body stringlengths 26 98.2k | body_hash int64 -9,222,864,604,528,158,000 9,221,803,474B | docstring stringlengths 1 16.8k | path stringlengths 5 230 | name stringlengths 1 96 | repository_name stringlengths 7 89 | lang stringclasses 1
value | body_without_docstring stringlengths 20 98.2k |
|---|---|---|---|---|---|---|---|
@property
@since('2.0.0')
def numIterations(self):
'\n Number of training iterations.\n '
return self._call_java('numIterations') | 3,231,421,437,338,347,000 | Number of training iterations. | python/pyspark/ml/regression.py | numIterations | AjithShetty2489/spark | python | @property
@since('2.0.0')
def numIterations(self):
'\n \n '
return self._call_java('numIterations') |
@property
@since('2.0.0')
def solver(self):
'\n The numeric solver used for training.\n '
return self._call_java('solver') | 7,895,560,103,752,479,000 | The numeric solver used for training. | python/pyspark/ml/regression.py | solver | AjithShetty2489/spark | python | @property
@since('2.0.0')
def solver(self):
'\n \n '
return self._call_java('solver') |
@property
@since('2.0.0')
def coefficientStandardErrors(self):
'\n Standard error of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_j... | -3,088,971,962,521,040,400 | Standard error of estimated coefficients and intercept.
If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,
then the last element returned corresponds to the intercept. | python/pyspark/ml/regression.py | coefficientStandardErrors | AjithShetty2489/spark | python | @property
@since('2.0.0')
def coefficientStandardErrors(self):
'\n Standard error of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_j... |
@property
@since('2.0.0')
def tValues(self):
'\n T-statistic of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('tValues') | 1,458,171,731,345,339,000 | T-statistic of estimated coefficients and intercept.
If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,
then the last element returned corresponds to the intercept. | python/pyspark/ml/regression.py | tValues | AjithShetty2489/spark | python | @property
@since('2.0.0')
def tValues(self):
'\n T-statistic of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('tValues') |
@property
@since('2.0.0')
def pValues(self):
'\n Two-sided p-value of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('pValues') | -4,774,701,551,750,324,000 | Two-sided p-value of estimated coefficients and intercept.
If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,
then the last element returned corresponds to the intercept. | python/pyspark/ml/regression.py | pValues | AjithShetty2489/spark | python | @property
@since('2.0.0')
def pValues(self):
'\n Two-sided p-value of estimated coefficients and intercept.\n\n If :py:attr:`GeneralizedLinearRegression.fitIntercept` is set to True,\n then the last element returned corresponds to the intercept.\n '
return self._call_java('pValues') |
@since('3.0.0')
def getFactorSize(self):
'\n Gets the value of factorSize or its default value.\n '
return self.getOrDefault(self.factorSize) | 1,791,135,980,955,474,200 | Gets the value of factorSize or its default value. | python/pyspark/ml/regression.py | getFactorSize | AjithShetty2489/spark | python | @since('3.0.0')
def getFactorSize(self):
'\n \n '
return self.getOrDefault(self.factorSize) |
@since('3.0.0')
def getFitLinear(self):
'\n Gets the value of fitLinear or its default value.\n '
return self.getOrDefault(self.fitLinear) | -4,194,490,835,834,387,000 | Gets the value of fitLinear or its default value. | python/pyspark/ml/regression.py | getFitLinear | AjithShetty2489/spark | python | @since('3.0.0')
def getFitLinear(self):
'\n \n '
return self.getOrDefault(self.fitLinear) |
@since('3.0.0')
def getMiniBatchFraction(self):
'\n Gets the value of miniBatchFraction or its default value.\n '
return self.getOrDefault(self.miniBatchFraction) | 3,609,176,603,815,900,000 | Gets the value of miniBatchFraction or its default value. | python/pyspark/ml/regression.py | getMiniBatchFraction | AjithShetty2489/spark | python | @since('3.0.0')
def getMiniBatchFraction(self):
'\n \n '
return self.getOrDefault(self.miniBatchFraction) |
@since('3.0.0')
def getInitStd(self):
'\n Gets the value of initStd or its default value.\n '
return self.getOrDefault(self.initStd) | 3,816,975,538,956,782,600 | Gets the value of initStd or its default value. | python/pyspark/ml/regression.py | getInitStd | AjithShetty2489/spark | python | @since('3.0.0')
def getInitStd(self):
'\n \n '
return self.getOrDefault(self.initStd) |
@keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
'\n __init__(self, featuresCol="features... | 3,828,870,183,181,281,300 | __init__(self, featuresCol="features", labelCol="label", predictionCol="prediction", factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-6, solver="adamW", seed=None) | python/pyspark/ml/regression.py | __init__ | AjithShetty2489/spark | python | @keyword_only
def __init__(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
'\n \n '
super(FMRegressor, s... |
@keyword_only
@since('3.0.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
'\n setParams(self, fea... | -4,552,423,437,633,609,700 | setParams(self, featuresCol="features", labelCol="label", predictionCol="prediction", factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-6, solver="adamW", seed=None)
Sets Params for ... | python/pyspark/ml/regression.py | setParams | AjithShetty2489/spark | python | @keyword_only
@since('3.0.0')
def setParams(self, featuresCol='features', labelCol='label', predictionCol='prediction', factorSize=8, fitIntercept=True, fitLinear=True, regParam=0.0, miniBatchFraction=1.0, initStd=0.01, maxIter=100, stepSize=1.0, tol=1e-06, solver='adamW', seed=None):
'\n setParams(self, fea... |
@since('3.0.0')
def setFactorSize(self, value):
'\n Sets the value of :py:attr:`factorSize`.\n '
return self._set(factorSize=value) | -8,753,162,586,989,364,000 | Sets the value of :py:attr:`factorSize`. | python/pyspark/ml/regression.py | setFactorSize | AjithShetty2489/spark | python | @since('3.0.0')
def setFactorSize(self, value):
'\n \n '
return self._set(factorSize=value) |
@since('3.0.0')
def setFitLinear(self, value):
'\n Sets the value of :py:attr:`fitLinear`.\n '
return self._set(fitLinear=value) | 4,134,348,066,261,796,000 | Sets the value of :py:attr:`fitLinear`. | python/pyspark/ml/regression.py | setFitLinear | AjithShetty2489/spark | python | @since('3.0.0')
def setFitLinear(self, value):
'\n \n '
return self._set(fitLinear=value) |
@since('3.0.0')
def setMiniBatchFraction(self, value):
'\n Sets the value of :py:attr:`miniBatchFraction`.\n '
return self._set(miniBatchFraction=value) | 5,309,665,991,897,720,000 | Sets the value of :py:attr:`miniBatchFraction`. | python/pyspark/ml/regression.py | setMiniBatchFraction | AjithShetty2489/spark | python | @since('3.0.0')
def setMiniBatchFraction(self, value):
'\n \n '
return self._set(miniBatchFraction=value) |
@since('3.0.0')
def setInitStd(self, value):
'\n Sets the value of :py:attr:`initStd`.\n '
return self._set(initStd=value) | 7,314,427,056,946,567,000 | Sets the value of :py:attr:`initStd`. | python/pyspark/ml/regression.py | setInitStd | AjithShetty2489/spark | python | @since('3.0.0')
def setInitStd(self, value):
'\n \n '
return self._set(initStd=value) |
@since('3.0.0')
def setMaxIter(self, value):
'\n Sets the value of :py:attr:`maxIter`.\n '
return self._set(maxIter=value) | 8,691,892,694,452,766,000 | Sets the value of :py:attr:`maxIter`. | python/pyspark/ml/regression.py | setMaxIter | AjithShetty2489/spark | python | @since('3.0.0')
def setMaxIter(self, value):
'\n \n '
return self._set(maxIter=value) |
@since('3.0.0')
def setStepSize(self, value):
'\n Sets the value of :py:attr:`stepSize`.\n '
return self._set(stepSize=value) | -6,862,698,385,601,250,000 | Sets the value of :py:attr:`stepSize`. | python/pyspark/ml/regression.py | setStepSize | AjithShetty2489/spark | python | @since('3.0.0')
def setStepSize(self, value):
'\n \n '
return self._set(stepSize=value) |
@since('3.0.0')
def setTol(self, value):
'\n Sets the value of :py:attr:`tol`.\n '
return self._set(tol=value) | 3,581,312,399,990,777,300 | Sets the value of :py:attr:`tol`. | python/pyspark/ml/regression.py | setTol | AjithShetty2489/spark | python | @since('3.0.0')
def setTol(self, value):
'\n \n '
return self._set(tol=value) |
@since('3.0.0')
def setSolver(self, value):
'\n Sets the value of :py:attr:`solver`.\n '
return self._set(solver=value) | 5,689,872,772,768,317,000 | Sets the value of :py:attr:`solver`. | python/pyspark/ml/regression.py | setSolver | AjithShetty2489/spark | python | @since('3.0.0')
def setSolver(self, value):
'\n \n '
return self._set(solver=value) |
@since('3.0.0')
def setSeed(self, value):
'\n Sets the value of :py:attr:`seed`.\n '
return self._set(seed=value) | 1,987,893,307,764,387,800 | Sets the value of :py:attr:`seed`. | python/pyspark/ml/regression.py | setSeed | AjithShetty2489/spark | python | @since('3.0.0')
def setSeed(self, value):
'\n \n '
return self._set(seed=value) |
@since('3.0.0')
def setFitIntercept(self, value):
'\n Sets the value of :py:attr:`fitIntercept`.\n '
return self._set(fitIntercept=value) | 4,746,861,393,854,669,000 | Sets the value of :py:attr:`fitIntercept`. | python/pyspark/ml/regression.py | setFitIntercept | AjithShetty2489/spark | python | @since('3.0.0')
def setFitIntercept(self, value):
'\n \n '
return self._set(fitIntercept=value) |
@since('3.0.0')
def setRegParam(self, value):
'\n Sets the value of :py:attr:`regParam`.\n '
return self._set(regParam=value) | 4,120,470,953,683,944,400 | Sets the value of :py:attr:`regParam`. | python/pyspark/ml/regression.py | setRegParam | AjithShetty2489/spark | python | @since('3.0.0')
def setRegParam(self, value):
'\n \n '
return self._set(regParam=value) |
@property
@since('3.0.0')
def intercept(self):
'\n Model intercept.\n '
return self._call_java('intercept') | -378,010,395,860,784,450 | Model intercept. | python/pyspark/ml/regression.py | intercept | AjithShetty2489/spark | python | @property
@since('3.0.0')
def intercept(self):
'\n \n '
return self._call_java('intercept') |
@property
@since('3.0.0')
def linear(self):
'\n Model linear term.\n '
return self._call_java('linear') | 8,724,079,305,889,703,000 | Model linear term. | python/pyspark/ml/regression.py | linear | AjithShetty2489/spark | python | @property
@since('3.0.0')
def linear(self):
'\n \n '
return self._call_java('linear') |
@property
@since('3.0.0')
def factors(self):
'\n Model factor term.\n '
return self._call_java('factors') | -1,686,756,612,127,754,800 | Model factor term. | python/pyspark/ml/regression.py | factors | AjithShetty2489/spark | python | @property
@since('3.0.0')
def factors(self):
'\n \n '
return self._call_java('factors') |
def min_time(x):
'my lib'
graph = GeometryTopology.Graph()
for i in range(h):
for j in range(w):
graph.add_node((i, j))
for i in range(h):
for j in range(w):
if (i > 0):
graph.add_edge((i, j), ((i - 1), j), weight=(1 if (s[(i - 1)][j] == '.') else ... | -785,816,328,218,200,000 | my lib | jp.atcoder/abc009/abc009_4/17183548.py | min_time | kagemeka/atcoder-submissions | python | def min_time(x):
graph = GeometryTopology.Graph()
for i in range(h):
for j in range(w):
graph.add_node((i, j))
for i in range(h):
for j in range(w):
if (i > 0):
graph.add_edge((i, j), ((i - 1), j), weight=(1 if (s[(i - 1)][j] == '.') else x))
... |
def group_by(keys, values=None, reduction=None, axis=0):
'construct a grouping object on the given keys, optionally performing the given reduction on the given values\n\n Parameters\n ----------\n keys : indexable object\n keys to group by\n values : array_like, optional\n sequence of valu... | 6,910,904,156,956,246,000 | construct a grouping object on the given keys, optionally performing the given reduction on the given values
Parameters
----------
keys : indexable object
keys to group by
values : array_like, optional
sequence of values, of the same length as keys
if a reduction function is provided, the given values are ... | numpy_indexed/grouping.py | group_by | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def group_by(keys, values=None, reduction=None, axis=0):
'construct a grouping object on the given keys, optionally performing the given reduction on the given values\n\n Parameters\n ----------\n keys : indexable object\n keys to group by\n values : array_like, optional\n sequence of valu... |
def __init__(self, keys, axis=0):
'\n Parameters\n ----------\n keys : indexable object\n sequence of keys to group by\n axis : int, optional\n axis to regard as the key-sequence, in case keys is multi-dimensional\n\n See Also\n --------\n numpy... | 6,020,760,228,939,865,000 | Parameters
----------
keys : indexable object
sequence of keys to group by
axis : int, optional
axis to regard as the key-sequence, in case keys is multi-dimensional
See Also
--------
numpy_indexed.as_index : for information regarding the casting rules to a valid Index object | numpy_indexed/grouping.py | __init__ | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def __init__(self, keys, axis=0):
'\n Parameters\n ----------\n keys : indexable object\n sequence of keys to group by\n axis : int, optional\n axis to regard as the key-sequence, in case keys is multi-dimensional\n\n See Also\n --------\n numpy... |
@property
def unique(self):
'unique keys'
return self.index.unique | -930,526,704,603,093,000 | unique keys | numpy_indexed/grouping.py | unique | EelcoHoogendoorn/Numpy_arraysetops_EP | python | @property
def unique(self):
return self.index.unique |
@property
def count(self):
'count of each unique key'
return self.index.count | 8,502,613,712,486,878,000 | count of each unique key | numpy_indexed/grouping.py | count | EelcoHoogendoorn/Numpy_arraysetops_EP | python | @property
def count(self):
return self.index.count |
@property
def inverse(self):
'mapping such that unique[inverse]==keys'
return self.index.inverse | 5,544,252,425,276,580,000 | mapping such that unique[inverse]==keys | numpy_indexed/grouping.py | inverse | EelcoHoogendoorn/Numpy_arraysetops_EP | python | @property
def inverse(self):
return self.index.inverse |
@property
def groups(self):
'int, number of groups formed by the keys'
return self.index.groups | 8,731,109,496,834,587,000 | int, number of groups formed by the keys | numpy_indexed/grouping.py | groups | EelcoHoogendoorn/Numpy_arraysetops_EP | python | @property
def groups(self):
return self.index.groups |
def split_iterable_as_iterable(self, values):
'Group iterable into iterables, in the order of the keys\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n iterable of items in values\n\n... | -1,514,213,191,221,959,200 | Group iterable into iterables, in the order of the keys
Parameters
----------
values : iterable of length equal to keys
iterable of values to be grouped
Yields
------
iterable of items in values
Notes
-----
Memory consumption depends on the amount of sorting required
Worst case, if index.sorter[-1] = 0, we need ... | numpy_indexed/grouping.py | split_iterable_as_iterable | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_iterable_as_iterable(self, values):
'Group iterable into iterables, in the order of the keys\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n iterable of items in values\n\n... |
def split_iterable_as_unordered_iterable(self, values):
'Group iterable into iterables, without regard for the ordering of self.index.unique\n key-group tuples are yielded as soon as they are complete\n\n Parameters\n ----------\n values : iterable of length equal to keys\n it... | 7,352,415,599,736,740,000 | Group iterable into iterables, without regard for the ordering of self.index.unique
key-group tuples are yielded as soon as they are complete
Parameters
----------
values : iterable of length equal to keys
iterable of values to be grouped
Yields
------
tuple of key, and a list of corresponding items in values
No... | numpy_indexed/grouping.py | split_iterable_as_unordered_iterable | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_iterable_as_unordered_iterable(self, values):
'Group iterable into iterables, without regard for the ordering of self.index.unique\n key-group tuples are yielded as soon as they are complete\n\n Parameters\n ----------\n values : iterable of length equal to keys\n it... |
def split_sequence_as_iterable(self, values):
'Group sequence into iterables\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n iterable of items in values\n\n Notes\n ---... | -1,918,695,829,166,377,500 | Group sequence into iterables
Parameters
----------
values : iterable of length equal to keys
iterable of values to be grouped
Yields
------
iterable of items in values
Notes
-----
This is the preferred method if values has random access, but we dont want it completely in memory.
Like a big memory mapped file, f... | numpy_indexed/grouping.py | split_sequence_as_iterable | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_sequence_as_iterable(self, values):
'Group sequence into iterables\n\n Parameters\n ----------\n values : iterable of length equal to keys\n iterable of values to be grouped\n\n Yields\n ------\n iterable of items in values\n\n Notes\n ---... |
def split_array_as_array(self, values):
'Group ndarray into ndarray by means of reshaping\n\n Parameters\n ----------\n values : ndarray_like, [index.size, ...]\n\n Returns\n -------\n ndarray, [groups, group_size, ...]\n values grouped by key\n\n Raises\n... | 4,391,596,414,982,254,000 | Group ndarray into ndarray by means of reshaping
Parameters
----------
values : ndarray_like, [index.size, ...]
Returns
-------
ndarray, [groups, group_size, ...]
values grouped by key
Raises
------
AssertionError
This operation is only possible if index.uniform==True | numpy_indexed/grouping.py | split_array_as_array | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_array_as_array(self, values):
'Group ndarray into ndarray by means of reshaping\n\n Parameters\n ----------\n values : ndarray_like, [index.size, ...]\n\n Returns\n -------\n ndarray, [groups, group_size, ...]\n values grouped by key\n\n Raises\n... |
def split_array_as_list(self, values):
'Group values as a list of arrays, or a jagged-array\n\n Parameters\n ----------\n values : ndarray, [keys, ...]\n\n Returns\n -------\n list of length self.groups of ndarray, [key_count, ...]\n '
values = np.asarray(values)... | -2,253,695,053,208,438,500 | Group values as a list of arrays, or a jagged-array
Parameters
----------
values : ndarray, [keys, ...]
Returns
-------
list of length self.groups of ndarray, [key_count, ...] | numpy_indexed/grouping.py | split_array_as_list | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split_array_as_list(self, values):
'Group values as a list of arrays, or a jagged-array\n\n Parameters\n ----------\n values : ndarray, [keys, ...]\n\n Returns\n -------\n list of length self.groups of ndarray, [key_count, ...]\n '
values = np.asarray(values)... |
def split(self, values):
'some sensible defaults'
try:
return self.split_array_as_array(values)
except:
return self.split_array_as_list(values) | 8,993,428,587,264,464,000 | some sensible defaults | numpy_indexed/grouping.py | split | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def split(self, values):
try:
return self.split_array_as_array(values)
except:
return self.split_array_as_list(values) |
def __call__(self, values):
'not sure how i feel about this. explicit is better than implict?'
return (self.unique, self.split(values)) | 6,196,984,892,751,246,000 | not sure how i feel about this. explicit is better than implict? | numpy_indexed/grouping.py | __call__ | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def __call__(self, values):
return (self.unique, self.split(values)) |
def reduce(self, values, operator=np.add, axis=0, dtype=None):
'Reduce the values over identical key groups, using the given ufunc\n reduction is over the first axis, which should have elements corresponding to the keys\n all other axes are treated indepenently for the sake of this reduction\n\n ... | -1,888,145,151,123,266,800 | Reduce the values over identical key groups, using the given ufunc
reduction is over the first axis, which should have elements corresponding to the keys
all other axes are treated indepenently for the sake of this reduction
Parameters
----------
values : ndarray, [keys, ...]
values to perform reduction over
opera... | numpy_indexed/grouping.py | reduce | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def reduce(self, values, operator=np.add, axis=0, dtype=None):
'Reduce the values over identical key groups, using the given ufunc\n reduction is over the first axis, which should have elements corresponding to the keys\n all other axes are treated indepenently for the sake of this reduction\n\n ... |
def sum(self, values, axis=0, dtype=None):
'compute the sum over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to sum per group\n axis : int, optional\n alternative reduction axis for values\n dtype : output dtype\n\n ... | -1,945,319,861,920,158,000 | compute the sum over each group
Parameters
----------
values : array_like, [keys, ...]
values to sum per group
axis : int, optional
alternative reduction axis for values
dtype : output dtype
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced ove... | numpy_indexed/grouping.py | sum | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def sum(self, values, axis=0, dtype=None):
'compute the sum over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to sum per group\n axis : int, optional\n alternative reduction axis for values\n dtype : output dtype\n\n ... |
def prod(self, values, axis=0, dtype=None):
'compute the product over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to multiply per group\n axis : int, optional\n alternative reduction axis for values\n dtype : output dtyp... | 4,575,069,133,977,734,000 | compute the product over each group
Parameters
----------
values : array_like, [keys, ...]
values to multiply per group
axis : int, optional
alternative reduction axis for values
dtype : output dtype
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, re... | numpy_indexed/grouping.py | prod | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def prod(self, values, axis=0, dtype=None):
'compute the product over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to multiply per group\n axis : int, optional\n alternative reduction axis for values\n dtype : output dtyp... |
def mean(self, values, axis=0, weights=None, dtype=None):
'compute the mean over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take average of per group\n axis : int, optional\n alternative reduction axis for values\n w... | 8,492,916,686,960,966,000 | compute the mean over each group
Parameters
----------
values : array_like, [keys, ...]
values to take average of per group
axis : int, optional
alternative reduction axis for values
weights : ndarray, [keys, ...], optional
weight to use for each value
dtype : output dtype
Returns
-------
unique: ndarray,... | numpy_indexed/grouping.py | mean | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def mean(self, values, axis=0, weights=None, dtype=None):
'compute the mean over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take average of per group\n axis : int, optional\n alternative reduction axis for values\n w... |
def var(self, values, axis=0, weights=None, dtype=None):
'compute the variance over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take variance of per group\n axis : int, optional\n alternative reduction axis for values\n\n ... | -4,991,949,059,122,910,000 | compute the variance over each group
Parameters
----------
values : array_like, [keys, ...]
values to take variance of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over g... | numpy_indexed/grouping.py | var | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def var(self, values, axis=0, weights=None, dtype=None):
'compute the variance over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take variance of per group\n axis : int, optional\n alternative reduction axis for values\n\n ... |
def std(self, values, axis=0, weights=None, dtype=None):
'standard deviation over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take standard deviation of per group\n axis : int, optional\n alternative reduction axis for value... | -5,936,213,147,425,417,000 | standard deviation over each group
Parameters
----------
values : array_like, [keys, ...]
values to take standard deviation of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduce... | numpy_indexed/grouping.py | std | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def std(self, values, axis=0, weights=None, dtype=None):
'standard deviation over each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take standard deviation of per group\n axis : int, optional\n alternative reduction axis for value... |
def median(self, values, axis=0, average=True):
'compute the median value over each group.\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to compute the median of per group\n axis : int, optional\n alternative reduction axis for values\n ... | -9,059,944,262,215,597,000 | compute the median value over each group.
Parameters
----------
values : array_like, [keys, ...]
values to compute the median of per group
axis : int, optional
alternative reduction axis for values
average : bool, optional
when average is true, the average of the two central values is taken for groups with... | numpy_indexed/grouping.py | median | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def median(self, values, axis=0, average=True):
'compute the median value over each group.\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to compute the median of per group\n axis : int, optional\n alternative reduction axis for values\n ... |
def mode(self, values, weights=None):
'compute the mode within each group.\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to compute the mode of per group\n weights : array_like, [keys], float, optional\n optional weight associated with each... | 4,774,019,377,929,014,000 | compute the mode within each group.
Parameters
----------
values : array_like, [keys, ...]
values to compute the mode of per group
weights : array_like, [keys], float, optional
optional weight associated with each entry in values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [gr... | numpy_indexed/grouping.py | mode | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def mode(self, values, weights=None):
'compute the mode within each group.\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to compute the mode of per group\n weights : array_like, [keys], float, optional\n optional weight associated with each... |
def min(self, values, axis=0):
'return the minimum within each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take minimum of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -----... | -2,479,186,574,521,301,000 | return the minimum within each group
Parameters
----------
values : array_like, [keys, ...]
values to take minimum of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over gr... | numpy_indexed/grouping.py | min | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def min(self, values, axis=0):
'return the minimum within each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take minimum of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -----... |
def max(self, values, axis=0):
'return the maximum within each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take maximum of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -----... | -4,892,151,556,389,857,000 | return the maximum within each group
Parameters
----------
values : array_like, [keys, ...]
values to take maximum of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
value array, reduced over gr... | numpy_indexed/grouping.py | max | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def max(self, values, axis=0):
'return the maximum within each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take maximum of per group\n axis : int, optional\n alternative reduction axis for values\n\n Returns\n -----... |
def first(self, values, axis=0):
'return values at first occurance of its associated key\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to pick the first value of per group\n axis : int, optional\n alternative reduction axis for values\n\n ... | 1,523,354,121,096,837,400 | return values at first occurance of its associated key
Parameters
----------
values : array_like, [keys, ...]
values to pick the first value of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
va... | numpy_indexed/grouping.py | first | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def first(self, values, axis=0):
'return values at first occurance of its associated key\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to pick the first value of per group\n axis : int, optional\n alternative reduction axis for values\n\n ... |
def last(self, values, axis=0):
'return values at last occurance of its associated key\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to pick the last value of per group\n axis : int, optional\n alternative reduction axis for values\n\n ... | 6,716,917,442,927,277,000 | return values at last occurance of its associated key
Parameters
----------
values : array_like, [keys, ...]
values to pick the last value of per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...]
valu... | numpy_indexed/grouping.py | last | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def last(self, values, axis=0):
'return values at last occurance of its associated key\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to pick the last value of per group\n axis : int, optional\n alternative reduction axis for values\n\n ... |
def any(self, values, axis=0):
'compute if any item evaluates to true in each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take boolean predicate over per group\n axis : int, optional\n alternative reduction axis for values\n\n ... | -577,262,749,674,790,800 | compute if any item evaluates to true in each group
Parameters
----------
values : array_like, [keys, ...]
values to take boolean predicate over per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...], np.b... | numpy_indexed/grouping.py | any | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def any(self, values, axis=0):
'compute if any item evaluates to true in each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take boolean predicate over per group\n axis : int, optional\n alternative reduction axis for values\n\n ... |
def all(self, values, axis=0):
'compute if all items evaluates to true in each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take boolean predicate over per group\n axis : int, optional\n alternative reduction axis for values\n\n ... | 1,479,049,310,855,061,500 | compute if all items evaluates to true in each group
Parameters
----------
values : array_like, [keys, ...]
values to take boolean predicate over per group
axis : int, optional
alternative reduction axis for values
Returns
-------
unique: ndarray, [groups]
unique keys
reduced : ndarray, [groups, ...], np.... | numpy_indexed/grouping.py | all | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def all(self, values, axis=0):
'compute if all items evaluates to true in each group\n\n Parameters\n ----------\n values : array_like, [keys, ...]\n values to take boolean predicate over per group\n axis : int, optional\n alternative reduction axis for values\n\n ... |
def argmin(self, values):
'return the index into values corresponding to the minimum value of the group\n\n Parameters\n ----------\n values : array_like, [keys]\n values to pick the argmin of per group\n\n Returns\n -------\n unique: ndarray, [groups]\n ... | -7,292,802,029,241,178,000 | return the index into values corresponding to the minimum value of the group
Parameters
----------
values : array_like, [keys]
values to pick the argmin of per group
Returns
-------
unique: ndarray, [groups]
unique keys
argmin : ndarray, [groups]
index into value array, representing the argmin per group | numpy_indexed/grouping.py | argmin | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def argmin(self, values):
'return the index into values corresponding to the minimum value of the group\n\n Parameters\n ----------\n values : array_like, [keys]\n values to pick the argmin of per group\n\n Returns\n -------\n unique: ndarray, [groups]\n ... |
def argmax(self, values):
'return the index into values corresponding to the maximum value of the group\n\n Parameters\n ----------\n values : array_like, [keys]\n values to pick the argmax of per group\n\n Returns\n -------\n unique: ndarray, [groups]\n ... | -2,912,817,621,899,028,000 | return the index into values corresponding to the maximum value of the group
Parameters
----------
values : array_like, [keys]
values to pick the argmax of per group
Returns
-------
unique: ndarray, [groups]
unique keys
argmax : ndarray, [groups]
index into value array, representing the argmax per group | numpy_indexed/grouping.py | argmax | EelcoHoogendoorn/Numpy_arraysetops_EP | python | def argmax(self, values):
'return the index into values corresponding to the maximum value of the group\n\n Parameters\n ----------\n values : array_like, [keys]\n values to pick the argmax of per group\n\n Returns\n -------\n unique: ndarray, [groups]\n ... |
def __init__(self, raw_gadget):
'\n Gadget constructor\n :param str raw_gadget: raw line output from ROPgadget\n '
self.offset = raw_gadget[:raw_gadget.find(':')]
self.instruction_string = raw_gadget[(raw_gadget.find(':') + 2):]
self.instructions = []
for instr in self.instructi... | 8,029,491,231,991,800,000 | Gadget constructor
:param str raw_gadget: raw line output from ROPgadget | src/static_analyzer/Gadget.py | __init__ | michaelbrownuc/GadgetSetAnalyzer | python | def __init__(self, raw_gadget):
'\n Gadget constructor\n :param str raw_gadget: raw line output from ROPgadget\n '
self.offset = raw_gadget[:raw_gadget.find(':')]
self.instruction_string = raw_gadget[(raw_gadget.find(':') + 2):]
self.instructions = []
for instr in self.instructi... |
def is_useless_op(self):
'\n :return boolean: Returns True if the first instruction opcode is in the "useless" list, False otherwise\n Default behavior is to consider opcodes useful unless otherwise observed.\n '
first_opcode = self.instructions[0].opcode
if first_opcod... | -8,508,034,393,575,901,000 | :return boolean: Returns True if the first instruction opcode is in the "useless" list, False otherwise
Default behavior is to consider opcodes useful unless otherwise observed. | src/static_analyzer/Gadget.py | is_useless_op | michaelbrownuc/GadgetSetAnalyzer | python | def is_useless_op(self):
'\n :return boolean: Returns True if the first instruction opcode is in the "useless" list, False otherwise\n Default behavior is to consider opcodes useful unless otherwise observed.\n '
first_opcode = self.instructions[0].opcode
if first_opcod... |
def contains_unusable_op(self):
'\n :return boolean: Returns True if any instruction opcode is unusable. False otherwise\n unusable instructions are Ring-0 opcodes that trap in user mode and some other exceptional ops.\n '
for instr in self.instructions:
if instr.o... | 5,801,953,904,756,299,000 | :return boolean: Returns True if any instruction opcode is unusable. False otherwise
unusable instructions are Ring-0 opcodes that trap in user mode and some other exceptional ops. | src/static_analyzer/Gadget.py | contains_unusable_op | michaelbrownuc/GadgetSetAnalyzer | python | def contains_unusable_op(self):
'\n :return boolean: Returns True if any instruction opcode is unusable. False otherwise\n unusable instructions are Ring-0 opcodes that trap in user mode and some other exceptional ops.\n '
for instr in self.instructions:
if instr.o... |
def is_gpi_only(self):
"\n :return boolean: Returns True if the gadget is a single instruction and starts with 'ret', 'jmp', or 'call',\n False otherwise\n "
if (len(self.instructions) == 1):
opcode = self.instructions[0].opcode
if (opcode.startswith('ret') ... | 4,594,664,549,577,642,500 | :return boolean: Returns True if the gadget is a single instruction and starts with 'ret', 'jmp', or 'call',
False otherwise | src/static_analyzer/Gadget.py | is_gpi_only | michaelbrownuc/GadgetSetAnalyzer | python | def is_gpi_only(self):
"\n :return boolean: Returns True if the gadget is a single instruction and starts with 'ret', 'jmp', or 'call',\n False otherwise\n "
if (len(self.instructions) == 1):
opcode = self.instructions[0].opcode
if (opcode.startswith('ret') ... |
def is_invalid_branch(self):
"\n :return boolean: Returns True if the gadget is 'jmp' or 'call' ending and the call target is a constant offset\n or does not target a recognized register family. False otherwise\n "
last_instr = self.instructions[(len(self.instructions) - 1)... | 5,528,830,466,194,106,000 | :return boolean: Returns True if the gadget is 'jmp' or 'call' ending and the call target is a constant offset
or does not target a recognized register family. False otherwise | src/static_analyzer/Gadget.py | is_invalid_branch | michaelbrownuc/GadgetSetAnalyzer | python | def is_invalid_branch(self):
"\n :return boolean: Returns True if the gadget is 'jmp' or 'call' ending and the call target is a constant offset\n or does not target a recognized register family. False otherwise\n "
last_instr = self.instructions[(len(self.instructions) - 1)... |
def has_invalid_ret_offset(self):
"\n :return boolean: Returns True if the gadget is 'ret' ending and contains a constant offset that is not byte\n aligned or is greater than 32 bytes, False otherwise\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (... | -862,678,852,484,315,100 | :return boolean: Returns True if the gadget is 'ret' ending and contains a constant offset that is not byte
aligned or is greater than 32 bytes, False otherwise | src/static_analyzer/Gadget.py | has_invalid_ret_offset | michaelbrownuc/GadgetSetAnalyzer | python | def has_invalid_ret_offset(self):
"\n :return boolean: Returns True if the gadget is 'ret' ending and contains a constant offset that is not byte\n aligned or is greater than 32 bytes, False otherwise\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (... |
def clobbers_created_value(self):
'\n :return boolean: Returns True if the gadget completely overwrites the value created in the first instruction,\n False otherwise.\n '
first_instr = self.instructions[0]
if ((not first_instr.creates_value()) or ('xchg' in first_instr.... | -1,797,705,343,943,502,000 | :return boolean: Returns True if the gadget completely overwrites the value created in the first instruction,
False otherwise. | src/static_analyzer/Gadget.py | clobbers_created_value | michaelbrownuc/GadgetSetAnalyzer | python | def clobbers_created_value(self):
'\n :return boolean: Returns True if the gadget completely overwrites the value created in the first instruction,\n False otherwise.\n '
first_instr = self.instructions[0]
if ((not first_instr.creates_value()) or ('xchg' in first_instr.... |
def creates_unusable_value(self):
'\n :return boolean: Returns True if the gadget creates a value in segment or extension registers, or are\n RIP-relative, or are constant memory locations; False otherwise.\n '
first_instr = self.instructions[0]
if ((first_instr.opcode ... | 2,964,850,469,619,353,600 | :return boolean: Returns True if the gadget creates a value in segment or extension registers, or are
RIP-relative, or are constant memory locations; False otherwise. | src/static_analyzer/Gadget.py | creates_unusable_value | michaelbrownuc/GadgetSetAnalyzer | python | def creates_unusable_value(self):
'\n :return boolean: Returns True if the gadget creates a value in segment or extension registers, or are\n RIP-relative, or are constant memory locations; False otherwise.\n '
first_instr = self.instructions[0]
if ((first_instr.opcode ... |
def contains_intermediate_GPI(self):
"\n :return boolean: Returns True if the gadget's intermediate instructions contain a GPI (or a generic interrupt),\n False otherwise.\n "
for i in range((len(self.instructions) - 1)):
cur_opcode = self.instructions[i].opcode
... | 1,243,145,747,006,903,300 | :return boolean: Returns True if the gadget's intermediate instructions contain a GPI (or a generic interrupt),
False otherwise. | src/static_analyzer/Gadget.py | contains_intermediate_GPI | michaelbrownuc/GadgetSetAnalyzer | python | def contains_intermediate_GPI(self):
"\n :return boolean: Returns True if the gadget's intermediate instructions contain a GPI (or a generic interrupt),\n False otherwise.\n "
for i in range((len(self.instructions) - 1)):
cur_opcode = self.instructions[i].opcode
... |
def clobbers_stack_pointer(self):
"\n :return boolean: Returns True if the ROP gadget's instructions assign a non-static value to the stack pointer\n register, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if last_instr.opcode.startsw... | -2,445,516,483,870,566,400 | :return boolean: Returns True if the ROP gadget's instructions assign a non-static value to the stack pointer
register, False otherwise. | src/static_analyzer/Gadget.py | clobbers_stack_pointer | michaelbrownuc/GadgetSetAnalyzer | python | def clobbers_stack_pointer(self):
"\n :return boolean: Returns True if the ROP gadget's instructions assign a non-static value to the stack pointer\n register, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if last_instr.opcode.startsw... |
def clobbers_indirect_target(self):
"\n :return boolean: Returns True if the JOP/COP gadget's instructions modify the indirect branch register in\n certain ways, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.star... | 5,501,789,693,138,077,000 | :return boolean: Returns True if the JOP/COP gadget's instructions modify the indirect branch register in
certain ways, False otherwise. | src/static_analyzer/Gadget.py | clobbers_indirect_target | michaelbrownuc/GadgetSetAnalyzer | python | def clobbers_indirect_target(self):
"\n :return boolean: Returns True if the JOP/COP gadget's instructions modify the indirect branch register in\n certain ways, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.star... |
def has_invalid_int_handler(self):
"\n :return boolean: Returns True if the gadget's instructions assign a non-static value to the stack pointer\n register, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswit... | 6,179,865,065,802,890,000 | :return boolean: Returns True if the gadget's instructions assign a non-static value to the stack pointer
register, False otherwise. | src/static_analyzer/Gadget.py | has_invalid_int_handler | michaelbrownuc/GadgetSetAnalyzer | python | def has_invalid_int_handler(self):
"\n :return boolean: Returns True if the gadget's instructions assign a non-static value to the stack pointer\n register, False otherwise.\n "
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswit... |
def is_rip_relative_indirect_branch(self):
'\n :return boolean: Returns True if the gadget is a JOP/COP gadget relying on a RIP relative indirect branch,\n False otherwise.\n '
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith... | -610,727,062,618,971,600 | :return boolean: Returns True if the gadget is a JOP/COP gadget relying on a RIP relative indirect branch,
False otherwise. | src/static_analyzer/Gadget.py | is_rip_relative_indirect_branch | michaelbrownuc/GadgetSetAnalyzer | python | def is_rip_relative_indirect_branch(self):
'\n :return boolean: Returns True if the gadget is a JOP/COP gadget relying on a RIP relative indirect branch,\n False otherwise.\n '
last_instr = self.instructions[(len(self.instructions) - 1)]
if (last_instr.opcode.startswith... |
def is_equal(self, rhs):
'\n :return boolean: Returns True if the gadgets are an exact match, including offset. Used for gadget locality.\n '
return ((self.offset == rhs.offset) and (self.instruction_string == rhs.instruction_string)) | 2,534,057,557,342,863,000 | :return boolean: Returns True if the gadgets are an exact match, including offset. Used for gadget locality. | src/static_analyzer/Gadget.py | is_equal | michaelbrownuc/GadgetSetAnalyzer | python | def is_equal(self, rhs):
'\n \n '
return ((self.offset == rhs.offset) and (self.instruction_string == rhs.instruction_string)) |
def is_duplicate(self, rhs):
'\n :return boolean: Returns True if the gadgets are a semantic match. Used for non-locality gadget metrics.\n Semantic match is defined as the exact same sequence of equivalent instructions.\n '
if (len(self.instructions) != len(rhs.instruction... | -8,467,245,155,612,059,000 | :return boolean: Returns True if the gadgets are a semantic match. Used for non-locality gadget metrics.
Semantic match is defined as the exact same sequence of equivalent instructions. | src/static_analyzer/Gadget.py | is_duplicate | michaelbrownuc/GadgetSetAnalyzer | python | def is_duplicate(self, rhs):
'\n :return boolean: Returns True if the gadgets are a semantic match. Used for non-locality gadget metrics.\n Semantic match is defined as the exact same sequence of equivalent instructions.\n '
if (len(self.instructions) != len(rhs.instruction... |
def is_JOP_COP_dispatcher(self):
"\n :return boolean: Returns True if the gadget is a JOP or COP dispatcher. Defined as a gadget that begins with a\n arithmetic operation on a register and ends with a branch to a deference of that register. Used\n to iterate th... | 5,951,576,296,575,257,000 | :return boolean: Returns True if the gadget is a JOP or COP dispatcher. Defined as a gadget that begins with a
arithmetic operation on a register and ends with a branch to a deference of that register. Used
to iterate through instructions in payload. Only restrictions on the arithmetic... | src/static_analyzer/Gadget.py | is_JOP_COP_dispatcher | michaelbrownuc/GadgetSetAnalyzer | python | def is_JOP_COP_dispatcher(self):
"\n :return boolean: Returns True if the gadget is a JOP or COP dispatcher. Defined as a gadget that begins with a\n arithmetic operation on a register and ends with a branch to a deference of that register. Used\n to iterate th... |
def is_JOP_COP_dataloader(self):
'\n :return boolean: Returns True if the gadget is a JOP or COP data loader. Defined as a gadget that begins with a\n pop opcode to a non-memory location, that is also not the target of the GPI. Used to pop a\n necessary value o... | 5,617,497,105,708,245,000 | :return boolean: Returns True if the gadget is a JOP or COP data loader. Defined as a gadget that begins with a
pop opcode to a non-memory location, that is also not the target of the GPI. Used to pop a
necessary value off stack en masse before redirecting to the dispatcher. | src/static_analyzer/Gadget.py | is_JOP_COP_dataloader | michaelbrownuc/GadgetSetAnalyzer | python | def is_JOP_COP_dataloader(self):
'\n :return boolean: Returns True if the gadget is a JOP or COP data loader. Defined as a gadget that begins with a\n pop opcode to a non-memory location, that is also not the target of the GPI. Used to pop a\n necessary value o... |
def is_JOP_initializer(self):
'\n :return boolean: Returns True if the gadget is a JOP Initializer. Defined as a gadget that begins with a\n "pop all" opcode, used to pop necessary values off stack en masse before redirecting to the\n dispatcher.\n '
... | 405,727,441,158,540,800 | :return boolean: Returns True if the gadget is a JOP Initializer. Defined as a gadget that begins with a
"pop all" opcode, used to pop necessary values off stack en masse before redirecting to the
dispatcher. | src/static_analyzer/Gadget.py | is_JOP_initializer | michaelbrownuc/GadgetSetAnalyzer | python | def is_JOP_initializer(self):
'\n :return boolean: Returns True if the gadget is a JOP Initializer. Defined as a gadget that begins with a\n "pop all" opcode, used to pop necessary values off stack en masse before redirecting to the\n dispatcher.\n '
... |
def is_JOP_trampoline(self):
'\n :return boolean: Returns True if the gadget is a JOP trampoline. Defined as a gadget that begins with a\n pop opcode to a non-memory location, and that ends in a dereference of that value. Used to\n redirect execution to value s... | -3,181,699,853,611,830,300 | :return boolean: Returns True if the gadget is a JOP trampoline. Defined as a gadget that begins with a
pop opcode to a non-memory location, and that ends in a dereference of that value. Used to
redirect execution to value stored in memory. | src/static_analyzer/Gadget.py | is_JOP_trampoline | michaelbrownuc/GadgetSetAnalyzer | python | def is_JOP_trampoline(self):
'\n :return boolean: Returns True if the gadget is a JOP trampoline. Defined as a gadget that begins with a\n pop opcode to a non-memory location, and that ends in a dereference of that value. Used to\n redirect execution to value s... |
def is_COP_initializer(self):
'\n :return boolean: Returns True if the gadget is a COP initializer. Defined as a gadget that begins with a\n "pop all" opcode, does not use register bx/cx/dx/di as the call target, and does not clobber\n bx/cx/dx or the call targ... | -943,675,825,263,414,400 | :return boolean: Returns True if the gadget is a COP initializer. Defined as a gadget that begins with a
"pop all" opcode, does not use register bx/cx/dx/di as the call target, and does not clobber
bx/cx/dx or the call target in an intermediate instruction | src/static_analyzer/Gadget.py | is_COP_initializer | michaelbrownuc/GadgetSetAnalyzer | python | def is_COP_initializer(self):
'\n :return boolean: Returns True if the gadget is a COP initializer. Defined as a gadget that begins with a\n "pop all" opcode, does not use register bx/cx/dx/di as the call target, and does not clobber\n bx/cx/dx or the call targ... |
def is_COP_strong_trampoline(self):
'\n :return boolean: Returns True if the gadget is a COP strong trampoline. Defined as a gadget that begins with a\n pop opcode, and contains at least one other pop operation. The last non-pop all operation must\n target the ... | -7,207,612,691,470,076,000 | :return boolean: Returns True if the gadget is a COP strong trampoline. Defined as a gadget that begins with a
pop opcode, and contains at least one other pop operation. The last non-pop all operation must
target the call target. | src/static_analyzer/Gadget.py | is_COP_strong_trampoline | michaelbrownuc/GadgetSetAnalyzer | python | def is_COP_strong_trampoline(self):
'\n :return boolean: Returns True if the gadget is a COP strong trampoline. Defined as a gadget that begins with a\n pop opcode, and contains at least one other pop operation. The last non-pop all operation must\n target the ... |
def is_COP_intrastack_pivot(self):
'\n :return boolean: Returns True if the gadget is a COP Intra-stack pivot gadget. Defined as a gadget that begins\n with an additive operation on the stack pointer register. Used to move around in shellcode\n during COP explo... | 9,165,429,799,068,683,000 | :return boolean: Returns True if the gadget is a COP Intra-stack pivot gadget. Defined as a gadget that begins
with an additive operation on the stack pointer register. Used to move around in shellcode
during COP exploits. Only restriction on the arithmetic operation is that the second... | src/static_analyzer/Gadget.py | is_COP_intrastack_pivot | michaelbrownuc/GadgetSetAnalyzer | python | def is_COP_intrastack_pivot(self):
'\n :return boolean: Returns True if the gadget is a COP Intra-stack pivot gadget. Defined as a gadget that begins\n with an additive operation on the stack pointer register. Used to move around in shellcode\n during COP explo... |
def check_contains_leave(self):
'\n :return void: Increases gadget\'s score if the gadget has an intermediate "leave" instruction.\n '
for i in range(1, (len(self.instructions) - 1)):
if (self.instructions[i].opcode == 'leave'):
self.score += 2.0
return | 7,210,409,693,577,871,000 | :return void: Increases gadget's score if the gadget has an intermediate "leave" instruction. | src/static_analyzer/Gadget.py | check_contains_leave | michaelbrownuc/GadgetSetAnalyzer | python | def check_contains_leave(self):
'\n :return void: Increases gadget\'s score if the gadget has an intermediate "leave" instruction.\n '
for i in range(1, (len(self.instructions) - 1)):
if (self.instructions[i].opcode == 'leave'):
self.score += 2.0
return |
def check_sp_target_of_operation(self):
"\n :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain\n operations on the stack pointer register family.\n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.... | -7,326,613,457,057,683,000 | :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain
operations on the stack pointer register family. | src/static_analyzer/Gadget.py | check_sp_target_of_operation | michaelbrownuc/GadgetSetAnalyzer | python | def check_sp_target_of_operation(self):
"\n :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain\n operations on the stack pointer register family.\n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.... |
def check_negative_sp_offsets(self):
"\n :return void: Increases gadget's score if its cumulative register offsets are negative.\n "
sp_offset = 0
for i in range(len(self.instructions)):
cur_instr = self.instructions[i]
if (cur_instr.opcode == 'push'):
sp_offset -= ... | -8,229,238,631,788,484,000 | :return void: Increases gadget's score if its cumulative register offsets are negative. | src/static_analyzer/Gadget.py | check_negative_sp_offsets | michaelbrownuc/GadgetSetAnalyzer | python | def check_negative_sp_offsets(self):
"\n \n "
sp_offset = 0
for i in range(len(self.instructions)):
cur_instr = self.instructions[i]
if (cur_instr.opcode == 'push'):
sp_offset -= 8
elif ((cur_instr.opcode == 'pop') and (cur_instr.op1 not in Instruction.regis... |
def check_contains_conditional_op(self):
"\n :return void: Increases gadget's score if it contains conditional instructions like jumps, sets, and moves.\n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (cur_instr.opcode.startswith('j') and (cur_... | -501,580,019,472,423,000 | :return void: Increases gadget's score if it contains conditional instructions like jumps, sets, and moves. | src/static_analyzer/Gadget.py | check_contains_conditional_op | michaelbrownuc/GadgetSetAnalyzer | python | def check_contains_conditional_op(self):
"\n \n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (cur_instr.opcode.startswith('j') and (cur_instr.opcode != 'jmp')):
self.score += 3.0
elif (('cmov' in cur_instr.opcode) or ('cmpx... |
def check_register_ops(self):
"\n :return void: Increases gadget's score if it contains operations on a value carrying or a bystander register\n "
first_instr = self.instructions[0]
if ((not first_instr.creates_value()) or ('xchg' in first_instr.opcode)):
first_family = None
else:
... | 409,782,009,336,981,060 | :return void: Increases gadget's score if it contains operations on a value carrying or a bystander register | src/static_analyzer/Gadget.py | check_register_ops | michaelbrownuc/GadgetSetAnalyzer | python | def check_register_ops(self):
"\n \n "
first_instr = self.instructions[0]
if ((not first_instr.creates_value()) or ('xchg' in first_instr.opcode)):
first_family = None
else:
first_family = Instruction.get_operand_register_family(first_instr.op1)
for i in range(1, (len(s... |
def check_branch_target_of_operation(self):
"\n :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain\n operations on the indirect branch target register family.\n "
last_instr = self.instructions[(len(self.instructions) - 1... | -2,168,993,783,908,520,400 | :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain
operations on the indirect branch target register family. | src/static_analyzer/Gadget.py | check_branch_target_of_operation | michaelbrownuc/GadgetSetAnalyzer | python | def check_branch_target_of_operation(self):
"\n :return void: Increases gadget's score if the gadget has an intermediate instruction that performs certain\n operations on the indirect branch target register family.\n "
last_instr = self.instructions[(len(self.instructions) - 1... |
def check_memory_writes(self):
"\n :return void: Increases gadget's score if the gadget has an instruction that writes to memory.\n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (... | 1,998,233,147,490,157,800 | :return void: Increases gadget's score if the gadget has an instruction that writes to memory. | src/static_analyzer/Gadget.py | check_memory_writes | michaelbrownuc/GadgetSetAnalyzer | python | def check_memory_writes(self):
"\n \n "
for i in range((len(self.instructions) - 1)):
cur_instr = self.instructions[i]
if (not cur_instr.creates_value()):
continue
if (('xchg' in cur_instr.opcode) and (('[' in cur_instr.op1) or ('[' in cur_instr.op2))):
... |
def proof_of_work(last_proof):
"\n Simple Proof of Work Algorithm\n - Find a number p' such that hash(pp') contains 6 leading\n zeroes, where p is the previous p'\n - p is the previous proof, and p' is the new proof\n "
print(f'''
Search for proof initialized.
''')
proof = 0
while (valid_... | 9,213,363,334,812,784,000 | Simple Proof of Work Algorithm
- Find a number p' such that hash(pp') contains 6 leading
zeroes, where p is the previous p'
- p is the previous proof, and p' is the new proof | client_mining_p/miner.py | proof_of_work | lambda-projects-lafriedel/Blockchain | python | def proof_of_work(last_proof):
"\n Simple Proof of Work Algorithm\n - Find a number p' such that hash(pp') contains 6 leading\n zeroes, where p is the previous p'\n - p is the previous proof, and p' is the new proof\n "
print(f'
Search for proof initialized.
')
proof = 0
while (valid_proo... |
def equal_up_to_global_phase(val: Any, other: Any, *, atol: Union[(int, float)]=1e-08) -> bool:
'Determine whether two objects are equal up to global phase.\n\n If `val` implements a `_equal_up_to_global_phase_` method then it is\n invoked and takes precedence over all other checks:\n - For complex primit... | -9,119,513,904,675,405,000 | Determine whether two objects are equal up to global phase.
If `val` implements a `_equal_up_to_global_phase_` method then it is
invoked and takes precedence over all other checks:
- For complex primitive type the magnitudes of the values are compared.
- For `val` and `other` both iterable of the same length, consec... | cirq-core/cirq/protocols/equal_up_to_global_phase_protocol.py | equal_up_to_global_phase | 95-martin-orion/Cirq | python | def equal_up_to_global_phase(val: Any, other: Any, *, atol: Union[(int, float)]=1e-08) -> bool:
'Determine whether two objects are equal up to global phase.\n\n If `val` implements a `_equal_up_to_global_phase_` method then it is\n invoked and takes precedence over all other checks:\n - For complex primit... |
@doc_private
def _equal_up_to_global_phase_(self, other: Any, *, atol: Union[(int, float)]) -> bool:
'Approximate comparator.\n\n Types implementing this protocol define their own logic for comparison\n with other types.\n\n Args:\n other: Target object for comparison of equality up ... | 556,117,564,477,804,200 | Approximate comparator.
Types implementing this protocol define their own logic for comparison
with other types.
Args:
other: Target object for comparison of equality up to global phase.
atol: The minimum absolute tolerance. See `np.isclose()`
documentation for details.
Returns:
True if objects a... | cirq-core/cirq/protocols/equal_up_to_global_phase_protocol.py | _equal_up_to_global_phase_ | 95-martin-orion/Cirq | python | @doc_private
def _equal_up_to_global_phase_(self, other: Any, *, atol: Union[(int, float)]) -> bool:
'Approximate comparator.\n\n Types implementing this protocol define their own logic for comparison\n with other types.\n\n Args:\n other: Target object for comparison of equality up ... |
@tf_export('train.load_checkpoint')
def load_checkpoint(ckpt_dir_or_file):
'Returns `CheckpointReader` for checkpoint found in `ckpt_dir_or_file`.\n\n If `ckpt_dir_or_file` resolves to a directory with multiple checkpoints,\n reader for the latest checkpoint is returned.\n\n Args:\n ckpt_dir_or_file: Director... | 3,253,334,963,162,104,000 | Returns `CheckpointReader` for checkpoint found in `ckpt_dir_or_file`.
If `ckpt_dir_or_file` resolves to a directory with multiple checkpoints,
reader for the latest checkpoint is returned.
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint
file.
Returns:
`CheckpointReader` object.... | tensorflow/python/training/checkpoint_utils.py | load_checkpoint | KodeWorker/tensorflow | python | @tf_export('train.load_checkpoint')
def load_checkpoint(ckpt_dir_or_file):
'Returns `CheckpointReader` for checkpoint found in `ckpt_dir_or_file`.\n\n If `ckpt_dir_or_file` resolves to a directory with multiple checkpoints,\n reader for the latest checkpoint is returned.\n\n Args:\n ckpt_dir_or_file: Director... |
@tf_export('train.load_variable')
def load_variable(ckpt_dir_or_file, name):
'Returns the tensor value of the given variable in the checkpoint.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n name: Name of the variable to return.\n\n Returns:\n A numpy `ndarray` wit... | -7,616,513,250,938,454,000 | Returns the tensor value of the given variable in the checkpoint.
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
name: Name of the variable to return.
Returns:
A numpy `ndarray` with a copy of the value of this variable. | tensorflow/python/training/checkpoint_utils.py | load_variable | KodeWorker/tensorflow | python | @tf_export('train.load_variable')
def load_variable(ckpt_dir_or_file, name):
'Returns the tensor value of the given variable in the checkpoint.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n name: Name of the variable to return.\n\n Returns:\n A numpy `ndarray` wit... |
@tf_export('train.list_variables')
def list_variables(ckpt_dir_or_file):
'Returns list of all variables in the checkpoint.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n\n Returns:\n List of tuples `(name, shape)`.\n '
reader = load_checkpoint(ckpt_dir_or_file)
... | 1,467,950,224,971,931,600 | Returns list of all variables in the checkpoint.
Args:
ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.
Returns:
List of tuples `(name, shape)`. | tensorflow/python/training/checkpoint_utils.py | list_variables | KodeWorker/tensorflow | python | @tf_export('train.list_variables')
def list_variables(ckpt_dir_or_file):
'Returns list of all variables in the checkpoint.\n\n Args:\n ckpt_dir_or_file: Directory with checkpoints file or path to checkpoint.\n\n Returns:\n List of tuples `(name, shape)`.\n '
reader = load_checkpoint(ckpt_dir_or_file)
... |
def wait_for_new_checkpoint(checkpoint_dir, last_checkpoint=None, seconds_to_sleep=1, timeout=None):
"Waits until a new checkpoint file is found.\n\n Args:\n checkpoint_dir: The directory in which checkpoints are saved.\n last_checkpoint: The last checkpoint path used or `None` if we're expecting\n a ch... | -1,605,284,766,611,941,000 | Waits until a new checkpoint file is found.
Args:
checkpoint_dir: The directory in which checkpoints are saved.
last_checkpoint: The last checkpoint path used or `None` if we're expecting
a checkpoint for the first time.
seconds_to_sleep: The number of seconds to sleep for before looking for a
new checkp... | tensorflow/python/training/checkpoint_utils.py | wait_for_new_checkpoint | KodeWorker/tensorflow | python | def wait_for_new_checkpoint(checkpoint_dir, last_checkpoint=None, seconds_to_sleep=1, timeout=None):
"Waits until a new checkpoint file is found.\n\n Args:\n checkpoint_dir: The directory in which checkpoints are saved.\n last_checkpoint: The last checkpoint path used or `None` if we're expecting\n a ch... |
@tf_export('train.checkpoints_iterator')
def checkpoints_iterator(checkpoint_dir, min_interval_secs=0, timeout=None, timeout_fn=None):
'Continuously yield new checkpoint files as they appear.\n\n The iterator only checks for new checkpoints when control flow has been\n reverted to it. This means it can miss check... | 8,677,676,642,965,667,000 | Continuously yield new checkpoint files as they appear.
The iterator only checks for new checkpoints when control flow has been
reverted to it. This means it can miss checkpoints if your code takes longer
to run between iterations than `min_interval_secs` or the interval at which
new checkpoints are written.
The `tim... | tensorflow/python/training/checkpoint_utils.py | checkpoints_iterator | KodeWorker/tensorflow | python | @tf_export('train.checkpoints_iterator')
def checkpoints_iterator(checkpoint_dir, min_interval_secs=0, timeout=None, timeout_fn=None):
'Continuously yield new checkpoint files as they appear.\n\n The iterator only checks for new checkpoints when control flow has been\n reverted to it. This means it can miss check... |
@tf_export(v1=['train.init_from_checkpoint'])
def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
"Replaces `tf.Variable` initializers so they load from a checkpoint file.\n\n Values are not loaded immediately, but when the initializer is run\n (typically by running a `tf.compat.v1.global_variables_initia... | -8,538,280,477,431,354,000 | Replaces `tf.Variable` initializers so they load from a checkpoint file.
Values are not loaded immediately, but when the initializer is run
(typically by running a `tf.compat.v1.global_variables_initializer` op).
Note: This overrides default initialization ops of specified variables and
redefines dtype.
Assignment m... | tensorflow/python/training/checkpoint_utils.py | init_from_checkpoint | KodeWorker/tensorflow | python | @tf_export(v1=['train.init_from_checkpoint'])
def init_from_checkpoint(ckpt_dir_or_file, assignment_map):
"Replaces `tf.Variable` initializers so they load from a checkpoint file.\n\n Values are not loaded immediately, but when the initializer is run\n (typically by running a `tf.compat.v1.global_variables_initia... |
def _init_from_checkpoint(ckpt_dir_or_file, assignment_map):
'See `init_from_checkpoint` for documentation.'
ckpt_file = _get_checkpoint_filename(ckpt_dir_or_file)
reader = load_checkpoint(ckpt_dir_or_file)
variable_map = reader.get_variable_to_shape_map()
for (tensor_name_in_ckpt, current_var_or_na... | -3,119,381,913,987,592,700 | See `init_from_checkpoint` for documentation. | tensorflow/python/training/checkpoint_utils.py | _init_from_checkpoint | KodeWorker/tensorflow | python | def _init_from_checkpoint(ckpt_dir_or_file, assignment_map):
ckpt_file = _get_checkpoint_filename(ckpt_dir_or_file)
reader = load_checkpoint(ckpt_dir_or_file)
variable_map = reader.get_variable_to_shape_map()
for (tensor_name_in_ckpt, current_var_or_name) in sorted(six.iteritems(assignment_map)):
... |
def _get_checkpoint_filename(ckpt_dir_or_file):
'Returns checkpoint filename given directory or specific checkpoint file.'
if gfile.IsDirectory(ckpt_dir_or_file):
return checkpoint_management.latest_checkpoint(ckpt_dir_or_file)
return ckpt_dir_or_file | -5,948,685,012,336,749,000 | Returns checkpoint filename given directory or specific checkpoint file. | tensorflow/python/training/checkpoint_utils.py | _get_checkpoint_filename | KodeWorker/tensorflow | python | def _get_checkpoint_filename(ckpt_dir_or_file):
if gfile.IsDirectory(ckpt_dir_or_file):
return checkpoint_management.latest_checkpoint(ckpt_dir_or_file)
return ckpt_dir_or_file |
def _set_checkpoint_initializer(variable, ckpt_file, tensor_name, slice_spec, name='checkpoint_initializer', write_version=saver_pb2.SaverDef.DIT):
"Overrides given variable's initialization op.\n\n Sets variable initializer to assign op that initializes variable from tensor's\n value in the checkpoint.\n\n Args... | 7,078,638,621,091,424,000 | Overrides given variable's initialization op.
Sets variable initializer to assign op that initializes variable from tensor's
value in the checkpoint.
Args:
variable: `tf.Variable` object.
ckpt_file: string, full path of the checkpoint.
tensor_name: Name of the tensor to load from the checkpoint.
slice_spec: S... | tensorflow/python/training/checkpoint_utils.py | _set_checkpoint_initializer | KodeWorker/tensorflow | python | def _set_checkpoint_initializer(variable, ckpt_file, tensor_name, slice_spec, name='checkpoint_initializer', write_version=saver_pb2.SaverDef.DIT):
"Overrides given variable's initialization op.\n\n Sets variable initializer to assign op that initializes variable from tensor's\n value in the checkpoint.\n\n Args... |
def _set_variable_or_list_initializer(variable_or_list, ckpt_file, tensor_name):
'Overrides initialization op of given variable or list of variables.\n\n Calls `_set_checkpoint_initializer` for each variable in the given list of\n variables.\n\n Args:\n variable_or_list: `tf.Variable` object or a list of `tf.... | 4,867,478,037,457,606,000 | Overrides initialization op of given variable or list of variables.
Calls `_set_checkpoint_initializer` for each variable in the given list of
variables.
Args:
variable_or_list: `tf.Variable` object or a list of `tf.Variable` objects.
ckpt_file: string, full path of the checkpoint.
tensor_name: Name of the tens... | tensorflow/python/training/checkpoint_utils.py | _set_variable_or_list_initializer | KodeWorker/tensorflow | python | def _set_variable_or_list_initializer(variable_or_list, ckpt_file, tensor_name):
'Overrides initialization op of given variable or list of variables.\n\n Calls `_set_checkpoint_initializer` for each variable in the given list of\n variables.\n\n Args:\n variable_or_list: `tf.Variable` object or a list of `tf.... |
def _collect_partitioned_variable(name, all_vars):
'Returns list of `tf.Variable` that comprise the partitioned variable.'
if ((name + '/part_0') in all_vars):
var = []
i = 0
while ((name + ('/part_%d' % i)) in all_vars):
var.append(all_vars[(name + ('/part_%d' % i))])
... | -3,062,386,609,698,979,300 | Returns list of `tf.Variable` that comprise the partitioned variable. | tensorflow/python/training/checkpoint_utils.py | _collect_partitioned_variable | KodeWorker/tensorflow | python | def _collect_partitioned_variable(name, all_vars):
if ((name + '/part_0') in all_vars):
var = []
i = 0
while ((name + ('/part_%d' % i)) in all_vars):
var.append(all_vars[(name + ('/part_%d' % i))])
i += 1
return var
return None |
def add_const(self, const):
'\n Add a constant to the environment, return its index.\n '
if isinstance(const, str):
const = utils.intern(const)
for (index, val) in enumerate(self.env.consts):
if (val is const):
break
else:
index = len(self.env.consts)
... | 3,270,935,112,231,871,000 | Add a constant to the environment, return its index. | numba/core/pythonapi.py | add_const | DrTodd13/numba | python | def add_const(self, const):
'\n \n '
if isinstance(const, str):
const = utils.intern(const)
for (index, val) in enumerate(self.env.consts):
if (val is const):
break
else:
index = len(self.env.consts)
self.env.consts.append(const)
return index |
def read_const(self, index):
'\n Look up constant number *index* inside the environment body.\n A borrowed reference is returned.\n\n The returned LLVM value may have NULL value at runtime which indicates\n an error at runtime.\n '
assert (index < len(self.env.consts))
bui... | 7,686,056,002,697,819,000 | Look up constant number *index* inside the environment body.
A borrowed reference is returned.
The returned LLVM value may have NULL value at runtime which indicates
an error at runtime. | numba/core/pythonapi.py | read_const | DrTodd13/numba | python | def read_const(self, index):
'\n Look up constant number *index* inside the environment body.\n A borrowed reference is returned.\n\n The returned LLVM value may have NULL value at runtime which indicates\n an error at runtime.\n '
assert (index < len(self.env.consts))
bui... |
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