query stringlengths 10 2k | document stringlengths 20 51.2k | metadata dict | negatives listlengths 0 101 | negative_scores listlengths 0 101 | document_score stringlengths 3 10 | document_rank stringclasses 102
values |
|---|---|---|---|---|---|---|
Builds a kfactor circulant matrix (A matrix with the structure of circulant matrices, but with the entries above the diagonal multiplied by the same factor.) The matrix is store in memory. | def factor_circulant_matrix(x, k):
n=len(x)
return circulant(x) * (tri(n,n, 0) + k*np.transpose(tri(n,n, -1))) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def make_k_matrix(self):\r\n K = self.uv_vol + self.Epsilon * self.guv_vol + \\\r\n (self.Epsilon / self.Beta) * self.uv_bound\r\n return K",
"def _K(m):\n M = m*(m - 1)/2\n K = np.zeros((M, m**2), dtype=np.int64)\n row = 0\n for j in range(1, m):\n col = (j - 1)*m + j... | [
"0.6495986",
"0.6089255",
"0.6045119",
"0.59890914",
"0.5949488",
"0.59035623",
"0.5859298",
"0.58462423",
"0.57634705",
"0.574443",
"0.5730508",
"0.5717386",
"0.56819576",
"0.566873",
"0.5568253",
"0.55545205",
"0.5523086",
"0.55172205",
"0.5492196",
"0.5491694",
"0.5478032"... | 0.78092545 | 0 |
Convert `data` to float64 or int(64|32|16|8); as fallback, return `data`. Assume `data` is of type `utf8` or a dictionary of utf8. | def _autocast_column(data: pyarrow.ChunkedArray) -> pyarrow.ChunkedArray:
# All-empty (and all-null) columns stay text
for chunk in data.iterchunks():
# https://arrow.apache.org/docs/format/Columnar.html#variable-size-binary-layout
_, offsets_buf, _ = chunk.buffers()
# If data has an off... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def try_float(data):\n try:\n return float(data)\n except (ValueError, TypeError ):\n return data",
"def float_from_string(data):\n return float(maybe_number(data))",
"def _float(data):\n try:\n return float(data)\n except ValueError as err:\n if data in ('None', 'NA'... | [
"0.748317",
"0.69878703",
"0.6950177",
"0.6709056",
"0.6397729",
"0.6396958",
"0.63965064",
"0.6293659",
"0.6279694",
"0.6161165",
"0.61318475",
"0.61182475",
"0.5992608",
"0.596135",
"0.5870688",
"0.5860507",
"0.58470494",
"0.58467394",
"0.5794232",
"0.5766028",
"0.5722432",... | 0.0 | -1 |
Parse CSV, TSV or other delimiterseparated text file. Raise LookupError for an `encoding` Python cannot handle. Raise UnicodeError when the file simply cannot be read as text. (e.g., a UTF16 file that does not start with a byteorder marker.) | def _parse_csv(
path: Path,
*,
settings: Settings = DEFAULT_SETTINGS,
encoding: Optional[str],
delimiter: Optional[str],
has_header: bool,
autoconvert_text_to_numbers: bool,
) -> ParseCsvResult:
warnings = []
with contextlib.ExitStack() as ctx:
n_bytes = path.stat().st_size
... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def _read(filename, encodings=['ascii', 'utf-8', 'utf-16', 'latin-1']):\n text = None\n\n for encoding in encodings:\n try:\n f = open(filename, encoding=encoding)\n text = f.read()\n f.close()\n except UnicodeDecodeError:\n f.close()\n except ... | [
"0.6560378",
"0.62868285",
"0.61147213",
"0.6035779",
"0.6029811",
"0.5926391",
"0.5827321",
"0.5804859",
"0.58034223",
"0.5774665",
"0.5699063",
"0.56861764",
"0.56857336",
"0.56818",
"0.56673634",
"0.5613292",
"0.56082886",
"0.55889183",
"0.5518909",
"0.5518909",
"0.5518909... | 0.60616267 | 3 |
Update the config information with the number of attention heads. | def update_heads(info,
heads):
info["model_params"]["boltzmann_dict"]["num_heads"] = heads
# Concatenate the fingerprints produced by the different heads
info["model_params"]["boltzmann_dict"]["head_pool"] = "concatenate"
readoutdict = info["model_params"]["readoutdict"]
feat_dim ... | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | [
"def update_config(self):\n self.channel_count = self.config_global['channel_count']\n self.pixel_count = self.config_global['pixel_count']\n self.pixel_index_max = self.pixel_count - 1\n self.repeat_count = self.config_global['repeat_count']\n self.repeat_snake = self.config_glob... | [
"0.5661511",
"0.5599164",
"0.54210174",
"0.53882116",
"0.5338775",
"0.5247799",
"0.5247248",
"0.5225227",
"0.51431704",
"0.5058479",
"0.49841285",
"0.49445143",
"0.49379683",
"0.48532596",
"0.4848556",
"0.48481622",
"0.4835506",
"0.48258802",
"0.48030823",
"0.48024145",
"0.47... | 0.5935313 | 0 |
Distribution parameter for the location. | def loc(self):
return self._gev_bijector.loc | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | ["def distribution(self) -> str:\n return pulumi.get(self, \"distribution\")","def loc(self):(...TRUNCATED) | ["0.65867454","0.62829775","0.62012964","0.59412557","0.57245076","0.56689125","0.5637412","0.556788(...TRUNCATED) | 0.0 | -1 |
Initialise an allergy storage with the given allergy score. | "def __init__(self, score):\n self.score = score\n self.list = [candidate[0] for candi(...TRUNCATED) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | ["def _krls_init(self, iALDth=1e-4, iMaxDict=1e3):\n \n dAldKRLS = {} # Initiali(...TRUNCATED) | ["0.5347252","0.5322024","0.5263044","0.5225015","0.5212653","0.51022935","0.5069073","0.50057083","(...TRUNCATED) | 0.5045687 | 7 |
"This returns a single entry corresponding to the Experiment Entity referred to by ExpEntityData. Th(...TRUNCATED) | "def getExperimentEntry(ExpEntityData):\n\n # Validate that ExpEntityData actually corresponds to(...TRUNCATED) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | ["def _get_full_entity(entity: spacy.tokens.Token) -> str:\n entity_string = SpacyEventExtrac(...TRUNCATED) | ["0.6376407","0.5688458","0.56678385","0.5637462","0.5474638","0.5404834","0.5358794","0.53286546","(...TRUNCATED) | 0.81062454 | 0 |
Initialize the sum to zero | def __init__(self, name):
super(Column, self).__init__(self, name)
self.sum = 0 | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | ["def reset_sum(self):\n self.sum_e = 0","def initial(self):\n return zero","def zero((...TRUNCATED) | ["0.7703882","0.71630156","0.69994444","0.6945991","0.6897512","0.67463124","0.6605019","0.65537924"(...TRUNCATED) | 0.0 | -1 |
"The `ComputeCluster` data source can be used to discover the ID of a cluster in vSphere. This is us(...TRUNCATED) | "def get_compute_cluster(datacenter_id: Optional[str] = None,\n name: Optiona(...TRUNCATED) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | ["def get_cluster_id(options):\n cluster = options.cluster\n datacenter = get_datacenter(optio(...TRUNCATED) | ["0.70518404","0.70052683","0.67735595","0.6768537","0.6736824","0.6736824","0.6736824","0.6736824",(...TRUNCATED) | 0.72311264 | 0 |
"The `ComputeCluster` data source can be used to discover the ID of a cluster in vSphere. This is us(...TRUNCATED) | "def get_compute_cluster_output(datacenter_id: Optional[pulumi.Input[Optional[str]]] = None,\n (...TRUNCATED) | {
"objective": {
"self": [],
"paired": [],
"triplet": [
[
"query",
"document",
"negatives"
]
]
}
} | ["def get_compute_cluster(datacenter_id: Optional[str] = None,\n name: Option(...TRUNCATED) | ["0.72311264","0.70518404","0.67735595","0.6768537","0.6736824","0.6736824","0.6736824","0.6736824",(...TRUNCATED) | 0.70052683 | 2 |
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