654", "0.74345076", "0.7309599", "0.7190313", "0.7023898", "0.6925536", "0.6913321", "0.6834885", "0.68147796", "0.67920685", "0.67106897", "0.66438466", "0.6625213", "0.66128635", "0.6594163", "0.65927273", "0.65840656", "0.65581256", "0.64...
0.0
-1
Parse position packet to extract x,y,z coordinates.
def handle_position(data: bytes) -> Tuple[bytes, str]: x, y, z = struct.unpack('fff', data[0:3 * 4]) return data[20:], f'Current Position (x,y,z): {x} {y} {z}'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def xyz_points(packet):\n if not isinstance(packet, tuple):\n packet = unpack(packet)\n\n x = []\n y = []\n z = []\n\n for b in range(AZIMUTH_BLOCK_COUNT):\n block = azimuth_block(b, packet)\n\n if not azimuth_valid(block):\n continue\n\n for c in range(CHANNEL...
[ "0.69323254", "0.69320357", "0.69288343", "0.6922433", "0.6886289", "0.67812216", "0.63687855", "0.6320784", "0.62680185", "0.61316466", "0.61238545", "0.6087155", "0.6069988", "0.6061023", "0.60496056", "0.59711367", "0.59693176", "0.5949054", "0.5853009", "0.58473074", "0.5...
0.7093595
0
Parse jump packet to determine whether character is jumping.
def handle_jump(data: bytes) -> Tuple[bytes, str]: jumping = struct.unpack('?', data[:1])[0] return data[1:], 'Jumping' if jumping else 'Falling'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def isJump(self) -> bool:\n ...", "def parseJump(cmds):\n if (len(cmds) != 0):\n parseExpr(cmds[0])\n parseJump(cmds[1:])", "def jump(self):\n if self.commandType() is C_COMMAND and SEMIC in self.currentCommand:\n return self.currentCommand.split(SE...
[ "0.615037", "0.6014653", "0.5796932", "0.5575382", "0.5509545", "0.53331184", "0.52808654", "0.5249864", "0.515087", "0.5149437", "0.512318", "0.50901484", "0.508259", "0.5030159", "0.5023107", "0.50212574", "0.5014617", "0.49220034", "0.48980102", "0.48829585", "0.48466754",...
0.6712948
0
Parse sneak packet to determine whether character is sneaking.
def handle_sneak(data: bytes) -> Tuple[bytes, str]: sneaking = not(struct.unpack('?', data[:1])[0]) return data[1:], 'Sneaking' if sneaking else 'Done sneaking'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def spoof_packet(packet):", "def is_valid_ssdp_packet(data: bytes) -> bool:\n return (\n bool(data)\n and b\"\\n\" in data\n and (\n data.startswith(b\"NOTIFY * HTTP/1.1\")\n or data.startswith(b\"M-SEARCH * HTTP/1.1\")\n or data.startswith(b\"HTTP/1.1 200...
[ "0.60442406", "0.5914778", "0.55621016", "0.5559887", "0.5418556", "0.5331959", "0.5268514", "0.517216", "0.5141348", "0.5118424", "0.5115155", "0.5101938", "0.5091775", "0.50372213", "0.5020283", "0.50183654", "0.4982059", "0.4971184", "0.4962405", "0.495", "0.49458483", "...
0.67788094
0
Parse slot packet to get new selected slot.
def handle_slot_select(data: bytes) -> Tuple[bytes, str]: new_slot = struct.unpack('B', data[:1])[0] return data[1:], f'New slot: {new_slot}'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def ParseSlot(self, G, node):\n slot = BLNlpClipsSlotMap()\n slot.Name = node\n return slot", "def slot(self):\n if self.__slot in ApexAP1000.SLOTS:\n return self.__slot\n else:\n raise ValueError('Bad slot number !')", "def get_slot(self, c):\n i...
[ "0.6871716", "0.6167444", "0.6067937", "0.5867668", "0.582912", "0.56749433", "0.5670136", "0.5644674", "0.55893064", "0.55682135", "0.5525981", "0.54996186", "0.5356382", "0.53551406", "0.5354744", "0.535286", "0.53276724", "0.5315347", "0.5280438", "0.5268219", "0.5253302",...
0.74431324
0
Parse shoot packet to get name and direction of weapon shot.
def handle_shoot(data: bytes) -> Tuple[bytes, str]: length = struct.unpack('H', data[:2])[0] name = data[2:length+2] direction = struct.unpack('fff', data[length+2:length+2+12]) return data[2+length:], f'Shot {name.decode()} in direction: {direction}'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def shoot(self, direction):\n self.type = self.boss.get_bullet_type()\n if self.type == 'shotgun':\n try:\n dx = abs(Laser.List[-1].x - self.x)\n dy = abs(Laser.List[-1].y - self.y)\n if dx < 50 and dy < 50 and self.type == 'shotgun':\n ...
[ "0.5890974", "0.5887054", "0.53437346", "0.5336487", "0.53323966", "0.52839124", "0.5244903", "0.51474124", "0.5147154", "0.51261395", "0.5072278", "0.5041751", "0.5030743", "0.50281894", "0.50211716", "0.49417537", "0.49294055", "0.49110854", "0.48818228", "0.48443696", "0.4...
0.6662206
0
Parse chat packet to get chat message.
def handle_chat(data: bytes) -> Tuple[bytes, str]: length = struct.unpack('H', data[:2])[0] message = data[2:2+length].decode(helpers.ENCODING) return data[2+length:], f'Sent message: "{message}"'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def _parse_message(self, data):\r\n if TwitchChatStream._check_has_ping(data):\r\n self._maybe_print('got ping')\r\n self._send_pong()\r\n\r\n channel_name_or_false = TwitchChatStream._check_has_channel(data)\r\n if channel_name_or_false:\r\n current_channel = ...
[ "0.7406516", "0.73291683", "0.67943984", "0.6733794", "0.672288", "0.66768646", "0.64275604", "0.63590896", "0.6345727", "0.62247163", "0.620205", "0.6130546", "0.6123919", "0.6123464", "0.6119081", "0.6103453", "0.609881", "0.60979277", "0.60835326", "0.60701495", "0.6068451...
0.73227745
2
Parse actor drop packet to get actor information. Message displays actor name and drop position. If the actor is a "Drop" object, send loot packet to server to automatically pick it up.
def handle_actor_drop(data: bytes) -> Tuple[bytes, str]: # TODO: reverse first 9 bytes item_id = struct.unpack('I', data[:4])[0] unknown = struct.unpack('I', data[4:8])[0] # noqa: F841 unknown2 = data[9] # noqa: F841 item_name_length = struct.unpack('H', data[9:11])[0] item_name = data[11:11+i...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def do_drop(self, arg):\r\n\r\n # put this value in a more suitably named variable\r\n itemToDrop = arg.lower()\r\n\r\n # get a list of all \"description words\" for each item in the inventory\r\n invDescWords = getAllDescWords(inventory)\r\n\r\n # find out if the player doesn't ...
[ "0.56872076", "0.55682516", "0.54184365", "0.5319463", "0.51151735", "0.50281775", "0.49502423", "0.494522", "0.49405202", "0.48522088", "0.47940865", "0.47803688", "0.47510865", "0.47133994", "0.4665043", "0.46581176", "0.46517873", "0.46223408", "0.46195015", "0.45776522", ...
0.72898984
0
Parse regionchange packet to get region name. Drop positions of initial actors, like GoldenEggs, is also revealed.
def handle_region_change(data: bytes) -> Tuple[bytes, str]: region_name_length = struct.unpack('H', data[:2])[0] region_name = data[2:2+region_name_length] return (data[2+region_name_length:], f'Changing to region: {region_name.decode().upper()}')
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_region_name(self, i):\n for region in self.regions:\n if region['id'] == i:\n return region['name']\n return 'Unknown Matchmaking Region'", "def region_name(self):\n return self.random_element(self._regions)[1]", "def parse(spec: str):\n parts = spe...
[ "0.59673536", "0.57557505", "0.5583435", "0.5512079", "0.5460583", "0.5374759", "0.53120506", "0.5214128", "0.51646787", "0.5143317", "0.51233065", "0.503153", "0.5028622", "0.49404567", "0.49021447", "0.4894185", "0.4893214", "0.48763806", "0.48636374", "0.48617592", "0.4861...
0.7242775
0
Parse itemacquire packet to get information about item. For example, indicate the type and amount of ammo received by a Drop.
def handle_item_acquire(data: bytes) -> Tuple[bytes, str]: item_name_length = struct.unpack('H', data[:2])[0] item_name = data[2:2+item_name_length].decode(helpers.ENCODING) amount = struct.unpack('I', data[2+item_name_length:2+item_name_length+4])[0] return data[2+item_name_l...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def parse_item(raw_item: str) -> Item:\n name, cost, damage, armor = raw_item.split()\n\n return Item(name, int(cost), int(damage), int(armor))", "def purchase(self, item_type):", "def test_acquire(self):\n cmd = Acquire(10, Discriminator(name='test_disc', params={'test_params': 1.0}),\n ...
[ "0.5597219", "0.5188092", "0.5177499", "0.5073031", "0.5053507", "0.47726688", "0.47510383", "0.47276434", "0.46652564", "0.46515086", "0.46486056", "0.4630913", "0.46084592", "0.4608282", "0.45737928", "0.4572363", "0.45675558", "0.4482317", "0.44680476", "0.44588447", "0.44...
0.76179355
0
Parse itempickup packet to get ID of item picked up. This packet can be used to implement autolooting.
def handle_item_pickup(data: bytes) -> Tuple[bytes, str]: item_id = struct.unpack('I', data[:4])[0] return data[4:], f'Picked up item with ID {item_id}'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def item_id(self):\n return self._item_id", "def get_itemId(self):\n return self.metadata['itemId']", "def item2id(self):\n if self._item2id is None:\n self._item2id = dict(zip(self.item_unique_vals, range(self.n_items)))\n return self._item2id", "def GetId(self,item):\...
[ "0.5675741", "0.5595556", "0.55021983", "0.5498978", "0.5446714", "0.53615916", "0.5318583", "0.5302795", "0.52846485", "0.52069044", "0.52050513", "0.5146597", "0.51271284", "0.5093275", "0.50868964", "0.50801", "0.50474125", "0.50447243", "0.5043063", "0.5026166", "0.497911...
0.6605895
0
Parse reload packet to get weapon reloaded and type and amount of ammo. This packet can also be used to implement autoreloading.
def handle_reload(data: bytes) -> Tuple[bytes, str]: try: weapon_name_length = struct.unpack('H', data[:2])[0] weapon_name = data[2:2+weapon_name_length].decode(helpers.ENCODING) ammo_name_length = struct.unpack('H', data[2+weapon_name_length:2+weapon...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def handle_loaded_ammo(data: bytes) -> Tuple[bytes, str]:\n weapon_name_length = struct.unpack('H', data[:2])[0]\n weapon_name = data[2:2+weapon_name_length].decode(helpers.ENCODING)\n loaded_ammo = struct.unpack('I',\n data[2+weapon_name_length:2+weapon_name_length+4])[0] ...
[ "0.66666114", "0.55387217", "0.5414622", "0.5111501", "0.50200164", "0.48626655", "0.4839229", "0.47635645", "0.47579595", "0.47329336", "0.47281036", "0.4692487", "0.46864903", "0.46703836", "0.46219566", "0.46208167", "0.46134442", "0.46068662", "0.4582399", "0.45680597", "...
0.7670467
0
Parse health packet to get amount of HP for actors.
def handle_health(data: bytes) -> Tuple[bytes, str]: actor_id, hp = struct.unpack('Ih', data[:6]) return data[6:], f'Actor {actor_id} has {hp} HP'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_hp():\n\n return character['HP']", "def get_character_health(character: dict):\r\n print(\"Your health is: %d\" % character['HP'])", "def health(self) -> Union[int, float]:\n return self.proto.health", "def health(self) -> Union[int, float]:\n return self.proto.health", "def get...
[ "0.61138475", "0.58834904", "0.58287996", "0.58287996", "0.57303226", "0.56599045", "0.557464", "0.55465996", "0.55047244", "0.54503375", "0.54475665", "0.54012614", "0.53854513", "0.5374126", "0.5319295", "0.53045714", "0.52863604", "0.52635986", "0.526149", "0.526149", "0.5...
0.72335124
0
Parse mana packet to get amount of mana player has.
def handle_mana(data: bytes) -> Tuple[bytes, str]: mana = struct.unpack('H', data[:2])[0] return data[2:], f'Player has {mana} mana'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def get_card_generic_mana(card_face: CardFace) -> int:\n if not card_face.mana_cost:\n return 0\n\n generic_mana = RE_GENERIC_MANA.search(card_face.mana_cost)\n if generic_mana:\n return int(generic_mana.group(1))\n return 0", "async def mana():\n acc = Account(\"travelfeed\")\n m...
[ "0.57014525", "0.56480294", "0.51965064", "0.5002724", "0.49907732", "0.4978547", "0.49368247", "0.4921005", "0.49195775", "0.48984864", "0.48687136", "0.4837379", "0.48320034", "0.4801529", "0.4791798", "0.4788704", "0.4764654", "0.467033", "0.464986", "0.46493697", "0.46481...
0.7122957
0
Parse actor state packet to get state of actors.
def handle_state(data: bytes) -> Tuple[bytes, str]: actor_id, state_length = struct.unpack('IH', data[:6]) state = data[6:6+state_length].decode(helpers.ENCODING) return data[6+state_length:], f'Actor {actor_id} in {state} state'
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def parse_state(self, state: str):\r\n state = state.strip()\r\n state = state.split(';')\r\n\r\n if len(state) < 2:\r\n print(state)\r\n return\r\n\r\n for field in state:\r\n split = field.split(':')\r\n if len(split) < 2:\r\n ...
[ "0.64426273", "0.58384633", "0.5417954", "0.54048204", "0.53565496", "0.5319129", "0.53103405", "0.530963", "0.52439404", "0.5221096", "0.515227", "0.5113325", "0.5079505", "0.5075995", "0.5031213", "0.5030043", "0.50287706", "0.502478", "0.50243676", "0.5004235", "0.49950925...
0.6912247
0
Parse attack packet to get actor that attacked a victim and the attack.
def handle_attack(data: bytes) -> Tuple[bytes, str]: attacker_id, attack_length = struct.unpack('IH', data[:6]) attack = data[6:6+attack_length].decode(helpers.ENCODING) victim_id = struct.unpack('I', data[6+attack_length:6+attack_length+4])[0] return (data[6+attack_length+4:], f'Actor {atta...
{ "objective": { "self": [], "paired": [], "triplet": [ [ "query", "document", "negatives" ] ] } }
[ "def parse_attack(self, name_index, bonus_index, damage_index, sheet=None):\n if self.character_data is None: raise Exception('You must call get_character() first.')\n\n wksht = sheet or self.character_data\n\n name = wksht.value(name_index)\n damage = wksht.value(damage_index)\n ...
[ "0.57115144", "0.5400416", "0.528136", "0.51143986", "0.5100148", "0.50714874", "0.49574938", "0.4811062", "0.48007146", "0.47944915", "0.4706924", "0.47052175", "0.46990165", "0.46683258", "0.46647388", "0.46605876", "0.4658123", "0.46417665", "0.46375567", "0.46187025", "0....
0.71671
0