242 lines
10 KiB
Python
242 lines
10 KiB
Python
"""Python wrappers around TensorFlow ops.
|
|
|
|
This file is MACHINE GENERATED! Do not edit.
|
|
"""
|
|
|
|
import collections
|
|
|
|
from tensorflow.python import pywrap_tfe as pywrap_tfe
|
|
from tensorflow.python.eager import context as _context
|
|
from tensorflow.python.eager import core as _core
|
|
from tensorflow.python.eager import execute as _execute
|
|
from tensorflow.python.framework import dtypes as _dtypes
|
|
from tensorflow.security.fuzzing.py import annotation_types as _atypes
|
|
|
|
from tensorflow.python.framework import op_def_registry as _op_def_registry
|
|
from tensorflow.python.framework import ops as _ops
|
|
from tensorflow.python.framework import op_def_library as _op_def_library
|
|
from tensorflow.python.util.deprecation import deprecated_endpoints
|
|
from tensorflow.python.util import dispatch as _dispatch
|
|
from tensorflow.python.util.tf_export import tf_export
|
|
|
|
from typing import TypeVar, List, Any
|
|
from typing_extensions import Annotated
|
|
|
|
def kmc2_chain_initialization(distances: Annotated[Any, _atypes.Float32], seed: Annotated[Any, _atypes.Int64], name=None) -> Annotated[Any, _atypes.Int64]:
|
|
r"""Returns the index of a data point that should be added to the seed set.
|
|
|
|
Entries in distances are assumed to be squared distances of candidate points to
|
|
the already sampled centers in the seed set. The op constructs one Markov chain
|
|
of the k-MC^2 algorithm and returns the index of one candidate point to be added
|
|
as an additional cluster center.
|
|
|
|
Args:
|
|
distances: A `Tensor` of type `float32`.
|
|
Vector with squared distances to the closest previously sampled cluster center
|
|
for each candidate point.
|
|
seed: A `Tensor` of type `int64`.
|
|
Scalar. Seed for initializing the random number generator.
|
|
name: A name for the operation (optional).
|
|
|
|
Returns:
|
|
A `Tensor` of type `int64`.
|
|
"""
|
|
_ctx = _context._context or _context.context()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "KMC2ChainInitialization", name, distances, seed)
|
|
return _result
|
|
except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
try:
|
|
return kmc2_chain_initialization_eager_fallback(
|
|
distances, seed, name=name, ctx=_ctx)
|
|
except _core._SymbolicException:
|
|
pass # Add nodes to the TensorFlow graph.
|
|
# Add nodes to the TensorFlow graph.
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
|
"KMC2ChainInitialization", distances=distances, seed=seed, name=name)
|
|
_result = _outputs[:]
|
|
if _execute.must_record_gradient():
|
|
_attrs = ()
|
|
_inputs_flat = _op.inputs
|
|
_execute.record_gradient(
|
|
"KMC2ChainInitialization", _inputs_flat, _attrs, _result)
|
|
_result, = _result
|
|
return _result
|
|
|
|
KMC2ChainInitialization = tf_export("raw_ops.KMC2ChainInitialization")(_ops.to_raw_op(kmc2_chain_initialization))
|
|
|
|
|
|
def kmc2_chain_initialization_eager_fallback(distances: Annotated[Any, _atypes.Float32], seed: Annotated[Any, _atypes.Int64], name, ctx) -> Annotated[Any, _atypes.Int64]:
|
|
distances = _ops.convert_to_tensor(distances, _dtypes.float32)
|
|
seed = _ops.convert_to_tensor(seed, _dtypes.int64)
|
|
_inputs_flat = [distances, seed]
|
|
_attrs = None
|
|
_result = _execute.execute(b"KMC2ChainInitialization", 1,
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
|
name=name)
|
|
if _execute.must_record_gradient():
|
|
_execute.record_gradient(
|
|
"KMC2ChainInitialization", _inputs_flat, _attrs, _result)
|
|
_result, = _result
|
|
return _result
|
|
|
|
|
|
def kmeans_plus_plus_initialization(points: Annotated[Any, _atypes.Float32], num_to_sample: Annotated[Any, _atypes.Int64], seed: Annotated[Any, _atypes.Int64], num_retries_per_sample: Annotated[Any, _atypes.Int64], name=None) -> Annotated[Any, _atypes.Float32]:
|
|
r"""Selects num_to_sample rows of input using the KMeans++ criterion.
|
|
|
|
Rows of points are assumed to be input points. One row is selected at random.
|
|
Subsequent rows are sampled with probability proportional to the squared L2
|
|
distance from the nearest row selected thus far till num_to_sample rows have
|
|
been sampled.
|
|
|
|
Args:
|
|
points: A `Tensor` of type `float32`.
|
|
Matrix of shape (n, d). Rows are assumed to be input points.
|
|
num_to_sample: A `Tensor` of type `int64`.
|
|
Scalar. The number of rows to sample. This value must not be larger than n.
|
|
seed: A `Tensor` of type `int64`.
|
|
Scalar. Seed for initializing the random number generator.
|
|
num_retries_per_sample: A `Tensor` of type `int64`.
|
|
Scalar. For each row that is sampled, this parameter
|
|
specifies the number of additional points to draw from the current
|
|
distribution before selecting the best. If a negative value is specified, a
|
|
heuristic is used to sample O(log(num_to_sample)) additional points.
|
|
name: A name for the operation (optional).
|
|
|
|
Returns:
|
|
A `Tensor` of type `float32`.
|
|
"""
|
|
_ctx = _context._context or _context.context()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "KmeansPlusPlusInitialization", name, points, num_to_sample,
|
|
seed, num_retries_per_sample)
|
|
return _result
|
|
except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
try:
|
|
return kmeans_plus_plus_initialization_eager_fallback(
|
|
points, num_to_sample, seed, num_retries_per_sample, name=name,
|
|
ctx=_ctx)
|
|
except _core._SymbolicException:
|
|
pass # Add nodes to the TensorFlow graph.
|
|
# Add nodes to the TensorFlow graph.
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
|
"KmeansPlusPlusInitialization", points=points,
|
|
num_to_sample=num_to_sample,
|
|
seed=seed,
|
|
num_retries_per_sample=num_retries_per_sample,
|
|
name=name)
|
|
_result = _outputs[:]
|
|
if _execute.must_record_gradient():
|
|
_attrs = ()
|
|
_inputs_flat = _op.inputs
|
|
_execute.record_gradient(
|
|
"KmeansPlusPlusInitialization", _inputs_flat, _attrs, _result)
|
|
_result, = _result
|
|
return _result
|
|
|
|
KmeansPlusPlusInitialization = tf_export("raw_ops.KmeansPlusPlusInitialization")(_ops.to_raw_op(kmeans_plus_plus_initialization))
|
|
|
|
|
|
def kmeans_plus_plus_initialization_eager_fallback(points: Annotated[Any, _atypes.Float32], num_to_sample: Annotated[Any, _atypes.Int64], seed: Annotated[Any, _atypes.Int64], num_retries_per_sample: Annotated[Any, _atypes.Int64], name, ctx) -> Annotated[Any, _atypes.Float32]:
|
|
points = _ops.convert_to_tensor(points, _dtypes.float32)
|
|
num_to_sample = _ops.convert_to_tensor(num_to_sample, _dtypes.int64)
|
|
seed = _ops.convert_to_tensor(seed, _dtypes.int64)
|
|
num_retries_per_sample = _ops.convert_to_tensor(num_retries_per_sample, _dtypes.int64)
|
|
_inputs_flat = [points, num_to_sample, seed, num_retries_per_sample]
|
|
_attrs = None
|
|
_result = _execute.execute(b"KmeansPlusPlusInitialization", 1,
|
|
inputs=_inputs_flat, attrs=_attrs, ctx=ctx,
|
|
name=name)
|
|
if _execute.must_record_gradient():
|
|
_execute.record_gradient(
|
|
"KmeansPlusPlusInitialization", _inputs_flat, _attrs, _result)
|
|
_result, = _result
|
|
return _result
|
|
|
|
_NearestNeighborsOutput = collections.namedtuple(
|
|
"NearestNeighbors",
|
|
["nearest_center_indices", "nearest_center_distances"])
|
|
|
|
|
|
def nearest_neighbors(points: Annotated[Any, _atypes.Float32], centers: Annotated[Any, _atypes.Float32], k: Annotated[Any, _atypes.Int64], name=None):
|
|
r"""Selects the k nearest centers for each point.
|
|
|
|
Rows of points are assumed to be input points. Rows of centers are assumed to be
|
|
the list of candidate centers. For each point, the k centers that have least L2
|
|
distance to it are computed.
|
|
|
|
Args:
|
|
points: A `Tensor` of type `float32`.
|
|
Matrix of shape (n, d). Rows are assumed to be input points.
|
|
centers: A `Tensor` of type `float32`.
|
|
Matrix of shape (m, d). Rows are assumed to be centers.
|
|
k: A `Tensor` of type `int64`.
|
|
Number of nearest centers to return for each point. If k is larger than m, then
|
|
only m centers are returned.
|
|
name: A name for the operation (optional).
|
|
|
|
Returns:
|
|
A tuple of `Tensor` objects (nearest_center_indices, nearest_center_distances).
|
|
|
|
nearest_center_indices: A `Tensor` of type `int64`.
|
|
nearest_center_distances: A `Tensor` of type `float32`.
|
|
"""
|
|
_ctx = _context._context or _context.context()
|
|
tld = _ctx._thread_local_data
|
|
if tld.is_eager:
|
|
try:
|
|
_result = pywrap_tfe.TFE_Py_FastPathExecute(
|
|
_ctx, "NearestNeighbors", name, points, centers, k)
|
|
_result = _NearestNeighborsOutput._make(_result)
|
|
return _result
|
|
except _core._NotOkStatusException as e:
|
|
_ops.raise_from_not_ok_status(e, name)
|
|
except _core._FallbackException:
|
|
pass
|
|
try:
|
|
return nearest_neighbors_eager_fallback(
|
|
points, centers, k, name=name, ctx=_ctx)
|
|
except _core._SymbolicException:
|
|
pass # Add nodes to the TensorFlow graph.
|
|
# Add nodes to the TensorFlow graph.
|
|
_, _, _op, _outputs = _op_def_library._apply_op_helper(
|
|
"NearestNeighbors", points=points, centers=centers, k=k, name=name)
|
|
_result = _outputs[:]
|
|
if _execute.must_record_gradient():
|
|
_attrs = ()
|
|
_inputs_flat = _op.inputs
|
|
_execute.record_gradient(
|
|
"NearestNeighbors", _inputs_flat, _attrs, _result)
|
|
_result = _NearestNeighborsOutput._make(_result)
|
|
return _result
|
|
|
|
NearestNeighbors = tf_export("raw_ops.NearestNeighbors")(_ops.to_raw_op(nearest_neighbors))
|
|
|
|
|
|
def nearest_neighbors_eager_fallback(points: Annotated[Any, _atypes.Float32], centers: Annotated[Any, _atypes.Float32], k: Annotated[Any, _atypes.Int64], name, ctx):
|
|
points = _ops.convert_to_tensor(points, _dtypes.float32)
|
|
centers = _ops.convert_to_tensor(centers, _dtypes.float32)
|
|
k = _ops.convert_to_tensor(k, _dtypes.int64)
|
|
_inputs_flat = [points, centers, k]
|
|
_attrs = None
|
|
_result = _execute.execute(b"NearestNeighbors", 2, inputs=_inputs_flat,
|
|
attrs=_attrs, ctx=ctx, name=name)
|
|
if _execute.must_record_gradient():
|
|
_execute.record_gradient(
|
|
"NearestNeighbors", _inputs_flat, _attrs, _result)
|
|
_result = _NearestNeighborsOutput._make(_result)
|
|
return _result
|
|
|