id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
|---|---|---|
148,089 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,090 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,091 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
def cumsum(input, dim, *,... | null |
148,092 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
def lu_solve(b, LU_data,... | null |
148,093 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,094 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
def flatten(input, start_... | null |
148,095 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,096 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,097 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
def flip(input, dims):
... | null |
148,098 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,099 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,100 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,101 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
def tril(input, diagonal=... | null |
148,102 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
def triu(input, diagonal=... | null |
148,103 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,104 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,105 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,106 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes, with_supported_dtypes
from ivy.functional.frontends.torch.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.torch import promote_types_of_torch_inputs
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.util... | null |
148,107 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,108 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def concat(tensors, dim=0, *, out=None):
return ivy.concat(... | null |
148,109 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def split(tensor, split_size_or_sections, dim=0):
if isinst... | null |
148,110 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def hstack(tensors, *, out=None):
return ivy.hstack(tensors... | null |
148,111 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,112 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def reshape(input, shape):
return ivy.reshape(input, shape)... | null |
148,113 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,114 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,115 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,116 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def stack(tensors, dim=0, *, out=None):
return ivy.stack(te... | null |
148,117 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def stack(tensors, dim=0, *, out=None):
return ivy.stack(te... | null |
148,118 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def gather(input, dim, index, *, sparse_grad=False, out=None):
... | null |
148,119 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def gather(input, dim, index, *, sparse_grad=False, out=None):
... | null |
148,120 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,121 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def moveaxis(input, source, destination):
return ivy.moveax... | null |
148,122 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,123 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,124 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def vstack(tensors, *, out=None):
return ivy.vstack(tensors... | null |
148,125 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,126 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,127 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def swapaxes(input, axis0, axis1):
import ivy
from ivy.utils.e... | null |
148,128 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def gather(input, dim, index, *, sparse_grad=False, out=None):
... | null |
148,129 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,130 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def split(tensor, split_size_or_sections, dim=0):
if isinst... | null |
148,131 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,132 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def swapaxes(input, axis0, axis1):
return ivy.swapaxes(inpu... | null |
148,133 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def reshape(input, shape):
return ivy.reshape(input, shape)... | null |
148,134 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,135 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
... | null |
148,136 | import ivy
from ivy.func_wrapper import with_unsupported_dtypes
from ivy.functional.frontends.torch.func_wrapper import (
to_ivy_arrays_and_back,
numpy_to_torch_style_args,
to_ivy_shape,
)
import ivy.functional.frontends.torch as torch_frontend
def nonzero(input, *, out=None, as_tuple=False):
import ivy
fr... | null |
148,137 | import ivy
import ivy.functional.frontends.torch as torch_frontend
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.is_local():
module = ivy.utils._importlib.import_cache[__name__]
else:
module = sys.modul... | null |
148,138 | import ivy
import ivy.functional.frontends.torch as torch_frontend
_default_dtype = torch_frontend.float32
def get_default_dtype():
return _default_dtype | null |
148,139 | import ivy
import ivy.functional.frontends.torch as torch_frontend
def promote_types(type1, type2, /):
return torch_frontend.promote_types_torch(type1, type2) | null |
148,140 | import ivy
import ivy.functional.frontends.torch as torch_frontend
_default_dtype = torch_frontend.float32
import ivy
from ivy.utils.exceptions import handle_exceptions
from ivy.functional.frontends import set_frontend_to_specific_version
if ivy.is_local():
module = ivy.utils._importlib.import_cach... | null |
148,141 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
from sklearn.utils.multiclass import type_of_target
from ivy.utils.exceptions import IvyValueError
def type_of_target(y, input_name="y"):
# purely utility function
unique_vals = len(ivy.unique_values(y))
if y.ndim == ... | null |
148,142 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
from sklearn.utils.multiclass import type_of_target
from ivy.utils.exceptions import IvyValueError
class IvyValueError(IvyException, ValueError):
def __init__(self, *messages, include_backend=False):
super().__init__(... | null |
148,143 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
from sklearn.utils.multiclass import type_of_target
from ivy.utils.exceptions import IvyValueError
class IvyValueError(IvyException, ValueError):
def __init__(self, *messages, include_backend=False):
def precision_score(y_t... | null |
148,144 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
from sklearn.utils.multiclass import type_of_target
from ivy.utils.exceptions import IvyValueError
class IvyValueError(IvyException, ValueError):
def __init__(self, *messages, include_backend=False):
super().__init__(... | null |
148,145 | import ivy
FEATURE_THRESHOLD = 1e-7
class SplitRecord:
def __init__(
self,
feature=0,
pos=0,
threshold=0.0,
improvement=-ivy.inf,
impurity_left=0.0,
impurity_right=0.0,
missing_go_to_left=False,
n_missing=0,... | null |
148,146 | import ivy
def sort(feature_values, samples, n):
if n == 0:
return
idx = ivy.argsort(feature_values)
return feature_values[idx], samples[idx] | null |
148,147 | from abc import ABC, abstractmethod
import ivy
def _move_sums_classification(
criterion, sum_1, sum_2, weighted_n_1, weighted_n_2, put_missing_in_1
):
for k in range(criterion.n_outputs):
n = int(criterion.n_classes[k])
sum_1[k, :n] = 0
sum_2[k, :n] = criterion.sum_total[k, :n]
wei... | null |
148,148 | from abc import ABCMeta, abstractmethod
import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
from ivy.functional.frontends.sklearn.utils.validation import column_or_1d
def train_test_split(
*arrays,
test_size=None,
train_size=None,
random_state=None,
shuffle=Tru... | null |
148,149 | import ivy
import numbers
from ivy.functional.frontends.numpy.func_wrapper import outputs_to_frontend_arrays
def make_circles(
n_samples=100, *, shuffle=True, noise=None, random_state=None, factor=0.8
):
# numbers.Integral also includes bool
if isinstance(n_samples, numbers.Integral):
n_samples_out... | null |
148,150 | import ivy
import numbers
from ivy.functional.frontends.numpy.func_wrapper import outputs_to_frontend_arrays
def make_moons(n_samples=100, *, shuffle=True, noise=None, random_state=None):
if isinstance(n_samples, numbers.Integral):
n_samples_out = n_samples // 2
n_samples_in = n_samples - n_samples... | null |
148,151 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
from ivy.func_wrapper import with_unsupported_dtypes
def as_float_array(X, *, copy=True, force_all_finite=True):
if X.dtype in [ivy.float32, ivy.float64]:
return X.copy_array() if copy else X
if ("bool" in X.dtype... | null |
148,152 | import ivy
from ivy.functional.frontends.numpy.func_wrapper import to_ivy_arrays_and_back
from ivy.func_wrapper import with_unsupported_dtypes
def column_or_1d(y, *, warn=False):
shape = y.shape
if len(shape) == 2 and shape[1] == 1:
y = ivy.reshape(y, (-1,))
elif len(shape) > 2:
raise Value... | null |
148,153 | from typing import Sequence, Union, Optional, Tuple, Callable
import numpy as np
import itertools
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
inputs_to_ivy_arrays,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
... | Create a function that evaluates both func and the gradient of func. Parameters ---------- func Function for which we compute the gradients of the output with respect to xs input. Returns ------- ret A function that returns both func and the gradient of func. Examples -------- With :class:`ivy.Array` input: >>> x = ivy... |
148,154 | from typing import Sequence, Union, Optional, Tuple, Callable
import numpy as np
import itertools
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
inputs_to_ivy_arrays,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
... | Call function func, and return func's Jacobian partial derivatives. Parameters ---------- func Function for which we compute the gradients of the output with respect to xs input. Returns ------- ret the Jacobian function Examples -------- With :class:`ivy.Array` input: >>> x = ivy.array([[4.6, 2.1, 5], [2.8, 1.3, 6.2]]... |
148,155 | from typing import Sequence, Union, Optional, Tuple, Callable
import numpy as np
import itertools
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
inputs_to_ivy_arrays,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
... | Update weights ws of some function, given the derivatives of some cost c with respect to ws, [dc/dw for w in ws], by applying Layerwise Adaptive Rate Scaling (LARS) method. Parameters ---------- w Weights of the function to be updated. dcdw Derivates of the cost c with respect to the weights ws, [dc/dw for w in ws]. lr... |
148,156 | from typing import Sequence, Union, Optional, Tuple, Callable
import numpy as np
import itertools
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
inputs_to_ivy_arrays,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
... | Update weights ws of some function, given the derivatives of some cost c with respect to ws, using ADAM update. `[reference] <https://en.wikipedia.org/wiki/Stochastic_gradient_descent#Adam>`_ Parameters ---------- w Weights of the function to be updated. dcdw Derivates of the cost c with respect to the weights ws, [dc/... |
148,157 | from typing import Sequence, Union, Optional, Tuple, Callable
import numpy as np
import itertools
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
inputs_to_ivy_arrays,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
... | Update weights ws of some function, given the derivatives of some cost c with respect to ws, [dc/dw for w in ws], by applying LAMB method. Parameters ---------- w Weights of the function to be updated. dcdw Derivates of the cost c with respect to the weights ws, [dc/dw for w in ws]. lr Learning rate(s), the rate(s) at ... |
148,158 | from typing import Union, Optional, Literal, List
import ivy
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
handle_backend_invalid,
)
from ivy.utils.exceptions import h... | Return the indices that sort an array ``x`` along a specified axis. Parameters ---------- x input array. axis axis along which to sort. If set to ``-1``, the function must sort along the last axis. Default: ``-1``. descending sort order. If ``True``, the returned indices sort ``x`` in descending order (by value). If ``... |
148,159 | from typing import Union, Optional, Literal, List
import ivy
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
handle_backend_invalid,
)
from ivy.utils.exceptions import h... | Return a sorted copy of an array. Parameters ---------- x input array axis axis along which to sort. If set to ``-1``, the function must sort along the last axis. Default: ``-1``. descending direction The direction in which to sort the values stable sort stability. If ``True``, the returned indices must maintain the re... |
148,160 | from typing import Union, Optional, Literal, List
import ivy
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
handle_backend_invalid,
)
from ivy.utils.exceptions import h... | Return a copy of an array sorted along the first axis. Parameters ---------- a array-like input. out optional output array, for writing the result to. Returns ------- ret sorted array of the same type and shape as a Examples -------- >>> a = ivy.asarray([[8, 9, 6],[6, 2, 6]]) >>> ivy.msort(a) ivy.array( [[6, 2, 6], [8,... |
148,161 | from typing import Union, Optional, Literal, List
import ivy
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
handle_backend_invalid,
)
from ivy.utils.exceptions import h... | Return the indices of the inserted elements in a sorted array. Parameters ---------- x Input array. If `sorter` is None, then it must be sorted in ascending order, otherwise `sorter` must be an array of indices that sort it. v specific elements to insert in array x1 side The specific elements' index is at the 'left' si... |
148,162 | from typing import List, Union, Optional
import ivy
from ivy.func_wrapper import (
handle_array_like_without_promotion,
handle_nestable,
handle_array_function,
inputs_to_ivy_arrays,
)
from ivy.utils.exceptions import handle_exceptions
import ivy.utils.backend.handler
from ivy.utils import check_for_bin... | Apply Layer Normalization over a mini-batch of inputs. Parameters ---------- x Input array normalized_idxs Indices to apply the normalization to. scale Learnable gamma variables for elementwise post-multiplication, default is ``None``. offset Learnable beta variables for elementwise post-addition, default is ``None``. ... |
148,163 | from numbers import Number
from typing import Union, Optional, Tuple
import ivy
from ivy.utils.backend import current_backend
from ivy.utils.exceptions import handle_exceptions
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
hand... | Return the indices of the maximum values along a specified axis. When the maximum value occurs multiple times, only the indices corresponding to the first occurrence are returned. Parameters ---------- x input array. Should have a numeric data type. axis axis along which to search. If None, the function must return the... |
148,164 | from numbers import Number
from typing import Union, Optional, Tuple
import ivy
from ivy.utils.backend import current_backend
from ivy.utils.exceptions import handle_exceptions
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
hand... | Return the indices of the minimum values along a specified axis. When the minimum value occurs multiple times, only the indices corresponding to the first occurrence are returned. Parameters ---------- x input array. Should have a numeric data type. axis axis along which to search. If None, the function must return the... |
148,165 | from numbers import Number
from typing import Union, Optional, Tuple
import ivy
from ivy.utils.backend import current_backend
from ivy.utils.exceptions import handle_exceptions
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
hand... | Return the indices of the array elements which are non-zero. .. note:: If ``x`` has a complex floating-point data type, non-zero elements are those elements having at least one component (real or imaginary) which is non-zero. .. note:: If ``x`` has a boolean data type, non-zeroelements are those elements which are equa... |
148,166 | from numbers import Number
from typing import Union, Optional, Tuple
import ivy
from ivy.utils.backend import current_backend
from ivy.utils.exceptions import handle_exceptions
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
hand... | Return elements chosen from x or y depending on condition. Parameters ---------- condition Where True, yield x1, otherwise yield x2. x1 values from which to choose when condition is True. x2 values from which to choose when condition is False. out optional output array, for writing the result to. It must have a shape t... |
148,167 | from numbers import Number
from typing import Union, Optional, Tuple
import ivy
from ivy.utils.backend import current_backend
from ivy.utils.exceptions import handle_exceptions
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
hand... | Return the indices of all non-zero elements of the input array. Parameters ---------- x input array, for which indices are desired. out optional output array, for writing the result to. It must have a shape that the inputs broadcast to. Returns ------- ret Indices of non-zero elements. Examples -------- With :class:`iv... |
148,168 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Compute the cholesky decomposition of the x matrix. Parameters ---------- x input array having shape (..., M, M) and whose innermost two dimensions form square symmetric positive-definite matrices. Should have a floating-point data type. upper If True, the result must be the upper-triangular Cholesky factor U. If False... |
148,169 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return cross product of 3-element vectors. If x1 and x2 are multi- dimensional arrays (i.e., both have a rank greater than 1), then the cross- product of each pair of corresponding 3-element vectors is independently computed. Parameters ---------- x1 first input array. Should have a numeric data type. x2 second input a... |
148,170 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the determinant of a square matrix (or a stack of square matrices)``x``. Parameters ---------- x input array having shape ``(..., M, M)`` and whose innermost two dimensions form square matrices. Should have a floating-point data type. out optional output array, for writing the result to. It must have a shape tha... |
148,171 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the specified diagonals of a matrix (or a stack of matrices) ``x``. Parameters ---------- x input array having shape ``(..., M, N)`` and whose innermost two dimensions form ``MxN`` matrices. offset offset specifying the off-diagonal relative to the main diagonal. - ``offset = 0``: the main diagonal. - ``offset >... |
148,172 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return an eigendecomposition x = QLQᵀ of a symmetric matrix (or a stack of symmetric matrices) ``x``, where ``Q`` is an orthogonal matrix (or a stack of matrices) and ``L`` is a vector (or a stack of vectors). .. note:: The function ``eig`` currently behaves like ``eigh``, as it requires complex number support, once co... |
148,173 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | r"""Return an eigendecomposition x = QLQᵀ of a symmetric matrix (or a stack of symmetric matrices) ``x``, where ``Q`` is an orthogonal matrix (or a stack of matrices) and ``L`` is a vector (or a stack of vectors). .. note:: The function ``eig`` will be added in a future version of the specification, as it requires comp... |
148,174 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the eigenvalues of a symmetric matrix (or a stack of symmetric matrices) x. .. note:: The function ``eig`` will be added in a future version of the specification, as it requires complex number support, once complex numbers are supported, each square matrix must be Hermitian. .. note:: Whether an array library ex... |
148,175 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the inner product of two vectors ``x1`` and ``x2``. Parameters ---------- x1 first one-dimensional input array of size N. Should have a numeric data type. a(N,) array_like First input vector. Input is flattened if not already 1-dimensional. x2 second one-dimensional input array of size M. Should have a numeric d... |
148,176 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the multiplicative inverse of a square matrix (or a stack of square matrices) ``x``. Parameters ---------- x input array having shape ``(..., M, M)`` and whose innermost two dimensions form square matrices. Should have a floating-point data type. out optional output array, for writing the result to. It must have... |
148,177 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Compute the matrix product. Parameters ---------- x1 first input array. Should have a numeric data type. Must have at least one dimension. x2 second input array. Should have a numeric data type. Must have at least one dimension. transpose_a if True, ``x1`` is transposed before multiplication. transpose_b if True, ``x2`... |
148,178 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Compute the matrix p-norm. Parameters ---------- x Input array having shape (..., M, N) and whose innermost two dimensions form MxN matrices. Should have a floating-point data type. ord order of the norm. The following mathematical norms must be supported: +------------------+---------------------------------+ | ord | ... |
148,179 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Raise a square matrix (or a stack of square matrices) x to an integer power n. Parameters ---------- x input array having shape (..., M, M) and whose innermost two dimensions form square matrices. Should have a floating-point data type. n integer exponent. Returns ------- ret if n is equal to zero, an array containing ... |
148,180 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the rank (i.e., number of non-zero singular values) of a matrix (or a stack of matrices). Parameters ---------- x input array having shape ``(..., M, N)`` and whose innermost two dimensions form ``MxN`` matrices. Should have a floating-point data type. atol absolute tolerance. When None it's considered to be zer... |
148,181 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the outer product of two vectors ``x1`` and ``x2``. Parameters ---------- x1 first one-dimensional input array of size N. Should have a numeric data type. a(N,) array_like First input vector. Input is flattened if not already 1-dimensional. x2 second one-dimensional input array of size M. Should have a numeric d... |
148,182 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the (Moore-Penrose) pseudo-inverse of a matrix (or a stack of matrices) ``x``. Parameters ---------- x input array having shape ``(..., M, N)`` and whose innermost two dimensions form ``MxN`` matrices. Should have a floating-point data type. rtol relative tolerance for small singular values. Singular values appr... |
148,183 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the qr decomposition x = QR of a full column rank matrix (or a stack of matrices), where Q is an orthonormal matrix (or a stack of matrices) and R is an upper-triangular matrix (or a stack of matrices). Parameters ---------- x input array having shape (..., M, N) and whose innermost two dimensions form MxN matri... |
148,184 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the sign and the natural logarithm of the absolute value of the determinant of a square matrix (or a stack of square matrices) ``x``. .. note:: The purpose of this function is to calculate the determinant more accurately when the determinant is either very small or very large, as calling ``det`` may overflow or ... |
148,185 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return a singular value decomposition A = USVh of a matrix (or a stack of matrices) ``x``, where ``U`` is a matrix (or a stack of matrices) with orthonormal columns, ``S`` is a vector of non-negative numbers (or stack of vectors), and ``Vh`` is a matrix (or a stack of matrices) with orthonormal rows. Parameters -------... |
148,186 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the singular values of a matrix (or a stack of matrices) ``x``. Parameters ---------- x input array having shape ``(..., M, N)`` and whose innermost two dimensions form ``MxN`` matrices. driver optional output array,name of the cuSOLVER method to be used. This keyword argument only works on CUDA inputs. Availabl... |
148,187 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return a tensor contraction of x1 and x2 over specific axes. .. note:: If either ``x1`` or ``x2`` has a complex floating-point data type, neither argument must be complex-conjugated or transposed. If conjugation and/or transposition is desired, these operations should explicitly performed prior to computing the general... |
148,188 | from typing import Union, Optional, Tuple, Literal, List, Sequence
import ivy
from ivy.utils.backend import current_backend
from ivy.func_wrapper import (
handle_array_function,
to_native_arrays_and_back,
handle_out_argument,
handle_nestable,
handle_array_like_without_promotion,
handle_device,
... | Return the sum along the specified diagonals of a matrix (or a stack of matrices) ``x``. **Special cases** Let ``N`` equal the number of elements over which to compute the sum. - If ``N`` is ``0``, the sum is ``0`` (i.e., the empty sum). For both real-valued and complex floating-point operands, special cases must be ha... |
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