repo stringlengths 2 99 | file stringlengths 13 225 | code stringlengths 0 18.3M | file_length int64 0 18.3M | avg_line_length float64 0 1.36M | max_line_length int64 0 4.26M | extension_type stringclasses 1
value |
|---|---|---|---|---|---|---|
scipy | scipy-main/scipy/_build_utils/compiler_helper.py | """
Helpers for detection of compiler features
"""
import tempfile
import os
import sys
from numpy.distutils.system_info import dict_append
def try_compile(compiler, code=None, flags=[], ext=None):
"""Returns True if the compiler is able to compile the given code"""
from distutils.errors import CompileError
... | 4,030 | 28.859259 | 80 | py |
scipy | scipy-main/scipy/_build_utils/system_info.py | def combine_dict(*dicts, **kw):
"""
Combine Numpy distutils style library configuration dictionaries.
Parameters
----------
*dicts
Dictionaries of keys. List-valued keys will be concatenated.
Otherwise, duplicate keys with different values result to
an error. The input argum... | 1,225 | 30.435897 | 77 | py |
scipy | scipy-main/scipy/_build_utils/tests/test_scipy_version.py | import re
import scipy
from numpy.testing import assert_
def test_valid_scipy_version():
# Verify that the SciPy version is a valid one (no .post suffix or other
# nonsense). See NumPy issue gh-6431 for an issue caused by an invalid
# version.
version_pattern = r"^[0-9]+\.[0-9]+\.[0-9]+(|a[0-9]|b[0-9... | 606 | 30.947368 | 76 | py |
scipy | scipy-main/scipy/_build_utils/tests/__init__.py | 0 | 0 | 0 | py | |
scipy | scipy-main/scipy/optimize/_differentialevolution.py | """
differential_evolution: The differential evolution global optimization algorithm
Added by Andrew Nelson 2014
"""
import warnings
import numpy as np
from scipy.optimize import OptimizeResult, minimize
from scipy.optimize._optimize import _status_message
from scipy._lib._util import check_random_state, MapWrapper, _... | 74,758 | 43.001766 | 86 | py |
scipy | scipy-main/scipy/optimize/_zeros_py.py | import warnings
from collections import namedtuple
import operator
from . import _zeros
from ._optimize import OptimizeResult, _call_callback_maybe_halt
import numpy as np
_iter = 100
_xtol = 2e-12
_rtol = 4 * np.finfo(float).eps
__all__ = ['newton', 'bisect', 'ridder', 'brentq', 'brenth', 'toms748',
'Roo... | 72,277 | 37.323436 | 83 | py |
scipy | scipy-main/scipy/optimize/lbfgsb.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _lbfgsb_py
__all__ = [ # noqa: F822
'LbfgsInvHessProduct',
'LinearOperator',
'MemoizeJac',
'OptimizeResult',
... | 929 | 23.473684 | 78 | py |
scipy | scipy-main/scipy/optimize/_trustregion_dogleg.py | """Dog-leg trust-region optimization."""
import numpy as np
import scipy.linalg
from ._trustregion import (_minimize_trust_region, BaseQuadraticSubproblem)
__all__ = []
def _minimize_dogleg(fun, x0, args=(), jac=None, hess=None,
**trust_region_options):
"""
Minimization of scalar functio... | 4,389 | 34.691057 | 81 | py |
scipy | scipy-main/scipy/optimize/_qap.py | import numpy as np
import operator
from . import (linear_sum_assignment, OptimizeResult)
from ._optimize import _check_unknown_options
from scipy._lib._util import check_random_state
import itertools
QUADRATIC_ASSIGNMENT_METHODS = ['faq', '2opt']
def quadratic_assignment(A, B, method="faq", options=None):
r"""
... | 27,658 | 37.150345 | 81 | py |
scipy | scipy-main/scipy/optimize/_nonlin.py | # Copyright (C) 2009, Pauli Virtanen <pav@iki.fi>
# Distributed under the same license as SciPy.
import sys
import numpy as np
from scipy.linalg import norm, solve, inv, qr, svd, LinAlgError
from numpy import asarray, dot, vdot
import scipy.sparse.linalg
import scipy.sparse
from scipy.linalg import get_blas_funcs
impo... | 49,031 | 30.270408 | 104 | py |
scipy | scipy-main/scipy/optimize/_minpack_py.py | import warnings
from . import _minpack
import numpy as np
from numpy import (atleast_1d, triu, shape, transpose, zeros, prod, greater,
asarray, inf,
finfo, inexact, issubdtype, dtype)
from scipy import linalg
from scipy.linalg import svd, cholesky, solve_triangular, LinAlgError
fr... | 43,032 | 37.388046 | 91 | py |
scipy | scipy-main/scipy/optimize/_optimize.py | #__docformat__ = "restructuredtext en"
# ******NOTICE***************
# optimize.py module by Travis E. Oliphant
#
# You may copy and use this module as you see fit with no
# guarantee implied provided you keep this notice in all copies.
# *****END NOTICE************
# A collection of optimization algorithms. Version 0... | 146,247 | 34.574799 | 101 | py |
scipy | scipy-main/scipy/optimize/_linprog_simplex.py | """Simplex method for linear programming
The *simplex* method uses a traditional, full-tableau implementation of
Dantzig's simplex algorithm [1]_, [2]_ (*not* the Nelder-Mead simplex).
This algorithm is included for backwards compatibility and educational
purposes.
.. versionadded:: 0.15.0
Warnings
--------
Th... | 24,725 | 36.350453 | 82 | py |
scipy | scipy-main/scipy/optimize/setup.py | import sys
import os.path
from os.path import join
from scipy._build_utils import numpy_nodepr_api
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info
from scipy._build_utils import (gfortran_legacy_flag... | 5,833 | 36.63871 | 78 | py |
scipy | scipy-main/scipy/optimize/_trustregion_exact.py | """Nearly exact trust-region optimization subproblem."""
import numpy as np
from scipy.linalg import (norm, get_lapack_funcs, solve_triangular,
cho_solve)
from ._trustregion import (_minimize_trust_region, BaseQuadraticSubproblem)
__all__ = ['_minimize_trustregion_exact',
'estimate... | 15,413 | 34.763341 | 110 | py |
scipy | scipy-main/scipy/optimize/_tstutils.py | r"""
Parameters used in test and benchmark methods.
Collections of test cases suitable for testing 1-D root-finders
'original': The original benchmarking functions.
Real-valued functions of real-valued inputs on an interval
with a zero.
f1, .., f3 are continuous and infinitely differentiable
f4 h... | 33,043 | 40.512563 | 116 | py |
scipy | scipy-main/scipy/optimize/_trustregion_ncg.py | """Newton-CG trust-region optimization."""
import math
import numpy as np
import scipy.linalg
from ._trustregion import (_minimize_trust_region, BaseQuadraticSubproblem)
__all__ = []
def _minimize_trust_ncg(fun, x0, args=(), jac=None, hess=None, hessp=None,
**trust_region_options):
"""
... | 4,580 | 35.070866 | 79 | py |
scipy | scipy-main/scipy/optimize/minpack2.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _minpack2
__all__ = [ # noqa: F822
'dcsrch',
'dcstep',
]
def __dir__():
return __all__
def __getattr__(name):
... | 769 | 24.666667 | 78 | py |
scipy | scipy-main/scipy/optimize/_milp.py | import warnings
import numpy as np
from scipy.sparse import csc_array, vstack, issparse
from ._highs._highs_wrapper import _highs_wrapper # type: ignore[import]
from ._constraints import LinearConstraint, Bounds
from ._optimize import OptimizeResult
from ._linprog_highs import _highs_to_scipy_status_message
def _con... | 15,129 | 37.596939 | 79 | py |
scipy | scipy-main/scipy/optimize/moduleTNC.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _moduleTNC
__all__ = [ # noqa: F822
]
def __dir__():
return __all__
def __getattr__(name):
if name not in __all__... | 746 | 24.758621 | 78 | py |
scipy | scipy-main/scipy/optimize/_root.py | """
Unified interfaces to root finding algorithms.
Functions
---------
- root : find a root of a vector function.
"""
__all__ = ['root']
import numpy as np
from warnings import warn
from ._optimize import MemoizeJac, OptimizeResult, _check_unknown_options
from ._minpack_py import _root_hybr, leastsq
from ._spectral... | 28,280 | 38.333797 | 81 | py |
scipy | scipy-main/scipy/optimize/_shgo.py | """shgo: The simplicial homology global optimisation algorithm."""
from collections import namedtuple
import time
import logging
import warnings
import sys
import numpy as np
from scipy import spatial
from scipy.optimize import OptimizeResult, minimize, Bounds
from scipy.optimize._optimize import MemoizeJac
from scip... | 62,233 | 37.993734 | 133 | py |
scipy | scipy-main/scipy/optimize/_linprog_doc.py | """
Created on Sat Aug 22 19:49:17 2020
@author: matth
"""
def _linprog_highs_doc(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None,
bounds=None, method='highs', callback=None,
maxiter=None, disp=False, presolve=True,
time_limit=None,
... | 61,943 | 42.166551 | 143 | py |
scipy | scipy-main/scipy/optimize/_linprog.py | """
A top-level linear programming interface.
.. versionadded:: 0.15.0
Functions
---------
.. autosummary::
:toctree: generated/
linprog
linprog_verbose_callback
linprog_terse_callback
"""
import numpy as np
from ._optimize import OptimizeResult, OptimizeWarning
from warnings import warn
from ._lin... | 29,666 | 40.608696 | 143 | py |
scipy | scipy-main/scipy/optimize/tnc.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _tnc
__all__ = [ # noqa: F822
'CONSTANT',
'FCONVERGED',
'INFEASIBLE',
'LOCALMINIMUM',
'LSFAIL',
'MAXFU... | 1,148 | 20.277778 | 78 | py |
scipy | scipy-main/scipy/optimize/_direct_py.py | from __future__ import annotations
from typing import ( # noqa: UP035
Any, Callable, Iterable, TYPE_CHECKING
)
import numpy as np
from scipy.optimize import OptimizeResult
from ._constraints import old_bound_to_new, Bounds
from ._direct import direct as _direct # type: ignore
if TYPE_CHECKING:
import numpy.... | 11,798 | 41.290323 | 79 | py |
scipy | scipy-main/scipy/optimize/optimize.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _optimize
__all__ = [ # noqa: F822
'Brent',
'FD_METHODS',
'LineSearchWarning',
'MapWrapper',
'MemoizeJac',... | 1,513 | 20.027778 | 78 | py |
scipy | scipy-main/scipy/optimize/_linprog_util.py | """
Method agnostic utility functions for linear progamming
"""
import numpy as np
import scipy.sparse as sps
from warnings import warn
from ._optimize import OptimizeWarning
from scipy.optimize._remove_redundancy import (
_remove_redundancy_svd, _remove_redundancy_pivot_sparse,
_remove_redundancy_pivot_dense,... | 62,773 | 40.217334 | 88 | py |
scipy | scipy-main/scipy/optimize/_trustregion.py | """Trust-region optimization."""
import math
import warnings
import numpy as np
import scipy.linalg
from ._optimize import (_check_unknown_options, _status_message,
OptimizeResult, _prepare_scalar_function,
_call_callback_maybe_halt)
from scipy.optimize._hessian_update_s... | 10,786 | 34.367213 | 79 | py |
scipy | scipy-main/scipy/optimize/_minimize.py | """
Unified interfaces to minimization algorithms.
Functions
---------
- minimize : minimization of a function of several variables.
- minimize_scalar : minimization of a function of one variable.
"""
__all__ = ['minimize', 'minimize_scalar']
from warnings import warn
import numpy as np
# unconstrained minimizati... | 47,713 | 42.976037 | 89 | py |
scipy | scipy-main/scipy/optimize/_linprog_ip.py | """Interior-point method for linear programming
The *interior-point* method uses the primal-dual path following algorithm
outlined in [1]_. This algorithm supports sparse constraint matrices and
is typically faster than the simplex methods, especially for large, sparse
problems. Note, however, that the solution return... | 45,749 | 39.630551 | 143 | py |
scipy | scipy-main/scipy/optimize/_cobyla_py.py | """
Interface to Constrained Optimization By Linear Approximation
Functions
---------
.. autosummary::
:toctree: generated/
fmin_cobyla
"""
import functools
from threading import RLock
import numpy as np
from scipy.optimize import _cobyla as cobyla
from ._optimize import OptimizeResult, _check_unknown_optio... | 10,662 | 33.508091 | 79 | py |
scipy | scipy-main/scipy/optimize/_differentiable_functions.py | import numpy as np
import scipy.sparse as sps
from ._numdiff import approx_derivative, group_columns
from ._hessian_update_strategy import HessianUpdateStrategy
from scipy.sparse.linalg import LinearOperator
FD_METHODS = ('2-point', '3-point', 'cs')
class ScalarFunction:
"""Scalar function and its derivatives.
... | 22,719 | 35.823339 | 79 | py |
scipy | scipy-main/scipy/optimize/_linprog_rs.py | """Revised simplex method for linear programming
The *revised simplex* method uses the method described in [1]_, except
that a factorization [2]_ of the basis matrix, rather than its inverse,
is efficiently maintained and used to solve the linear systems at each
iteration of the algorithm.
.. versionadded:: 1.3.0
Re... | 23,149 | 39.401396 | 79 | py |
scipy | scipy-main/scipy/optimize/linesearch.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _linesearch
__all__ = [ # noqa: F822
'LineSearchWarning',
'line_search',
'line_search_BFGS',
'line_search_armi... | 1,007 | 24.846154 | 78 | py |
scipy | scipy-main/scipy/optimize/_dual_annealing.py | # Dual Annealing implementation.
# Copyright (c) 2018 Sylvain Gubian <sylvain.gubian@pmi.com>,
# Yang Xiang <yang.xiang@pmi.com>
# Author: Sylvain Gubian, Yang Xiang, PMP S.A.
"""
A Dual Annealing global optimization algorithm
"""
import numpy as np
from scipy.optimize import OptimizeResult
from scipy.optimize import... | 30,363 | 41.348675 | 86 | py |
scipy | scipy-main/scipy/optimize/_basinhopping.py | """
basinhopping: The basinhopping global optimization algorithm
"""
import numpy as np
import math
import inspect
import scipy.optimize
from scipy._lib._util import check_random_state
__all__ = ['basinhopping']
_params = (inspect.Parameter('res_new', kind=inspect.Parameter.KEYWORD_ONLY),
inspect.Paramete... | 30,657 | 39.660477 | 104 | py |
scipy | scipy-main/scipy/optimize/_trustregion_krylov.py | from ._trustregion import (_minimize_trust_region)
from ._trlib import (get_trlib_quadratic_subproblem)
__all__ = ['_minimize_trust_krylov']
def _minimize_trust_krylov(fun, x0, args=(), jac=None, hess=None, hessp=None,
inexact=True, **trust_region_options):
"""
Minimization of a sca... | 3,030 | 44.924242 | 86 | py |
scipy | scipy-main/scipy/optimize/slsqp.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _slsqp_py
__all__ = [ # noqa: F822
'OptimizeResult',
'append',
'approx_derivative',
'approx_jacobian',
'ar... | 1,044 | 21.234043 | 78 | py |
scipy | scipy-main/scipy/optimize/_spectral.py | """
Spectral Algorithm for Nonlinear Equations
"""
import collections
import numpy as np
from scipy.optimize import OptimizeResult
from scipy.optimize._optimize import _check_unknown_options
from ._linesearch import _nonmonotone_line_search_cruz, _nonmonotone_line_search_cheng
class _NoConvergence(Exception):
pas... | 7,920 | 29.70155 | 103 | py |
scipy | scipy-main/scipy/optimize/_remove_redundancy.py | """
Routines for removing redundant (linearly dependent) equations from linear
programming equality constraints.
"""
# Author: Matt Haberland
import numpy as np
from scipy.linalg import svd
from scipy.linalg.interpolative import interp_decomp
import scipy
from scipy.linalg.blas import dtrsm
def _row_count(A):
""... | 18,767 | 34.885277 | 88 | py |
scipy | scipy-main/scipy/optimize/__init__.py | """
=====================================================
Optimization and root finding (:mod:`scipy.optimize`)
=====================================================
.. currentmodule:: scipy.optimize
.. toctree::
:hidden:
optimize.cython_optimize
SciPy ``optimize`` provides functions for minimizing (or maximi... | 12,880 | 27.816555 | 94 | py |
scipy | scipy-main/scipy/optimize/minpack.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _minpack_py
__all__ = [ # noqa: F822
'LEASTSQ_FAILURE',
'LEASTSQ_SUCCESS',
'LinAlgError',
'OptimizeResult',
... | 1,277 | 19.95082 | 78 | py |
scipy | scipy-main/scipy/optimize/_constraints.py | """Constraints definition for minimize."""
import numpy as np
from ._hessian_update_strategy import BFGS
from ._differentiable_functions import (
VectorFunction, LinearVectorFunction, IdentityVectorFunction)
from ._optimize import OptimizeWarning
from warnings import warn, catch_warnings, simplefilter
from numpy.te... | 22,552 | 37.552137 | 88 | py |
scipy | scipy-main/scipy/optimize/_group_columns.py | """
Pythran implementation of columns grouping for finite difference Jacobian
estimation. Used by ._numdiff.group_columns and based on the Cython version.
"""
import numpy as np
#pythran export group_dense(int, int, intc[:,:])
#pythran export group_dense(int, int, int[:,:])
def group_dense(m, n, A):
B = A.T # Tr... | 2,659 | 26.142857 | 76 | py |
scipy | scipy-main/scipy/optimize/_tnc.py | # TNC Python interface
# @(#) $Jeannot: tnc.py,v 1.11 2005/01/28 18:27:31 js Exp $
# Copyright (c) 2004-2005, Jean-Sebastien Roy (js@jeannot.org)
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Soft... | 16,678 | 38.337264 | 84 | py |
scipy | scipy-main/scipy/optimize/cobyla.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _cobyla_py
__all__ = [ # noqa: F822
'OptimizeResult',
'RLock',
'fmin_cobyla',
'functools',
'izip',
'sy... | 840 | 25.28125 | 78 | py |
scipy | scipy-main/scipy/optimize/nonlin.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _nonlin
__all__ = [ # noqa: F822
'Anderson',
'BroydenFirst',
'BroydenSecond',
'DiagBroyden',
'ExcitingMixi... | 1,418 | 20.5 | 78 | py |
scipy | scipy-main/scipy/optimize/_slsqp_py.py | """
This module implements the Sequential Least Squares Programming optimization
algorithm (SLSQP), originally developed by Dieter Kraft.
See http://www.netlib.org/toms/733
Functions
---------
.. autosummary::
:toctree: generated/
approx_jacobian
fmin_slsqp
"""
__all__ = ['approx_jacobian', 'fmin_slsqp']... | 18,767 | 36.164356 | 80 | py |
scipy | scipy-main/scipy/optimize/_linprog_highs.py | """HiGHS Linear Optimization Methods
Interface to HiGHS linear optimization software.
https://highs.dev/
.. versionadded:: 1.5.0
References
----------
.. [1] Q. Huangfu and J.A.J. Hall. "Parallelizing the dual revised simplex
method." Mathematical Programming Computation, 10 (1), 119-142,
2018.... | 17,571 | 38.845805 | 79 | py |
scipy | scipy-main/scipy/optimize/_numdiff.py | """Routines for numerical differentiation."""
import functools
import numpy as np
from numpy.linalg import norm
from scipy.sparse.linalg import LinearOperator
from ..sparse import issparse, csc_matrix, csr_matrix, coo_matrix, find
from ._group_columns import group_dense, group_sparse
def _adjust_scheme_to_bounds(x0,... | 28,279 | 36.112861 | 79 | py |
scipy | scipy-main/scipy/optimize/_linesearch.py | """
Functions
---------
.. autosummary::
:toctree: generated/
line_search_armijo
line_search_wolfe1
line_search_wolfe2
scalar_search_wolfe1
scalar_search_wolfe2
"""
from warnings import warn
from scipy.optimize import _minpack2 as minpack2
import numpy as np
__all__ = ['LineSearchWarning', 'l... | 27,044 | 29.353535 | 81 | py |
scipy | scipy-main/scipy/optimize/_nnls.py | import numpy as np
from scipy.linalg import solve
__all__ = ['nnls']
def nnls(A, b, maxiter=None, *, atol=None):
"""
Solve ``argmin_x || Ax - b ||_2`` for ``x>=0``.
This problem, often called as NonNegative Least Squares, is a convex
optimization problem with convex constraints. It typically arises... | 5,189 | 31.236025 | 79 | py |
scipy | scipy-main/scipy/optimize/_hessian_update_strategy.py | """Hessian update strategies for quasi-Newton optimization methods."""
import numpy as np
from numpy.linalg import norm
from scipy.linalg import get_blas_funcs
from warnings import warn
__all__ = ['HessianUpdateStrategy', 'BFGS', 'SR1']
class HessianUpdateStrategy:
"""Interface for implementing Hessian update s... | 15,830 | 35.816279 | 80 | py |
scipy | scipy-main/scipy/optimize/zeros.py | # This file is not meant for public use and will be removed in SciPy v2.0.0.
# Use the `scipy.optimize` namespace for importing the functions
# included below.
import warnings
from . import _zeros_py
__all__ = [ # noqa: F822
'CONVERGED',
'CONVERR',
'INPROGRESS',
'RootResults',
'SIGNERR',
'TO... | 1,008 | 21.422222 | 78 | py |
scipy | scipy-main/scipy/optimize/_lbfgsb_py.py | """
Functions
---------
.. autosummary::
:toctree: generated/
fmin_l_bfgs_b
"""
## License for the Python wrapper
## ==============================
## Copyright (c) 2004 David M. Cooke <cookedm@physics.mcmaster.ca>
## Permission is hereby granted, free of charge, to any person obtaining a
## copy of this so... | 18,877 | 36.907631 | 92 | py |
scipy | scipy-main/scipy/optimize/_root_scalar.py | """
Unified interfaces to root finding algorithms for real or complex
scalar functions.
Functions
---------
- root : find a root of a scalar function.
"""
import numpy as np
from . import _zeros_py as optzeros
from ._numdiff import approx_derivative
__all__ = ['root_scalar']
ROOT_SCALAR_METHODS = ['bisect', 'brentq... | 19,556 | 36.180608 | 137 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/canonical_constraint.py | import numpy as np
import scipy.sparse as sps
class CanonicalConstraint:
"""Canonical constraint to use with trust-constr algorithm.
It represents the set of constraints of the form::
f_eq(x) = 0
f_ineq(x) <= 0
where ``f_eq`` and ``f_ineq`` are evaluated by a single function, see
be... | 12,538 | 31.069054 | 79 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/setup.py | def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('_trustregion_constr', parent_package, top_path)
config.add_data_dir('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(**... | 357 | 31.545455 | 75 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/minimize_trustregion_constr.py | import time
import numpy as np
from scipy.sparse.linalg import LinearOperator
from .._differentiable_functions import VectorFunction
from .._constraints import (
NonlinearConstraint, LinearConstraint, PreparedConstraint, strict_bounds)
from .._hessian_update_strategy import BFGS
from .._optimize import OptimizeResu... | 25,330 | 44.233929 | 86 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/report.py | """Progress report printers."""
from __future__ import annotations
class ReportBase:
COLUMN_NAMES: list[str] = NotImplemented
COLUMN_WIDTHS: list[int] = NotImplemented
ITERATION_FORMATS: list[str] = NotImplemented
@classmethod
def print_header(cls):
fmt = ("|"
+ "|".join([f... | 1,818 | 33.980769 | 75 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/__init__.py | """This module contains the equality constrained SQP solver."""
from .minimize_trustregion_constr import _minimize_trustregion_constr
__all__ = ['_minimize_trustregion_constr']
| 180 | 24.857143 | 69 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/projections.py | """Basic linear factorizations needed by the solver."""
from scipy.sparse import (bmat, csc_matrix, eye, issparse)
from scipy.sparse.linalg import LinearOperator
import scipy.linalg
import scipy.sparse.linalg
try:
from sksparse.cholmod import cholesky_AAt
sksparse_available = True
except ImportError:
impor... | 13,105 | 31.280788 | 80 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/qp_subproblem.py | """Equality-constrained quadratic programming solvers."""
from scipy.sparse import (linalg, bmat, csc_matrix)
from math import copysign
import numpy as np
from numpy.linalg import norm
__all__ = [
'eqp_kktfact',
'sphere_intersections',
'box_intersections',
'box_sphere_intersections',
'inside_box_b... | 22,592 | 34.412226 | 88 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/equality_constrained_sqp.py | """Byrd-Omojokun Trust-Region SQP method."""
from scipy.sparse import eye as speye
from .projections import projections
from .qp_subproblem import modified_dogleg, projected_cg, box_intersections
import numpy as np
from numpy.linalg import norm
__all__ = ['equality_constrained_sqp']
def default_scaling(x):
n, =... | 8,592 | 38.417431 | 79 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/tr_interior_point.py | """Trust-region interior point method.
References
----------
.. [1] Byrd, Richard H., Mary E. Hribar, and Jorge Nocedal.
"An interior point algorithm for large-scale nonlinear
programming." SIAM Journal on Optimization 9.4 (1999): 877-900.
.. [2] Byrd, Richard H., Guanghui Liu, and Jorge Nocedal.
... | 13,802 | 38.778098 | 78 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/tests/test_projections.py | import numpy as np
import scipy.linalg
from scipy.sparse import csc_matrix
from scipy.optimize._trustregion_constr.projections \
import projections, orthogonality
from numpy.testing import (TestCase, assert_array_almost_equal,
assert_equal, assert_allclose)
try:
from sksparse.cholmod... | 8,834 | 40.093023 | 76 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/tests/test_qp_subproblem.py | import numpy as np
from scipy.sparse import csc_matrix
from scipy.optimize._trustregion_constr.qp_subproblem \
import (eqp_kktfact,
projected_cg,
box_intersections,
sphere_intersections,
box_sphere_intersections,
modified_dogleg)
from scipy.optimize._trust... | 27,719 | 41.910217 | 79 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/tests/__init__.py | 0 | 0 | 0 | py | |
scipy | scipy-main/scipy/optimize/_trustregion_constr/tests/test_canonical_constraint.py | import numpy as np
from numpy.testing import assert_array_equal, assert_equal
from scipy.optimize._constraints import (NonlinearConstraint, Bounds,
PreparedConstraint)
from scipy.optimize._trustregion_constr.canonical_constraint \
import CanonicalConstraint, initial_constrai... | 9,869 | 32.232323 | 79 | py |
scipy | scipy-main/scipy/optimize/_trustregion_constr/tests/test_report.py | import numpy as np
from scipy.optimize import minimize, Bounds
def test_gh10880():
# checks that verbose reporting works with trust-constr for
# bound-contrained problems
bnds = Bounds(1, 2)
opts = {'maxiter': 1000, 'verbose': 2}
minimize(lambda x: x**2, x0=2., method='trust-constr',
b... | 1,070 | 31.454545 | 63 | py |
scipy | scipy-main/scipy/optimize/_highs/setup.py | """
setup.py for HiGHS scipy interface
Some CMake files are used to create source lists for compilation
"""
from datetime import datetime
import os
from os.path import join
from scipy._lib._highs_utils import _highs_dir
def pre_build_hook(build_ext, ext):
from scipy._build_utils.compiler_helper import get_cxx_... | 6,062 | 34.046243 | 78 | py |
scipy | scipy-main/scipy/optimize/_highs/__init__.py | 0 | 0 | 0 | py | |
scipy | scipy-main/scipy/optimize/_highs/cython/__init__.py | 0 | 0 | 0 | py | |
scipy | scipy-main/scipy/optimize/_highs/cython/src/__init__.py | 0 | 0 | 0 | py | |
scipy | scipy-main/scipy/optimize/_shgo_lib/_complex.py | """Base classes for low memory simplicial complex structures."""
import copy
import logging
import itertools
import decimal
from functools import cache
import numpy
from ._vertex import (VertexCacheField, VertexCacheIndex)
class Complex:
"""
Base class for a simplicial complex described as a cache of vertic... | 50,352 | 40.03749 | 79 | py |
scipy | scipy-main/scipy/optimize/_shgo_lib/_vertex.py | import collections
from abc import ABC, abstractmethod
import numpy as np
from scipy._lib._util import MapWrapper
class VertexBase(ABC):
"""
Base class for a vertex.
"""
def __init__(self, x, nn=None, index=None):
"""
Initiation of a vertex object.
Parameters
-------... | 13,997 | 29.364425 | 79 | py |
scipy | scipy-main/scipy/optimize/_shgo_lib/__init__.py | 0 | 0 | 0 | py | |
scipy | scipy-main/scipy/optimize/tests/test__root.py | """
Unit tests for optimization routines from _root.py.
"""
from numpy.testing import assert_, assert_equal
from pytest import raises as assert_raises, warns as assert_warns
import numpy as np
from scipy.optimize import root
class TestRoot:
def test_tol_parameter(self):
# Check that the minimize() tol= a... | 3,727 | 32.285714 | 79 | py |
scipy | scipy-main/scipy/optimize/tests/test__numdiff.py | import math
from itertools import product
import numpy as np
from numpy.testing import assert_allclose, assert_equal, assert_
from pytest import raises as assert_raises
from scipy.sparse import csr_matrix, csc_matrix, lil_matrix
from scipy.optimize._numdiff import (
_adjust_scheme_to_bounds, approx_derivative, c... | 31,338 | 37.452761 | 84 | py |
scipy | scipy-main/scipy/optimize/tests/test_cython_optimize.py | """
Test Cython optimize zeros API functions: ``bisect``, ``ridder``, ``brenth``,
and ``brentq`` in `scipy.optimize.cython_optimize`, by finding the roots of a
3rd order polynomial given a sequence of constant terms, ``a0``, and fixed 1st,
2nd, and 3rd order terms in ``args``.
.. math::
f(x, a0, args) = ((args[2... | 2,638 | 27.376344 | 79 | py |
scipy | scipy-main/scipy/optimize/tests/test_lsq_common.py | from numpy.testing import assert_, assert_allclose, assert_equal
from pytest import raises as assert_raises
import numpy as np
from scipy.optimize._lsq.common import (
step_size_to_bound, find_active_constraints, make_strictly_feasible,
CL_scaling_vector, intersect_trust_region, build_quadratic_1d,
minimiz... | 9,500 | 30.88255 | 78 | py |
scipy | scipy-main/scipy/optimize/tests/test_regression.py | """Regression tests for optimize.
"""
import numpy as np
from numpy.testing import assert_almost_equal
from pytest import raises as assert_raises
import scipy.optimize
class TestRegression:
def test_newton_x0_is_0(self):
# Regression test for gh-1601
tgt = 1
res = scipy.optimize.newton(... | 1,077 | 25.292683 | 62 | py |
scipy | scipy-main/scipy/optimize/tests/test_slsqp.py | """
Unit test for SLSQP optimization.
"""
from numpy.testing import (assert_, assert_array_almost_equal,
assert_allclose, assert_equal)
from pytest import raises as assert_raises
import pytest
import numpy as np
from scipy.optimize import fmin_slsqp, minimize, Bounds, NonlinearConstraint
c... | 23,260 | 37.195402 | 88 | py |
scipy | scipy-main/scipy/optimize/tests/test_trustregion_krylov.py | """
Unit tests for Krylov space trust-region subproblem solver.
To run it in its simplest form::
nosetests test_optimize.py
"""
import numpy as np
from scipy.optimize._trlib import (get_trlib_quadratic_subproblem)
from numpy.testing import (assert_,
assert_almost_equal,
... | 6,587 | 37.526316 | 89 | py |
scipy | scipy-main/scipy/optimize/tests/test_nnls.py | import numpy as np
from numpy.testing import assert_allclose
from pytest import raises as assert_raises
from scipy.optimize import nnls
class TestNNLS:
def setup_method(self):
self.rng = np.random.default_rng(1685225766635251)
def test_nnls(self):
a = np.arange(25.0).reshape(-1, 5)
x ... | 1,549 | 33.444444 | 78 | py |
scipy | scipy-main/scipy/optimize/tests/test__spectral.py | import itertools
import numpy as np
from numpy import exp
from numpy.testing import assert_, assert_equal
from scipy.optimize import root
def test_performance():
# Compare performance results to those listed in
# [Cheng & Li, IMA J. Num. An. 29, 814 (2008)]
# and
# [W. La Cruz, J.M. Martinez, M. Ray... | 6,597 | 29.976526 | 120 | py |
scipy | scipy-main/scipy/optimize/tests/test_nonlin.py | """ Unit tests for nonlinear solvers
Author: Ondrej Certik
May 2007
"""
from numpy.testing import assert_
import pytest
from scipy.optimize import _nonlin as nonlin, root
from numpy import diag, dot
from numpy.linalg import inv
import numpy as np
from .test_minpack import pressure_network
SOLVERS = {'anderson': nonl... | 17,228 | 33.527054 | 79 | py |
scipy | scipy-main/scipy/optimize/tests/test_minpack.py | """
Unit tests for optimization routines from minpack.py.
"""
import warnings
import pytest
from numpy.testing import (assert_, assert_almost_equal, assert_array_equal,
assert_array_almost_equal, assert_allclose,
assert_warns, suppress_warnings)
from pytest import ... | 40,676 | 36.629047 | 113 | py |
scipy | scipy-main/scipy/optimize/tests/test_hessian_update_strategy.py | import numpy as np
from copy import deepcopy
from numpy.linalg import norm
from numpy.testing import (TestCase, assert_array_almost_equal,
assert_array_equal, assert_array_less)
from scipy.optimize import (BFGS, SR1)
class Rosenbrock:
"""Rosenbrock function.
The following optimizat... | 10,112 | 47.38756 | 77 | py |
scipy | scipy-main/scipy/optimize/tests/test_trustregion.py | """
Unit tests for trust-region optimization routines.
To run it in its simplest form::
nosetests test_optimize.py
"""
import pytest
import numpy as np
from numpy.testing import assert_, assert_equal, assert_allclose
from scipy.optimize import (minimize, rosen, rosen_der, rosen_hess,
ros... | 4,701 | 40.610619 | 78 | py |
scipy | scipy-main/scipy/optimize/tests/test_constraint_conversion.py | """
Unit test for constraint conversion
"""
import numpy as np
from numpy.testing import (assert_array_almost_equal,
assert_allclose, assert_warns, suppress_warnings)
import pytest
from scipy.optimize import (NonlinearConstraint, LinearConstraint,
OptimizeWarning,... | 11,887 | 42.229091 | 83 | py |
scipy | scipy-main/scipy/optimize/tests/test_optimize.py | """
Unit tests for optimization routines from optimize.py
Authors:
Ed Schofield, Nov 2005
Andrew Straw, April 2008
To run it in its simplest form::
nosetests test_optimize.py
"""
import itertools
import platform
import numpy as np
from numpy.testing import (assert_allclose, assert_equal,
... | 119,295 | 37.934726 | 102 | py |
scipy | scipy-main/scipy/optimize/tests/test_direct.py | """
Unit test for DIRECT optimization algorithm.
"""
from numpy.testing import (assert_allclose,
assert_array_less)
import pytest
import numpy as np
from scipy.optimize import direct, Bounds
class TestDIRECT:
def setup_method(self):
self.fun_calls = 0
self.bounds_sphere... | 13,152 | 40.231975 | 78 | py |
scipy | scipy-main/scipy/optimize/tests/test__dual_annealing.py | # Dual annealing unit tests implementation.
# Copyright (c) 2018 Sylvain Gubian <sylvain.gubian@pmi.com>,
# Yang Xiang <yang.xiang@pmi.com>
# Author: Sylvain Gubian, PMP S.A.
"""
Unit tests for the dual annealing global optimizer
"""
from scipy.optimize import dual_annealing, Bounds
from scipy.optimize._dual_annealing... | 15,173 | 38.931579 | 79 | py |
scipy | scipy-main/scipy/optimize/tests/test__basinhopping.py | """
Unit tests for the basin hopping global minimization algorithm.
"""
import copy
from numpy.testing import (assert_almost_equal, assert_equal, assert_,
assert_allclose)
import pytest
from pytest import raises as assert_raises
import numpy as np
from numpy import cos, sin
from scipy.optim... | 18,897 | 34.927757 | 79 | py |
scipy | scipy-main/scipy/optimize/tests/test__remove_redundancy.py | """
Unit test for Linear Programming via Simplex Algorithm.
"""
# TODO: add tests for:
# https://github.com/scipy/scipy/issues/5400
# https://github.com/scipy/scipy/issues/6690
import numpy as np
from numpy.testing import (
assert_,
assert_allclose,
assert_equal)
from .test_linprog import magic_square
fr... | 6,799 | 28.694323 | 77 | py |
scipy | scipy-main/scipy/optimize/tests/test_linear_assignment.py | # Author: Brian M. Clapper, G. Varoquaux, Lars Buitinck
# License: BSD
from numpy.testing import assert_array_equal
import pytest
import numpy as np
from scipy.optimize import linear_sum_assignment
from scipy.sparse import random
from scipy.sparse._sputils import matrix
from scipy.sparse.csgraph import min_weight_fu... | 4,085 | 33.923077 | 78 | py |
scipy | scipy-main/scipy/optimize/tests/test_tnc.py | """
Unit tests for TNC optimization routine from tnc.py
"""
import pytest
from numpy.testing import assert_allclose, assert_equal
import numpy as np
from math import pow
from scipy import optimize
class TestTnc:
"""TNC non-linear optimization.
These tests are taken from Prof. K. Schittkowski's test example... | 12,700 | 35.708092 | 78 | py |
scipy | scipy-main/scipy/optimize/tests/test_linprog.py | """
Unit test for Linear Programming
"""
import sys
import platform
import numpy as np
from numpy.testing import (assert_, assert_allclose, assert_equal,
assert_array_less, assert_warns, suppress_warnings)
from pytest import raises as assert_raises
from scipy.optimize import linprog, Optimiz... | 96,628 | 38.232237 | 107 | py |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.