optimization¶

symmetry

Bindings for core::optimization namespace

class pyrosetta.rosetta.core.optimization.ArmijoLineMinimization¶

Bases: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm

Armijo(self: pyrosetta.rosetta.core.optimization.ArmijoLineMinimization, init_step: float, func_eval: pyrosetta.rosetta.core.optimization.func_1d) → float¶: C++: core::optimization::ArmijoLineMinimization::Armijo(double, class core::optimization::func_1d &) –> double

__call__(self: pyrosetta.rosetta.core.optimization.ArmijoLineMinimization, curr_pos: pyrosetta.rosetta.utility.vector1_double, curr_dir: pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::ArmijoLineMinimization::operator()(class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) –> double

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(self: pyrosetta.rosetta.core.optimization.ArmijoLineMinimization, score_fxn: pyrosetta.rosetta.core.optimization.Multifunc, nonmonotone: bool, dim: int) → None¶

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

cubic_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::cubic_interpolation(double, double, double, double, double, double) –> double

fetch_stored_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, get_derivs: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::LineMinimizationAlgorithm::fetch_stored_derivatives(class utility::vector1<double, class std::allocator<double> > &) –> void

nonmonotone(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) → bool¶: C++: core::optimization::LineMinimizationAlgorithm::nonmonotone() –> bool

provide_stored_derivatives(self: pyrosetta.rosetta.core.optimization.ArmijoLineMinimization) → bool¶: C++: core::optimization::ArmijoLineMinimization::provide_stored_derivatives() –> bool

quadratic_deriv_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::quadratic_deriv_interpolation(double, double, double, double, double, double) –> double

quadratic_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::quadratic_interpolation(double, double, double, double, double) –> double

secant_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, deriv1: float, point2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::secant_interpolation(double, double, double, double) –> double

silent(*args, **kwargs)¶

Overloaded function.

silent(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) -> bool

C++: core::optimization::LineMinimizationAlgorithm::silent() –> bool

silent(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, s_in: bool) -> None

C++: core::optimization::LineMinimizationAlgorithm::silent(bool) –> void

store_current_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, curr_derivs: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::LineMinimizationAlgorithm::store_current_derivatives(class utility::vector1<double, class std::allocator<double> > &) –> void

class pyrosetta.rosetta.core.optimization.AtomTreeMinimizer¶

Bases: pybind11_builtins.pybind11_object

High-level atom tree minimizer class

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.AtomTreeMinimizer) -> None
__init__(self: pyrosetta.rosetta.core.optimization.AtomTreeMinimizer, arg0: pyrosetta.rosetta.core.optimization.AtomTreeMinimizer) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

assign(self: pyrosetta.rosetta.core.optimization.AtomTreeMinimizer, : pyrosetta.rosetta.core.optimization.AtomTreeMinimizer) → pyrosetta.rosetta.core.optimization.AtomTreeMinimizer¶: C++: core::optimization::AtomTreeMinimizer::operator=(const class core::optimization::AtomTreeMinimizer &) –> class core::optimization::AtomTreeMinimizer &

check_setup(self: pyrosetta.rosetta.core.optimization.AtomTreeMinimizer, pose: pyrosetta.rosetta.core.pose.Pose, move_map: pyrosetta.rosetta.core.kinematics.MoveMap, scorefxn: pyrosetta.rosetta.core.scoring.ScoreFunction, options: core::optimization::MinimizerOptions) → None¶

Do consistency checks for minimizer setup.

C++: core::optimization::AtomTreeMinimizer::check_setup(const class core::pose::Pose &, const class core::kinematics::MoveMap &, const class core::scoring::ScoreFunction &, const class core::optimization::MinimizerOptions &) const –> void

deriv_check_result(self: pyrosetta.rosetta.core.optimization.AtomTreeMinimizer) → core::optimization::NumericalDerivCheckResult¶

After minimization has concluded, the user may access the deriv-check result,: assuming that they have run the AtomTreeMinimizer with deriv_check = true;

C++: core::optimization::AtomTreeMinimizer::deriv_check_result() const –> class std::shared_ptr<class core::optimization::NumericalDerivCheckResult>

run(self: pyrosetta.rosetta.core.optimization.AtomTreeMinimizer, pose: pyrosetta.rosetta.core.pose.Pose, move_map: pyrosetta.rosetta.core.kinematics.MoveMap, scorefxn: pyrosetta.rosetta.core.scoring.ScoreFunction, options: core::optimization::MinimizerOptions) → float¶

run minimization and return the final score at minimization’s conclusion.: Virtual allowing derived classes to mascarade as AtomTreeMinimizers. Non-const so that it can modify its deriv_check_result_ object.

C++: core::optimization::AtomTreeMinimizer::run(class core::pose::Pose &, const class core::kinematics::MoveMap &, const class core::scoring::ScoreFunction &, const class core::optimization::MinimizerOptions &) –> double

class pyrosetta.rosetta.core.optimization.AtomTreeMultifunc¶

Bases: pyrosetta.rosetta.core.optimization.Multifunc

Atom tree multifunction class

__call__(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, vars: pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::AtomTreeMultifunc::operator()(const class utility::vector1<double, class std::allocator<double> > &) const –> double

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, arg0: pyrosetta.rosetta.core.pose.Pose, arg1: core::optimization::MinimizerMap, arg2: pyrosetta.rosetta.core.scoring.ScoreFunction) -> None

doc

__init__(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, arg0: pyrosetta.rosetta.core.pose.Pose, arg1: core::optimization::MinimizerMap, arg2: pyrosetta.rosetta.core.scoring.ScoreFunction, arg3: bool) -> None

doc

__init__(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, pose_in: pyrosetta.rosetta.core.pose.Pose, min_map_in: core::optimization::MinimizerMap, scorefxn_in: pyrosetta.rosetta.core.scoring.ScoreFunction, deriv_check_in: bool, deriv_check_verbose_in: bool) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

abort_min(self: pyrosetta.rosetta.core.optimization.Multifunc, : pyrosetta.rosetta.utility.vector1_double) → bool¶

Christophe added the following to allow premature end of minimization: If you want to abort the minimizer under specific circonstances overload this function and return true if you want to stop, false if you want to continue. FOR THE MOMENT, ONLY IN DFPMIN!

C++: core::optimization::Multifunc::abort_min(const class utility::vector1<double, class std::allocator<double> > &) const –> bool

dfunc(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::AtomTreeMultifunc::dfunc(const class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) const –> void

dump(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, vars: pyrosetta.rosetta.utility.vector1_double, vars2: pyrosetta.rosetta.utility.vector1_double) → None¶

Error state reached – derivative does not match gradient

C++: core::optimization::AtomTreeMultifunc::dump(const class utility::vector1<double, class std::allocator<double> > &, const class utility::vector1<double, class std::allocator<double> > &) const –> void

set_deriv_check_result(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, deriv_check_result: core::optimization::NumericalDerivCheckResult) → None¶: C++: core::optimization::AtomTreeMultifunc::set_deriv_check_result(class std::shared_ptr<class core::optimization::NumericalDerivCheckResult>) –> void

class pyrosetta.rosetta.core.optimization.BrentLineMinimization¶

Bases: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm

BRENT(self: pyrosetta.rosetta.core.optimization.BrentLineMinimization, AX: float, BX: float, CX: float, FA: float, FB: float, FC: float, TOL: float, func_eval: pyrosetta.rosetta.core.optimization.func_1d) → float¶: C++: core::optimization::BrentLineMinimization::BRENT(const double, const double, const double, double &, double &, const double, const double, class core::optimization::func_1d &) –> double

MNBRAK(self: pyrosetta.rosetta.core.optimization.BrentLineMinimization, AX: float, BX: float, CX: float, FA: float, FB: float, FC: float, func_eval: pyrosetta.rosetta.core.optimization.func_1d) → None¶: C++: core::optimization::BrentLineMinimization::MNBRAK(double &, double &, double &, double &, double &, double &, class core::optimization::func_1d &) const –> void

__call__(self: pyrosetta.rosetta.core.optimization.BrentLineMinimization, curr_pos: pyrosetta.rosetta.utility.vector1_double, curr_dir: pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::BrentLineMinimization::operator()(class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) –> double

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(self: pyrosetta.rosetta.core.optimization.BrentLineMinimization, score_fxn: pyrosetta.rosetta.core.optimization.Multifunc, dim: int) → None¶

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

cubic_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::cubic_interpolation(double, double, double, double, double, double) –> double

fetch_stored_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, get_derivs: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::LineMinimizationAlgorithm::fetch_stored_derivatives(class utility::vector1<double, class std::allocator<double> > &) –> void

nonmonotone(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) → bool¶: C++: core::optimization::LineMinimizationAlgorithm::nonmonotone() –> bool

provide_stored_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) → bool¶: C++: core::optimization::LineMinimizationAlgorithm::provide_stored_derivatives() –> bool

quadratic_deriv_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::quadratic_deriv_interpolation(double, double, double, double, double, double) –> double

quadratic_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::quadratic_interpolation(double, double, double, double, double) –> double

secant_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, deriv1: float, point2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::secant_interpolation(double, double, double, double) –> double

set_deriv_cutoff(self: pyrosetta.rosetta.core.optimization.BrentLineMinimization, val: float) → None¶: C++: core::optimization::BrentLineMinimization::set_deriv_cutoff(const double &) –> void

silent(*args, **kwargs)¶

Overloaded function.

silent(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) -> bool

C++: core::optimization::LineMinimizationAlgorithm::silent() –> bool

silent(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, s_in: bool) -> None

C++: core::optimization::LineMinimizationAlgorithm::silent(bool) –> void

store_current_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, curr_derivs: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::LineMinimizationAlgorithm::store_current_derivatives(class utility::vector1<double, class std::allocator<double> > &) –> void

class pyrosetta.rosetta.core.optimization.CartesianMinimizer¶

Bases: pybind11_builtins.pybind11_object

High-level atom tree minimizer class

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(self: pyrosetta.rosetta.core.optimization.CartesianMinimizer) → None¶

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

assign(self: pyrosetta.rosetta.core.optimization.CartesianMinimizer, : pyrosetta.rosetta.core.optimization.CartesianMinimizer) → pyrosetta.rosetta.core.optimization.CartesianMinimizer¶: C++: core::optimization::CartesianMinimizer::operator=(const class core::optimization::CartesianMinimizer &) –> class core::optimization::CartesianMinimizer &

deriv_check_result(self: pyrosetta.rosetta.core.optimization.CartesianMinimizer) → core::optimization::NumericalDerivCheckResult¶

After minimization has concluded, the user may access the deriv-check result,: assuming that they have run the CartesianMinimizer with deriv_check = true;

C++: core::optimization::CartesianMinimizer::deriv_check_result() const –> class std::shared_ptr<class core::optimization::NumericalDerivCheckResult>

run(self: pyrosetta.rosetta.core.optimization.CartesianMinimizer, pose: pyrosetta.rosetta.core.pose.Pose, move_map: pyrosetta.rosetta.core.kinematics.MoveMap, scorefxn: pyrosetta.rosetta.core.scoring.ScoreFunction, options: core::optimization::MinimizerOptions) → float¶

run minimization and return the final score at minimization’s conclusion.: Virtual allowing derived classes to mascarade as CartesianMinimizers. Non-const so that it can modify its deriv_check_result_ object.

C++: core::optimization::CartesianMinimizer::run(class core::pose::Pose &, const class core::kinematics::MoveMap &, const class core::scoring::ScoreFunction &, const class core::optimization::MinimizerOptions &) –> double

class pyrosetta.rosetta.core.optimization.CartesianMultifunc¶

Bases: pyrosetta.rosetta.core.optimization.Multifunc

Atom tree multifunction class

__call__(self: pyrosetta.rosetta.core.optimization.CartesianMultifunc, vars: pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::CartesianMultifunc::operator()(const class utility::vector1<double, class std::allocator<double> > &) const –> double

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.CartesianMultifunc, arg0: pyrosetta.rosetta.core.pose.Pose, arg1: pyrosetta.rosetta.core.optimization.CartesianMinimizerMap, arg2: pyrosetta.rosetta.core.scoring.ScoreFunction) -> None

doc

__init__(self: pyrosetta.rosetta.core.optimization.CartesianMultifunc, arg0: pyrosetta.rosetta.core.pose.Pose, arg1: pyrosetta.rosetta.core.optimization.CartesianMinimizerMap, arg2: pyrosetta.rosetta.core.scoring.ScoreFunction, arg3: bool) -> None

doc

__init__(self: pyrosetta.rosetta.core.optimization.CartesianMultifunc, pose_in: pyrosetta.rosetta.core.pose.Pose, min_map_in: pyrosetta.rosetta.core.optimization.CartesianMinimizerMap, scorefxn_in: pyrosetta.rosetta.core.scoring.ScoreFunction, deriv_check_in: bool, deriv_check_verbose_in: bool) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

abort_min(self: pyrosetta.rosetta.core.optimization.Multifunc, : pyrosetta.rosetta.utility.vector1_double) → bool¶

Christophe added the following to allow premature end of minimization: If you want to abort the minimizer under specific circonstances overload this function and return true if you want to stop, false if you want to continue. FOR THE MOMENT, ONLY IN DFPMIN!

C++: core::optimization::Multifunc::abort_min(const class utility::vector1<double, class std::allocator<double> > &) const –> bool

dfunc(self: pyrosetta.rosetta.core.optimization.CartesianMultifunc, vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::CartesianMultifunc::dfunc(const class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) const –> void

dump(self: pyrosetta.rosetta.core.optimization.CartesianMultifunc, vars: pyrosetta.rosetta.utility.vector1_double, vars2: pyrosetta.rosetta.utility.vector1_double) → None¶

Error state reached – derivative does not match gradient

C++: core::optimization::CartesianMultifunc::dump(const class utility::vector1<double, class std::allocator<double> > &, const class utility::vector1<double, class std::allocator<double> > &) const –> void

set_deriv_check_result(self: pyrosetta.rosetta.core.optimization.CartesianMultifunc, deriv_check_result: core::optimization::NumericalDerivCheckResult) → None¶: C++: core::optimization::CartesianMultifunc::set_deriv_check_result(class std::shared_ptr<class core::optimization::NumericalDerivCheckResult>) –> void

class pyrosetta.rosetta.core.optimization.ConvergenceTest¶

Bases: pybind11_builtins.pybind11_object

Rough outline of how to structure this:

Make a base ‘minimizer’ class

Sub-class into univariate and multivariate minimizers -> actually, could we treat linmin as an instance of multivariate

minimization, with a single pass of steepest descent

The trick is how to mix and match convergence criteria, descent direction generation, and line minimization schemes

convergence criteria could be a function or a functor. Descent direction algorithms probably need to be functors, since they have different storage needs.

__call__(self: pyrosetta.rosetta.core.optimization.ConvergenceTest, Fnew: float, Fold: float) → bool¶: C++: core::optimization::ConvergenceTest::operator()(double, double) –> bool

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.ConvergenceTest) -> None
__init__(self: pyrosetta.rosetta.core.optimization.ConvergenceTest, arg0: pyrosetta.rosetta.core.optimization.ConvergenceTest) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

assign(self: pyrosetta.rosetta.core.optimization.ConvergenceTest, : pyrosetta.rosetta.core.optimization.ConvergenceTest) → pyrosetta.rosetta.core.optimization.ConvergenceTest¶: C++: core::optimization::ConvergenceTest::operator=(const class core::optimization::ConvergenceTest &) –> class core::optimization::ConvergenceTest &

class pyrosetta.rosetta.core.optimization.EItem¶

Bases: pybind11_builtins.pybind11_object

Inner class for Genetic Algorithm, hold one population with some additional info

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.EItem) -> None
__init__(self: pyrosetta.rosetta.core.optimization.EItem, vn: pyrosetta.rosetta.utility.vector1_double) -> None
__init__(self: pyrosetta.rosetta.core.optimization.EItem, arg0: pyrosetta.rosetta.core.optimization.EItem) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

sort_R_function(e1: pyrosetta.rosetta.core.optimization.EItem, e2: pyrosetta.rosetta.core.optimization.EItem) → bool¶: C++: core::optimization::EItem::sort_R_function(const class core::optimization::EItem &, const class core::optimization::EItem &) –> bool

class pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm¶

Bases: pybind11_builtins.pybind11_object

__call__(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, : pyrosetta.rosetta.utility.vector1_double, : pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::LineMinimizationAlgorithm::operator()(class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) –> double

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, score_fxn: pyrosetta.rosetta.core.optimization.Multifunc, dimension: int) -> None
__init__(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, arg0: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

cubic_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::cubic_interpolation(double, double, double, double, double, double) –> double

fetch_stored_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, get_derivs: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::LineMinimizationAlgorithm::fetch_stored_derivatives(class utility::vector1<double, class std::allocator<double> > &) –> void

nonmonotone(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) → bool¶: C++: core::optimization::LineMinimizationAlgorithm::nonmonotone() –> bool

provide_stored_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) → bool¶: C++: core::optimization::LineMinimizationAlgorithm::provide_stored_derivatives() –> bool

quadratic_deriv_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::quadratic_deriv_interpolation(double, double, double, double, double, double) –> double

quadratic_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::quadratic_interpolation(double, double, double, double, double) –> double

secant_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, deriv1: float, point2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::secant_interpolation(double, double, double, double) –> double

silent(*args, **kwargs)¶

Overloaded function.

silent(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) -> bool

C++: core::optimization::LineMinimizationAlgorithm::silent() –> bool

silent(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, s_in: bool) -> None

C++: core::optimization::LineMinimizationAlgorithm::silent(bool) –> void

store_current_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, curr_derivs: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::LineMinimizationAlgorithm::store_current_derivatives(class utility::vector1<double, class std::allocator<double> > &) –> void

class pyrosetta.rosetta.core.optimization.Minimizer¶

Bases: pybind11_builtins.pybind11_object

Simple low-level minimizer class

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.Minimizer, func_in: pyrosetta.rosetta.core.optimization.Multifunc, options_in: pyrosetta.rosetta.core.optimization.MinimizerOptions) -> None
__init__(self: pyrosetta.rosetta.core.optimization.Minimizer, arg0: pyrosetta.rosetta.core.optimization.Minimizer) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

run(self: pyrosetta.rosetta.core.optimization.Minimizer, phipsi_inout: pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::Minimizer::run(class utility::vector1<double, class std::allocator<double> > &) –> double

class pyrosetta.rosetta.core.optimization.Multifunc¶

Bases: pybind11_builtins.pybind11_object

Multifunction interface class

__call__(self: pyrosetta.rosetta.core.optimization.Multifunc, phipsi: pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::Multifunc::operator()(const class utility::vector1<double, class std::allocator<double> > &) const –> double

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__¶: Initialize self. See help(type(self)) for accurate signature.

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

abort_min(self: pyrosetta.rosetta.core.optimization.Multifunc, : pyrosetta.rosetta.utility.vector1_double) → bool¶

Christophe added the following to allow premature end of minimization: If you want to abort the minimizer under specific circonstances overload this function and return true if you want to stop, false if you want to continue. FOR THE MOMENT, ONLY IN DFPMIN!

C++: core::optimization::Multifunc::abort_min(const class utility::vector1<double, class std::allocator<double> > &) const –> bool

dfunc(self: pyrosetta.rosetta.core.optimization.Multifunc, phipsi: pyrosetta.rosetta.utility.vector1_double, dE_dphipsi: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::Multifunc::dfunc(const class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) const –> void

dump(self: pyrosetta.rosetta.core.optimization.Multifunc, : pyrosetta.rosetta.utility.vector1_double, : pyrosetta.rosetta.utility.vector1_double) → None¶

Error state reached – derivative does not match gradient: Derived classes have the oportunity to now output and or analyze the two vars assignments vars, vars+delta where the derivatives are incorrect.

C++: core::optimization::Multifunc::dump(const class utility::vector1<double, class std::allocator<double> > &, const class utility::vector1<double, class std::allocator<double> > &) const –> void

class pyrosetta.rosetta.core.optimization.Particle¶

Bases: pybind11_builtins.pybind11_object

Simple data container for PSO algorithm.

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.Particle, size: int) -> None
__init__(self: pyrosetta.rosetta.core.optimization.Particle, p_in: pyrosetta.rosetta.utility.vector1_double) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__(self: pyrosetta.rosetta.core.optimization.Particle) → str¶

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

assign(self: pyrosetta.rosetta.core.optimization.Particle, : pyrosetta.rosetta.core.optimization.Particle) → pyrosetta.rosetta.core.optimization.Particle¶: C++: core::optimization::Particle::operator=(const class core::optimization::Particle &) –> class core::optimization::Particle &

ensure_size(self: pyrosetta.rosetta.core.optimization.Particle, minsize: int) → None¶

Make sure that all arrays are large enough – prevents index-out-of-bound errors.

C++: core::optimization::Particle::ensure_size(unsigned long) –> void

fitness_pbest(self: pyrosetta.rosetta.core.optimization.Particle) → float¶: C++: core::optimization::Particle::fitness_pbest() const –> double

pbest(self: pyrosetta.rosetta.core.optimization.Particle) → pyrosetta.rosetta.utility.vector1_double¶

This is why data should be private: you get to ensure it’s valid when you read it.

C++: core::optimization::Particle::pbest() const –> const class utility::vector1<double, class std::allocator<double> > &

score(self: pyrosetta.rosetta.core.optimization.Particle, f: pyrosetta.rosetta.core.optimization.Multifunc) → float¶: C++: core::optimization::Particle::score(class core::optimization::Multifunc &) –> double

set_score(self: pyrosetta.rosetta.core.optimization.Particle, new_score: float) → float¶: C++: core::optimization::Particle::set_score(double &) –> double

class pyrosetta.rosetta.core.optimization.ParticleSwarmMinimizer¶

Bases: pybind11_builtins.pybind11_object

Particle Swarm Optimization engine.

Algorithm details based heavily on

Chen, Liu, Huang, Hwang, Ho (2006). “SODOCK: Swarm Optimization for Highly Flexible Protein-Ligand Docking” J Comput Chem 28: 612-623, 2007

Also on: http://en.wikipedia.org/wiki/Particle_swarm_optimization http://www.swarmintelligence.org/

One can imagine writing another version that distributed the work via MPI…

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.ParticleSwarmMinimizer, p_min: pyrosetta.rosetta.utility.vector1_double, p_max: pyrosetta.rosetta.utility.vector1_double) -> None
__init__(self: pyrosetta.rosetta.core.optimization.ParticleSwarmMinimizer, arg0: pyrosetta.rosetta.core.optimization.ParticleSwarmMinimizer) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

assign(self: pyrosetta.rosetta.core.optimization.ParticleSwarmMinimizer, : pyrosetta.rosetta.core.optimization.ParticleSwarmMinimizer) → pyrosetta.rosetta.core.optimization.ParticleSwarmMinimizer¶: C++: core::optimization::ParticleSwarmMinimizer::operator=(const class core::optimization::ParticleSwarmMinimizer &) –> class core::optimization::ParticleSwarmMinimizer &

class pyrosetta.rosetta.core.optimization.SingleResidueMultifunc¶

Bases: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc

A streamlined AtomTreeMultifunc designed specifically for RTMIN.

Evaluates only the energies between the specified residue and the rest of the Pose, assuming the nbr_atoms do not move (as in rotamer trials and repacking). Could probably be sped up further with a customized dfunc(). DFPMIN seems to spend most of its time in func() rather than dfunc(), so there’s not as much to gain there anyway.

__call__(self: pyrosetta.rosetta.core.optimization.SingleResidueMultifunc, vars: pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::SingleResidueMultifunc::operator()(const class utility::vector1<double, class std::allocator<double> > &) const –> double

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(*args, **kwargs)¶

Overloaded function.

__init__(self: pyrosetta.rosetta.core.optimization.SingleResidueMultifunc, arg0: pyrosetta.rosetta.core.pose.Pose, arg1: int, arg2: pyrosetta.rosetta.core.optimization.MinimizerMap, arg3: pyrosetta.rosetta.core.scoring.ScoreFunction, arg4: utility::graph::Graph) -> None

doc

__init__(self: pyrosetta.rosetta.core.optimization.SingleResidueMultifunc, arg0: pyrosetta.rosetta.core.pose.Pose, arg1: int, arg2: pyrosetta.rosetta.core.optimization.MinimizerMap, arg3: pyrosetta.rosetta.core.scoring.ScoreFunction, arg4: utility::graph::Graph, arg5: bool) -> None

doc

__init__(self: pyrosetta.rosetta.core.optimization.SingleResidueMultifunc, pose_in: pyrosetta.rosetta.core.pose.Pose, rsd_id_in: int, min_map_in: pyrosetta.rosetta.core.optimization.MinimizerMap, scorefxn_in: pyrosetta.rosetta.core.scoring.ScoreFunction, packer_neighbor_graph_in: utility::graph::Graph, deriv_check_in: bool, deriv_check_verbose_in: bool) -> None

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

abort_min(self: pyrosetta.rosetta.core.optimization.Multifunc, : pyrosetta.rosetta.utility.vector1_double) → bool¶

Christophe added the following to allow premature end of minimization: If you want to abort the minimizer under specific circonstances overload this function and return true if you want to stop, false if you want to continue. FOR THE MOMENT, ONLY IN DFPMIN!

C++: core::optimization::Multifunc::abort_min(const class utility::vector1<double, class std::allocator<double> > &) const –> bool

dfunc(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::AtomTreeMultifunc::dfunc(const class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) const –> void

dump(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, vars: pyrosetta.rosetta.utility.vector1_double, vars2: pyrosetta.rosetta.utility.vector1_double) → None¶

Error state reached – derivative does not match gradient

C++: core::optimization::AtomTreeMultifunc::dump(const class utility::vector1<double, class std::allocator<double> > &, const class utility::vector1<double, class std::allocator<double> > &) const –> void

set_deriv_check_result(self: pyrosetta.rosetta.core.optimization.AtomTreeMultifunc, deriv_check_result: core::optimization::NumericalDerivCheckResult) → None¶: C++: core::optimization::AtomTreeMultifunc::set_deriv_check_result(class std::shared_ptr<class core::optimization::NumericalDerivCheckResult>) –> void

class pyrosetta.rosetta.core.optimization.StrongWolfeLineMinimization¶

Bases: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm

StrongWolfe(self: pyrosetta.rosetta.core.optimization.StrongWolfeLineMinimization, init_step: float, func_eval: pyrosetta.rosetta.core.optimization.func_1d) → float¶: C++: core::optimization::StrongWolfeLineMinimization::StrongWolfe(double, class core::optimization::func_1d &) –> double

__call__(self: pyrosetta.rosetta.core.optimization.StrongWolfeLineMinimization, curr_pos: pyrosetta.rosetta.utility.vector1_double, curr_dir: pyrosetta.rosetta.utility.vector1_double) → float¶: C++: core::optimization::StrongWolfeLineMinimization::operator()(class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) –> double

__delattr__¶: Implement delattr(self, name).

__dir__() → list¶: default dir() implementation

__eq__¶: Return self==value.

__format__()¶: default object formatter

__ge__¶: Return self>=value.

__getattribute__¶: Return getattr(self, name).

__gt__¶: Return self>value.

__hash__¶: Return hash(self).

__init__(self: pyrosetta.rosetta.core.optimization.StrongWolfeLineMinimization, score_fxn: pyrosetta.rosetta.core.optimization.Multifunc, nonmonotone: bool, dim: int) → None¶

__init_subclass__()¶

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__le__¶: Return self<=value.

__lt__¶: Return self<value.

__ne__¶: Return self!=value.

__new__()¶: Create and return a new object. See help(type) for accurate signature.

__reduce__()¶: helper for pickle

__reduce_ex__()¶: helper for pickle

__repr__¶: Return repr(self).

__setattr__¶: Implement setattr(self, name, value).

__sizeof__() → int¶: size of object in memory, in bytes

__str__¶: Return str(self).

__subclasshook__()¶

Abstract classes can override this to customize issubclass().

This is invoked early on by abc.ABCMeta.__subclasscheck__(). It should return True, False or NotImplemented. If it returns NotImplemented, the normal algorithm is used. Otherwise, it overrides the normal algorithm (and the outcome is cached).

cubic_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::cubic_interpolation(double, double, double, double, double, double) –> double

fetch_stored_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, get_derivs: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::LineMinimizationAlgorithm::fetch_stored_derivatives(class utility::vector1<double, class std::allocator<double> > &) –> void

nonmonotone(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) → bool¶: C++: core::optimization::LineMinimizationAlgorithm::nonmonotone() –> bool

provide_stored_derivatives(self: pyrosetta.rosetta.core.optimization.StrongWolfeLineMinimization) → bool¶: C++: core::optimization::StrongWolfeLineMinimization::provide_stored_derivatives() –> bool

quadratic_deriv_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::quadratic_deriv_interpolation(double, double, double, double, double, double) –> double

quadratic_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, func1: float, deriv1: float, point2: float, func2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::quadratic_interpolation(double, double, double, double, double) –> double

secant_interpolation(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, point1: float, deriv1: float, point2: float, deriv2: float) → float¶: C++: core::optimization::LineMinimizationAlgorithm::secant_interpolation(double, double, double, double) –> double

silent(*args, **kwargs)¶

Overloaded function.

silent(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm) -> bool

C++: core::optimization::LineMinimizationAlgorithm::silent() –> bool

silent(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, s_in: bool) -> None

C++: core::optimization::LineMinimizationAlgorithm::silent(bool) –> void

store_current_derivatives(self: pyrosetta.rosetta.core.optimization.LineMinimizationAlgorithm, curr_derivs: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::LineMinimizationAlgorithm::store_current_derivatives(class utility::vector1<double, class std::allocator<double> > &) –> void

zoom(self: pyrosetta.rosetta.core.optimization.StrongWolfeLineMinimization, alpha_low: float, func_low: float, deriv_low: float, alpha_high: float, func_high: float, deriv_high: float, func_zero: float, deriv_zero: float, func_return: float, func_eval: pyrosetta.rosetta.core.optimization.func_1d) → float¶: C++: core::optimization::StrongWolfeLineMinimization::zoom(double, double, double, double, double, double, double, double, double &, class core::optimization::func_1d &) –> double

pyrosetta.rosetta.core.optimization.atom_tree_dfunc(pose: pyrosetta.rosetta.core.pose.Pose, min_map: pyrosetta.rosetta.core.optimization.MinimizerMap, scorefxn: pyrosetta.rosetta.core.scoring.ScoreFunction, vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::atom_tree_dfunc(class core::pose::Pose &, class core::optimization::MinimizerMap &, const class core::scoring::ScoreFunction &, const class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) –> void

pyrosetta.rosetta.core.optimization.atom_tree_get_atompairE_deriv(pose: pyrosetta.rosetta.core.pose.Pose, min_map: pyrosetta.rosetta.core.optimization.MinimizerMap, scorefxn: pyrosetta.rosetta.core.scoring.ScoreFunction) → None¶: C++: core::optimization::atom_tree_get_atompairE_deriv(class core::pose::Pose &, class core::optimization::MinimizerMap &, const class core::scoring::ScoreFunction &) –> void

pyrosetta.rosetta.core.optimization.cart_numerical_derivative_check(min_map: pyrosetta.rosetta.core.optimization.CartesianMinimizerMap, func: pyrosetta.rosetta.core.optimization.CartesianMultifunc, start_vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double, deriv_check_result: pyrosetta.rosetta.core.optimization.NumericalDerivCheckResult, verbose: bool) → None¶: C++: core::optimization::cart_numerical_derivative_check(const class core::optimization::CartesianMinimizerMap &, const class core::optimization::CartesianMultifunc &, const class utility::vector1<double, class std::allocator<double> > &, const class utility::vector1<double, class std::allocator<double> > &, class std::shared_ptr<class core::optimization::NumericalDerivCheckResult>, const bool) –> void

pyrosetta.rosetta.core.optimization.cartesian_collect_atompairE_deriv(pose: pyrosetta.rosetta.core.pose.Pose, min_map: pyrosetta.rosetta.core.optimization.CartesianMinimizerMap, scorefxn: pyrosetta.rosetta.core.scoring.ScoreFunction, dE_dvars: pyrosetta.rosetta.utility.vector1_double, scale: float) → None¶: C++: core::optimization::cartesian_collect_atompairE_deriv(class core::pose::Pose &, class core::optimization::CartesianMinimizerMap &, const class core::scoring::ScoreFunction &, class utility::vector1<double, class std::allocator<double> > &, double) –> void

pyrosetta.rosetta.core.optimization.cartesian_collect_torsional_deriv(pose: pyrosetta.rosetta.core.pose.Pose, min_map: pyrosetta.rosetta.core.optimization.CartesianMinimizerMap, scorefxn: pyrosetta.rosetta.core.scoring.ScoreFunction, dE_dvars: pyrosetta.rosetta.utility.vector1_double, scale: float) → None¶: C++: core::optimization::cartesian_collect_torsional_deriv(class core::pose::Pose &, class core::optimization::CartesianMinimizerMap &, const class core::scoring::ScoreFunction &, class utility::vector1<double, class std::allocator<double> > &, double) –> void

pyrosetta.rosetta.core.optimization.cartesian_dfunc(pose: pyrosetta.rosetta.core.pose.Pose, min_map: pyrosetta.rosetta.core.optimization.CartesianMinimizerMap, scorefxn: pyrosetta.rosetta.core.scoring.ScoreFunction, vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double) → None¶: C++: core::optimization::cartesian_dfunc(class core::pose::Pose &, class core::optimization::CartesianMinimizerMap &, const class core::scoring::ScoreFunction &, const class utility::vector1<double, class std::allocator<double> > &, class utility::vector1<double, class std::allocator<double> > &) –> void

pyrosetta.rosetta.core.optimization.numerical_derivative_check(min_map: pyrosetta.rosetta.core.optimization.MinimizerMap, func: pyrosetta.rosetta.core.optimization.Multifunc, start_vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double, deriv_check_result: pyrosetta.rosetta.core.optimization.NumericalDerivCheckResult, verbose: bool) → None¶: C++: core::optimization::numerical_derivative_check(const class core::optimization::MinimizerMap &, const class core::optimization::Multifunc &, const class utility::vector1<double, class std::allocator<double> > &, const class utility::vector1<double, class std::allocator<double> > &, class std::shared_ptr<class core::optimization::NumericalDerivCheckResult>, const bool) –> void

pyrosetta.rosetta.core.optimization.simple_numeric_deriv_check(*args, **kwargs)¶

Overloaded function.

simple_numeric_deriv_check(func: pyrosetta.rosetta.core.optimization.Multifunc, start_vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double, send_to_stdout: bool, verbose: bool) -> pyrosetta.rosetta.core.optimization.SimpleDerivCheckResult
simple_numeric_deriv_check(func: pyrosetta.rosetta.core.optimization.Multifunc, start_vars: pyrosetta.rosetta.utility.vector1_double, dE_dvars: pyrosetta.rosetta.utility.vector1_double, send_to_stdout: bool, verbose: bool, nsteps: int) -> pyrosetta.rosetta.core.optimization.SimpleDerivCheckResult

Numeric deriv check for Multifuncs other than the AtomTreeMultifunc.

C++: core::optimization::simple_numeric_deriv_check(const class core::optimization::Multifunc &, const class utility::vector1<double, class std::allocator<double> > &, const class utility::vector1<double, class std::allocator<double> > &, bool, bool, unsigned long) –> class core::optimization::SimpleDerivCheckResult

pyrosetta.rosetta.core.optimization.torsional_derivative_from_cartesian_derivatives(atom: pyrosetta.rosetta.core.kinematics.tree.Atom, dof_node: pyrosetta.rosetta.core.optimization.DOF_Node, dof_deriv: float, torsion_scale_factor: float) → float¶: C++: core::optimization::torsional_derivative_from_cartesian_derivatives(const class core::kinematics::tree::Atom &, const class core::optimization::DOF_Node &, double, double) –> double