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 |
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def _make_fake_dataset_fn():
'Returns a dataset that emulates a remote storage data source.\n\n Returns a dataset factory which creates a dataset with 100 elements that\n emulates the performance characteristic of a file-based dataset stored in a\n remote storage. In particular, the first element will take an or... | -4,219,913,051,384,786,400 | Returns a dataset that emulates a remote storage data source.
Returns a dataset factory which creates a dataset with 100 elements that
emulates the performance characteristic of a file-based dataset stored in a
remote storage. In particular, the first element will take an order of
magnitude longer to produce than the ... | tensorflow/python/data/experimental/benchmarks/parallel_interleave_benchmark.py | _make_fake_dataset_fn | 1244783394/tensorflow | python | def _make_fake_dataset_fn():
'Returns a dataset that emulates a remote storage data source.\n\n Returns a dataset factory which creates a dataset with 100 elements that\n emulates the performance characteristic of a file-based dataset stored in a\n remote storage. In particular, the first element will take an or... |
def benchmark_parallel_interleave_v1(self):
'Benchmark for parallel interleave that does not support autotuning.'
def dataset_fn():
return dataset_ops.Dataset.range(1).repeat().apply(interleave_ops.parallel_interleave(_make_fake_dataset_fn(), cycle_length=10))
self._benchmark(dataset_fn=dataset_fn,... | 6,597,294,758,033,310,000 | Benchmark for parallel interleave that does not support autotuning. | tensorflow/python/data/experimental/benchmarks/parallel_interleave_benchmark.py | benchmark_parallel_interleave_v1 | 1244783394/tensorflow | python | def benchmark_parallel_interleave_v1(self):
def dataset_fn():
return dataset_ops.Dataset.range(1).repeat().apply(interleave_ops.parallel_interleave(_make_fake_dataset_fn(), cycle_length=10))
self._benchmark(dataset_fn=dataset_fn, iters=100, num_elements=1000) |
def benchmark_parallel_interleave_v2(self):
'Benchmark for parallel interleave that supports autotuning.'
def dataset_fn():
return dataset_ops.Dataset.range(1).repeat().interleave(_make_fake_dataset_fn(), cycle_length=10, num_parallel_calls=dataset_ops.AUTOTUNE)
self._benchmark(dataset_fn=dataset_f... | -5,564,878,944,003,852,000 | Benchmark for parallel interleave that supports autotuning. | tensorflow/python/data/experimental/benchmarks/parallel_interleave_benchmark.py | benchmark_parallel_interleave_v2 | 1244783394/tensorflow | python | def benchmark_parallel_interleave_v2(self):
def dataset_fn():
return dataset_ops.Dataset.range(1).repeat().interleave(_make_fake_dataset_fn(), cycle_length=10, num_parallel_calls=dataset_ops.AUTOTUNE)
self._benchmark(dataset_fn=dataset_fn, iters=100, num_elements=1000) |
def MA(ma, close):
'Compute Moving Average\n\n Args:\n ma (float): MA value\n close (float): Close value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (ma < close):
return Recommendation.buy
elif (ma > close):
return Recommend... | -8,902,239,209,966,890,000 | Compute Moving Average
Args:
ma (float): MA value
close (float): Close value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | MA | Chizkiyahu/python-tradingview-ta | python | def MA(ma, close):
'Compute Moving Average\n\n Args:\n ma (float): MA value\n close (float): Close value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (ma < close):
return Recommendation.buy
elif (ma > close):
return Recommend... |
def RSI(rsi, rsi1):
'Compute Relative Strength Index\n\n Args:\n rsi (float): RSI value\n rsi1 (float): RSI[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if ((rsi < 30) and (rsi1 > rsi)):
return Recommendation.buy
elif ((rsi > 7... | -4,906,149,037,489,024,000 | Compute Relative Strength Index
Args:
rsi (float): RSI value
rsi1 (float): RSI[1] value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | RSI | Chizkiyahu/python-tradingview-ta | python | def RSI(rsi, rsi1):
'Compute Relative Strength Index\n\n Args:\n rsi (float): RSI value\n rsi1 (float): RSI[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if ((rsi < 30) and (rsi1 > rsi)):
return Recommendation.buy
elif ((rsi > 7... |
def Stoch(k, d, k1, d1):
'Compute Stochastic\n\n Args:\n k (float): Stoch.K value\n d (float): Stoch.D value\n k1 (float): Stoch.K[1] value\n d1 (float): Stoch.D[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if ((k < 2... | -4,613,719,220,800,345,000 | Compute Stochastic
Args:
k (float): Stoch.K value
d (float): Stoch.D value
k1 (float): Stoch.K[1] value
d1 (float): Stoch.D[1] value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | Stoch | Chizkiyahu/python-tradingview-ta | python | def Stoch(k, d, k1, d1):
'Compute Stochastic\n\n Args:\n k (float): Stoch.K value\n d (float): Stoch.D value\n k1 (float): Stoch.K[1] value\n d1 (float): Stoch.D[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if ((k < 2... |
def CCI20(cci20, cci201):
'Compute Commodity Channel Index 20\n\n Args:\n cci20 (float): CCI20 value\n cci201 ([type]): CCI20[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if ((cci20 < (- 100)) and (cci20 > cci201)):
return Recommen... | 7,493,960,804,903,274,000 | Compute Commodity Channel Index 20
Args:
cci20 (float): CCI20 value
cci201 ([type]): CCI20[1] value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | CCI20 | Chizkiyahu/python-tradingview-ta | python | def CCI20(cci20, cci201):
'Compute Commodity Channel Index 20\n\n Args:\n cci20 (float): CCI20 value\n cci201 ([type]): CCI20[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if ((cci20 < (- 100)) and (cci20 > cci201)):
return Recommen... |
def ADX(adx, adxpdi, adxndi, adxpdi1, adxndi1):
'Compute Average Directional Index\n\n Args:\n adx (float): ADX value\n adxpdi (float): ADX+DI value\n adxndi (float): ADX-DI value\n adxpdi1 (float): ADX+DI[1] value\n adxndi1 (float): ADX-DI[1] value\n\n ... | 8,872,061,564,006,650,000 | Compute Average Directional Index
Args:
adx (float): ADX value
adxpdi (float): ADX+DI value
adxndi (float): ADX-DI value
adxpdi1 (float): ADX+DI[1] value
adxndi1 (float): ADX-DI[1] value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | ADX | Chizkiyahu/python-tradingview-ta | python | def ADX(adx, adxpdi, adxndi, adxpdi1, adxndi1):
'Compute Average Directional Index\n\n Args:\n adx (float): ADX value\n adxpdi (float): ADX+DI value\n adxndi (float): ADX-DI value\n adxpdi1 (float): ADX+DI[1] value\n adxndi1 (float): ADX-DI[1] value\n\n ... |
def AO(ao, ao1):
'Compute Awesome Oscillator\n\n Args:\n ao (float): AO value\n ao1 (float): AO[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (((ao > 0) and (ao1 < 0)) or ((ao > 0) and (ao1 > 0) and (ao > ao1))):
return Recommend... | -6,713,277,984,516,991,000 | Compute Awesome Oscillator
Args:
ao (float): AO value
ao1 (float): AO[1] value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | AO | Chizkiyahu/python-tradingview-ta | python | def AO(ao, ao1):
'Compute Awesome Oscillator\n\n Args:\n ao (float): AO value\n ao1 (float): AO[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (((ao > 0) and (ao1 < 0)) or ((ao > 0) and (ao1 > 0) and (ao > ao1))):
return Recommend... |
def Mom(mom, mom1):
'Compute Momentum\n\n Args:\n mom (float): Mom value\n mom1 (float): Mom[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (mom < mom1):
return Recommendation.buy
elif (mom > mom1):
return Recommendati... | -8,346,860,407,921,247,000 | Compute Momentum
Args:
mom (float): Mom value
mom1 (float): Mom[1] value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | Mom | Chizkiyahu/python-tradingview-ta | python | def Mom(mom, mom1):
'Compute Momentum\n\n Args:\n mom (float): Mom value\n mom1 (float): Mom[1] value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (mom < mom1):
return Recommendation.buy
elif (mom > mom1):
return Recommendati... |
def MACD(macd, signal):
'Compute Moving Average Convergence/Divergence\n\n Args:\n macd (float): MACD.macd value\n signal (float): MACD.signal value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (macd > signal):
return Recommendation.buy
... | -7,220,129,794,616,158,000 | Compute Moving Average Convergence/Divergence
Args:
macd (float): MACD.macd value
signal (float): MACD.signal value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | MACD | Chizkiyahu/python-tradingview-ta | python | def MACD(macd, signal):
'Compute Moving Average Convergence/Divergence\n\n Args:\n macd (float): MACD.macd value\n signal (float): MACD.signal value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (macd > signal):
return Recommendation.buy
... |
def BBBuy(close, bblower):
'Compute Bull Bear Buy\n\n Args:\n close (float): close value\n bblower (float): BB.lower value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (close < bblower):
return Recommendation.buy
else:
return... | 3,170,778,078,137,031,000 | Compute Bull Bear Buy
Args:
close (float): close value
bblower (float): BB.lower value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | BBBuy | Chizkiyahu/python-tradingview-ta | python | def BBBuy(close, bblower):
'Compute Bull Bear Buy\n\n Args:\n close (float): close value\n bblower (float): BB.lower value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (close < bblower):
return Recommendation.buy
else:
return... |
def BBSell(close, bbupper):
'Compute Bull Bear Sell\n\n Args:\n close (float): close value\n bbupper (float): BB.upper value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (close > bbupper):
return Recommendation.sell
else:
ret... | -1,134,133,646,265,981,600 | Compute Bull Bear Sell
Args:
close (float): close value
bbupper (float): BB.upper value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | BBSell | Chizkiyahu/python-tradingview-ta | python | def BBSell(close, bbupper):
'Compute Bull Bear Sell\n\n Args:\n close (float): close value\n bbupper (float): BB.upper value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (close > bbupper):
return Recommendation.sell
else:
ret... |
def PSAR(psar, open):
'Compute Parabolic Stop-And-Reverse\n\n Args:\n psar (float): P.SAR value\n open (float): open value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (psar < open):
return Recommendation.buy
elif (psar > open):
... | 2,251,847,869,837,323,800 | Compute Parabolic Stop-And-Reverse
Args:
psar (float): P.SAR value
open (float): open value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | PSAR | Chizkiyahu/python-tradingview-ta | python | def PSAR(psar, open):
'Compute Parabolic Stop-And-Reverse\n\n Args:\n psar (float): P.SAR value\n open (float): open value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (psar < open):
return Recommendation.buy
elif (psar > open):
... |
def Recommend(value):
'Compute Recommend\n\n Args:\n value (float): recommend value\n\n Returns:\n string: "STRONG_BUY", "BUY", "NEUTRAL", "SELL", "STRONG_SELL", or "ERROR"\n '
if ((value >= (- 1)) and (value < (- 0.5))):
return Recommendation.strong_sell
e... | -4,807,892,968,998,064,000 | Compute Recommend
Args:
value (float): recommend value
Returns:
string: "STRONG_BUY", "BUY", "NEUTRAL", "SELL", "STRONG_SELL", or "ERROR" | tradingview_ta/technicals.py | Recommend | Chizkiyahu/python-tradingview-ta | python | def Recommend(value):
'Compute Recommend\n\n Args:\n value (float): recommend value\n\n Returns:\n string: "STRONG_BUY", "BUY", "NEUTRAL", "SELL", "STRONG_SELL", or "ERROR"\n '
if ((value >= (- 1)) and (value < (- 0.5))):
return Recommendation.strong_sell
e... |
def Simple(value):
'Compute Simple\n\n Args:\n value (float): Rec.X value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (value == (- 1)):
return Recommendation.sell
elif (value == 1):
return Recommendation.buy
else:
return Re... | -4,654,071,787,963,070,000 | Compute Simple
Args:
value (float): Rec.X value
Returns:
string: "BUY", "SELL", or "NEUTRAL" | tradingview_ta/technicals.py | Simple | Chizkiyahu/python-tradingview-ta | python | def Simple(value):
'Compute Simple\n\n Args:\n value (float): Rec.X value\n\n Returns:\n string: "BUY", "SELL", or "NEUTRAL"\n '
if (value == (- 1)):
return Recommendation.sell
elif (value == 1):
return Recommendation.buy
else:
return Re... |
def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used... | -2,043,395,450,413,527,600 | Applies matrix multiplication to two tensors. `matmul` follows
the complete broadcast rules,
and its behavior is consistent with `np.matmul`.
Currently, the input tensors' number of dimensions can be any, `matmul` can be used to
achieve the `dot`, `matmul` and `batchmatmul`.
The actual behavior depends on the shapes ... | python/paddle/tensor/linalg.py | matmul | DevilCarp/Paddle | python | def matmul(x, y, transpose_x=False, transpose_y=False, name=None):
"\n Applies matrix multiplication to two tensors. `matmul` follows\n the complete broadcast rules,\n and its behavior is consistent with `np.matmul`.\n\n Currently, the input tensors' number of dimensions can be any, `matmul` can be used... |
def norm(x, p='fro', axis=None, keepdim=False, name=None):
"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api s... | 2,276,984,511,793,815,300 | Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean
or 2-norm, and in general the p-norm for p > 0) of a given tensor.
.. note::
This norm API is different from `numpy.linalg.norm`.
This api supports high-order input tensors (rank >= 3), and certain axis need to be pointed out to calc... | python/paddle/tensor/linalg.py | norm | DevilCarp/Paddle | python | def norm(x, p='fro', axis=None, keepdim=False, name=None):
"\n\n Returns the matrix norm (Frobenius) or vector norm (the 1-norm, the Euclidean\n or 2-norm, and in general the p-norm for p > 0) of a given tensor.\n\n .. note::\n This norm API is different from `numpy.linalg.norm`.\n This api s... |
def dist(x, y, p=2, name=None):
'\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy\'s broadcasting <https://docs.scipy.org/doc/... | -1,655,034,432,683,055,400 | This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure
of distance. The shapes of x and y must be broadcastable. The definition is as follows, for
details, please refer to the `numpy's broadcasting <https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html>`_:
- Each input ha... | python/paddle/tensor/linalg.py | dist | DevilCarp/Paddle | python | def dist(x, y, p=2, name=None):
'\n\n This OP returns the p-norm of (x - y). It is not a norm in a strict sense, only as a measure\n of distance. The shapes of x and y must be broadcastable. The definition is as follows, for\n details, please refer to the `numpy\'s broadcasting <https://docs.scipy.org/doc/... |
def cond(x, p=None, name=None):
"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``,... | -8,999,208,571,252,141,000 | Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.
Args:
x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions
for ``p`` in ``(2, -2)``, or of shape ``(*, n, n)`` where every matrix is invertible f... | python/paddle/tensor/linalg.py | cond | DevilCarp/Paddle | python | def cond(x, p=None, name=None):
"\n\n Computes the condition number of a matrix or batches of matrices with respect to a matrix norm ``p``.\n\n Args:\n x (Tensor): The input tensor could be tensor of shape ``(*, m, n)`` where ``*`` is zero or more batch dimensions\n for ``p`` in ``(2, -2)``,... |
def dot(x, y, name=None):
"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tenso... | -8,485,889,732,085,839,000 | This operator calculates inner product for vectors.
.. note::
Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix
is the batch dimension, which means that the vectors of multiple batches are dotted.
Parameters:
x(Tensor): 1-D or 2-D ``Tensor``. Its dtype should be ``float32``, ``fl... | python/paddle/tensor/linalg.py | dot | DevilCarp/Paddle | python | def dot(x, y, name=None):
"\n This operator calculates inner product for vectors.\n\n .. note::\n Support 1-d and 2-d Tensor. When it is 2d, the first dimension of this matrix\n is the batch dimension, which means that the vectors of multiple batches are dotted.\n\n Parameters:\n x(Tenso... |
def cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):
"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional... | 4,961,629,382,206,763,000 | Estimate the covariance matrix of the input variables, given data and weights.
A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.
For example, for an N-dimensional samples X=[x1,x2,…xN]T, then the covariance matrix
element Cij is the covariance of xi and xj. Th... | python/paddle/tensor/linalg.py | cov | DevilCarp/Paddle | python | def cov(x, rowvar=True, ddof=True, fweights=None, aweights=None, name=None):
"\n Estimate the covariance matrix of the input variables, given data and weights.\n\n A covariance matrix is a square matrix, indicate the covariance of each pair variables in the input matrix.\n For example, for an N-dimensional... |
def t(input, name=None):
"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, fl... | 3,878,725,866,431,119,400 | Transpose <=2-D tensor.
0-D and 1-D tensors are returned as it is and 2-D tensor is equal to
the paddle.transpose function which perm dimensions set 0 and 1.
Args:
input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, float64, int32.
name(str, optional): The default value ... | python/paddle/tensor/linalg.py | t | DevilCarp/Paddle | python | def t(input, name=None):
"\n Transpose <=2-D tensor.\n 0-D and 1-D tensors are returned as it is and 2-D tensor is equal to\n the paddle.transpose function which perm dimensions set 0 and 1.\n\n Args:\n input (Tensor): The input Tensor. It is a N-D (N<=2) Tensor of data types float16, float32, fl... |
def cross(x, y, axis=None, name=None):
'\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor):... | -527,705,269,943,561,300 | Computes the cross product between two tensors along an axis.
Inputs must have the same shape, and the length of their axes should be equal to 3.
If `axis` is not given, it defaults to the first axis found with the length 3.
Args:
x (Tensor): The first input tensor.
y (Tensor): The second input tensor.
ax... | python/paddle/tensor/linalg.py | cross | DevilCarp/Paddle | python | def cross(x, y, axis=None, name=None):
'\n Computes the cross product between two tensors along an axis.\n\n Inputs must have the same shape, and the length of their axes should be equal to 3.\n If `axis` is not given, it defaults to the first axis found with the length 3.\n\n Args:\n x (Tensor):... |
def cholesky(x, upper=False, name=None):
'\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-trian... | -1,156,870,987,024,628,700 | Computes the Cholesky decomposition of one symmetric positive-definite
matrix or batches of symmetric positive-definite matrice.
If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,
and the returned matrix :math:`U` is upper-triangular. Otherwise, the
decomposition has the form :math:`A = LL^{T}`... | python/paddle/tensor/linalg.py | cholesky | DevilCarp/Paddle | python | def cholesky(x, upper=False, name=None):
'\n Computes the Cholesky decomposition of one symmetric positive-definite\n matrix or batches of symmetric positive-definite matrice.\n\n If `upper` is `True`, the decomposition has the form :math:`A = U^{T}U` ,\n and the returned matrix :math:`U` is upper-trian... |
def matrix_rank(x, tol=None, hermitian=False, name=None):
'\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specifi... | 1,160,058,177,991,084,800 | Computes the rank of a matrix.
The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,
or the number of eigenvalues in absolute value that are greater than the specified `tol` threshold when hermitian=True.
Args:
x (Tensor): The input tensor.... | python/paddle/tensor/linalg.py | matrix_rank | DevilCarp/Paddle | python | def matrix_rank(x, tol=None, hermitian=False, name=None):
'\n Computes the rank of a matrix.\n\n The rank of a matrix is the number of singular values that are greater than the specified `tol` threshold when hermitian=False,\n or the number of eigenvalues in absolute value that are greater than the specifi... |
def bmm(x, y, name=None):
'\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): Th... | -4,711,183,802,654,545,000 | Applies batched matrix multiplication to two tensors.
Both of the two input tensors must be three-dementional and share the same batch size.
if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.
Args:
x (Tensor): The input Tensor.
y (Tensor): The input Tensor.
name(s... | python/paddle/tensor/linalg.py | bmm | DevilCarp/Paddle | python | def bmm(x, y, name=None):
'\n Applies batched matrix multiplication to two tensors.\n\n Both of the two input tensors must be three-dementional and share the same batch size.\n\n if x is a (b, m, k) tensor, y is a (b, k, n) tensor, the output will be a (b, m, n) tensor.\n\n Args:\n x (Tensor): Th... |
def histogram(input, bins=100, min=0, max=0, name=None):
'\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) ... | 8,785,959,902,747,494,000 | Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.
If min and max are both zero, the minimum and maximum values of the data are used.
Args:
input (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor
s... | python/paddle/tensor/linalg.py | histogram | DevilCarp/Paddle | python | def histogram(input, bins=100, min=0, max=0, name=None):
'\n Computes the histogram of a tensor. The elements are sorted into equal width bins between min and max.\n If min and max are both zero, the minimum and maximum values of the data are used.\n\n Args:\n input (Tensor): A Tensor(or LoDTensor) ... |
def bincount(x, weights=None, minlength=0, name=None):
'\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape ... | -7,411,482,120,546,404,000 | Computes frequency of each value in the input tensor.
Args:
x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.
weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape as input. Default is None.
minlength (int, optional): Minimum number of bins. ... | python/paddle/tensor/linalg.py | bincount | DevilCarp/Paddle | python | def bincount(x, weights=None, minlength=0, name=None):
'\n Computes frequency of each value in the input tensor. \n\n Args:\n x (Tensor): A Tensor with non-negative integer. Should be 1-D tensor.\n weights (Tensor, optional): Weight for each value in the input tensor. Should have the same shape ... |
def mv(x, vec, name=None):
'\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , T... | 7,252,601,793,221,310,000 | Performs a matrix-vector product of the matrix x and the vector vec.
Args:
x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x
should be one of float32, float64.
vec (Tensor): A tensor with shape :math:`[N]` , The data type of the input Tensor x
should be one of... | python/paddle/tensor/linalg.py | mv | DevilCarp/Paddle | python | def mv(x, vec, name=None):
'\n Performs a matrix-vector product of the matrix x and the vector vec.\n\n Args:\n x (Tensor): A tensor with shape :math:`[M, N]` , The data type of the input Tensor x\n should be one of float32, float64.\n vec (Tensor): A tensor with shape :math:`[N]` , T... |
def det(x, name=None):
'\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n ... | 4,513,317,354,980,321,000 | Calculates determinant value of a square matrix or batches of square matrices.
Args:
x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size
`(*, n, n)` where `*` is one or more batch dimensions.
Returns:
y (Tensor):the determinant value of a square matrix ... | python/paddle/tensor/linalg.py | det | DevilCarp/Paddle | python | def det(x, name=None):
'\n Calculates determinant value of a square matrix or batches of square matrices.\n Args:\n x (Tensor): input (Tensor): the input matrix of size `(n, n)` or the batch of matrices of size\n `(*, n, n)` where `*` is one or more batch dimensions.\n Returns:\n ... |
def slogdet(x, name=None):
"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determ... | 9,101,923,281,703,332,000 | Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.
The determinant can be computed with ``sign * exp(logabsdet)
Supports input of float, double
Note that for matrices that have zero determinant, this returns ``(0, -inf)``
Args:
x (Tensor):... | python/paddle/tensor/linalg.py | slogdet | DevilCarp/Paddle | python | def slogdet(x, name=None):
"\n Calculates the sign and natural logarithm of the absolute value of a square matrix's or batches square matrices' determinant.\n The determinant can be computed with ``sign * exp(logabsdet)\n\n Supports input of float, double\n\n Note that for matrices that have zero determ... |
def svd(x, full_matrices=False, name=None):
"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tenso... | 7,655,087,064,594,550,000 | Computes the singular value decomposition of one matrix or a batch of regular matrices.
Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:
.. math::
X = U * diag(S) * VT
Args:
x (Tensor): The input tensor. Its shape should be `[..., N, M]`,
where `...` is zer... | python/paddle/tensor/linalg.py | svd | DevilCarp/Paddle | python | def svd(x, full_matrices=False, name=None):
"\n Computes the singular value decomposition of one matrix or a batch of regular matrices.\n\n Let :math:`X` be the input matrix or a batch of input matrices, the output should satisfies:\n\n .. math::\n X = U * diag(S) * VT\n\n Args:\n x (Tenso... |
def matrix_power(x, n, name=None):
"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `... | -4,744,617,923,970,205,000 | Computes the n-th power of a square matrix or a batch of square matrices.
Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be
an exponent, the equation should be:
.. math::
Out = X ^ {n}
Specifically,
- If `n > 0`, it returns the matrix or a batch of matrices raised to the power
of `n`.... | python/paddle/tensor/linalg.py | matrix_power | DevilCarp/Paddle | python | def matrix_power(x, n, name=None):
"\n Computes the n-th power of a square matrix or a batch of square matrices.\n\n Let :math:`X` be a sqaure matrix or a batch of square matrices, :math:`n` be\n an exponent, the equation should be:\n\n .. math::\n Out = X ^ {n}\n\n Specifically,\n\n - If `... |
def qr(x, mode='reduced', name=None):
'\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n ... | -753,448,886,194,518,700 | Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).
Args:
x (Tensor): The input tensor. Its shape should be `[..., M, N]`,
where ... is zero or more batch dimensions. M and N can be arbitrary
positive number. The data type of x should be float32 or float... | python/paddle/tensor/linalg.py | qr | DevilCarp/Paddle | python | def qr(x, mode='reduced', name=None):
'\n Computes the QR decomposition of one matrix or batches of matrice (backward is unsupported now).\n\n Args:\n x (Tensor): The input tensor. Its shape should be `[..., M, N]`,\n where ... is zero or more batch dimensions. M and N can be arbitrary\n ... |
def lu(x, pivot=True, get_infos=False, name=None):
"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to Tru... | -1,885,591,820,543,059,200 | Computes the LU factorization of an N-D(N>=2) matrix x.
Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and
upper triangular matrix U are combined to a single LU matrix.
Pivoting is done if pivot is set to True.
P mat can be get by pivots:
# ones = eye(rows) #eye matrix of rank rows
# fo... | python/paddle/tensor/linalg.py | lu | DevilCarp/Paddle | python | def lu(x, pivot=True, get_infos=False, name=None):
"\n Computes the LU factorization of an N-D(N>=2) matrix x. \n\n Returns the LU factorization(inplace x) and Pivots. low triangular matrix L and \n upper triangular matrix U are combined to a single LU matrix.\n\n Pivoting is done if pivot is set to Tru... |
def lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):
"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n ... | -8,822,091,043,951,989,000 | Unpack L U and P to single matrix tensor .
unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .
P mat can be get by pivots:
# ones = eye(rows) #eye matrix of rank rows
# for i in range(cols):
# swap(ones[i], ones[pivots[i]])
Args:
x (Tensor): The LU tensor get from paddle.linalg.lu, whic... | python/paddle/tensor/linalg.py | lu_unpack | DevilCarp/Paddle | python | def lu_unpack(x, y, unpack_ludata=True, unpack_pivots=True, name=None):
"\n Unpack L U and P to single matrix tensor . \n unpack L and U matrix from LU, unpack permutation matrix P from Pivtos .\n\n P mat can be get by pivots:\n # ones = eye(rows) #eye matrix of rank rows\n # for i in range(cols):\n ... |
def eig(x, name=None):
'\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed... | 4,681,175,119,224,986,000 | This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.
.. note::
If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.
If only eigenvalues is needed, please use :ref:`paddle.linalg.eigvals` instead.... | python/paddle/tensor/linalg.py | eig | DevilCarp/Paddle | python | def eig(x, name=None):
'\n This API performs the eigenvalue decomposition of a square matrix or a batch of square matrices.\n\n .. note::\n If the matrix is a Hermitian or a real symmetric matrix, please use :ref:`paddle.linalg.eigh` instead, which is much faster.\n If only eigenvalues is needed... |
def eigvals(x, name=None):
'\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A ... | -910,280,352,137,155,100 | Compute the eigenvalues of one or more general matrices.
Warning:
The gradient kernel of this operator does not yet developed.
If you need back propagation through this operator, please replace it with paddle.linalg.eig.
Args:
x (Tensor): A square matrix or a batch of square matrices whose eigenvalues wil... | python/paddle/tensor/linalg.py | eigvals | DevilCarp/Paddle | python | def eigvals(x, name=None):
'\n Compute the eigenvalues of one or more general matrices.\n\n Warning:\n The gradient kernel of this operator does not yet developed.\n If you need back propagation through this operator, please replace it with paddle.linalg.eig.\n\n Args:\n x (Tensor): A ... |
def multi_dot(x, name=None):
'\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last ... | 1,592,148,319,123,424,000 | Multi_dot is an operator that calculates multiple matrix multiplications.
Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not
support batched inputs.
The input tensor in [x] must be 2-D except for the first and last can be 1-D.
If the first tensor is a 1-D vector of shape(... | python/paddle/tensor/linalg.py | multi_dot | DevilCarp/Paddle | python | def multi_dot(x, name=None):
'\n Multi_dot is an operator that calculates multiple matrix multiplications.\n\n Supports inputs of float16(only GPU support), float32 and float64 dtypes. This function does not\n support batched inputs.\n\n The input tensor in [x] must be 2-D except for the first and last ... |
def eigh(x, UPLO='L', name=None):
'\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64... | -1,568,547,505,044,493,800 | Compute the eigenvalues and eigenvectors of a
complex Hermitian (conjugate symmetric) or a real symmetric matrix.
Args:
x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x
should be one of float32, float64, complex64, complex128.
UPLO(str, optional): (string, default... | python/paddle/tensor/linalg.py | eigh | DevilCarp/Paddle | python | def eigh(x, UPLO='L', name=None):
'\n Compute the eigenvalues and eigenvectors of a\n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[*, N, N]` , The data type of the input Tensor x\n should be one of float32, float64... |
def pinv(x, rcond=1e-15, hermitian=False, name=None):
"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n ... | 9,053,780,694,763,835,000 | Calculate pseudo inverse via SVD(singular value decomposition)
of one matrix or batches of regular matrix.
.. math::
if hermitian == False:
x = u * s * vt (SVD)
out = v * 1/s * ut
else:
x = u * s * ut (eigh)
out = u * 1/s * u.conj().transpose(-2,-1)
If x is hermitian or symm... | python/paddle/tensor/linalg.py | pinv | DevilCarp/Paddle | python | def pinv(x, rcond=1e-15, hermitian=False, name=None):
"\n Calculate pseudo inverse via SVD(singular value decomposition)\n of one matrix or batches of regular matrix.\n\n .. math::\n\n if hermitian == False:\n x = u * s * vt (SVD)\n out = v * 1/s * ut\n else:\n ... |
def solve(x, y, name=None):
'\n Computes the solution of a square system of linear equations with a unique solution for input \'X\' and \'Y\'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. ... | -3,942,150,556,993,506,300 | Computes the solution of a square system of linear equations with a unique solution for input 'X' and 'Y'.
Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be
a vector/matrix or a batch of vectors/matrices, the equation should be:
.. math::
Out = X^-1 * Y
Specifically,
- This system of li... | python/paddle/tensor/linalg.py | solve | DevilCarp/Paddle | python | def solve(x, y, name=None):
'\n Computes the solution of a square system of linear equations with a unique solution for input \'X\' and \'Y\'.\n Let :math: `X` be a sqaure matrix or a batch of square matrices, :math:`Y` be\n a vector/matrix or a batch of vectors/matrices, the equation should be:\n\n .. ... |
def triangular_solve(x, y, upper=True, transpose=False, unitriangular=False, name=None):
'\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batche... | -3,320,546,878,268,656,600 | Computes the solution of a system of equations with a triangular coefficient matrix `x` and
multiple right-hand sides `y` .
Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs
is also batches.
Args:
x (Tensor): The input triangular coefficient matrix. Its shape shoul... | python/paddle/tensor/linalg.py | triangular_solve | DevilCarp/Paddle | python | def triangular_solve(x, y, upper=True, transpose=False, unitriangular=False, name=None):
'\n Computes the solution of a system of equations with a triangular coefficient matrix `x` and\n multiple right-hand sides `y` .\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batche... |
def cholesky_solve(x, y, upper=False, name=None):
'\n Solves a linear system of equations A @ X = B, given A\'s Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor):... | -8,255,322,614,350,314,000 | Solves a linear system of equations A @ X = B, given A's Cholesky factor matrix u and matrix B.
Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs
is also batches.
Args:
x (Tensor): The input matrix which is upper or lower triangular Cholesky factor of square matri... | python/paddle/tensor/linalg.py | cholesky_solve | DevilCarp/Paddle | python | def cholesky_solve(x, y, upper=False, name=None):
'\n Solves a linear system of equations A @ X = B, given A\'s Cholesky factor matrix u and matrix B.\n\n Input `x` and `y` is 2D matrices or batches of 2D matrices. If the inputs are batches, the outputs\n is also batches.\n\n Args:\n x (Tensor):... |
def eigvalsh(x, UPLO='L', name=None):
"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64... | -352,417,534,785,145,600 | Computes the eigenvalues of a
complex Hermitian (conjugate symmetric) or a real symmetric matrix.
Args:
x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x
should be one of float32, float64, complex64, complex128.
UPLO(str, optional): Lower triangular part of a (‘L’... | python/paddle/tensor/linalg.py | eigvalsh | DevilCarp/Paddle | python | def eigvalsh(x, UPLO='L', name=None):
"\n Computes the eigenvalues of a \n complex Hermitian (conjugate symmetric) or a real symmetric matrix.\n\n Args:\n x (Tensor): A tensor with shape :math:`[_, M, M]` , The data type of the input Tensor x\n should be one of float32, float64, complex64... |
def lstsq(x, y, rcond=None, driver=None, name=None):
'\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y ... | 7,536,901,320,083,422,000 | Computes a solution to
the least squares problem of a system of linear equations.
Args:
x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``
should be one of float32, float64.
y (Tensor): A tensor with shape ``(*, M, K)`` , the data type of the input Tensor ``y``
... | python/paddle/tensor/linalg.py | lstsq | DevilCarp/Paddle | python | def lstsq(x, y, rcond=None, driver=None, name=None):
'\n Computes a solution to\n the least squares problem of a system of linear equations.\n\n Args:\n x (Tensor): A tensor with shape ``(*, M, N)`` , the data type of the input Tensor ``x``\n should be one of float32, float64.\n y ... |
def frobenius_norm(input, dim=None, keepdim=False, name=None):
'\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimension... | 8,133,598,796,588,167,000 | The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.
Args:
input (Variable): Tensor, data type float32, float64.
dim (list, optional): None for last two dimensions.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default False. | python/paddle/tensor/linalg.py | frobenius_norm | DevilCarp/Paddle | python | def frobenius_norm(input, dim=None, keepdim=False, name=None):
'\n The frobenius norm OP is to calculate the frobenius norm of certain two dimensions of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n dim (list, optional): None for last two dimension... |
def vector_norm(input, porder=None, axis=None, keepdim=False, asvector=False, name=None):
'\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n... | -1,317,694,213,258,792,200 | Calculate the p-order vector norm for certain dimension of Tensor `input`.
Args:
input (Variable): Tensor, data type float32, float64.
porder (float, optional): None for porder=2.0.
axis (int, optional): None for last dimension.
keepdim (bool, optional): Whether keep the dimensions as the `input`, Default Fals... | python/paddle/tensor/linalg.py | vector_norm | DevilCarp/Paddle | python | def vector_norm(input, porder=None, axis=None, keepdim=False, asvector=False, name=None):
'\n Calculate the p-order vector norm for certain dimension of Tensor `input`.\n Args:\n input (Variable): Tensor, data type float32, float64.\n porder (float, optional): None for porder=2.0.\n... |
def p_matrix_norm(input, porder=1.0, axis=axis, keepdim=False, name=None):
'\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n '
block = LayerHelper('norm', **locals())
out = block.create_variable_for_type_infer... | -4,288,087,778,360,846,000 | NOTE:
This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm. | python/paddle/tensor/linalg.py | p_matrix_norm | DevilCarp/Paddle | python | def p_matrix_norm(input, porder=1.0, axis=axis, keepdim=False, name=None):
'\n NOTE:\n This function actually treats the matrix as flattened vector to calculate vector norm instead of matrix norm.\n '
block = LayerHelper('norm', **locals())
out = block.create_variable_for_type_infer... |
def mat_norm(input, porder=1.0, axis=None):
'\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n '
reduce_all = (True if ((axis is None) or (axis == [])) else False)
axis = (axis if ((axis != None... | 1,976,357,735,964,301,800 | NOTE:
Calculate the matrix norm of a square matrix or batches of square matrices,
when porder is in (1, -1, inf, -inf) | python/paddle/tensor/linalg.py | mat_norm | DevilCarp/Paddle | python | def mat_norm(input, porder=1.0, axis=None):
'\n NOTE:\n Calculate the matrix norm of a square matrix or batches of square matrices,\n when porder is in (1, -1, inf, -inf)\n '
reduce_all = (True if ((axis is None) or (axis == [])) else False)
axis = (axis if ((axis != None... |
def fro_norm(input, porder=2, axis=[(- 1)]):
'\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n '
reduce_all = (True if ((axis is None) or (axis == [])) else False)
keepdim = False
if paddle.in_dynamic_mode():
pow_out = _C_ops.po... | -7,539,193,297,382,894,000 | NOTE:
Calculate the frobenius norm of a square matrix or batches of square matrices. | python/paddle/tensor/linalg.py | fro_norm | DevilCarp/Paddle | python | def fro_norm(input, porder=2, axis=[(- 1)]):
'\n NOTE:\n Calculate the frobenius norm of a square matrix or batches of square matrices.\n '
reduce_all = (True if ((axis is None) or (axis == [])) else False)
keepdim = False
if paddle.in_dynamic_mode():
pow_out = _C_ops.po... |
def svd_norm(input, porder, axis=[(- 1)]):
'\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n '
reduce_all = (True if ((axis is None) or (axis == [])) else False)
... | -4,169,968,877,713,200,600 | NOTE:
Calculate the matrix norm, which is related to singular values, of a matrix
or batches of matrices, including nuclear norm, 2-norm and (-2)-norm. | python/paddle/tensor/linalg.py | svd_norm | DevilCarp/Paddle | python | def svd_norm(input, porder, axis=[(- 1)]):
'\n NOTE:\n Calculate the matrix norm, which is related to singular values, of a matrix\n or batches of matrices, including nuclear norm, 2-norm and (-2)-norm.\n '
reduce_all = (True if ((axis is None) or (axis == [])) else False)
... |
def testV1ScaleIOPersistentVolumeSource(self):
'\n Test V1ScaleIOPersistentVolumeSource\n '
pass | 5,658,198,493,830,950,000 | Test V1ScaleIOPersistentVolumeSource | kubernetes/test/test_v1_scale_io_persistent_volume_source.py | testV1ScaleIOPersistentVolumeSource | MiaoRachelYu/python | python | def testV1ScaleIOPersistentVolumeSource(self):
'\n \n '
pass |
def compute_positivity(dico):
" This computes the positivity score of each statement. \n Takes a dictionary with each statement as liste item and the corresponding interlocutor's name in names item \n \n "
dico_score = defaultdict((lambda : list()))
for (name, liste) in dico.items():
neg_... | 1,236,163,244,579,036,700 | This computes the positivity score of each statement.
Takes a dictionary with each statement as liste item and the corresponding interlocutor's name in names item | RA_project/code_python/image_score_posi.py | compute_positivity | erialc-cal/NLP-FOMC | python | def compute_positivity(dico):
" This computes the positivity score of each statement. \n Takes a dictionary with each statement as liste item and the corresponding interlocutor's name in names item \n \n "
dico_score = defaultdict((lambda : list()))
for (name, liste) in dico.items():
neg_... |
def __init__(self, code):
'code: int - index of macro to run'
self.code = code | -4,599,207,852,549,858,300 | code: int - index of macro to run | homevision_netio_controller/controller.py | __init__ | jackoson/homevision-netio-controller | python | def __init__(self, code):
self.code = code |
def __init__(self, command):
'command: string - command to send'
self.command = command | -6,435,207,489,040,474,000 | command: string - command to send | homevision_netio_controller/controller.py | __init__ | jackoson/homevision-netio-controller | python | def __init__(self, command):
self.command = command |
def __init__(self, ip_address, port, auth, on_off_appliance_codes={}, actions={}, process_actions={}, var_queries={}, flag_queries={}, flag_return_values={True: ['True', 'On', 'Yes', 'Occupied', 'Set', '1'], False: ['False', 'Off', 'No', 'Vacant', 'Clear', '0']}, on_off_commands=None):
'\n Args:\n ip_addres... | -5,714,372,172,230,657,000 | Args:
ip_address: string
port: int
auth: string
- key for authenticating with netio
on_off_appliance_codes: dict[string] => int - codes to be fed to 'on_off_commands' for each appliance
actions: dict[string] => Macro/Command/(_, _, ...) - named actions to be completed
process_actions: dict[string] => {"... | homevision_netio_controller/controller.py | __init__ | jackoson/homevision-netio-controller | python | def __init__(self, ip_address, port, auth, on_off_appliance_codes={}, actions={}, process_actions={}, var_queries={}, flag_queries={}, flag_return_values={True: ['True', 'On', 'Yes', 'Occupied', 'Set', '1'], False: ['False', 'Off', 'No', 'Vacant', 'Clear', '0']}, on_off_commands=None):
'\n Args:\n ip_addres... |
def on_off_command(self, details):
'Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {"appliance": string, "state": string} \n '
if ('appliance' not in details):
... | -79,934,147,174,809,070 | Send an on or off command to an appliance
Sends the specified command to the homevision through netio interface to control the specified appliance.
Args:
details: {"appliance": string, "state": string} | homevision_netio_controller/controller.py | on_off_command | jackoson/homevision-netio-controller | python | def on_off_command(self, details):
'Send an on or off command to an appliance\n \n Sends the specified command to the homevision through netio interface to control the specified appliance.\n \n Args:\n details: {"appliance": string, "state": string} \n '
if ('appliance' not in details):
... |
def action_command(self, details):
'Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {"command": string} \n '
if ('command' not in details):
raise Exception('Command not specified')
if (details['command'] not... | -232,058,001,206,219,100 | Send an action command
Sends the specified command to the homevision through netio interface.
Args:
details: {"command": string} | homevision_netio_controller/controller.py | action_command | jackoson/homevision-netio-controller | python | def action_command(self, details):
'Send an action command\n \n Sends the specified command to the homevision through netio interface.\n \n Args:\n details: {"command": string} \n '
if ('command' not in details):
raise Exception('Command not specified')
if (details['command'] not... |
def start_stop_command(self, details):
'Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {"action": string, "process": string} \n '
if ('action' not in details):
raise Exceptio... | -596,609,483,046,554,400 | Starts or stops a process
Sends the specified command to the homevision through netio interface to control the specified process.
Args:
details: {"action": string, "process": string} | homevision_netio_controller/controller.py | start_stop_command | jackoson/homevision-netio-controller | python | def start_stop_command(self, details):
'Starts or stops a process\n \n Sends the specified command to the homevision through netio interface to control the specified process.\n \n Args:\n details: {"action": string, "process": string} \n '
if ('action' not in details):
raise Exceptio... |
def var_query(self, details):
'Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {"query": string} \n '
if ('query' not in details):
raise Exception('query not specified')
if (details['query'] ... | -7,702,968,477,016,100,000 | Returns the answer to a query on variable
Returns the answer to a query on the specified variable using netio
Args:
details: {"query": string} | homevision_netio_controller/controller.py | var_query | jackoson/homevision-netio-controller | python | def var_query(self, details):
'Returns the answer to a query on variable\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {"query": string} \n '
if ('query' not in details):
raise Exception('query not specified')
if (details['query'] ... |
def flag_query(self, details):
'Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {"query": string} \n '
if ('query' not in details):
raise Exception('query not specified')
if (details['query'] not... | -8,740,578,671,466,509,000 | Returns the answer to a query on flag
Returns the answer to a query on the specified variable using netio
Args:
details: {"query": string} | homevision_netio_controller/controller.py | flag_query | jackoson/homevision-netio-controller | python | def flag_query(self, details):
'Returns the answer to a query on flag\n \n Returns the answer to a query on the specified variable using netio\n \n Args:\n details: {"query": string} \n '
if ('query' not in details):
raise Exception('query not specified')
if (details['query'] not... |
def I():
'Identity operator.'
return np.identity(2) | 4,501,568,749,302,991,000 | Identity operator. | quantum.py | I | duboviy/misc | python | def I():
return np.identity(2) |
def X():
'X-rotation, negation operator.'
return np.identity(2)[..., ::(- 1)] | -164,720,657,164,185,280 | X-rotation, negation operator. | quantum.py | X | duboviy/misc | python | def X():
return np.identity(2)[..., ::(- 1)] |
def H():
'Adamara operator, superposition.'
return (np.array([[1, 1], [1, (- 1)]]) / np.sqrt(2)) | -3,200,611,757,409,645,600 | Adamara operator, superposition. | quantum.py | H | duboviy/misc | python | def H():
return (np.array([[1, 1], [1, (- 1)]]) / np.sqrt(2)) |
def SWAP():
'Swap 2 qubits'
m = np.identity(4)
m[[1, 2]] = m[[2, 1]]
return m | -3,730,938,159,623,653,400 | Swap 2 qubits | quantum.py | SWAP | duboviy/misc | python | def SWAP():
m = np.identity(4)
m[[1, 2]] = m[[2, 1]]
return m |
def CX():
'Controlled negation.'
m = np.identity(4)
m[[3, 2]] = m[[2, 3]]
return m | -5,465,632,063,229,900,000 | Controlled negation. | quantum.py | CX | duboviy/misc | python | def CX():
m = np.identity(4)
m[[3, 2]] = m[[2, 3]]
return m |
@classmethod
def schema(cls) -> dict:
"we're overriding the schema classmethod to enable some post-processing"
schema = super().schema()
schema = cls.change_format_to_oneOf(schema)
return cls.resolve_refs(schema) | 2,795,126,804,326,684,700 | we're overriding the schema classmethod to enable some post-processing | airbyte-integrations/connectors/source-tiktok-marketing/source_tiktok_marketing/spec.py | schema | 99designs/airbyte | python | @classmethod
def schema(cls) -> dict:
schema = super().schema()
schema = cls.change_format_to_oneOf(schema)
return cls.resolve_refs(schema) |
@javaConstructorOverload(java_imports['Long'], (make_sig(['long'], 'void'), (metaLong,)), (make_sig([java_imports['String']], 'void'), (str,)))
def __init__(self, *args, **kwargs):
'\n Instantiates a new Long\n\n Signatures:\n\n Long(long value)\n Long(String s)\n\n Arguments:\n\n... | 9,194,312,380,020,609,000 | Instantiates a new Long
Signatures:
Long(long value)
Long(String s)
Arguments:
Long(long value)
value -- The long to wrap in the object
Long (String s)
s -- The string representing the long | TASSELpy/java/lang/Long.py | __init__ | er432/TASSELpy | python | @javaConstructorOverload(java_imports['Long'], (make_sig(['long'], 'void'), (metaLong,)), (make_sig([java_imports['String']], 'void'), (str,)))
def __init__(self, *args, **kwargs):
'\n Instantiates a new Long\n\n Signatures:\n\n Long(long value)\n Long(String s)\n\n Arguments:\n\n... |
async def async_setup_entry(hass: HomeAssistant, config_entry: ConfigEntry, async_add_entities) -> None:
'Set up discovered lights.'
devs = []
for dev in hass.data[AQUALINK_DOMAIN][DOMAIN]:
devs.append(HassAqualinkLight(dev))
async_add_entities(devs, True) | 4,148,767,531,453,222,000 | Set up discovered lights. | homeassistant/components/iaqualink/light.py | async_setup_entry | 0xFEEDC0DE64/homeassistant-core | python | async def async_setup_entry(hass: HomeAssistant, config_entry: ConfigEntry, async_add_entities) -> None:
devs = []
for dev in hass.data[AQUALINK_DOMAIN][DOMAIN]:
devs.append(HassAqualinkLight(dev))
async_add_entities(devs, True) |
@property
def name(self) -> str:
'Return the name of the light.'
return self.dev.label | 331,499,236,811,538,800 | Return the name of the light. | homeassistant/components/iaqualink/light.py | name | 0xFEEDC0DE64/homeassistant-core | python | @property
def name(self) -> str:
return self.dev.label |
@property
def is_on(self) -> bool:
'Return whether the light is on or off.'
return self.dev.is_on | -7,804,622,775,240,501,000 | Return whether the light is on or off. | homeassistant/components/iaqualink/light.py | is_on | 0xFEEDC0DE64/homeassistant-core | python | @property
def is_on(self) -> bool:
return self.dev.is_on |
@refresh_system
async def async_turn_on(self, **kwargs) -> None:
'Turn on the light.\n\n This handles brightness and light effects for lights that do support\n them.\n '
brightness = kwargs.get(ATTR_BRIGHTNESS)
effect = kwargs.get(ATTR_EFFECT)
if effect:
effect = AqualinkLig... | -9,093,624,857,957,879,000 | Turn on the light.
This handles brightness and light effects for lights that do support
them. | homeassistant/components/iaqualink/light.py | async_turn_on | 0xFEEDC0DE64/homeassistant-core | python | @refresh_system
async def async_turn_on(self, **kwargs) -> None:
'Turn on the light.\n\n This handles brightness and light effects for lights that do support\n them.\n '
brightness = kwargs.get(ATTR_BRIGHTNESS)
effect = kwargs.get(ATTR_EFFECT)
if effect:
effect = AqualinkLig... |
@refresh_system
async def async_turn_off(self, **kwargs) -> None:
'Turn off the light.'
(await self.dev.turn_off()) | -5,875,403,621,456,464,000 | Turn off the light. | homeassistant/components/iaqualink/light.py | async_turn_off | 0xFEEDC0DE64/homeassistant-core | python | @refresh_system
async def async_turn_off(self, **kwargs) -> None:
(await self.dev.turn_off()) |
@property
def brightness(self) -> int:
'Return current brightness of the light.\n\n The scale needs converting between 0-100 and 0-255.\n '
return ((self.dev.brightness * 255) / 100) | -8,752,403,519,390,625,000 | Return current brightness of the light.
The scale needs converting between 0-100 and 0-255. | homeassistant/components/iaqualink/light.py | brightness | 0xFEEDC0DE64/homeassistant-core | python | @property
def brightness(self) -> int:
'Return current brightness of the light.\n\n The scale needs converting between 0-100 and 0-255.\n '
return ((self.dev.brightness * 255) / 100) |
@property
def effect(self) -> str:
'Return the current light effect if supported.'
return AqualinkLightEffect(self.dev.effect).name | 4,174,574,449,923,972,600 | Return the current light effect if supported. | homeassistant/components/iaqualink/light.py | effect | 0xFEEDC0DE64/homeassistant-core | python | @property
def effect(self) -> str:
return AqualinkLightEffect(self.dev.effect).name |
@property
def effect_list(self) -> list:
'Return supported light effects.'
return list(AqualinkLightEffect.__members__) | 9,045,013,550,262,554,000 | Return supported light effects. | homeassistant/components/iaqualink/light.py | effect_list | 0xFEEDC0DE64/homeassistant-core | python | @property
def effect_list(self) -> list:
return list(AqualinkLightEffect.__members__) |
@property
def supported_features(self) -> int:
'Return the list of features supported by the light.'
if self.dev.is_dimmer:
return SUPPORT_BRIGHTNESS
if self.dev.is_color:
return SUPPORT_EFFECT
return 0 | 2,749,345,628,372,254,700 | Return the list of features supported by the light. | homeassistant/components/iaqualink/light.py | supported_features | 0xFEEDC0DE64/homeassistant-core | python | @property
def supported_features(self) -> int:
if self.dev.is_dimmer:
return SUPPORT_BRIGHTNESS
if self.dev.is_color:
return SUPPORT_EFFECT
return 0 |
def is_language(self, s, expected_lang):
' Check if the language of the segment cannot be reliably identified\n as another language. If another than the expected language is\n detected return False '
expected_lang = expected_lang.lower()
if self.valid_languages:
assert (expected_lang i... | 5,011,547,454,327,538,000 | Check if the language of the segment cannot be reliably identified
as another language. If another than the expected language is
detected return False | baseline/filter_hunalign_bitext.py | is_language | christianbuck/CorpusMining | python | def is_language(self, s, expected_lang):
' Check if the language of the segment cannot be reliably identified\n as another language. If another than the expected language is\n detected return False '
expected_lang = expected_lang.lower()
if self.valid_languages:
assert (expected_lang i... |
@abstractmethod
def find_files(self, directory: Union[(str, Path)], extensions: Optional[Union[(Sequence, str)]]=None, keywords: Optional[Union[(list, str)]]=None, hemisphere: Optional[str]=None, stimulation: Optional[str]=None, medication: Optional[str]=None, exclude: Optional[Union[(str, list)]]=None, verbose: bool=F... | 3,752,076,389,859,590,700 | Find files in directory with optional
keywords and extensions. | src/pte/filetools/filefinder_abc.py | find_files | richardkoehler/pte | python | @abstractmethod
def find_files(self, directory: Union[(str, Path)], extensions: Optional[Union[(Sequence, str)]]=None, keywords: Optional[Union[(list, str)]]=None, hemisphere: Optional[str]=None, stimulation: Optional[str]=None, medication: Optional[str]=None, exclude: Optional[Union[(str, list)]]=None, verbose: bool=F... |
@abstractmethod
def filter_files(self, keywords: Optional[Union[(str, list)]]=None, hemisphere: Optional[str]=None, stimulation: Optional[str]=None, medication: Optional[str]=None, exclude: Optional[Union[(str, list)]]=None, verbose: bool=False) -> None:
'Filter list of filepaths for given parameters.' | -1,411,756,026,739,772,400 | Filter list of filepaths for given parameters. | src/pte/filetools/filefinder_abc.py | filter_files | richardkoehler/pte | python | @abstractmethod
def filter_files(self, keywords: Optional[Union[(str, list)]]=None, hemisphere: Optional[str]=None, stimulation: Optional[str]=None, medication: Optional[str]=None, exclude: Optional[Union[(str, list)]]=None, verbose: bool=False) -> None:
|
def _find_files(self, directory: Union[(Path, str)], extensions: Optional[Union[(list, str)]]=None) -> None:
'Find files in directory with optional extensions.\n\n Args:\n directory (string)\n keywords (list): e.g. ["SelfpacedRota", "ButtonPress] (optional)\n extensions (list... | 56,373,943,236,067,064 | Find files in directory with optional extensions.
Args:
directory (string)
keywords (list): e.g. ["SelfpacedRota", "ButtonPress] (optional)
extensions (list): e.g. [".json" or "tsv"] (optional)
verbose (bool): verbosity level (optional, default=True) | src/pte/filetools/filefinder_abc.py | _find_files | richardkoehler/pte | python | def _find_files(self, directory: Union[(Path, str)], extensions: Optional[Union[(list, str)]]=None) -> None:
'Find files in directory with optional extensions.\n\n Args:\n directory (string)\n keywords (list): e.g. ["SelfpacedRota", "ButtonPress] (optional)\n extensions (list... |
def _filter_files(self, keywords: Optional[Union[(str, list[str])]]=None, hemisphere: Optional[str]=None, stimulation: Optional[str]=None, medication: Optional[str]=None, exclude: Optional[Union[(str, list[str])]]=None) -> None:
'Filter filepaths for given parameters.'
filtered_files = self.files
if exclude... | 7,211,439,597,194,100,000 | Filter filepaths for given parameters. | src/pte/filetools/filefinder_abc.py | _filter_files | richardkoehler/pte | python | def _filter_files(self, keywords: Optional[Union[(str, list[str])]]=None, hemisphere: Optional[str]=None, stimulation: Optional[str]=None, medication: Optional[str]=None, exclude: Optional[Union[(str, list[str])]]=None) -> None:
filtered_files = self.files
if exclude:
if (not isinstance(exclude, li... |
def irfft2(a, s=None, axes=((- 2), (- 1)), norm=None):
'\n Compute the 2-dimensional inverse FFT of a real array.\n\n Parameters\n ----------\n a : array_like\n The input tensor\n s : sequence of ints, optional\n Shape of the inverse FFT.\n axes : sequence of ints, optional\n ... | 31,699,221,590,624,984 | Compute the 2-dimensional inverse FFT of a real array.
Parameters
----------
a : array_like
The input tensor
s : sequence of ints, optional
Shape of the inverse FFT.
axes : sequence of ints, optional
The axes over which to compute the inverse fft.
Default is the last two axes.
norm : {None, "ortho"}, o... | mars/tensor/fft/irfft2.py | irfft2 | JeffroMF/mars | python | def irfft2(a, s=None, axes=((- 2), (- 1)), norm=None):
'\n Compute the 2-dimensional inverse FFT of a real array.\n\n Parameters\n ----------\n a : array_like\n The input tensor\n s : sequence of ints, optional\n Shape of the inverse FFT.\n axes : sequence of ints, optional\n ... |
def __init__(self, conf, router_conf, db, agent):
'Create a new Router'
self.conf = conf
self.router_conf = router_conf
self.db = db
self.agent = agent | 7,695,343,446,000,534,000 | Create a new Router | autopush/router/webpush.py | __init__ | Acidburn0zzz/autopush | python | def __init__(self, conf, router_conf, db, agent):
self.conf = conf
self.router_conf = router_conf
self.db = db
self.agent = agent |
def register(self, uaid, router_data, app_id, *args, **kwargs):
'No additional routing data' | -4,153,625,044,012,655,000 | No additional routing data | autopush/router/webpush.py | register | Acidburn0zzz/autopush | python | def register(self, uaid, router_data, app_id, *args, **kwargs):
|
def amend_endpoint_response(self, response, router_data):
'Stubbed out for this router' | 4,586,840,260,404,607,500 | Stubbed out for this router | autopush/router/webpush.py | amend_endpoint_response | Acidburn0zzz/autopush | python | def amend_endpoint_response(self, response, router_data):
|
@inlineCallbacks
def route_notification(self, notification, uaid_data):
"Route a notification to an internal node, and store it if the node\n can't deliver immediately or is no longer a valid node\n "
node_id = uaid_data.get('node_id')
uaid = uaid_data['uaid']
router = self.db.router
i... | 3,801,602,032,211,172,400 | Route a notification to an internal node, and store it if the node
can't deliver immediately or is no longer a valid node | autopush/router/webpush.py | route_notification | Acidburn0zzz/autopush | python | @inlineCallbacks
def route_notification(self, notification, uaid_data):
"Route a notification to an internal node, and store it if the node\n can't deliver immediately or is no longer a valid node\n "
node_id = uaid_data.get('node_id')
uaid = uaid_data['uaid']
router = self.db.router
i... |
def _send_notification(self, uaid, node_id, notification):
'Send a notification to a specific node_id\n\n This version of the overriden method includes the necessary crypto\n headers for the notification.\n\n :type notification: autopush.utils.WebPushNotification\n\n '
payload = noti... | 4,112,651,449,687,784,400 | Send a notification to a specific node_id
This version of the overriden method includes the necessary crypto
headers for the notification.
:type notification: autopush.utils.WebPushNotification | autopush/router/webpush.py | _send_notification | Acidburn0zzz/autopush | python | def _send_notification(self, uaid, node_id, notification):
'Send a notification to a specific node_id\n\n This version of the overriden method includes the necessary crypto\n headers for the notification.\n\n :type notification: autopush.utils.WebPushNotification\n\n '
payload = noti... |
def _send_notification_check(self, uaid, node_id):
'Send a command to the node to check for notifications'
url = ((node_id + '/notif/') + uaid)
return self.agent.request('PUT', url.encode('utf8')).addCallback(IgnoreBody.ignore) | 4,989,087,466,341,468,000 | Send a command to the node to check for notifications | autopush/router/webpush.py | _send_notification_check | Acidburn0zzz/autopush | python | def _send_notification_check(self, uaid, node_id):
url = ((node_id + '/notif/') + uaid)
return self.agent.request('PUT', url.encode('utf8')).addCallback(IgnoreBody.ignore) |
def _save_notification(self, uaid_data, notification):
'Saves a notification, returns a deferred.\n\n This version of the overridden method saves each individual message\n to the message table along with relevant request headers if\n available.\n\n :type uaid_data: dict\n\n '
... | -1,176,066,011,258,479,600 | Saves a notification, returns a deferred.
This version of the overridden method saves each individual message
to the message table along with relevant request headers if
available.
:type uaid_data: dict | autopush/router/webpush.py | _save_notification | Acidburn0zzz/autopush | python | def _save_notification(self, uaid_data, notification):
'Saves a notification, returns a deferred.\n\n This version of the overridden method saves each individual message\n to the message table along with relevant request headers if\n available.\n\n :type uaid_data: dict\n\n '
... |
def _eat_db_err(self, fail):
'errBack for ignoring provisioned throughput errors'
fail.trap(ClientError) | -5,169,902,337,626,011,000 | errBack for ignoring provisioned throughput errors | autopush/router/webpush.py | _eat_db_err | Acidburn0zzz/autopush | python | def _eat_db_err(self, fail):
fail.trap(ClientError) |
def save_bloguser_extra_profile(backend, user, response, *args, **kwargs):
'\n see more:\n http://python-social-auth.readthedocs.io/en/latest/use_cases.html#retrieve-google-friends\n http://python-social-auth.readthedocs.io/en/latest/pipeline.html\n :param backend:\n :param user:\n :param ... | 8,284,591,256,816,238,000 | see more:
http://python-social-auth.readthedocs.io/en/latest/use_cases.html#retrieve-google-friends
http://python-social-auth.readthedocs.io/en/latest/pipeline.html
:param backend:
:param user:
:param response:
:param args:
:param kwargs:
:return: | apps/bloguser/pipline.py | save_bloguser_extra_profile | Jennei/MyBlog | python | def save_bloguser_extra_profile(backend, user, response, *args, **kwargs):
'\n see more:\n http://python-social-auth.readthedocs.io/en/latest/use_cases.html#retrieve-google-friends\n http://python-social-auth.readthedocs.io/en/latest/pipeline.html\n :param backend:\n :param user:\n :param ... |
def extractMichilunWordpressCom(item):
"\n\tParser for 'michilun.wordpress.com'\n\t"
bad = ['Recommendations and Reviews']
if any([(tmp in item['tags']) for tmp in bad]):
return None
(vol, chp, frag, postfix) = extractVolChapterFragmentPostfix(item['title'])
if ((not (chp or vol)) or ('previ... | 2,736,643,888,499,868,000 | Parser for 'michilun.wordpress.com' | WebMirror/management/rss_parser_funcs/feed_parse_extractMichilunWordpressCom.py | extractMichilunWordpressCom | fake-name/ReadableWebProxy | python | def extractMichilunWordpressCom(item):
"\n\t\n\t"
bad = ['Recommendations and Reviews']
if any([(tmp in item['tags']) for tmp in bad]):
return None
(vol, chp, frag, postfix) = extractVolChapterFragmentPostfix(item['title'])
if ((not (chp or vol)) or ('preview' in item['title'].lower())):
... |
def _recursive_apply(self, block):
'\n This function is "applied" to every child in the block. This function in turn\n registers the forward hook to each module. It helps logging the input output tensors\n of that module.\n '
if (block in self.registered_blocks):
self.logger.... | 8,230,639,869,853,194,000 | This function is "applied" to every child in the block. This function in turn
registers the forward hook to each module. It helps logging the input output tensors
of that module. | smdebug/mxnet/hook.py | _recursive_apply | arjkesh/sagemaker-debugger | python | def _recursive_apply(self, block):
'\n This function is "applied" to every child in the block. This function in turn\n registers the forward hook to each module. It helps logging the input output tensors\n of that module.\n '
if (block in self.registered_blocks):
self.logger.... |
@error_handling_agent.catch_smdebug_errors()
def register_block(self, block):
'\n This function registers the forward hook. If user wants to register the hook\n for every child in the given block, then the function calls "apply" API for\n registration of the hook.\n The hook is registere... | 3,938,190,178,072,743,400 | This function registers the forward hook. If user wants to register the hook
for every child in the given block, then the function calls "apply" API for
registration of the hook.
The hook is registered recursively, if user has specified the collections that are more than
the default collectors viz. gradients, weight an... | smdebug/mxnet/hook.py | register_block | arjkesh/sagemaker-debugger | python | @error_handling_agent.catch_smdebug_errors()
def register_block(self, block):
'\n This function registers the forward hook. If user wants to register the hook\n for every child in the given block, then the function calls "apply" API for\n registration of the hook.\n The hook is registere... |
def get_virtual_machine_scale_set(expand: Optional[str]=None, resource_group_name: Optional[str]=None, vm_scale_set_name: Optional[str]=None, opts: Optional[pulumi.InvokeOptions]=None) -> AwaitableGetVirtualMachineScaleSetResult:
"\n Describes a Virtual Machine Scale Set.\n API Version: 2021-03-01.\n\n\n :... | -7,936,196,944,535,669,000 | Describes a Virtual Machine Scale Set.
API Version: 2021-03-01.
:param str expand: The expand expression to apply on the operation. 'UserData' retrieves the UserData property of the VM scale set that was provided by the user during the VM scale set Create/Update operation
:param str resource_group_name: The name of t... | sdk/python/pulumi_azure_native/compute/get_virtual_machine_scale_set.py | get_virtual_machine_scale_set | polivbr/pulumi-azure-native | python | def get_virtual_machine_scale_set(expand: Optional[str]=None, resource_group_name: Optional[str]=None, vm_scale_set_name: Optional[str]=None, opts: Optional[pulumi.InvokeOptions]=None) -> AwaitableGetVirtualMachineScaleSetResult:
"\n Describes a Virtual Machine Scale Set.\n API Version: 2021-03-01.\n\n\n :... |
@property
@pulumi.getter(name='additionalCapabilities')
def additional_capabilities(self) -> Optional['outputs.AdditionalCapabilitiesResponse']:
'\n Specifies additional capabilities enabled or disabled on the Virtual Machines in the Virtual Machine Scale Set. For instance: whether the Virtual Machines have ... | -4,984,097,992,721,295,000 | Specifies additional capabilities enabled or disabled on the Virtual Machines in the Virtual Machine Scale Set. For instance: whether the Virtual Machines have the capability to support attaching managed data disks with UltraSSD_LRS storage account type. | sdk/python/pulumi_azure_native/compute/get_virtual_machine_scale_set.py | additional_capabilities | polivbr/pulumi-azure-native | python | @property
@pulumi.getter(name='additionalCapabilities')
def additional_capabilities(self) -> Optional['outputs.AdditionalCapabilitiesResponse']:
'\n \n '
return pulumi.get(self, 'additional_capabilities') |
@property
@pulumi.getter(name='automaticRepairsPolicy')
def automatic_repairs_policy(self) -> Optional['outputs.AutomaticRepairsPolicyResponse']:
'\n Policy for automatic repairs.\n '
return pulumi.get(self, 'automatic_repairs_policy') | -5,255,793,026,184,793,000 | Policy for automatic repairs. | sdk/python/pulumi_azure_native/compute/get_virtual_machine_scale_set.py | automatic_repairs_policy | polivbr/pulumi-azure-native | python | @property
@pulumi.getter(name='automaticRepairsPolicy')
def automatic_repairs_policy(self) -> Optional['outputs.AutomaticRepairsPolicyResponse']:
'\n \n '
return pulumi.get(self, 'automatic_repairs_policy') |
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