diff --git a/_sources/install.rst b/_sources/install.rst index 6649ea97..a9edf412 100644 --- a/_sources/install.rst +++ b/_sources/install.rst @@ -17,8 +17,6 @@ pygmo has the following **mandatory** runtime dependencies: Additionally, pygmo has the following **optional** runtime dependencies: -* `dill `__, which can be used as an - alternative serialization backend, * `Matplotlib `__, which is used by a few plotting utilities, * `NetworkX `__, which is used for diff --git a/algorithm.html b/algorithm.html index 87b0a20a..70443475 100644 --- a/algorithm.html +++ b/algorithm.html @@ -432,7 +432,7 @@

Algorithm classRaises
    -

  • NotImplementedError – if uda does not implement the mandatory method detailed above

    • +

  • NotImplementedError – if uda does not implement the mandatory method detailed above

  • unspecified – any exception thrown by methods of the UDA invoked during construction, the deep copy of the UDA, the constructor of the underlying C++ class, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function @@ -468,25 +468,25 @@

    Algorithm classalgorithm instance. The behaviour of this function depends on the value -of t (which must be a type) and on the type of the internal UDA:

    +of t (which must be a type) and on the type of the internal UDA:

    • if the type of the UDA is t, then a reference to the UDA will be returned (this mirrors the behaviour of the corresponding C++ method pagmo::algorithm::extract()),

      • -

    • if t is object and the UDA is a Python object (as opposed to an +

    • if t is object and the UDA is a Python object (as opposed to an exposed C++ algorithm), then a reference to the UDA will be returned (this allows to extract a Python UDA without knowing its type),

      • -

    • otherwise, None will be returned.

      • +

    • otherwise, None will be returned.

    Parameters
    -

    t (type) – the type of the user-defined algorithm to extract

    +

    t (type) – the type of the user-defined algorithm to extract

    Returns
    -

    a reference to the internal user-defined algorithm, or None if the extraction fails

    +

    a reference to the internal user-defined algorithm, or None if the extraction fails

    Raises
    -

    TypeError – if t is not a type

    +

    TypeError – if t is not a type

    Examples

    @@ -521,7 +521,7 @@

    Algorithm class

    extra info about the UDA

Return type
-

str

+

str

Raises

unspecified – any exception thrown by the get_extra_info() method of the UDA

@@ -540,7 +540,7 @@

Algorithm class

the algorithm’s name

Return type
-

str

+

str

@@ -617,17 +617,17 @@

Algorithm class is_(t)#

Check the type of the user-defined algorithm.

-

This method returns False if extract(t) returns -None, and True otherwise.

+

This method returns False if extract(t) returns +None, and True otherwise.

Parameters
-

t (type) – the type that will be compared to the type of the UDA

+

t (type) – the type that will be compared to the type of the UDA

Returns

whether the UDA is of type t or not

Return type
-

bool

+

bool

Raises

unspecified – any exception thrown by extract()

@@ -648,14 +648,14 @@

Algorithm classevolve() method of the UDA for all stochastic variables. If the UDA provides a set_seed() method, then its set_seed() method will be invoked. Otherwise, an error will be raised. The seed parameter must be non-negative.

-

The set_seed() method of the UDA must be able to take an int as input parameter.

+

The set_seed() method of the UDA must be able to take an int as input parameter.

Parameters
-

seed (int) – the random seed

+

seed (int) – the random seed

Raises
    -

  • NotImplementedError – if the UDA does not provide a set_seed() method

    • +

  • NotImplementedError – if the UDA does not provide a set_seed() method

  • unspecified – any exception raised by the set_seed() method of the UDA or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -670,14 +670,14 @@

Algorithm classset_verbosity() method, then its set_verbosity() method will be invoked. Otherwise, an error will be raised.

The exact meaning of the input parameter level is dependent on the UDA.

-

The set_verbosity() method of the UDA must be able to take an int as input parameter.

+

The set_verbosity() method of the UDA must be able to take an int as input parameter.

Parameters
-

level (int) – the desired verbosity level

+

level (int) – the desired verbosity level

Raises
    -

  • NotImplementedError – if the UDA does not provide a set_verbosity() method

    • +

  • NotImplementedError – if the UDA does not provide a set_verbosity() method

  • unspecified – any exception raised by the set_verbosity() method of the UDA or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

diff --git a/algorithms.html b/algorithms.html index 93c9f1d8..3fe0969f 100644 --- a/algorithms.html +++ b/algorithms.html @@ -400,7 +400,7 @@

Contents

class pygmo.scipy_optimize(args=(), method: typing.Optional[str] = None, tol: typing.Optional[float] = None, callback: typing.Optional[typing.Callable[[typing.Any], typing.Any]] = None, options: typing.Optional[typing.MutableMapping[str, typing.Any]] = None, selection: pygmo.core.s_policy = Selection policy name: Select best C++ class name: pagmo::select_best  Extra info: Absolute migration rate: 1 )#
-

This class is a user defined algorithm (UDA) providing a wrapper around the function scipy.optimize.minimize().

+

This class is a user defined algorithm (UDA) providing a wrapper around the function scipy.optimize.minimize().

This wraps several well-known local optimization algorithms:

    @@ -440,7 +440,7 @@

    Contents

The constructor initializes a wrapper instance for a specific algorithm. -Construction arguments are those options of scipy.optimize.minimize() that are not problem-specific. +Construction arguments are those options of scipy.optimize.minimize() that are not problem-specific. Problem-specific options, for example the bounds, constraints and the existence of a gradient and hessian, are deduced from the problem in the population given to the evolve function.

@@ -455,7 +455,7 @@

Contents

Raises
-

ValueError – If method is not one of Nelder-Mead Powell, CG, BFGS, Newton-CG, L-BFGS-B, TNC, COBYLA, SLSQP, trust-constr, dogleg, trust-ncg, trust-exact, trust-krylov or None.

+

ValueError – If method is not one of Nelder-Mead Powell, CG, BFGS, Newton-CG, L-BFGS-B, TNC, COBYLA, SLSQP, trust-constr, dogleg, trust-ncg, trust-exact, trust-krylov or None.

@@ -472,9 +472,9 @@

Contents

Raises
    -

  • ValueError – If the problem has constraints, but during construction a method was selected that cannot deal with them.

    • -

  • ValueError – If the problem contains multiple objectives

    • -

  • ValueError – If the problem is stochastic

    • +

  • ValueError – If the problem has constraints, but during construction a method was selected that cannot deal with them.

    • +

  • ValueError – If the problem contains multiple objectives

    • +

  • ValueError – If the problem is stochastic

  • unspecified – any exception thrown by the member functions of the problem

@@ -483,20 +483,20 @@

Contents

-get_name() → str#
+get_name() → str#

Returns the method name if one was selected, scipy.optimize.minimize otherwise

-set_verbosity(level: int) → None#
+set_verbosity(level: int) → None#

Modifies the ‘disp’ parameter in the options dict, which prints out a final convergence message.

Parameters

level – Every verbosity level above zero prints out a convergence message.

Raises
-

ValueError – If options dict was given in instance constructor and has options conflicting with verbosity level

+

ValueError – If options dict was given in instance constructor and has options conflicting with verbosity level

@@ -546,24 +546,24 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • ker (int) – kernel size

    • -

  • q (float) – convergence speed parameter

    • -

  • oracle (float) – oracle parameter

    • -

  • acc (float) – accuracy parameter

    • -

  • threshold (int) – threshold parameter

    • -

  • n_gen_mark (int) – std convergence speed parameter

    • -

  • impstop (int) – improvement stopping criterion

    • -

  • evalstop (int) – evaluation stopping criterion

    • -

  • focus (float) – focus parameter

    • -

  • memory (bool) – memory parameter

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • ker (int) – kernel size

    • +

  • q (float) – convergence speed parameter

    • +

  • oracle (float) – oracle parameter

    • +

  • acc (float) – accuracy parameter

    • +

  • threshold (int) – threshold parameter

    • +

  • n_gen_mark (int) – std convergence speed parameter

    • +

  • impstop (int) – improvement stopping criterion

    • +

  • evalstop (int) – evaluation stopping criterion

    • +

  • focus (float) – focus parameter

    • +

  • memory (bool) – memory parameter

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • -

  • ValueError – if either acc is not >=0, focus is not >=0 or q is not >=0, +

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • +

  • ValueError – if either acc is not >=0, focus is not >=0 or q is not >=0, threshold is not in [1,gen] when gen!=0 and memory==false, or threshold is not in >=1 when gen!=0 and memory==true

@@ -580,18 +580,18 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, Best, Kernel, Oracle, dx, dp, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • Best (float), best fitness function value

    • -

  • Kernel (int), kernel size

    • -

  • Oracle (float), oracle parameter

    • -

  • dx (float), sum of the absolute value of the difference between the variables’ values of the best and worst solutions

    • -

  • dp (float), absolute value of the difference between the worst and best solutions’ penalty values

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • Best (float), best fitness function value

    • +

  • Kernel (int), kernel size

    • +

  • Oracle (float), oracle parameter

    • +

  • dx (float), sum of the absolute value of the difference between the variables’ values of the best and worst solutions

    • +

  • dp (float), absolute value of the difference between the worst and best solutions’ penalty values

Return type
-

list of tuple

+

list of tuple

Examples

@@ -629,7 +629,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -659,21 +659,21 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • ker (int) – kernel size

    • -

  • q (float) – convergence speed parameter

    • -

  • threshold (int) – threshold parameter

    • -

  • n_gen_mark (int) – std convergence speed parameter

    • -

  • evalstop (int) – evaluation stopping criterion

    • -

  • focus (float) – focus parameter

    • -

  • memory (bool) – memory parameter

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • ker (int) – kernel size

    • +

  • q (float) – convergence speed parameter

    • +

  • threshold (int) – threshold parameter

    • +

  • n_gen_mark (int) – std convergence speed parameter

    • +

  • evalstop (int) – evaluation stopping criterion

    • +

  • focus (float) – focus parameter

    • +

  • memory (bool) – memory parameter

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • -

  • ValueError – if either focus is < 0, threshold is not in [0,*gen*] when gen is > 0 and memory is False, or if threshold is not >=1 when gen is > 0 and memory is True

    • +

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • +

  • ValueError – if either focus is < 0, threshold is not in [0,*gen*] when gen is > 0 and memory is False, or if threshold is not >=1 when gen is > 0 and memory is True

@@ -688,14 +688,14 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, ideal_point, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

  • ideal_point (1D numpy array), the ideal point of the current population (cropped to max 5 dimensions only in the screen output)

Return type
-

list of tuple

+

list of tuple

Examples

@@ -727,7 +727,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -769,14 +769,14 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • -

  • ValueError – if gen is not >=3

    • +

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • +

  • ValueError – if gen is not >=3

@@ -791,15 +791,15 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, ideal_point, where:

    -

  • Gen (int), generation number

    • -

  • alpha (float), fitness function value of alpha

    • -

  • beta (float), fitness function value of beta

    • -

  • delta (float), fitness function value of delta

    • +

  • Gen (int), generation number

    • +

  • alpha (float), fitness function value of alpha

    • +

  • beta (float), fitness function value of beta

    • +

  • delta (float), fitness function value of delta

Return type
-

list of tuple

+

list of tuple

Examples

@@ -832,7 +832,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -847,15 +847,15 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • limit (int) – maximum number of trials for abandoning a source

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • limit (int) – maximum number of trials for abandoning a source

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen, limit or seed is negative or greater than an implementation-defined value

    • -

  • ValueError – if limit is not greater than 0

    • +

  • OverflowError – if gen, limit or seed is negative or greater than an implementation-defined value

    • +

  • ValueError – if limit is not greater than 0

@@ -870,15 +870,15 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, Current best, Best, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • Current best (float), the best fitness currently in the population

    • -

  • Best (float), the best fitness found

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • Current best (float), the best fitness currently in the population

    • +

  • Best (float), the best fitness found

Return type
-

list of tuple

+

list of tuple

Examples

@@ -911,7 +911,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -926,19 +926,19 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • F (float) – weight coefficient (dafault value is 0.8)

    • -

  • CR (float) – crossover probability (dafault value is 0.9)

    • -

  • variant (int) – mutation variant (dafault variant is 2: /rand/1/exp)

    • -

  • ftol (float) – stopping criteria on the f tolerance (default is 1e-6)

    • -

  • xtol (float) – stopping criteria on the x tolerance (default is 1e-6)

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • F (float) – weight coefficient (dafault value is 0.8)

    • +

  • CR (float) – crossover probability (dafault value is 0.9)

    • +

  • variant (int) – mutation variant (dafault variant is 2: /rand/1/exp)

    • +

  • ftol (float) – stopping criteria on the f tolerance (default is 1e-6)

    • +

  • xtol (float) – stopping criteria on the x tolerance (default is 1e-6)

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen, variant or seed is negative or greater than an implementation-defined value

    • -

  • ValueError – if F, CR are not in [0,1] or variant is not in [1, 10]

    • +

  • OverflowError – if gen, variant or seed is negative or greater than an implementation-defined value

    • +

  • ValueError – if F, CR are not in [0,1] or variant is not in [1, 10]

@@ -977,16 +977,16 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, Best, dx, df, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • Best (float), the best fitness function currently in the population

    • -

  • dx (float), the norm of the distance to the population mean of the mutant vectors

    • -

  • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • Best (float), the best fitness function currently in the population

    • +

  • dx (float), the norm of the distance to the population mean of the mutant vectors

    • +

  • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

Return type
-

list of tuple

+

list of tuple

Examples

@@ -1020,7 +1020,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -1035,13 +1035,13 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations to consider (each generation will compute the objective function once)

    • -

  • seed (int) – seed used by the internal random number generator

    • +

  • gen (int) – number of generations to consider (each generation will compute the objective function once)

    • +

  • seed (int) – seed used by the internal random number generator

Raises
    -

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • +

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -1061,16 +1061,16 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, Best, Improvement, Mutations

    -

  • Gen (int), generation.

    • -

  • Fevals (int), number of functions evaluation made.

    • -

  • Best (float), the best fitness function found so far.

    • -

  • Improvement (float), improvement made by the last mutation.

    • -

  • Mutations (float), number of mutated components for the decision vector.

    • +

  • Gen (int), generation.

    • +

  • Fevals (int), number of functions evaluation made.

    • +

  • Best (float), the best fitness function found so far.

    • +

  • Improvement (float), improvement made by the last mutation.

    • +

  • Mutations (float), number of mutated components for the decision vector.

Return type
-

list of tuple

+

list of tuple

Examples

@@ -1108,7 +1108,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -1164,22 +1164,22 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations.

    • -

  • cr (float) – crossover probability.

    • -

  • eta_c (float) – distribution index for sbx crossover. This parameter is inactive if other types of crossover are selected.

    • -

  • m (float) – mutation probability.

    • -

  • param_m (float) – distribution index (polynomial mutation), gaussian width (gaussian mutation) or inactive (uniform mutation)

    • -

  • param_s (float) – the number of best individuals to use in “truncated” selection or the size of the tournament in tournament selection.

    • -

  • crossover (str) – the crossover strategy. One of exponential, binomial, single or sbx

    • -

  • mutation (str) – the mutation strategy. One of gaussian, polynomial or uniform.

    • -

  • selection (str) – the selection strategy. One of tournament, “truncated”.

    • -

  • seed (int) – seed used by the internal random number generator

    • +

  • gen (int) – number of generations.

    • +

  • cr (float) – crossover probability.

    • +

  • eta_c (float) – distribution index for sbx crossover. This parameter is inactive if other types of crossover are selected.

    • +

  • m (float) – mutation probability.

    • +

  • param_m (float) – distribution index (polynomial mutation), gaussian width (gaussian mutation) or inactive (uniform mutation)

    • +

  • param_s (float) – the number of best individuals to use in “truncated” selection or the size of the tournament in tournament selection.

    • +

  • crossover (str) – the crossover strategy. One of exponential, binomial, single or sbx

    • +

  • mutation (str) – the mutation strategy. One of gaussian, polynomial or uniform.

    • +

  • selection (str) – the selection strategy. One of tournament, “truncated”.

    • +

  • seed (int) – seed used by the internal random number generator

Raises
    -

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • -

  • ValueError – if cr is not in [0,1], if eta_c is not in [1,100], if m is not in [0,1], input_f mutation +

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • +

  • ValueError – if cr is not in [0,1], if eta_c is not in [1,100], if m is not in [0,1], input_f mutation is not one of gaussian, uniform or polynomial, if selection not one of “roulette”, “truncated” or crossover is not one of exponential, binomial, sbx, single, if param_m is not in [0,1] and mutation is not polynomial, if mutation is not in [1,100] and mutation is polynomial

    • @@ -1201,14 +1201,14 @@

    Algorithms exposed from C++
    Returns

    at each logged epoch, the values Gen, Fevals, Best, Improvement

    -

    Gen (int), generation. -Fevals (int), number of functions evaluation made. -Best (float), the best fitness function found so far. -Improvement (float), improvement made by the last generation.

    +

    Gen (int), generation. +Fevals (int), number of functions evaluation made. +Best (float), the best fitness function found so far. +Improvement (float), improvement made by the last generation.

    Return type
    -

    list of tuple

    +

    list of tuple

    Examples

    @@ -1246,7 +1246,7 @@

    Algorithms exposed from C++

    the random seed of the population

Return type
-

int

+

int

@@ -1261,19 +1261,19 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • variant (int) – mutation variant (dafault variant is 2: /rand/1/exp)

    • -

  • variant_adptv (int) – F and CR parameter adaptation scheme to be used (one of 1..2)

    • -

  • ftol (float) – stopping criteria on the x tolerance (default is 1e-6)

    • -

  • xtol (float) – stopping criteria on the f tolerance (default is 1e-6)

    • -

  • memory (bool) – when true the adapted parameters CR anf F are not reset between successive calls to the evolve method

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • variant (int) – mutation variant (dafault variant is 2: /rand/1/exp)

    • +

  • variant_adptv (int) – F and CR parameter adaptation scheme to be used (one of 1..2)

    • +

  • ftol (float) – stopping criteria on the x tolerance (default is 1e-6)

    • +

  • xtol (float) – stopping criteria on the f tolerance (default is 1e-6)

    • +

  • memory (bool) – when true the adapted parameters CR anf F are not reset between successive calls to the evolve method

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen, variant, variant_adptv or seed is negative or greater than an implementation-defined value

    • -

  • ValueError – if variant is not in [1,18] or variant_adptv is not in [0,1]

    • +

  • OverflowError – if gen, variant, variant_adptv or seed is negative or greater than an implementation-defined value

    • +

  • ValueError – if variant is not in [1,18] or variant_adptv is not in [0,1]

@@ -1336,18 +1336,18 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, Best, F, CR, dx, df, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • Best (float), the best fitness function currently in the population

    • -

  • F (float), the value of the adapted paramter F used to create the best so far

    • -

  • CR (float), the value of the adapted paramter CR used to create the best so far

    • -

  • dx (float), the norm of the distance to the population mean of the mutant vectors

    • -

  • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • Best (float), the best fitness function currently in the population

    • +

  • F (float), the value of the adapted paramter F used to create the best so far

    • +

  • CR (float), the value of the adapted paramter CR used to create the best so far

    • +

  • dx (float), the norm of the distance to the population mean of the mutant vectors

    • +

  • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

Return type
-

list of tuple

+

list of tuple

Examples

@@ -1381,7 +1381,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -1397,19 +1397,19 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • +

  • gen (int) – number of generations

  • allowed_variants (array-like object) – allowed mutation variants, each one being a number in [1, 18]

    • -

  • variant_adptv (int) – F and CR parameter adaptation scheme to be used (one of 1..2)

    • -

  • ftol (float) – stopping criteria on the x tolerance (default is 1e-6)

    • -

  • xtol (float) – stopping criteria on the f tolerance (default is 1e-6)

    • -

  • memory (bool) – when true the adapted parameters CR anf F are not reset between successive calls to the evolve method

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • variant_adptv (int) – F and CR parameter adaptation scheme to be used (one of 1..2)

    • +

  • ftol (float) – stopping criteria on the x tolerance (default is 1e-6)

    • +

  • xtol (float) – stopping criteria on the f tolerance (default is 1e-6)

    • +

  • memory (bool) – when true the adapted parameters CR anf F are not reset between successive calls to the evolve method

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen, variant, variant_adptv or seed is negative or greater than an implementation-defined value

    • -

  • ValueError – if each id in variant_adptv is not in [1,18] or variant_adptv is not in [0,1]

    • +

  • OverflowError – if gen, variant, variant_adptv or seed is negative or greater than an implementation-defined value

    • +

  • ValueError – if each id in variant_adptv is not in [1,18] or variant_adptv is not in [0,1]

@@ -1472,19 +1472,19 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, Best, F, CR, Variant, dx, df, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • Best (float), the best fitness function currently in the population

    • -

  • F (float), the value of the adapted paramter F used to create the best so far

    • -

  • CR (float), the value of the adapted paramter CR used to create the best so far

    • -

  • Variant (int), the mutation variant used to create the best so far

    • -

  • dx (float), the norm of the distance to the population mean of the mutant vectors

    • -

  • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • Best (float), the best fitness function currently in the population

    • +

  • F (float), the value of the adapted paramter F used to create the best so far

    • +

  • CR (float), the value of the adapted paramter CR used to create the best so far

    • +

  • Variant (int), the mutation variant used to create the best so far

    • +

  • dx (float), the norm of the distance to the population mean of the mutant vectors

    • +

  • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

Return type
-

list of tuple

+

list of tuple

Examples

@@ -1518,7 +1518,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -1533,23 +1533,23 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • cc (float) – backward time horizon for the evolution path (by default is automatically assigned)

    • -

  • cs (float) – makes partly up for the small variance loss in case the indicator is zero (by default is automatically assigned)

    • -

  • c1 (float) – learning rate for the rank-one update of the covariance matrix (by default is automatically assigned)

    • -

  • cmu (float) – learning rate for the rank-mu update of the covariance matrix (by default is automatically assigned)

    • -

  • sigma0 (float) – initial step-size

    • -

  • ftol (float) – stopping criteria on the x tolerance

    • -

  • xtol (float) – stopping criteria on the f tolerance

    • -

  • memory (bool) – when true the adapted parameters are not reset between successive calls to the evolve method

    • -

  • force_bounds (bool) – when true the box bounds are enforced. The fitness will never be called outside the bounds but the covariance matrix adaptation mechanism will worsen

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • cc (float) – backward time horizon for the evolution path (by default is automatically assigned)

    • +

  • cs (float) – makes partly up for the small variance loss in case the indicator is zero (by default is automatically assigned)

    • +

  • c1 (float) – learning rate for the rank-one update of the covariance matrix (by default is automatically assigned)

    • +

  • cmu (float) – learning rate for the rank-mu update of the covariance matrix (by default is automatically assigned)

    • +

  • sigma0 (float) – initial step-size

    • +

  • ftol (float) – stopping criteria on the x tolerance

    • +

  • xtol (float) – stopping criteria on the f tolerance

    • +

  • memory (bool) – when true the adapted parameters are not reset between successive calls to the evolve method

    • +

  • force_bounds (bool) – when true the box bounds are enforced. The fitness will never be called outside the bounds but the covariance matrix adaptation mechanism will worsen

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen is negative or greater than an implementation-defined value

    • -

  • ValueError – if cc, cs, c1, cmu are not in [0,1] or -1

    • +

  • OverflowError – if gen is negative or greater than an implementation-defined value

    • +

  • ValueError – if cc, cs, c1, cmu are not in [0,1] or -1

@@ -1564,17 +1564,17 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, Best, dx, df, sigma, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • Best (float), the best fitness function currently in the population

    • -

  • dx (float), the norm of the distance to the population mean of the mutant vectors

    • -

  • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

    • -

  • sigma (float), the current step-size

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • Best (float), the best fitness function currently in the population

    • +

  • dx (float), the norm of the distance to the population mean of the mutant vectors

    • +

  • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

    • +

  • sigma (float), the current step-size

Return type
-

list of tuple

+

list of tuple

Examples

@@ -1608,7 +1608,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -1638,23 +1638,23 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • weight_generation (str) – method used to generate the weights, one of “grid”, “low discrepancy” or “random”

    • -

  • decomposition (str) – method used to decompose the objectives, one of “tchebycheff”, “weighted” or “bi”

    • -

  • neighbours (int) – size of the weight’s neighborhood

    • -

  • CR (float) – crossover parameter in the Differential Evolution operator

    • -

  • F (float) – parameter for the Differential Evolution operator

    • -

  • eta_m (float) – distribution index used by the polynomial mutation

    • -

  • realb (float) – chance that the neighbourhood is considered at each generation, rather than the whole population (only if preserve_diversity is true)

    • -

  • limit (int) – maximum number of copies reinserted in the population (only if m_preserve_diversity is true)

    • -

  • preserve_diversity (bool) – when true activates diversity preservation mechanisms

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • weight_generation (str) – method used to generate the weights, one of “grid”, “low discrepancy” or “random”

    • +

  • decomposition (str) – method used to decompose the objectives, one of “tchebycheff”, “weighted” or “bi”

    • +

  • neighbours (int) – size of the weight’s neighborhood

    • +

  • CR (float) – crossover parameter in the Differential Evolution operator

    • +

  • F (float) – parameter for the Differential Evolution operator

    • +

  • eta_m (float) – distribution index used by the polynomial mutation

    • +

  • realb (float) – chance that the neighbourhood is considered at each generation, rather than the whole population (only if preserve_diversity is true)

    • +

  • limit (int) – maximum number of copies reinserted in the population (only if m_preserve_diversity is true)

    • +

  • preserve_diversity (bool) – when true activates diversity preservation mechanisms

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen, neighbours, seed or limit are negative or greater than an implementation-defined value

    • -

  • ValueError – if either decomposition is not one of ‘tchebycheff’, ‘weighted’ or ‘bi’, +

  • OverflowError – if gen, neighbours, seed or limit are negative or greater than an implementation-defined value

    • +

  • ValueError – if either decomposition is not one of ‘tchebycheff’, ‘weighted’ or ‘bi’, weight_generation is not one of ‘random’, ‘low discrepancy’ or ‘grid’, CR or F or realb are not in [0.,1.] or eta_m is negative, if neighbours is not >=2

@@ -1671,15 +1671,15 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, ADR, ideal_point, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • ADF (float), Average Decomposed Fitness, that is the average across all decomposed problem of the single objective decomposed fitness along the corresponding direction

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • ADF (float), Average Decomposed Fitness, that is the average across all decomposed problem of the single objective decomposed fitness along the corresponding direction

  • ideal_point (array), the ideal point of the current population (cropped to max 5 dimensions only in the screen output)

Return type
-

list of tuple

+

list of tuple

Examples

@@ -1712,7 +1712,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -1727,23 +1727,23 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • weight_generation (str) – method used to generate the weights, one of “grid”, “low discrepancy” or “random”

    • -

  • decomposition (str) – method used to decompose the objectives, one of “tchebycheff”, “weighted” or “bi”

    • -

  • neighbours (int) – size of the weight’s neighborhood

    • -

  • CR (float) – crossover parameter in the Differential Evolution operator

    • -

  • F (float) – parameter for the Differential Evolution operator

    • -

  • eta_m (float) – distribution index used by the polynomial mutation

    • -

  • realb (float) – chance that the neighbourhood is considered at each generation, rather than the whole population (only if preserve_diversity is true)

    • -

  • limit (int) – maximum number of copies reinserted in the population (only if m_preserve_diversity is true)

    • -

  • preserve_diversity (bool) – when true activates diversity preservation mechanisms

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • weight_generation (str) – method used to generate the weights, one of “grid”, “low discrepancy” or “random”

    • +

  • decomposition (str) – method used to decompose the objectives, one of “tchebycheff”, “weighted” or “bi”

    • +

  • neighbours (int) – size of the weight’s neighborhood

    • +

  • CR (float) – crossover parameter in the Differential Evolution operator

    • +

  • F (float) – parameter for the Differential Evolution operator

    • +

  • eta_m (float) – distribution index used by the polynomial mutation

    • +

  • realb (float) – chance that the neighbourhood is considered at each generation, rather than the whole population (only if preserve_diversity is true)

    • +

  • limit (int) – maximum number of copies reinserted in the population (only if m_preserve_diversity is true)

    • +

  • preserve_diversity (bool) – when true activates diversity preservation mechanisms

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen, neighbours, seed or limit are negative or greater than an implementation-defined value

    • -

  • ValueError – if either decomposition is not one of ‘tchebycheff’, ‘weighted’ or ‘bi’, +

  • OverflowError – if gen, neighbours, seed or limit are negative or greater than an implementation-defined value

    • +

  • ValueError – if either decomposition is not one of ‘tchebycheff’, ‘weighted’ or ‘bi’, weight_generation is not one of ‘random’, ‘low discrepancy’ or ‘grid’, CR or F or realb are not in [0.,1.] or eta_m is negative, if neighbours is not >=2

@@ -1760,15 +1760,15 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, ADR, ideal_point, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • ADF (float), Average Decomposed Fitness, that is the average across all decomposed problem of the single objective decomposed fitness along the corresponding direction

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • ADF (float), Average Decomposed Fitness, that is the average across all decomposed problem of the single objective decomposed fitness along the corresponding direction

  • ideal_point (array), the ideal point of the current population (cropped to max 5 dimensions only in the screen output)

Return type
-

list of tuple

+

list of tuple

Examples

@@ -1801,7 +1801,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -1831,16 +1831,16 @@

Algorithms exposed from C++
Parameters
    -

  • max_fevals (int) – maximum number of function evaluation

    • -

  • start_range (float) – start range (dafault value is .1)

    • -

  • stop_range (float) – stop range (dafault value is .01)

    • -

  • reduction_coeff (float) – range reduction coefficient (dafault value is .5)

    • +

  • max_fevals (int) – maximum number of function evaluation

    • +

  • start_range (float) – start range (dafault value is .1)

    • +

  • stop_range (float) – stop range (dafault value is .01)

    • +

  • reduction_coeff (float) – range reduction coefficient (dafault value is .5)

Raises
    -

  • OverflowError – if max_fevals is negative or greater than an implementation-defined value

    • -

  • ValueError – if start_range is not in (0, 1], if stop_range is not in (start_range, 1] or if reduction_coeff is not in (0,1)

    • +

  • OverflowError – if max_fevals is negative or greater than an implementation-defined value

    • +

  • ValueError – if start_range is not in (0, 1], if stop_range is not in (start_range, 1] or if reduction_coeff is not in (0,1)

@@ -1856,14 +1856,14 @@

Algorithms exposed from C++Returns

at each logged epoch, the values``Fevals``, Best, Range, where:

    -

  • Fevals (int), number of functions evaluation made

    • -

  • Best (float), the best fitness function currently in the population

    • -

  • Range (float), the range used to vary the chromosome (relative to the box bounds width)

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • Best (float), the best fitness function currently in the population

    • +

  • Range (float), the range used to vary the chromosome (relative to the box bounds width)

Return type
-

list of tuple

+

list of tuple

Examples

@@ -1929,13 +1929,13 @@

Algorithms exposed from C++

the individual replacement policy or index

Return type
-

int or str

+

int or str

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • -

  • ValueError – if the attribute is set to an invalid string

    • -

  • TypeError – if the attribute is set to a value of an invalid type

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • ValueError – if the attribute is set to an invalid string

    • +

  • TypeError – if the attribute is set to a value of an invalid type

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -1964,13 +1964,13 @@

Algorithms exposed from C++

the individual selection policy or index

Return type
-

int or str

+

int or str

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • -

  • ValueError – if the attribute is set to an invalid string

    • -

  • TypeError – if the attribute is set to a value of an invalid type

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • ValueError – if the attribute is set to an invalid string

    • +

  • TypeError – if the attribute is set to a value of an invalid type

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -1984,13 +1984,13 @@

Algorithms exposed from C++"random" selection/replacement policies.

Parameters
-

seed (int) – the value that will be used to seed the random number generator used by the "random" +

seed (int) – the value that will be used to seed the random number generator used by the "random" election/replacement policies (see selection and replacement)

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -2008,20 +2008,20 @@

Algorithms exposed from C++
Parameters
    -

  • Ts (float) – starting temperature

    • -

  • Tf (float) – final temperature

    • -

  • n_T_adj (int) – number of temperature adjustments in the annealing schedule

    • -

  • n_range_adj (int) – number of adjustments of the search range performed at a constant temperature

    • -

  • bin_size (int) – number of mutations that are used to compute the acceptance rate

    • -

  • start_range (float) – starting range for mutating the decision vector

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • Ts (float) – starting temperature

    • +

  • Tf (float) – final temperature

    • +

  • n_T_adj (int) – number of temperature adjustments in the annealing schedule

    • +

  • n_range_adj (int) – number of adjustments of the search range performed at a constant temperature

    • +

  • bin_size (int) – number of mutations that are used to compute the acceptance rate

    • +

  • start_range (float) – starting range for mutating the decision vector

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if n_T_adj, n_range_adj or bin_size are negative or greater than an implementation-defined value

    • -

  • ValueError – if Ts is not in (0, inf), if Tf is not in (0, inf), if Tf > Ts or if start_range is not in (0,1]

    • -

  • ValueError – if n_T_adj is not strictly positive or if n_range_adj is not strictly positive

    • +

  • OverflowError – if n_T_adj, n_range_adj or bin_size are negative or greater than an implementation-defined value

    • +

  • ValueError – if Ts is not in (0, inf), if Tf is not in (0, inf), if Tf > Ts or if start_range is not in (0,1]

    • +

  • ValueError – if n_T_adj is not strictly positive or if n_range_adj is not strictly positive

@@ -2038,16 +2038,16 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Fevals, Best, Current, Mean range, Temperature, where:

    -

  • Fevals (int), number of functions evaluation made

    • -

  • Best (float), the best fitness function found so far

    • -

  • Current (float), last fitness sampled

    • -

  • Mean range (float), the mean search range across the decision vector components (relative to the box bounds width)

    • -

  • Temperature (float), the current temperature

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • Best (float), the best fitness function found so far

    • +

  • Current (float), last fitness sampled

    • +

  • Mean range (float), the mean search range across the decision vector components (relative to the box bounds width)

    • +

  • Temperature (float), the current temperature

Return type
-

list of tuple

+

list of tuple

Examples

@@ -2095,7 +2095,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -2121,13 +2121,13 @@

Algorithms exposed from C++

the individual replacement policy or index

Return type
-

int or str

+

int or str

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • -

  • ValueError – if the attribute is set to an invalid string

    • -

  • TypeError – if the attribute is set to a value of an invalid type

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • ValueError – if the attribute is set to an invalid string

    • +

  • TypeError – if the attribute is set to a value of an invalid type

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -2156,13 +2156,13 @@

Algorithms exposed from C++

the individual selection policy or index

Return type
-

int or str

+

int or str

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • -

  • ValueError – if the attribute is set to an invalid string

    • -

  • TypeError – if the attribute is set to a value of an invalid type

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • ValueError – if the attribute is set to an invalid string

    • +

  • TypeError – if the attribute is set to a value of an invalid type

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -2176,13 +2176,13 @@

Algorithms exposed from C++"random" selection/replacement policies.

Parameters
-

seed (int) – the value that will be used to seed the random number generator used by the "random" +

seed (int) – the value that will be used to seed the random number generator used by the "random" election/replacement policies (see selection and replacement)

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -2200,23 +2200,23 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • omega (float) – inertia weight (or constriction factor)

    • -

  • eta1 (float) – social component

    • -

  • eta2 (float) – cognitive component

    • -

  • max_vel (float) – maximum allowed particle velocities (normalized with respect to the bounds width)

    • -

  • variant (int) – algorithmic variant

    • -

  • neighb_type (int) – swarm topology (defining each particle’s neighbours)

    • -

  • neighb_param (int) – topology parameter (defines how many neighbours to consider)

    • -

  • memory (bool) – when true the velocities are not reset between successive calls to the evolve method

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • omega (float) – inertia weight (or constriction factor)

    • +

  • eta1 (float) – social component

    • +

  • eta2 (float) – cognitive component

    • +

  • max_vel (float) – maximum allowed particle velocities (normalized with respect to the bounds width)

    • +

  • variant (int) – algorithmic variant

    • +

  • neighb_type (int) – swarm topology (defining each particle’s neighbours)

    • +

  • neighb_param (int) – topology parameter (defines how many neighbours to consider)

    • +

  • memory (bool) – when true the velocities are not reset between successive calls to the evolve method

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen or seed is negative or greater than an implementation-defined value

    • -

  • ValueError – if omega is not in the [0,1] interval, if eta1, eta2 are not in the [0,4] interval, if max_vel is not in ]0,1]

    • -

  • ValueError – variant is not one of 1 .. 6, if neighb_type is not one of 1 .. 4 or if neighb_param is zero

    • +

  • OverflowError – if gen or seed is negative or greater than an implementation-defined value

    • +

  • ValueError – if omega is not in the [0,1] interval, if eta1, eta2 are not in the [0,4] interval, if max_vel is not in ]0,1]

    • +

  • ValueError – variant is not one of 1 .. 6, if neighb_type is not one of 1 .. 4 or if neighb_param is zero

@@ -2265,17 +2265,17 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, gbest, Mean Vel., Mean lbest, Avg. Dist., where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • gbest (float), the best fitness function found so far by the the swarm

    • -

  • Mean Vel. (float), the average particle velocity (normalized)

    • -

  • Mean lbest (float), the average fitness of the current particle locations

    • -

  • Avg. Dist. (float), the average distance between particles (normalized)

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • gbest (float), the best fitness function found so far by the the swarm

    • +

  • Mean Vel. (float), the average particle velocity (normalized)

    • +

  • Mean lbest (float), the average fitness of the current particle locations

    • +

  • Avg. Dist. (float), the average distance between particles (normalized)

Return type
-

list of tuple

+

list of tuple

Examples

@@ -2313,7 +2313,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -2330,23 +2330,23 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • omega (float) – inertia weight (or constriction factor)

    • -

  • eta1 (float) – social component

    • -

  • eta2 (float) – cognitive component

    • -

  • max_vel (float) – maximum allowed particle velocities (normalized with respect to the bounds width)

    • -

  • variant (int) – algorithmic variant

    • -

  • neighb_type (int) – swarm topology (defining each particle’s neighbours)

    • -

  • neighb_param (int) – topology parameter (defines how many neighbours to consider)

    • -

  • memory (bool) – when true the velocities are not reset between successive calls to the evolve method

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • omega (float) – inertia weight (or constriction factor)

    • +

  • eta1 (float) – social component

    • +

  • eta2 (float) – cognitive component

    • +

  • max_vel (float) – maximum allowed particle velocities (normalized with respect to the bounds width)

    • +

  • variant (int) – algorithmic variant

    • +

  • neighb_type (int) – swarm topology (defining each particle’s neighbours)

    • +

  • neighb_param (int) – topology parameter (defines how many neighbours to consider)

    • +

  • memory (bool) – when true the velocities are not reset between successive calls to the evolve method

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen or seed is negative or greater than an implementation-defined value

    • -

  • ValueError – if omega is not in the [0,1] interval, if eta1, eta2 are not in the [0,1] interval, if max_vel is not in ]0,1]

    • -

  • ValueError – variant is not one of 1 .. 6, if neighb_type is not one of 1 .. 4 or if neighb_param is zero

    • +

  • OverflowError – if gen or seed is negative or greater than an implementation-defined value

    • +

  • ValueError – if omega is not in the [0,1] interval, if eta1, eta2 are not in the [0,1] interval, if max_vel is not in ]0,1]

    • +

  • ValueError – variant is not one of 1 .. 6, if neighb_type is not one of 1 .. 4 or if neighb_param is zero

@@ -2395,17 +2395,17 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, gbest, Mean Vel., Mean lbest, Avg. Dist., where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • -

  • gbest (float), the best fitness function found so far by the the swarm

    • -

  • Mean Vel. (float), the average particle velocity (normalized)

    • -

  • Mean lbest (float), the average fitness of the current particle locations

    • -

  • Avg. Dist. (float), the average distance between particles (normalized)

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

    • +

  • gbest (float), the best fitness function found so far by the the swarm

    • +

  • Mean Vel. (float), the average particle velocity (normalized)

    • +

  • Mean lbest (float), the average fitness of the current particle locations

    • +

  • Avg. Dist. (float), the average distance between particles (normalized)

Return type
-

list of tuple

+

list of tuple

Examples

@@ -2443,7 +2443,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -2472,18 +2472,18 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations

    • -

  • cr (float) – crossover probability

    • -

  • eta_c (float) – distribution index for crossover

    • -

  • m (float) – mutation probability

    • -

  • eta_m (float) – distribution index for mutation

    • -

  • seed (int) – seed used by the internal random number generator (default is random)

    • +

  • gen (int) – number of generations

    • +

  • cr (float) – crossover probability

    • +

  • eta_c (float) – distribution index for crossover

    • +

  • m (float) – mutation probability

    • +

  • eta_m (float) – distribution index for mutation

    • +

  • seed (int) – seed used by the internal random number generator (default is random)

Raises
    -

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • -

  • ValueError – if either cr is not in [0,1[, eta_c is not in [0,100[, m is not in [0,1], or +

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • +

  • ValueError – if either cr is not in [0,1[, eta_c is not in [0,100[, m is not in [0,1], or eta_m is not in [0,100[

@@ -2499,14 +2499,14 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, ideal_point, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

  • ideal_point (1D numpy array), the ideal point of the current population (cropped to max 5 dimensions only in the screen output)

Return type
-

list of tuple

+

list of tuple

Examples

@@ -2538,7 +2538,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -2568,21 +2568,21 @@

Algorithms exposed from C++
Parameters
    -

  • gen (int) – number of generations to evolve

    • -

  • omega (float) – particles’ inertia weight

    • -

  • c1 (float) – magnitude of the force, applied to the particle’s velocity, in the direction of its previous best position.

    • -

  • c2 (float) – magnitude of the force, applied to the particle’s velocity, in the direction of its global best position.

    • -

  • chi (float) – velocity scaling factor.

    • -

  • v_coeff (float) – velocity coefficient.

    • -

  • leader_selection_range (int) – leader selection range.

    • -

  • diversity_mechanism (str) – leader selection range.

    • -

  • memory (bool) – memory parameter.

    • +

  • gen (int) – number of generations to evolve

    • +

  • omega (float) – particles’ inertia weight

    • +

  • c1 (float) – magnitude of the force, applied to the particle’s velocity, in the direction of its previous best position.

    • +

  • c2 (float) – magnitude of the force, applied to the particle’s velocity, in the direction of its global best position.

    • +

  • chi (float) – velocity scaling factor.

    • +

  • v_coeff (float) – velocity coefficient.

    • +

  • leader_selection_range (int) – leader selection range.

    • +

  • diversity_mechanism (str) – leader selection range.

    • +

  • memory (bool) – memory parameter.

Raises
    -

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • -

  • ValueError – if either omega < 0 or c1 <= 0 or c2 <= 0 or chi <= 0, if omega > 1,

    • +

  • OverflowError – if gen or seed are negative or greater than an implementation-defined value

    • +

  • ValueError – if either omega < 0 or c1 <= 0 or c2 <= 0 or chi <= 0, if omega > 1,

  • if v_coeff <= 0 or v_coeff > 1, if leader_selection_range > 100, if diversity_mechanism != "crowding distance", or != "niche count", or != "max min" –

@@ -2598,14 +2598,14 @@

Algorithms exposed from C++Returns

at each logged epoch, the values Gen, Fevals, ideal_point, where:

    -

  • Gen (int), generation number

    • -

  • Fevals (int), number of functions evaluation made

    • +

  • Gen (int), generation number

    • +

  • Fevals (int), number of functions evaluation made

  • ideal_point (1D numpy array), the ideal point of the current population (cropped to max 5 dimensions only in the screen output)

Return type
-

list of tuple

+

list of tuple

Examples

@@ -2637,7 +2637,7 @@

Algorithms exposed from C++

the random seed of the population

Return type
-

int

+

int

@@ -2694,17 +2694,17 @@

Algorithms exposed from C++Parameters

  • algo – an algorithm or a user-defined algorithm, either C++ or Python (if -algo is None, a compass_search algorithm will be used in its stead)

    • -

  • stop (int) – consecutive runs of the inner algorithm that need to result in no improvement for +algo is None, a compass_search algorithm will be used in its stead)

    • +

  • stop (int) – consecutive runs of the inner algorithm that need to result in no improvement for mbh to stop

    • -

  • perturb (float or array-like object) – the perturbation to be applied to each component

    • -

  • seed (int) – seed used by the internal random number generator (if seed is None, a +

  • perturb (float or array-like object) – the perturbation to be applied to each component

    • +

  • seed (int) – seed used by the internal random number generator (if seed is None, a randomly-generated value will be used in its stead)

Raises
    -

  • ValueError – if perturb (or one of its components, if perturb is an array) is not in the +

  • ValueError – if perturb (or one of its components, if perturb is an array) is not in the (0,1] range

  • unspecified – any exception thrown by the constructor of pygmo.algorithm, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function @@ -2722,16 +2722,16 @@

    Algorithms exposed from C++Returns

    at each call of the inner algorithm, the values Fevals, Best, Violated, Viol. Norm and Trial, where:

      -

    • Fevals (int), the number of fitness evaluations made

      • -

    • Best (float), the objective function of the best fitness currently in the population

      • -

    • Violated (int), the number of constraints currently violated by the best solution

      • -

    • Viol. Norm (float), the norm of the violation (discounted already by the constraints tolerance)

      • -

    • Trial (int), the trial number (which will determine the algorithm stop)

      • +

    • Fevals (int), the number of fitness evaluations made

      • +

    • Best (float), the objective function of the best fitness currently in the population

      • +

    • Violated (int), the number of constraints currently violated by the best solution

      • +

    • Viol. Norm (float), the norm of the violation (discounted already by the constraints tolerance)

      • +

    • Trial (int), the trial number (which will determine the algorithm stop)

    Return type
    -

    list of tuple

    +

    list of tuple

    Examples

    @@ -2777,7 +2777,7 @@

    Algorithms exposed from C++

    the seed value

Return type
-

int

+

int

@@ -2791,7 +2791,7 @@

Algorithms exposed from C++

the verbosity level

Return type
-

int

+

int

@@ -2892,16 +2892,16 @@

Algorithms exposed from C++
Parameters
    -

  • iter (int) – number of iterations (i.e., calls to the inner algorithm evolve)

    • +

  • iter (int) – number of iterations (i.e., calls to the inner algorithm evolve)

  • algo – an algorithm or a user-defined algorithm, either C++ or Python (if -algo is None, a de algorithm will be used in its stead)

    • -

  • seed (int) – seed used by the internal random number generator (if seed is None, a +algo is None, a de algorithm will be used in its stead)

    • +

  • seed (int) – seed used by the internal random number generator (if seed is None, a randomly-generated value will be used in its stead)

Raises
    -

  • ValueError – if iters is negative or greater than an implementation-defined value

    • +

  • ValueError – if iters is negative or greater than an implementation-defined value

  • unspecified – any exception thrown by the constructor of pygmo.algorithm, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    • @@ -2919,18 +2919,18 @@

    Algorithms exposed from C++

    at each call of the inner algorithm, the values Iters, Fevals, Best, Infeasibility, Violated, Viol. Norm and N. Feasible, where:

      -

    • Iters (int), the number of iterations made (i.e. calls to the evolve method of the inner algorithm)

      • -

    • Fevals (int), the number of fitness evaluations made

      • -

    • Best (float), the objective function of the best fitness currently in the population

      • -

    • Infeasibility (float), the aggregated (and normalized) infeasibility value of Best

      • -

    • Violated (int), the number of constraints currently violated by the best solution

      • -

    • Viol. Norm (float), the norm of the violation (discounted already by the constraints tolerance)

      • -

    • N. Feasible (int), the number of feasible individuals currently in the population.

      • +

    • Iters (int), the number of iterations made (i.e. calls to the evolve method of the inner algorithm)

      • +

    • Fevals (int), the number of fitness evaluations made

      • +

    • Best (float), the objective function of the best fitness currently in the population

      • +

    • Infeasibility (float), the aggregated (and normalized) infeasibility value of Best

      • +

    • Violated (int), the number of constraints currently violated by the best solution

      • +

    • Viol. Norm (float), the norm of the violation (discounted already by the constraints tolerance)

      • +

    • N. Feasible (int), the number of feasible individuals currently in the population.

Return type
-

list of tuple

+

list of tuple

Examples

@@ -3104,12 +3104,12 @@

Algorithms exposed from C++
Parameters
-

solver (str) – the name of the NLopt algorithm that will be used by this nlopt object

+

solver (str) – the name of the NLopt algorithm that will be used by this nlopt object

Raises
    -

  • RuntimeError – if the NLopt version is not at least 2

    • -

  • ValueError – if solver is not one of the allowed algorithm names

    • +

  • RuntimeError – if the NLopt version is not at least 2

    • +

  • ValueError – if solver is not one of the allowed algorithm names

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3174,11 +3174,11 @@

Algorithms exposed from C++

the value of the ftol_abs stopping criterion

Return type
-

float

+

float

Raises
    -

  • ValueError – if, when setting this property, a NaN is passed

    • +

  • ValueError – if, when setting this property, a NaN is passed

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3198,11 +3198,11 @@

Algorithms exposed from C++

the value of the ftol_rel stopping criterion

Return type
-

float

+

float

Raises
    -

  • ValueError – if, when setting this property, a NaN is passed

    • +

  • ValueError – if, when setting this property, a NaN is passed

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3219,7 +3219,7 @@

Algorithms exposed from C++

the NLopt return code for the last optimisation run, or NLOPT_SUCCESS if no optimisations have been run yet

Return type
-

int

+

int

Raises

unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -3245,7 +3245,7 @@

Algorithms exposed from C++

the optimisation log

Return type
-

list

+

list

Raises

unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -3263,7 +3263,7 @@

Algorithms exposed from C++

the name of the NLopt solver used during construction

Return type
-

str

+

str

Raises

unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -3316,7 +3316,7 @@

Algorithms exposed from C++

the value of the maxeval stopping criterion

Return type
-

int

+

int

Raises

unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -3336,7 +3336,7 @@

Algorithms exposed from C++

the value of the maxtime stopping criterion

Return type
-

float

+

float

Raises

unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -3366,13 +3366,13 @@

Algorithms exposed from C++

the individual replacement policy or index

Return type
-

int or str

+

int or str

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • -

  • ValueError – if the attribute is set to an invalid string

    • -

  • TypeError – if the attribute is set to a value of an invalid type

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • ValueError – if the attribute is set to an invalid string

    • +

  • TypeError – if the attribute is set to a value of an invalid type

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3401,13 +3401,13 @@

Algorithms exposed from C++

the individual selection policy or index

Return type
-

int or str

+

int or str

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • -

  • ValueError – if the attribute is set to an invalid string

    • -

  • TypeError – if the attribute is set to a value of an invalid type

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • ValueError – if the attribute is set to an invalid string

    • +

  • TypeError – if the attribute is set to a value of an invalid type

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3421,13 +3421,13 @@

Algorithms exposed from C++"random" selection/replacement policies.

Parameters
-

seed (int) – the value that will be used to seed the random number generator used by the "random" +

seed (int) – the value that will be used to seed the random number generator used by the "random" election/replacement policies (see selection and replacement)

Raises
    -

  • OverflowError – if the attribute is set to an integer which is negative or too large

    • +

  • OverflowError – if the attribute is set to an integer which is negative or too large

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3447,11 +3447,11 @@

Algorithms exposed from C++

the value of the stopval stopping criterion

Return type
-

float

+

float

Raises
    -

  • ValueError – if, when setting this property, a NaN is passed

    • +

  • ValueError – if, when setting this property, a NaN is passed

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3471,11 +3471,11 @@

Algorithms exposed from C++

the value of the xtol_abs stopping criterion

Return type
-

float

+

float

Raises
    -

  • ValueError – if, when setting this property, a NaN is passed

    • +

  • ValueError – if, when setting this property, a NaN is passed

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3495,11 +3495,11 @@

Algorithms exposed from C++

the value of the xtol_rel stopping criterion

Return type
-

float

+

float

Raises
    -

  • ValueError – if, when setting this property, a NaN is passed

    • +

  • ValueError – if, when setting this property, a NaN is passed

  • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

@@ -3531,9 +3531,9 @@

Algorithms exposed from C++ -

  • string options (i.e., the type of the option is str),

    • -

  • integer options (i.e., the type of the option is int),

    • -

  • numeric options (i.e., the type of the option is float).

    • +

  • string options (i.e., the type of the option is str),

    • +

  • integer options (i.e., the type of the option is int),

    • +

  • numeric options (i.e., the type of the option is float).

    • The full list of options is available on the Ipopt website. pygmo.ipopt allows to configure any Ipopt option via methods such as set_string_options(), @@ -3621,7 +3621,7 @@

    Algorithms exposed from C++

    a name-value dictionary of optimisation integer options

    Return type
    -

    dict of str-int pairs

    +

    dict of str-int pairs

    Examples

    @@ -3643,7 +3643,7 @@

    Algorithms exposed from C++

    the Ipopt return code for the last optimisation run, or Ipopt::Solve_Succeeded if no optimisations have been run yet

    Return type
    -

    int

    +

    int

    Raises

    unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -3676,7 +3676,7 @@

    Algorithms exposed from C++

    the optimisation log

    Return type
    -

    list

    +

    list

    Raises

    unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -3707,7 +3707,7 @@

    Algorithms exposed from C++

    a name-value dictionary of optimisation numeric options

    Return type
    -

    dict of str-float pairs

    +

    dict of str-float pairs

    Examples

    @@ -3729,7 +3729,7 @@

    Algorithms exposed from C++

    a name-value dictionary of optimisation string options

    Return type
    -

    dict of str-str pairs

    +

    dict of str-str pairs

    Examples

    @@ -3763,13 +3763,13 @@

    Algorithms exposed from C++

    the individual replacement policy or index

    Return type
    -

    int or str

    +

    int or str

    Raises
      -

    • OverflowError – if the attribute is set to an integer which is negative or too large

      • -

    • ValueError – if the attribute is set to an invalid string

      • -

    • TypeError – if the attribute is set to a value of an invalid type

      • +

    • OverflowError – if the attribute is set to an integer which is negative or too large

      • +

    • ValueError – if the attribute is set to an invalid string

      • +

    • TypeError – if the attribute is set to a value of an invalid type

    • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    @@ -3849,13 +3849,13 @@

    Algorithms exposed from C++

    the individual selection policy or index

    Return type
    -

    int or str

    +

    int or str

    Raises
      -

    • OverflowError – if the attribute is set to an integer which is negative or too large

      • -

    • ValueError – if the attribute is set to an invalid string

      • -

    • TypeError – if the attribute is set to a value of an invalid type

      • +

    • OverflowError – if the attribute is set to an integer which is negative or too large

      • +

    • ValueError – if the attribute is set to an invalid string

      • +

    • TypeError – if the attribute is set to a value of an invalid type

    • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    @@ -3872,8 +3872,8 @@

    Algorithms exposed from C++
    Parameters
      -

    • name (str) – the name of the option

      • -

    • value (int) – the value of the option

      • +

    • name (str) – the name of the option

      • +

    • value (int) – the value of the option

    Raises
    @@ -3910,7 +3910,7 @@

    Algorithms exposed from C++
    Parameters
    -

    opts (dict of str-int pairs) – the name-value map that will be used to set the options

    +

    opts (dict of str-int pairs) – the name-value map that will be used to set the options

    Raises

    unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -3946,8 +3946,8 @@

    Algorithms exposed from C++
    Parameters
      -

    • name (str) – the name of the option

      • -

    • value (float) – the value of the option

      • +

    • name (str) – the name of the option

      • +

    • value (float) – the value of the option

    Raises
    @@ -3984,7 +3984,7 @@

    Algorithms exposed from C++
    Parameters
    -

    opts (dict of str-float pairs) – the name-value map that will be used to set the options

    +

    opts (dict of str-float pairs) – the name-value map that will be used to set the options

    Raises

    unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -4017,13 +4017,13 @@

    Algorithms exposed from C++"random" selection/replacement policies.

    Parameters
    -

    seed (int) – the value that will be used to seed the random number generator used by the "random" +

    seed (int) – the value that will be used to seed the random number generator used by the "random" election/replacement policies (see selection and replacement)

    Raises
      -

    • OverflowError – if the attribute is set to an integer which is negative or too large

      • +

    • OverflowError – if the attribute is set to an integer which is negative or too large

    • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    @@ -4040,8 +4040,8 @@

    Algorithms exposed from C++
    Parameters
      -

    • name (str) – the name of the option

      • -

    • value (str) – the value of the option

      • +

    • name (str) – the name of the option

      • +

    • value (str) – the value of the option

    Raises
    @@ -4078,7 +4078,7 @@

    Algorithms exposed from C++
    Parameters
    -

    opts (dict of str-str pairs) – the name-value map that will be used to set the options

    +

    opts (dict of str-str pairs) – the name-value map that will be used to set the options

    Raises

    unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -4138,19 +4138,19 @@

    Algorithms exposed from C++
    Parameters
      -

    • gen (int) – number of generations to consider (each generation will compute the objective function once)

      • -

    • phmcr (float) – probability of choosing from memory (similar to a crossover probability)

      • -

    • ppar_min (float) – minimum pitch adjustment rate. (similar to a mutation rate)

      • -

    • ppar_max (float) – maximum pitch adjustment rate. (similar to a mutation rate)

      • -

    • bw_min (float) – minimum distance bandwidth. (similar to a mutation width)

      • -

    • bw_max (float) – maximum distance bandwidth. (similar to a mutation width)

      • -

    • seed (int) – seed used by the internal random number generator

      • +

    • gen (int) – number of generations to consider (each generation will compute the objective function once)

      • +

    • phmcr (float) – probability of choosing from memory (similar to a crossover probability)

      • +

    • ppar_min (float) – minimum pitch adjustment rate. (similar to a mutation rate)

      • +

    • ppar_max (float) – maximum pitch adjustment rate. (similar to a mutation rate)

      • +

    • bw_min (float) – minimum distance bandwidth. (similar to a mutation width)

      • +

    • bw_max (float) – maximum distance bandwidth. (similar to a mutation width)

      • +

    • seed (int) – seed used by the internal random number generator

    Raises
      -

    • OverflowError – if gen or seed are negative or greater than an implementation-defined value

      • -

    • ValueError – if phmcr is not in the ]0,1[ interval, ppar_min or ppar_max are not in the ]0,1[ +

    • OverflowError – if gen or seed are negative or greater than an implementation-defined value

      • +

    • ValueError – if phmcr is not in the ]0,1[ interval, ppar_min or ppar_max are not in the ]0,1[ interval, min/max quantities are less than/greater than max/min quantities, bw_min is negative.

    • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

      • @@ -4170,19 +4170,19 @@

      Algorithms exposed from C++Returns

      at each logged epoch, the values Fevals, ppar, bw, dx, df, Violated, Viol. Norm,``ideal``

        -

      • Fevals (int), number of functions evaluation made.

        • -

      • ppar (float), the pitch adjustment rate.

        • -

      • bw (float), the distance bandwidth.

        • -

      • dx (float), the population flatness evaluated as the distance between the decisions vector of the best and of the worst individual (or -1 in a multiobjective case).

        • -

      • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual (or -1 in a multiobjective case).

        • -

      • Violated (int), the number of constraints violated by the current decision vector.

        • -

      • Viol. Norm (float), the constraints violation norm for the current decision vector.

        • +

      • Fevals (int), number of functions evaluation made.

        • +

      • ppar (float), the pitch adjustment rate.

        • +

      • bw (float), the distance bandwidth.

        • +

      • dx (float), the population flatness evaluated as the distance between the decisions vector of the best and of the worst individual (or -1 in a multiobjective case).

        • +

      • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual (or -1 in a multiobjective case).

        • +

      • Violated (int), the number of constraints violated by the current decision vector.

        • +

      • Viol. Norm (float), the constraints violation norm for the current decision vector.

      • ideal_point (1D numpy array), the ideal point of the current population (cropped to max 5 dimensions only in the screen output)

      Return type
      -

      list of tuple

      +

      list of tuple

    Examples

    @@ -4221,7 +4221,7 @@

    Algorithms exposed from C++

    the random seed of the population

    Return type
    -

    int

    +

    int

    @@ -4236,22 +4236,22 @@

    Algorithms exposed from C++
    Parameters
      -

    • gen (int) – number of generations

      • -

    • eta_mu (float) – learning rate for mean update (if -1 will be automatically selected to be 1)

      • -

    • eta_sigma (float) – learning rate for step-size update (if -1 will be automatically selected)

      • -

    • eta_b (float) – learning rate for the covariance matrix update (if -1 will be automatically selected)

      • -

    • sigma0 (float) – the initial search width will be sigma0 * (ub - lb) (if -1 will be automatically selected to be 1)

      • -

    • ftol (float) – stopping criteria on the x tolerance

      • -

    • xtol (float) – stopping criteria on the f tolerance

      • -

    • memory (bool) – when true the adapted parameters are not reset between successive calls to the evolve method

      • -

    • force_bounds (bool) – when true the box bounds are enforced. The fitness will never be called outside the bounds but the covariance matrix adaptation mechanism will worsen

      • -

    • seed (int) – seed used by the internal random number generator (default is random)

      • +

    • gen (int) – number of generations

      • +

    • eta_mu (float) – learning rate for mean update (if -1 will be automatically selected to be 1)

      • +

    • eta_sigma (float) – learning rate for step-size update (if -1 will be automatically selected)

      • +

    • eta_b (float) – learning rate for the covariance matrix update (if -1 will be automatically selected)

      • +

    • sigma0 (float) – the initial search width will be sigma0 * (ub - lb) (if -1 will be automatically selected to be 1)

      • +

    • ftol (float) – stopping criteria on the x tolerance

      • +

    • xtol (float) – stopping criteria on the f tolerance

      • +

    • memory (bool) – when true the adapted parameters are not reset between successive calls to the evolve method

      • +

    • force_bounds (bool) – when true the box bounds are enforced. The fitness will never be called outside the bounds but the covariance matrix adaptation mechanism will worsen

      • +

    • seed (int) – seed used by the internal random number generator (default is random)

    Raises
      -

    • OverflowError – if gen is negative or greater than an implementation-defined value

      • -

    • ValueError – if eta_mu, eta_sigma, eta_b, sigma0 are not in ]0,1] or -1

      • +

    • OverflowError – if gen is negative or greater than an implementation-defined value

      • +

    • ValueError – if eta_mu, eta_sigma, eta_b, sigma0 are not in ]0,1] or -1

    @@ -4266,17 +4266,17 @@

    Algorithms exposed from C++Returns

    at each logged epoch, the values Gen, Fevals, Best, dx, df, sigma, where:

      -

    • Gen (int), generation number

      • -

    • Fevals (int), number of functions evaluation made

      • -

    • Best (float), the best fitness function currently in the population

      • -

    • dx (float), the norm of the distance to the population mean of the mutant vectors

      • -

    • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

      • -

    • sigma (float), the current step-size

      • +

    • Gen (int), generation number

      • +

    • Fevals (int), number of functions evaluation made

      • +

    • Best (float), the best fitness function currently in the population

      • +

    • dx (float), the norm of the distance to the population mean of the mutant vectors

      • +

    • df (float), the population flatness evaluated as the distance between the fitness of the best and of the worst individual

      • +

    • sigma (float), the current step-size

    Return type
    -

    list of tuple

    +

    list of tuple

    Examples

    @@ -4310,7 +4310,7 @@

    Algorithms exposed from C++

    the random seed of the population

    Return type
    -

    int

    +

    int

    diff --git a/archipelago.html b/archipelago.html index 354a204e..4fe2a606 100644 --- a/archipelago.html +++ b/archipelago.html @@ -416,7 +416,7 @@

    Archipelago class
    Parameters
      -

    • n (int) – the number of islands in the archipelago

      • +

    • n (int) – the number of islands in the archipelago

    • t – a user-defined topology (either Python or C++), or an instance of topology

    @@ -427,16 +427,16 @@

    Archipelago classpopulation) – a population

  • prob – a user-defined problem (either Python or C++), or an instance of problem

  • b – a user-defined batch fitness evaluator (either Python or C++), or an instance of bfe

    • -

  • pop_size (int) – the number of individuals for each island

    • +

  • pop_size (int) – the number of individuals for each island

  • r_pol – a user-defined replacement policy (either Python or C++), or an instance of r_policy

  • s_pol – a user-defined selection policy (either Python or C++), or an instance of s_policy

    • -

  • seed (int) – the random seed

    • +

  • seed (int) – the random seed

    • Raises
      -

    • TypeError – if n is not an integral type

      • -

    • ValueError – if n is negative

      • +

    • TypeError – if n is not an integral type

      • +

    • ValueError – if n is negative

    • unspecified – any exception thrown by the constructor of island, by the underlying C++ constructor, push_back() or by the public interface of topology

      • @@ -503,7 +503,7 @@

      Archipelago class
      Parameters
      -

      n (int) – the parameter that will be passed to pygmo.island.evolve()

      +

      n (int) – the parameter that will be passed to pygmo.island.evolve()

      Raises

      unspecified – any exception thrown by pygmo.island.evolve()

      @@ -520,7 +520,7 @@

      Archipelago class

      the fitness vectors of the islands’ champions

      Return type
      -

      list of 1D NumPy float arrays

      +

      list of 1D NumPy float arrays

      Raises

      unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, @@ -538,7 +538,7 @@

      Archipelago class

      the decision vectors of the islands’ champions

      Return type
      -

      list of 1D NumPy float arrays

      +

      list of 1D NumPy float arrays

      Raises

      unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, @@ -565,7 +565,7 @@

      Archipelago classget_migrants_db()#

      During the evolution of an archipelago, islands will periodically store the individuals selected for migration in a migrant database. -This is a list of tuple objects whose +This is a list of tuple objects whose size is equal to the number of islands in the archipelago, and which contains the current candidate outgoing migrants for each island.

      The migrants tuples consist of 3 values each:

      @@ -581,7 +581,7 @@

      Archipelago class

      a copy of the database of migrants

      Return type
      -

      list

      +

      list

      Raises

      unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, @@ -595,20 +595,20 @@

      Archipelago classget_migration_log()#

      Each time an individual migrates from an island (the source) to another (the destination), an entry will be added to the migration log. -The entry is a tuple of 6 elements containing:

      +The entry is a tuple of 6 elements containing:

      • a timestamp of the migration,

      • the ID of the individual that migrated,

      • the decision and fitness vectors of the individual that migrated,

      • the indices of the source and destination islands.

      -

      The migration log is a list of migration entries.

      +

      The migration log is a list of migration entries.

      Returns

      a copy of the migration log

      Return type
      -

      list

      +

      list

      Raises

      unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, @@ -667,9 +667,9 @@

      Archipelago class
      Raises
        -

      • ValueError – if, when using positional arguments, there are more than 1 positional arguments, +

      • ValueError – if, when using positional arguments, there are more than 1 positional arguments, or if keyword arguments are also used at the same time

        • -

      • TypeError – if, when using a single positional argument, the type of that argument +

      • TypeError – if, when using a single positional argument, the type of that argument is not island

      • unspecified – any exception thrown by the constructor of island, pygmo.topology.push_back() or by the underlying C++ method

        • @@ -694,7 +694,7 @@

        Archipelago classset_migrants_db(mig)#

        During the evolution of an archipelago, islands will periodically store the individuals selected for migration in a migrant database. -This is a list of tuple objects whose +This is a list of tuple objects whose size is equal to the number of islands in the archipelago, and which contains the current candidate outgoing migrants for each island.

        The migrants tuples consist of 3 values each:

        @@ -710,7 +710,7 @@

        Archipelago class
        Parameters
        -

        mig (list) – the new database of migrants

        +

        mig (list) – the new database of migrants

        Raises

        unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, diff --git a/bfe.html b/bfe.html index 24f346e7..68fc96b7 100644 --- a/bfe.html +++ b/bfe.html @@ -436,7 +436,7 @@

        Batch fitness evaluator
        Raises
          -

        • NotImplementedError – if udbfe does not implement the mandatory methods detailed above

          • +

        • NotImplementedError – if udbfe does not implement the mandatory methods detailed above

        • unspecified – any exception thrown by methods of the UDBFE invoked during construction, the deep copy of the UDBFE, the constructor of the underlying C++ class, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function @@ -473,7 +473,7 @@

          Batch fitness evaluator
          Raises
            -

          • ValueError – if dvs or the return value produced by the UDBFE are incompatible with the input problem prob

            • +

          • ValueError – if dvs or the return value produced by the UDBFE are incompatible with the input problem prob

          • unspecified – any exception raised by the invocation of the UDBFE, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

          @@ -498,25 +498,25 @@

          Batch fitness evaluator

          Extract the user-defined batch fitness evaluator (bfe).

          This method allows to extract a reference to the user-defined bfe (UDBFE) stored within this bfe instance. The behaviour of this function depends on the value -of t (which must be a type) and on the type of the internal UDBFE:

          +of t (which must be a type) and on the type of the internal UDBFE:

          • if the type of the UDBFE is t, then a reference to the UDBFE will be returned (this mirrors the behaviour of the corresponding C++ method pagmo::bfe::extract()),

            • -

          • if t is object and the UDBFE is a Python object (as opposed to an +

          • if t is object and the UDBFE is a Python object (as opposed to an exposed C++ bfe), then a reference to the UDBFE will be returned (this allows to extract a Python UDBFE without knowing its type),

            • -

          • otherwise, None will be returned.

            • +

          • otherwise, None will be returned.

          Parameters
          -

          t (type) – the type of the user-defined bfe to extract

          +

          t (type) – the type of the user-defined bfe to extract

          Returns
          -

          a reference to the internal user-defined bfe, or None if the extraction fails

          +

          a reference to the internal user-defined bfe, or None if the extraction fails

          Raises
          -

          TypeError – if t is not a type

          +

          TypeError – if t is not a type

          Examples

          @@ -549,7 +549,7 @@

          Batch fitness evaluator

          extra info about the UDBFE

          Return type
          -

          str

          +

          str

          Raises

          unspecified – any exception thrown by the get_extra_info() method of the UDBFE

          @@ -568,7 +568,7 @@

          Batch fitness evaluator

          the bfe’s name

          Return type
          -

          str

          +

          str

      @@ -595,17 +595,17 @@

      Batch fitness evaluator is_(t)#

      Check the type of the user-defined batch fitness evaluator.

      -

      This method returns False if extract(t) returns -None, and True otherwise.

      +

      This method returns False if extract(t) returns +None, and True otherwise.

      Parameters
      -

      t (type) – the type that will be compared to the type of the UDBFE

      +

      t (type) – the type that will be compared to the type of the UDBFE

      Returns

      whether the UDBFE is of type t or not

      Return type
      -

      bool

      +

      bool

      Raises

      unspecified – any exception thrown by extract()

      diff --git a/bfes.html b/bfes.html index 8f3a4592..807f755f 100644 --- a/bfes.html +++ b/bfes.html @@ -405,9 +405,9 @@

      Batch evaluators implemented in Pythonmultiprocessing module.

      +standard Python multiprocessing module.

      The evaluations of the decision vectors are dispatched to the processes -of a global pool shared between +of a global pool shared between different instances of mp_bfe. The pool is created either implicitly by the construction of the first mp_bfe object or explicitly via the init_pool() @@ -441,14 +441,14 @@

      Batch evaluators implemented in Python
      Parameters
      -

      chunksize (int or None) – if not None, this positive integral represents +

      chunksize (int or None) – if not None, this positive integral represents the approximate number of decision vectors that are processed by each task submitted to the process pool by the call operator

      Raises
        -

      • TypeError – if chunksize is neither None nor a value of an integral type

        • -

      • ValueError – if chunksize is not strictly positive

        • +

      • TypeError – if chunksize is neither None nor a value of an integral type

        • +

      • ValueError – if chunksize is not strictly positive

      • unspecified – any exception thrown by init_pool()

      @@ -464,7 +464,7 @@

      Batch evaluators implemented in Python

      a string containing information about the number of processes in the pool

      Return type
      -

      str

      +

      str

      Raises

      unspecified – any exception thrown by get_pool_size()

      @@ -481,7 +481,7 @@

      Batch evaluators implemented in Python

      "Multiprocessing batch fitness evaluator"

      Return type
      -

      str

      +

      str

      @@ -497,7 +497,7 @@

      Batch evaluators implemented in Python

      the current size of the pool

      Return type
      -

      int

      +

      int

      Raises

      unspecified – any exception thrown by init_pool()

      @@ -514,14 +514,14 @@

      Batch evaluators implemented in Python
      Parameters
      -

      processes (None or an int) – the size of the pool (if None, the size of the pool will be +

      processes (None or an int) – the size of the pool (if None, the size of the pool will be equal to the number of logical CPUs on the system)

      Raises
        -

      • ValueError – if the pool does not exist yet and the function is being called from a thread different +

      • ValueError – if the pool does not exist yet and the function is being called from a thread different from the main one, or if processes is a non-positive value

        • -

      • TypeError – if processes is not None and not an int

        • +

      • TypeError – if processes is not None and not an int

      @@ -536,12 +536,12 @@

      Batch evaluators implemented in Python
      Parameters
      -

      processes (int) – the desired number of processes in the pool

      +

      processes (int) – the desired number of processes in the pool

      Raises
        -

      • TypeError – if the processes argument is not an int

        • -

      • ValueError – if the processes argument is not strictly positive

        • +

      • TypeError – if the processes argument is not an int

        • +

      • ValueError – if the processes argument is not strictly positive

      • unspecified – any exception thrown by init_pool()

      @@ -572,10 +572,10 @@

      Batch evaluators implemented in Pythonipyparallel.LoadBalancedView instance which is +via an ipyparallel.LoadBalancedView instance which is created either implicitly when the first fitness evaluation is run, or explicitly via the init_view() method. The -LoadBalancedView instance is a global object shared +LoadBalancedView instance is a global object shared among all the ipyparallel batch fitness evaluators.

      See also

      @@ -590,7 +590,7 @@

      Batch evaluators implemented in Python

      a string with extra information about the status of the evaluator

      Return type
      -

      str

      +

      str

      @@ -604,7 +604,7 @@

      Batch evaluators implemented in Python

      "Ipyparallel batch fitness evaluator"

    Return type
    -

    str

    +

    str

    @@ -613,39 +613,39 @@

    Batch evaluators implemented in Python static init_view(client_args=[], client_kwargs={}, view_args=[], view_kwargs={})#

    Init the ipyparallel view.

    -

    This method will initialise the ipyparallel.LoadBalancedView +

    This method will initialise the ipyparallel.LoadBalancedView which is used by all ipyparallel evaluators to submit the evaluation tasks -to an ipyparallel cluster. If the ipyparallel.LoadBalancedView +to an ipyparallel cluster. If the ipyparallel.LoadBalancedView has already been created, this method will perform no action.

    The input arguments client_args and client_kwargs are forwarded as positional and keyword arguments to the construction of an -ipyparallel.Client instance. From the constructed client, -an ipyparallel.LoadBalancedView instance is then created +ipyparallel.Client instance. From the constructed client, +an ipyparallel.LoadBalancedView instance is then created via the ipyparallel.Client.load_balanced_view() method, to which the positional and keyword arguments view_args and view_kwargs are passed.

    Note that usually it is not necessary to explicitly invoke this -method: an ipyparallel.LoadBalancedView is automatically +method: an ipyparallel.LoadBalancedView is automatically constructed with default settings the first time a batch evaluation task is submitted to an ipyparallel evaluator. This method should be used only if it is necessary to pass custom arguments to the construction -of the ipyparallel.Client or ipyparallel.LoadBalancedView +of the ipyparallel.Client or ipyparallel.LoadBalancedView objects.

    Parameters
      -

    • client_args (list) – the positional arguments used for the +

    • client_args (list) – the positional arguments used for the construction of the client

      • -

    • client_kwargs (dict) – the keyword arguments used for the +

    • client_kwargs (dict) – the keyword arguments used for the construction of the client

      • -

    • view_args (list) – the positional arguments used for the +

    • view_args (list) – the positional arguments used for the construction of the view

      • -

    • view_kwargs (dict) – the keyword arguments used for the +

    • view_kwargs (dict) – the keyword arguments used for the construction of the view

    Raises
    -

    unspecified – any exception thrown by the constructor of ipyparallel.Client +

    unspecified – any exception thrown by the constructor of ipyparallel.Client or by the ipyparallel.Client.load_balanced_view() method

    @@ -655,7 +655,7 @@

    Batch evaluators implemented in Python static shutdown_view()#

    Destroy the ipyparallel view.

    -

    This method will destroy the ipyparallel.LoadBalancedView +

    This method will destroy the ipyparallel.LoadBalancedView currently being used by the ipyparallel evaluators for submitting evaluation tasks to an ipyparallel cluster. The view can be re-inited implicitly by submitting a new evaluation task, or by invoking diff --git a/con_utils.html b/con_utils.html index 7a110685..9f50b831 100644 --- a/con_utils.html +++ b/con_utils.html @@ -408,16 +408,16 @@

    Constrained optimization utilities

    Raises
      -

    • OverflowError – if nec is negative or greater than an implementation-defined value

      • -

    • ValueError – if f1 and f2 do not have equal size \(n\), if f1 does not have at least size 1, +

    • OverflowError – if nec is negative or greater than an implementation-defined value

      • +

    • ValueError – if f1 and f2 do not have equal size \(n\), if f1 does not have at least size 1, if nec is larger than \(n-1\) (too many constraints) or if the size of tol is not \(n - 1\)

      • -

    • TypeError – if f1, f2 or tol cannot be converted to a vector of floats

      • +

    • TypeError – if f1, f2 or tol cannot be converted to a vector of floats

    Returns
    @@ -460,23 +460,23 @@

    Constrained optimization utilitiesParameters
    Raises
      -

    • OverflowError – if nec is negative or greater than an implementation-defined value

      • -

    • ValueError – if the input fitness vectors do not have all the same size \(n >=1\), or if nec is larger than \(n-1\) (too many constraints) +

    • OverflowError – if nec is negative or greater than an implementation-defined value

      • +

    • ValueError – if the input fitness vectors do not have all the same size \(n >=1\), or if nec is larger than \(n-1\) (too many constraints) or if the size of tol is not equal to \(n-1\)

      • -

    • TypeError – if input_f cannot be converted to a vector of vector of floats or tol cannot be converted to a vector of floats.

      • +

    • TypeError – if input_f cannot be converted to a vector of vector of floats or tol cannot be converted to a vector of floats.

    Returns

    the indexes of the sorted fitnesses vectors.

    Return type
    -

    list of 1D NumPy int array

    +

    list of 1D NumPy int array

    Examples

    diff --git a/generic_utils.html b/generic_utils.html index 7fcab91d..4d6ea9b5 100644 --- a/generic_utils.html +++ b/generic_utils.html @@ -402,11 +402,11 @@

    Generic optimization utilities

    a random decision vector within the problem’s bounds

    Return type
    -

    numpy.ndarray

    +

    numpy.ndarray

    Raises
      -

    • ValueError – if the problem’s bounds are not finite or larger than an implementation-defined limit

      • +

    • ValueError – if the problem’s bounds are not finite or larger than an implementation-defined limit

    • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    @@ -432,19 +432,19 @@

    Generic optimization utilitiesParameters

    • prob (problem) – the input problem

      • -

    • n (int) – the number of decision vectors that will be generated

      • +

    • n (int) – the number of decision vectors that will be generated

    Returns

    a batch of random decision vectors within the problem’s bounds, laid out contiguously in a 1D array

    Return type
    -

    numpy.ndarray

    +

    numpy.ndarray

    Raises
      -

    • OverflowError – in case of (unlikely) overflows

      • -

    • ValueError – if the problem’s bounds are not finite or larger than an implementation-defined limit

      • +

    • OverflowError – in case of (unlikely) overflows

      • +

    • ValueError – if the problem’s bounds are not finite or larger than an implementation-defined limit

    • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    @@ -461,7 +461,7 @@

    Generic optimization utilities
    Parameters
    -

    seed (int) – the new global seed for random number generation

    +

    seed (int) – the new global seed for random number generation

    diff --git a/genetic_op_utils.html b/genetic_op_utils.html index a82fbd0c..ad0332b6 100644 --- a/genetic_op_utils.html +++ b/genetic_op_utils.html @@ -395,21 +395,21 @@

    Generic optimization utilitiesint) – the integer dimension of the chromosome

    -

  • p_cr (float) – crossover probability

    • -

  • eta_c (float) – crossover distribution index

    • -

  • seed (int) – seed used by the internal random number generator

    • +

  • nix (int) – the integer dimension of the chromosome

    • +

  • p_cr (float) – crossover probability

    • +

  • eta_c (float) – crossover distribution index

    • +

  • seed (int) – seed used by the internal random number generator

    • Returns
    -

    of numpy.ndarray: containing the two crossovered chromosomes

    +

    of numpy.ndarray: containing the two crossovered chromosomes

    Return type
    -

    tuple

    +

    tuple

    Raises
      -

    • ValueError – if bounds parent1 parent2 are not of equal length, if lower bounds are not less +

    • ValueError – if bounds parent1 parent2 are not of equal length, if lower bounds are not less or equal to the upper bounds, if the nix is larger than the parent size or if infinite values are detected in bounds, p_cr or eta_c

    • unspecified – any exception thrown by failiures at the intersection between C++ and Python (e.g., @@ -431,21 +431,21 @@

      Generic optimization utilities

    Returns
    -

    of numpy.ndarray: containing the two crossovered chromosomes

    +

    of numpy.ndarray: containing the two crossovered chromosomes

    Return type
    -

    tuple

    +

    tuple

    Raises
    Raises

    • unspecified – any exception thrown by the callable object when called on x.

      • -

    • TypeError – if x cannot be converted to a vector of floats or callable is not callable.

      • +

    • TypeError – if x cannot be converted to a vector of floats or callable is not callable.

    Returns
    @@ -444,13 +444,13 @@

    Gradient and Hessians utilities

    Raises

    • unspecified – any exception thrown by the callable object when called on x.

      • -

    • TypeError – if x cannot be converted to a vector of floats or callable is not callable.

      • +

    • TypeError – if x cannot be converted to a vector of floats or callable is not callable.

    Returns
    @@ -490,13 +490,13 @@

    Gradient and Hessians utilities
    Raises

    • unspecified – any exception thrown by the callable object when called on x.

      • -

    • TypeError – if x cannot be converted to a vector of floats or callable is not callable.

      • +

    • TypeError – if x cannot be converted to a vector of floats or callable is not callable.

    Returns
    diff --git a/hv_utils.html b/hv_utils.html index 94f02086..f20fa9cd 100644 --- a/hv_utils.html +++ b/hv_utils.html @@ -392,7 +392,7 @@

    Hypervolume utilities

    points (2d array-like object) – the points

    Raises
    -

    ValueError – if points is inconsistent

    +

    ValueError – if points is inconsistent

    Examples

    @@ -409,7 +409,7 @@

    Hypervolume utilities

    pop (population) – the input population

    Raises
    -

    ValueError – if pop contains a single-objective or a constrained problem

    +

    ValueError – if pop contains a single-objective or a constrained problem

    Examples

    @@ -436,10 +436,10 @@

    Hypervolume utilities

    the computed hypervolume assuming ref_point as reference point

    Return type
    -

    float

    +

    float

    Raises
    -

    ValueError – if ref_point is not dominated by the nadir point

    +

    ValueError – if ref_point is not dominated by the nadir point

    See also the docs of the C++ class pagmo::hypervolume::compute().

    @@ -465,7 +465,7 @@

    Hypervolume utilities

    1D NumPy float array

    Raises
    -

    ValueError – if ref_point is not suitable

    +

    ValueError – if ref_point is not suitable

    See also the docs of the C++ class pagmo::hypervolume::contributions().

    @@ -478,7 +478,7 @@

    Hypervolume utilities
    Parameters
      -

    • idx (int) – index of the point

      • +

    • idx (int) – index of the point

    • ref_point (array-like object) – the reference point

    • hv_algo (deriving from _hv_algorithm) – hypervolume algorithm to be used

    @@ -491,8 +491,8 @@

    Hypervolume utilities
    Raises
      -

    • ValueError – if ref_point is not suitable or if idx is out of bounds

      • -

    • OverflowError – if idx is negative or greater than an implementation-defined value

      • +

    • ValueError – if ref_point is not suitable or if idx is out of bounds

      • +

    • OverflowError – if idx is negative or greater than an implementation-defined value

    @@ -511,7 +511,7 @@

    Hypervolume utilities

    Raises
    -

    ValueError – if ref_point is not suitable

    +

    ValueError – if ref_point is not suitable

    See also the docs of the C++ class pagmo::hypervolume::greatest_contributor().

    @@ -529,7 +529,7 @@

    Hypervolume utilities
    Raises
    -

    ValueError – if ref_point is not suitable

    +

    ValueError – if ref_point is not suitable

    See also the docs of the C++ class pagmo::hypervolume::least_contributor().

    @@ -544,7 +544,7 @@

    Hypervolume utilities
    Parameters
    -

    offset (float) – the reference point

    +

    offset (float) – the reference point

    Returns

    the reference point

    @@ -568,10 +568,10 @@

    Hypervolume utilities_hv_algorithm

    Parameters
    -

    stop_dimension (int) – the input population

    +

    stop_dimension (int) – the input population

    Raises
    -

    OverflowError – if stop_dimension is negative or greater than an implementation-defined value

    +

    OverflowError – if stop_dimension is negative or greater than an implementation-defined value

    Examples

    diff --git a/install.html b/install.html index dfbe7dff..dbaf2c5c 100644 --- a/install.html +++ b/install.html @@ -416,8 +416,6 @@

    Dependencies -

  • dill, which can be used as an -alternative serialization backend,

  • Matplotlib, which is used by a few plotting utilities,

  • NetworkX, which is used for diff --git a/island.html b/island.html index 910071a8..4f182a92 100644 --- a/island.html +++ b/island.html @@ -462,7 +462,7 @@

    Island classKeyError exception will be raised.

    +

    If the keyword arguments list is invalid, a KeyError exception will be raised.

    This class is the Python counterpart of the C++ class pagmo::island.

    Keyword Arguments
    @@ -472,15 +472,15 @@

    Island classpopulation) – a population

  • prob – a user-defined problem (either Python or C++), or an instance of problem

  • b – a user-defined batch fitness evaluator (either Python or C++), or an instance of bfe

    • -

  • size (int) – the number of individuals

    • +

  • size (int) – the number of individuals

  • r_pol – a user-defined replacement policy (either Python or C++), or an instance of r_policy

  • s_pol – a user-defined selection policy (either Python or C++), or an instance of s_policy

    • -

  • seed (int) – the random seed (if not specified, it will be randomly-generated)

    • +

  • seed (int) – the random seed (if not specified, it will be randomly-generated)

    • Raises
      -

    • KeyError – if the set of keyword arguments is invalid

      • +

    • KeyError – if the set of keyword arguments is invalid

    • unspecified – any exception thrown by the invoked C++ constructors, the deep copy of the UDI, the constructors of algorithm and population, failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function @@ -516,14 +516,14 @@

      Island class
      Parameters
      -

      n (int) – the number of times the run_evolve() method of the UDI will be called within the evolution task +

      n (int) – the number of times the run_evolve() method of the UDI will be called within the evolution task (this corresponds also to the number of times migration can happen, if the island belongs to an archipelago)

      Raises
        -

      • IndexError – if the island is part of an archipelago and during migration an invalid island index is used (this can +

      • IndexError – if the island is part of an archipelago and during migration an invalid island index is used (this can happen if the archipelago’s topology is malformed)

        • -

      • OverflowError – if n is negative or larger than an implementation-defined value

        • +

      • OverflowError – if n is negative or larger than an implementation-defined value

      • unspecified – any exception thrown by the public interface of archipelago, the public interface of the replacement/selection policies, the underlying C++ method, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

        • @@ -538,25 +538,25 @@

        Island classisland instance. The behaviour of this function depends on the value -of t (which must be a type) and on the type of the internal UDI:

        +of t (which must be a type) and on the type of the internal UDI:

        • if the type of the UDI is t, then a reference to the UDI will be returned (this mirrors the behaviour of the corresponding C++ method pagmo::island::extract()),

          • -

        • if t is object and the UDI is a Python object (as opposed to an +

        • if t is object and the UDI is a Python object (as opposed to an exposed C++ island), then a reference to the UDI will be returned (this allows to extract a Python UDI without knowing its type),

          • -

        • otherwise, None will be returned.

          • +

        • otherwise, None will be returned.

        Parameters
        -

        t (type) – the type of the user-defined island to extract

        +

        t (type) – the type of the user-defined island to extract

        Returns
        -

        a reference to the internal user-defined island, or None if the extraction fails

        +

        a reference to the internal user-defined island, or None if the extraction fails

        Raises
        -

        TypeError – if t is not a type

        +

        TypeError – if t is not a type

        Examples

        @@ -610,7 +610,7 @@

        Island class

        extra info about the UDI

      Return type
      -

      str

      +

      str

      Raises

      unspecified – any exception thrown by the get_extra_info() method of the UDI

      @@ -630,7 +630,7 @@

      Island class

      the name of the UDI

      Return type
      -

      str

      +

      str

      Raises

      unspecified – any exception thrown by the get_name() method of the UDI

      @@ -688,17 +688,17 @@

      Island class is_(t)#

      Check the type of the user-defined island.

      -

      This method returns False if extract(t) returns -None, and True otherwise.

      +

      This method returns False if extract(t) returns +None, and True otherwise.

      Parameters
      -

      t (type) – the type that will be compared to the type of the UDI

      +

      t (type) – the type that will be compared to the type of the UDI

      Returns

      whether the UDI is of type t or not

      Return type
      -

      bool

      +

      bool

      Raises

      unspecified – any exception thrown by extract()

      diff --git a/islands.html b/islands.html index 0027c711..760a4c5e 100644 --- a/islands.html +++ b/islands.html @@ -403,17 +403,17 @@

      Islands implemented in PythonNew in version 2.10: The use_pool parameter (in previous versions, mp_island always used a process pool).

      This user-defined island (UDI) will dispatch evolution tasks to an external Python process -using the facilities provided by the standard Python multiprocessing module.

      -

      If the construction argument use_pool is True, then a process from a global -pool shared between different instances of +using the facilities provided by the standard Python multiprocessing module.

      +

      If the construction argument use_pool is True, then a process from a global +pool shared between different instances of mp_island will be used. The pool is created either implicitly by the construction of the first mp_island object or explicitly via the init_pool() static method. The default number of processes in the pool is equal to the number of logical CPUs on the current machine. The pool’s size can be queried via get_pool_size(), and changed via resize_pool(). The pool can be stopped via shutdown_pool().

      -

      If use_pool is False, each evolution launched by an mp_island will be offloaded -to a new process which will then be terminated at the end of the evolution.

      +

      If use_pool is False, each evolution launched by an mp_island will be offloaded +to a new process which will then be terminated at the end of the evolution.

      Generally speaking, a process pool will be faster (and will use fewer resources) than spawning a new process for every evolution. A process pool, however, by its very nature limits the number of evolutions that can be run simultaneously on the system, and it introduces a serializing behaviour that might not be desirable @@ -443,13 +443,13 @@

      Islands implemented in Python
      Parameters
      -

      use_pool (bool) – if True, a process from a global pool will be used to run the evolution, otherwise a new +

      use_pool (bool) – if True, a process from a global pool will be used to run the evolution, otherwise a new process will be spawned for each evolution

      Raises

      @@ -464,7 +464,7 @@

      Islands implemented in Python

      a string containing information about the state of the island (e.g., number of processes in the pool, ID of the evolution process, etc.)

      Return type
      -

      str

      +

      str

      Raises

      unspecified – any exception thrown by get_pool_size()

      @@ -481,7 +481,7 @@

      Islands implemented in Python

      "Multiprocessing island"

      Return type
      -

      str

      +

      str

    @@ -497,7 +497,7 @@

    Islands implemented in Python

    the current size of the pool

    Return type
    -

    int

    +

    int

    Raises

    unspecified – any exception thrown by init_pool()

    @@ -514,14 +514,14 @@

    Islands implemented in Python
    Parameters
    -

    processes (None or an int) – the size of the pool (if None, the size of the pool will be +

    processes (None or an int) – the size of the pool (if None, the size of the pool will be equal to the number of logical CPUs on the system)

    Raises
      -

    • ValueError – if the pool does not exist yet and the function is being called from a thread different +

    • ValueError – if the pool does not exist yet and the function is being called from a thread different from the main one, or if processes is a non-positive value

      • -

    • TypeError – if processes is not None and not an int

      • +

    • TypeError – if processes is not None and not an int

    @@ -534,13 +534,13 @@

    Islands implemented in Python
    Returns
    -

    the ID of the process running the current evolution, or None if no evolution is ongoing

    +

    the ID of the process running the current evolution, or None if no evolution is ongoing

    Return type
    -

    int

    +

    int

    Raises
    -

    ValueError – if the island is using a process pool

    +

    ValueError – if the island is using a process pool

    @@ -554,12 +554,12 @@

    Islands implemented in Python
    Parameters
    -

    processes (int) – the desired number of processes in the pool

    +

    processes (int) – the desired number of processes in the pool

    Raises
      -

    • TypeError – if the processes argument is not an int

      • -

    • ValueError – if the processes argument is not strictly positive

      • +

    • TypeError – if the processes argument is not an int

      • +

    • ValueError – if the processes argument is not strictly positive

    • unspecified – any exception thrown by init_pool()

    @@ -586,11 +586,11 @@

    Islands implemented in Python

    a tuple of 2 elements containing algo (i.e., the algorithm object that was used for the evolution) and the evolved population

    Return type
    -

    tuple

    +

    tuple

    Raises
      -

    • RuntimeError – if the pool was manually shut down via shutdown_pool()

      • +

    • RuntimeError – if the pool was manually shut down via shutdown_pool()

    • unspecified – any exception thrown by the evolution, by the (de)serialization of the input arguments or of the return value, or by the public interface of the process pool

      • @@ -620,10 +620,10 @@

      Islands implemented in Python
      Returns
      -

      True if this island uses a process pool, False otherwise

      +

      True if this island uses a process pool, False otherwise

      Return type
      -

      bool

      +

      bool

    @@ -635,10 +635,10 @@

    Islands implemented in Pythonclass pygmo.ipyparallel_island#

    Ipyparallel island.

    This user-defined island (UDI) will dispatch evolution tasks to an ipyparallel cluster. -The communication with the cluster is managed via an ipyparallel.LoadBalancedView +The communication with the cluster is managed via an ipyparallel.LoadBalancedView instance which is created either implicitly when the first evolution is run, or explicitly via the init_view() method. The -LoadBalancedView instance is a global object shared among all the +LoadBalancedView instance is a global object shared among all the ipyparallel islands.

    See also

    @@ -653,7 +653,7 @@

    Islands implemented in Python

    a string with extra information about the status of the island

    Return type
    -

    str

    +

    str

    @@ -667,7 +667,7 @@

    Islands implemented in Python

    "Ipyparallel island"

    Return type
    -

    str

    +

    str

    @@ -679,39 +679,39 @@

    Islands implemented in Python

    New in version 2.12.

    -

    This method will initialise the ipyparallel.LoadBalancedView +

    This method will initialise the ipyparallel.LoadBalancedView which is used by all ipyparallel islands to submit the evolution tasks -to an ipyparallel cluster. If the ipyparallel.LoadBalancedView +to an ipyparallel cluster. If the ipyparallel.LoadBalancedView has already been created, this method will perform no action.

    The input arguments client_args and client_kwargs are forwarded as positional and keyword arguments to the construction of an -ipyparallel.Client instance. From the constructed client, -an ipyparallel.LoadBalancedView instance is then created +ipyparallel.Client instance. From the constructed client, +an ipyparallel.LoadBalancedView instance is then created via the ipyparallel.Client.load_balanced_view() method, to which the positional and keyword arguments view_args and view_kwargs are passed.

    Note that usually it is not necessary to explicitly invoke this -method: an ipyparallel.LoadBalancedView is automatically +method: an ipyparallel.LoadBalancedView is automatically constructed with default settings the first time an evolution task is submitted to an ipyparallel island. This method should be used only if it is necessary to pass custom arguments to the construction -of the ipyparallel.Client or ipyparallel.LoadBalancedView +of the ipyparallel.Client or ipyparallel.LoadBalancedView objects.

    Parameters
      -

    • client_args (list) – the positional arguments used for the +

    • client_args (list) – the positional arguments used for the construction of the client

      • -

    • client_kwargs (dict) – the keyword arguments used for the +

    • client_kwargs (dict) – the keyword arguments used for the construction of the client

      • -

    • view_args (list) – the positional arguments used for the +

    • view_args (list) – the positional arguments used for the construction of the view

      • -

    • view_kwargs (dict) – the keyword arguments used for the +

    • view_kwargs (dict) – the keyword arguments used for the construction of the view

    Raises
    -

    unspecified – any exception thrown by the constructor of ipyparallel.Client +

    unspecified – any exception thrown by the constructor of ipyparallel.Client or by the ipyparallel.Client.load_balanced_view() method

    @@ -723,7 +723,7 @@

    Islands implemented in Pythonpopulation pop using the input algorithm algo, and return algo and the evolved population. The evolution -task is submitted to the ipyparallel cluster via a global ipyparallel.LoadBalancedView +task is submitted to the ipyparallel cluster via a global ipyparallel.LoadBalancedView instance initialised either implicitly by the first invocation of this method, or by an explicit call to the init_view() method.

    @@ -737,11 +737,11 @@

    Islands implemented in Python

    a tuple of 2 elements containing algo (i.e., the algorithm object that was used for the evolution) and the evolved population

    Return type
    -

    tuple

    +

    tuple

    Raises

    unspecified – any exception thrown by the evolution, by the creation of a - ipyparallel.LoadBalancedView, or by the sumission of the evolution task + ipyparallel.LoadBalancedView, or by the sumission of the evolution task to the ipyparallel cluster

    @@ -754,7 +754,7 @@

    Islands implemented in Python

    New in version 2.12.

    -

    This method will destroy the ipyparallel.LoadBalancedView +

    This method will destroy the ipyparallel.LoadBalancedView currently being used by the ipyparallel islands for submitting evolution tasks to an ipyparallel cluster. The view can be re-inited implicitly by submitting a new evolution task, or by invoking @@ -799,7 +799,7 @@

    Islands implemented in Python
    Parameters
    -

    use_pool (bool) – a boolean flag signalling whether or not a thread pool should be used by the island

    +

    use_pool (bool) – a boolean flag signalling whether or not a thread pool should be used by the island

    diff --git a/misc.html b/misc.html index 6f560ab7..9a4862cb 100644 --- a/misc.html +++ b/misc.html @@ -478,16 +478,15 @@

    Miscellaneacloudpickle -module, which extends the capabilities of the standard pickle module with support +module, which extends the capabilities of the standard pickle module with support for lambdas, functions and classes defined interactively in the __main__ module, etc.

    In some specific cases, however, different serialization backends might work better than cloudpickle, and thus pygmo provides the possibility for the cognizant user to switch to another serialization backend.

    The valid backends are:

      -

    • 'pickle' (i.e., the standard Python pickle module),

      • -

    • 'cloudpickle',

      • -

    • 'dill' (from the dill library).

      • +

    • 'pickle' (i.e., the standard Python pickle module),

      • +

    • 'cloudpickle'.

    Warning

    @@ -497,13 +496,12 @@

    Miscellanea
    Parameters
    -

    name (str) – the name of the desired backend

    +

    name (str) – the name of the desired backend

    Raises
      -

    • TypeError – if name is not a str

      • -

    • ValueError – if name is not one of ['pickle', 'cloudpickle', 'dill']

      • -

    • ImportError – if name is 'dill' but the dill module is not installed

      • +

    • TypeError – if name is not a str

      • +

    • ValueError – if name is not one of ['pickle', 'cloudpickle']

    @@ -521,7 +519,7 @@

    Miscellanea

    the current serialization backend (as a Python module)

    Return type
    -

    types.ModuleType

    +

    types.ModuleType

    diff --git a/mo_utils.html b/mo_utils.html index 2008b8a6..01cd6282 100644 --- a/mo_utils.html +++ b/mo_utils.html @@ -394,22 +394,22 @@

    Multi-objective optimization utilitiesRaises
      -

    • ValueError – if points is malformed

      • -

    • TypeError – if points cannot be converted to a vector of vector floats

      • +

    • ValueError – if points is malformed

      • +

    • TypeError – if points cannot be converted to a vector of vector floats

    Returns

    (ndf, dl, dc, ndr), where:

      -

    • ndf (list of 1D NumPy int array): the non dominated fronts

      • -

    • dl (list of 1D NumPy int array): the domination list

      • +

    • ndf (list of 1D NumPy int array): the non dominated fronts

      • +

    • dl (list of 1D NumPy int array): the domination list

    • dc (1D NumPy int array): the domination count

    • ndr (1D NumPy int array): the non domination ranks

    Return type
    -

    tuple

    +

    tuple

    Examples

    @@ -432,8 +432,8 @@

    Multi-objective optimization utilitiesRaises
      -

    • ValueError – if points is malformed

      • -

    • TypeError – if points cannot be converted to a vector of vector floats

      • +

    • ValueError – if points is malformed

      • +

    • TypeError – if points cannot be converted to a vector of vector floats

    Returns
    @@ -458,8 +458,8 @@

    Multi-objective optimization utilitiesRaises
      -

    • ValueError – if points is malformed

      • -

    • TypeError – if points cannot be converted to a vector of vector floats

      • +

    • ValueError – if points is malformed

      • +

    • TypeError – if points cannot be converted to a vector of vector floats

    Returns
    @@ -489,15 +489,15 @@

    Multi-objective optimization utilitiesRaises
      -

    • ValueError – if the dimensions of obj1 and obj2 are different

      • -

    • TypeError – if obj1 or obj2 cannot be converted to a vector of vector floats

      • +

    • ValueError – if the dimensions of obj1 and obj2 are different

      • +

    • TypeError – if obj1 or obj2 cannot be converted to a vector of vector floats

    Returns
    -

    True if obj1 is dominating obj2, False otherwise.

    +

    True if obj1 is dominating obj2, False otherwise.

    Return type
    -

    bool

    +

    bool

    Examples

    @@ -522,8 +522,8 @@

    Multi-objective optimization utilitiesRaises
      -

    • ValueError – if points contain anything else than 2 dimensional objectives

      • -

    • TypeError – if points cannot be converted to a vector of vector floats

      • +

    • ValueError – if points contain anything else than 2 dimensional objectives

      • +

    • TypeError – if points cannot be converted to a vector of vector floats

    Returns
    @@ -556,8 +556,8 @@

    Multi-objective optimization utilitiesRaises
      -

    • ValueError – if points does not contain at least two points, or is malformed

      • -

    • TypeError – if points cannot be converted to a vector of vector floats

      • +

    • ValueError – if points does not contain at least two points, or is malformed

      • +

    • TypeError – if points cannot be converted to a vector of vector floats

    Returns
    @@ -598,7 +598,7 @@

    Multi-objective optimization utilitiesRaises
    Returns
    @@ -633,13 +633,13 @@

    Multi-objective optimization utilitiesParameters
    Raises
    Returns
    @@ -691,13 +691,13 @@

    Multi-objective optimization utilities\(\boldsymbol \lambda\)

  • ref_point (array-like object) – the reference point \(\mathbf z^*\) . It is not used if method is "weighted"

    • -

  • method (str) – the decomposition method: one of "weighted", "tchebycheff" or "bi"

    • +

  • method (str) – the decomposition method: one of "weighted", "tchebycheff" or "bi"

    • Raises
      -

    • ValueError – if objs, weight and ref_point have different sizes or if method is not one of "weighted", "tchebycheff" or "bi".

      • -

    • TypeError – if weights or ref_point or objs cannot be converted to a vector of floats.

      • +

    • ValueError – if objs, weight and ref_point have different sizes or if method is not one of "weighted", "tchebycheff" or "bi".

      • +

    • TypeError – if weights or ref_point or objs cannot be converted to a vector of floats.

    Returns
    @@ -739,16 +739,16 @@

    Multi-objective optimization utilities
    Parameters
      -

    • n_f (int) – number of the objective vectors

      • -

    • n_w (int) – number of the weights \(\boldsymbol \lambda\)

      • -

    • method (str) – the weight generation method: one of "grid", "random", or "low discrepancy"

      • -

    • seed (int) – seed used by the internal random number generator

      • +

    • n_f (int) – number of the objective vectors

      • +

    • n_w (int) – number of the weights \(\boldsymbol \lambda\)

      • +

    • method (str) – the weight generation method: one of "grid", "random", or "low discrepancy"

      • +

    • seed (int) – seed used by the internal random number generator

    Raises
      -

    • OverflowError – if n_f, n_w or seed are negative or greater than an implementation-defined value

      • -

    • ValueError – if n_f and n_w are not compatible with the selected weight generation method or if method is not +

    • OverflowError – if n_f, n_w or seed are negative or greater than an implementation-defined value

      • +

    • ValueError – if n_f and n_w are not compatible with the selected weight generation method or if method is not one of "grid", "random" or "low discrepancy"

    diff --git a/plotting.html b/plotting.html index 8a6e5456..dfa7f647 100644 --- a/plotting.html +++ b/plotting.html @@ -391,9 +391,9 @@

    Plotting utilitiesParameters

    • points (2d array-like) – points to plot

      • -

    • marker (str) – matplotlib marker used to plot the points

      • -

    • comp (list) – Components to be considered in the two dimensional plot (useful in many-objectives cases)

      • -

    • axes – plot axes (if None, new axes will be created)

      • +

    • marker (str) – matplotlib marker used to plot the points

      • +

    • comp (list) – Components to be considered in the two dimensional plot (useful in many-objectives cases)

      • +

    • axes – plot axes (if None, new axes will be created)

    Returns
    diff --git a/population.html b/population.html index ee973922..fece63fd 100644 --- a/population.html +++ b/population.html @@ -406,20 +406,20 @@

    Population classParameters

    • prob – a user-defined problem (either Python or C++), or an instance of problem -(if prob is None, a default-constructed problem will be used +(if prob is None, a default-constructed problem will be used in its stead)

      • -

    • size (int) – the number of individuals

      • +

    • size (int) – the number of individuals

    • b – a user-defined batch fitness evaluator (either Python or C++), or an instance of bfe -(if b is None, the evaluation of the population’s individuals will be performed +(if b is None, the evaluation of the population’s individuals will be performed in sequential mode)

      • -

    • seed (int) – the random seed (if seed is None, a randomly-generated value will be used +

    • seed (int) – the random seed (if seed is None, a randomly-generated value will be used in its stead)

    Raises
      -

    • TypeError – if size is not an int or seed is not None and not an int

      • -

    • OverflowError – is size or seed are negative

      • +

    • TypeError – if size is not an int or seed is not None and not an int

      • +

    • OverflowError – is size or seed are negative

    • unspecified – any exception thrown by the invoked C++ constructors, by the constructor of problem, or the constructor of bfe, or by failures at the intersection between C++ and @@ -437,18 +437,18 @@

      Population classpygmo.sort_population_mo() function.

      Parameters
      -

      tol (float or array-like object) – scalar tolerance or vector of tolerances to be applied to each constraints. By default, the +

      tol (float or array-like object) – scalar tolerance or vector of tolerances to be applied to each constraints. By default, the c_tol attribute from the population’s problem is used.

      Returns

      the index of the best individual

      Return type
      -

      int

      +

      int

      Raises
        -

      • ValueError – if the problem is multiobjective and thus a best individual is not well defined, or if the population is empty

        • +

      • ValueError – if the problem is multiobjective and thus a best individual is not well defined, or if the population is empty

      • unspecified – any exception thrown by pagmo::sort_population_con()

      @@ -459,7 +459,7 @@

      Population class property champion_f#

      Champion’s fitness vector.

      -

      This read-only property contains an array of float representing the fitness vector of the population’s champion.

      +

      This read-only property contains an array of float representing the fitness vector of the population’s champion.

      Note

      If the problem is stochastic, the champion is the individual that had the lowest fitness for @@ -475,7 +475,7 @@

      Population classRaises
        -

      • ValueError – if the current problem is not single objective

        • +

      • ValueError – if the current problem is not single objective

      • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

      @@ -487,7 +487,7 @@

      Population class property champion_x#

      Champion’s decision vector.

      -

      This read-only property contains an array of float representing the decision vector of the population’s champion.

      +

      This read-only property contains an array of float representing the decision vector of the population’s champion.

      Note

      If the problem is stochastic the champion is the individual that had the lowest fitness for @@ -503,7 +503,7 @@

      Population classRaises
        -

      • ValueError – if the current problem is not single objective

        • +

      • ValueError – if the current problem is not single objective

      • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

      @@ -560,7 +560,7 @@

      Population class

      the random seed of the population

      Return type
      -

      int

      +

      int

    @@ -614,7 +614,7 @@

    Population classRaises
      -

    • ValueError – if the dimensions of x or f (if provided) are incompatible with the population’s problem

      • +

    • ValueError – if the dimensions of x or f (if provided) are incompatible with the population’s problem

    • unspecified – any exception thrown by pygmo.problem.fitness() or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    @@ -631,7 +631,7 @@

    Population class

    a random decision vector within the problem’s bounds

    Return type
    -

    numpy.ndarray

    +

    numpy.ndarray

    Raises

    unspecified – any exception thrown by pygmo.random_decision_vector()

    @@ -652,13 +652,13 @@

    Population class
    Parameters
      -

    • i (int) – individual’s index in the population

      • +

    • i (int) – individual’s index in the population

    • x (array-like object) – a decision vector (chromosome)

    Raises
      -

    • ValueError – if i is invalid, or if x has the wrong dimensions (i.e., the dimension is +

    • ValueError – if i is invalid, or if x has the wrong dimensions (i.e., the dimension is inconsistent with the problem’s properties)

    • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

      • @@ -679,14 +679,14 @@

      Population class
      Parameters
        -

      • i (int) – individual’s index in the population

        • +

      • i (int) – individual’s index in the population

      • x (array-like object) – a decision vector (chromosome)

      • f (array-like object) – a fitness vector

      Raises
        -

      • ValueError – if i is invalid, or if x or f have the wrong dimensions (i.e., their dimensions are +

      • ValueError – if i is invalid, or if x or f have the wrong dimensions (i.e., their dimensions are inconsistent with the problem’s properties)

      • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

        • @@ -705,17 +705,17 @@

        Population classpygmo.sort_population_mo() function.

        Parameters
        -

        tol (float or array-like object) – scalar tolerance or vector of tolerances to be applied to each constraints

        +

        tol (float or array-like object) – scalar tolerance or vector of tolerances to be applied to each constraints

        Returns

        the index of the worst individual

        Return type
        -

        int

        +

        int

        Raises
          -

        • ValueError – if the problem is multiobjective and thus a worst individual is not well defined, or if the population is empty

          • +

        • ValueError – if the problem is multiobjective and thus a worst individual is not well defined, or if the population is empty

        • unspecified – any exception thrown by pygmo.sort_population_con()

        diff --git a/problem.html b/problem.html index b6466c19..99ce9c32 100644 --- a/problem.html +++ b/problem.html @@ -482,8 +482,8 @@

        Problem classRaises
          -

        • NotImplementedError – if udp does not implement the mandatory methods detailed above

          • -

        • ValueError – if the number of objectives of the UDP is zero, the number of objectives, +

        • NotImplementedError – if udp does not implement the mandatory methods detailed above

          • +

        • ValueError – if the number of objectives of the UDP is zero, the number of objectives, equality or inequality constraints is larger than an implementation-defined value, the problem bounds are invalid (e.g., they contain NaNs, the dimensionality of the lower bounds is different from the dimensionality of the upper bounds, etc. - note that infinite bounds are allowed), @@ -529,7 +529,7 @@

          Problem classRaises
            -

          • ValueError – if dvs and/or the return value are not compatible with the problem’s properties

            • +

          • ValueError – if dvs and/or the return value are not compatible with the problem’s properties

          • unspecified – any exception thrown by the batch_fitness() method of the UDP, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

          @@ -541,7 +541,7 @@

          Problem class property c_tol#

          Constraints tolerance.

          -

          This property contains an array of float that are used when checking for constraint feasibility. +

          This property contains an array of float that are used when checking for constraint feasibility. The dimension of the array is \(n_{ec} + n_{ic}\) (i.e., the total number of constraints), and the array is zero-filled on problem construction.

          This property can also be set via a scalar, instead of an array. In such case, all the tolerances @@ -555,7 +555,7 @@

          Problem classRaises
            -

          • ValueError – if, when setting this property, the size of the input array differs from the number +

          • ValueError – if, when setting this property, the size of the input array differs from the number of constraints of the problem or if any element of the array is negative or NaN

          • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

            • @@ -583,25 +583,25 @@

            Problem classproblem instance. The behaviour of this function depends on the value -of t (which must be a type) and on the type of the internal UDP:

            +of t (which must be a type) and on the type of the internal UDP:

            • if the type of the UDP is t, then a reference to the UDP will be returned (this mirrors the behaviour of the corresponding C++ method pagmo::problem::extract()),

              • -

            • if t is object and the UDP is a Python object (as opposed to an +

            • if t is object and the UDP is a Python object (as opposed to an exposed C++ problem), then a reference to the UDP will be returned (this allows to extract a Python UDP without knowing its type),

              • -

            • otherwise, None will be returned.

              • +

            • otherwise, None will be returned.

            Parameters
            -

            t (type) – the type of the user-defined problem to extract

            +

            t (type) – the type of the user-defined problem to extract

            Returns
            -

            a reference to the internal user-defined problem, or None if the extraction fails

            +

            a reference to the internal user-defined problem, or None if the extraction fails

            Raises
            -

            TypeError – if t is not a type

            +

            TypeError – if t is not a type

            Examples

            @@ -643,7 +643,7 @@

            Problem class

            bool

          Raises
          -

          ValueError – if the size of f is not the same as the output of +

          ValueError – if the size of f is not the same as the output of get_nf()

        @@ -699,7 +699,7 @@

        Problem classRaises
        Return type
        -

        tuple

        +

        tuple

        Raises

        unspecified – any exception thrown by the invoked method of the underlying C++ class, or failures at the @@ -743,7 +743,7 @@

        Problem class

        extra info about the UDP

        Return type
        -

        str

        +

        str

        Raises

        unspecified – any exception thrown by the get_extra_info() method of the UDP

        @@ -763,7 +763,7 @@

        Problem class

        the number of times fitness() was successfully called

        Return type
        -

        int

        +

        int

      @@ -780,7 +780,7 @@

      Problem class

      the number of times gradient() was successfully called

    Return type
    -

    int

    +

    int

    @@ -797,7 +797,7 @@

    Problem class

    the number of times hessians() was successfully called

    Return type
    -

    int

    +

    int

    @@ -833,7 +833,7 @@

    Problem class

    the problem’s name

    Return type
    -

    str

    +

    str

    @@ -849,7 +849,7 @@

    Problem class

    the total number of constraints of the problem

    Return type
    -

    int

    +

    int

    @@ -864,7 +864,7 @@

    Problem class

    the continuous dimension of the problem

    Return type
    -

    int

    +

    int

    @@ -874,7 +874,7 @@

    Problem classget_nec()#

    Number of equality constraints.

    This method will return \(n_{ec}\), the number of equality constraints of the problem.

    -

    The optional get_nec() method of the UDP must return the number of equality constraints as an int. +

    The optional get_nec() method of the UDP must return the number of equality constraints as an int. If the UDP does not implement the get_nec() method, zero equality constraints will be assumed. The number of equality constraints returned by the UDP is checked upon the construction of a problem.

    @@ -883,7 +883,7 @@

    Problem class

    the number of equality constraints of the problem

    Return type
    -

    int

    +

    int

    @@ -899,7 +899,7 @@

    Problem class

    the dimension of the fitness

    Return type
    -

    int

    +

    int

    @@ -909,7 +909,7 @@

    Problem classget_nic()#

    Number of inequality constraints.

    This method will return \(n_{ic}\), the number of inequality constraints of the problem.

    -

    The optional get_nic() method of the UDP must return the number of inequality constraints as an int. +

    The optional get_nic() method of the UDP must return the number of inequality constraints as an int. If the UDP does not implement the get_nic() method, zero inequality constraints will be assumed. The number of inequality constraints returned by the UDP is checked upon the construction of a problem.

    @@ -918,7 +918,7 @@

    Problem class

    the number of inequality constraints of the problem

    Return type
    -

    int

    +

    int

    @@ -928,7 +928,7 @@

    Problem classget_nix()#

    Integer dimension of the problem.

    This method will return \(n_{ix}\), the integer dimension of the problem.

    -

    The optional get_nix() method of the UDP must return the problem’s integer dimension as an int. +

    The optional get_nix() method of the UDP must return the problem’s integer dimension as an int. If the UDP does not implement the get_nix() method, a zero integer dimension will be assumed. The integer dimension returned by the UDP is checked upon the construction of a problem.

    @@ -937,7 +937,7 @@

    Problem class

    the integer dimension of the problem

    Return type
    -

    int

    +

    int

    @@ -947,7 +947,7 @@

    Problem classget_nobj()#

    Number of objectives.

    This method will return \(n_{obj}\), the number of objectives of the problem.

    -

    The optional get_nobj() method of the UDP must return the number of objectives as an int. +

    The optional get_nobj() method of the UDP must return the number of objectives as an int. If the UDP does not implement the get_nobj() method, a single-objective optimizaztion problem will be assumed. The number of objectives returned by the UDP is checked upon the construction of a problem.

    @@ -956,7 +956,7 @@

    Problem class

    the number of objectives of the problem

    Return type
    -

    int

    +

    int

    @@ -972,7 +972,7 @@

    Problem class

    the dimension of the problem

    Return type
    -

    int

    +

    int

    @@ -1045,10 +1045,10 @@

    Problem classRaises
      -

    • ValueError – if either the length of dv differs from the value returned by get_nx(), or +

    • ValueError – if either the length of dv differs from the value returned by get_nx(), or the returned gradient vector does not have the same size as the vector returned by gradient_sparsity()

      • -

    • NotImplementedError – if the UDP does not provide a gradient() method

      • +

    • NotImplementedError – if the UDP does not provide a gradient() method

    • unspecified – any exception thrown by the gradient() method of the UDP, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    @@ -1081,14 +1081,14 @@

    Problem classRaises
      -

    • ValueError – if the NumPy array returned by the UDP does not satisfy the requirements described above (e.g., invalid +

    • ValueError – if the NumPy array returned by the UDP does not satisfy the requirements described above (e.g., invalid shape, dimensions, etc.), at least one element of the returned iterable Python object does not consist of a collection of exactly 2 elements, or the sparsity pattern returned by the UDP is invalid (specifically, if it is not strictly sorted lexicographically, or if the indices in the pattern are incompatible with the properties of the problem, or if the size of the returned pattern is different from the size recorded upon construction)

      • -

    • OverflowError – if the NumPy array returned by the UDP contains integer values which are negative or outside an +

    • OverflowError – if the NumPy array returned by the UDP contains integer values which are negative or outside an implementation-defined range

    • unspecified – any exception thrown by the underlying C++ function, the PyArray_FROM_OTF() function from the NumPy C API, or @@ -1287,14 +1287,14 @@

      Problem class

      the hessians of dv

    Return type
    -

    list of 1D NumPy float array

    +

    list of 1D NumPy float array

    Raises
      -

    • ValueError – if the length of dv differs from the value returned by get_nx(), or +

    • ValueError – if the length of dv differs from the value returned by get_nx(), or the length of returned hessians does not match the corresponding hessians sparsity pattern dimensions, or the size of the return value is not equal to the fitness dimension

      • -

    • NotImplementedError – if the UDP does not provide a hessians() method

      • +

    • NotImplementedError – if the UDP does not provide a hessians() method

    • unspecified – any exception thrown by the hessians() method of the UDP, or by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

    @@ -1326,17 +1326,17 @@

    Problem class

    the hessians sparsity patterns

    Return type
    -

    list of 2D Numpy int array

    +

    list of 2D Numpy int array

    Raises
      -

    • ValueError – if the NumPy arrays returned by the UDP do not satisfy the requirements described above (e.g., invalid +

    • ValueError – if the NumPy arrays returned by the UDP do not satisfy the requirements described above (e.g., invalid shape, dimensions, etc.), at least one element of a returned iterable Python object does not consist of a collection of exactly 2 elements, or if a sparsity pattern returned by the UDP is invalid (specifically, if it is not strictly sorted lexicographically, if the indices in the pattern are incompatible with the properties of the problem or if the size of the pattern differs from the size recorded upon construction)

      • -

    • OverflowError – if the NumPy arrays returned by the UDP contain integer values which are negative or outside an +

    • OverflowError – if the NumPy arrays returned by the UDP contain integer values which are negative or outside an implementation-defined range

    • unspecified – any exception thrown by the underlying C++ function, the PyArray_FROM_OTF() function from the NumPy C API, or @@ -1357,7 +1357,7 @@

      Problem class
      Parameters
      -

      n (int) – the amount by which the internal counter of fitness evaluations will be increased

      +

      n (int) – the amount by which the internal counter of fitness evaluations will be increased

      Raises

      unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., @@ -1370,17 +1370,17 @@

      Problem class is_(t)#

      Check the type of the user-defined problem.

      -

      This method returns False if extract(t) returns -None, and True otherwise.

      +

      This method returns False if extract(t) returns +None, and True otherwise.

      Parameters
      -

      t (type) – the type that will be compared to the type of the UDP

      +

      t (type) – the type that will be compared to the type of the UDP

      Returns

      whether the UDP is of type t or not

      Return type
      -

      bool

      +

      bool

      Raises

      unspecified – any exception thrown by extract()

      @@ -1402,15 +1402,15 @@

      Problem classset_seed() method, then its set_seed() method will be invoked. Otherwise, an error will be raised. The seed parameter must be non-negative.

      -

      The set_seed() method of the UDP must be able to take an int as input parameter.

      +

      The set_seed() method of the UDP must be able to take an int as input parameter.

      Parameters
      -

      seed (int) – the desired seed value

      +

      seed (int) – the desired seed value

      Raises
        -

      • NotImplementedError – if the UDP does not provide a set_seed() method

        • -

      • OverflowError – if seed is negative

        • +

      • NotImplementedError – if the UDP does not provide a set_seed() method

        • +

      • OverflowError – if seed is negative

      • unspecified – any exception raised by the set_seed() method of the UDP or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

      diff --git a/problems.html b/problems.html index accbff3f..c7f61925 100644 --- a/problems.html +++ b/problems.html @@ -449,13 +449,13 @@

      Contents

      Parameters

      • prob – a problem or a user-defined problem, either C++ or Python (if -prob is None, a null_problem will be used in its stead)

        • +prob is None, a null_problem will be used in its stead)

      • kwargs – the dictionary of decorators to be applied to the functions of the input problem

      Raises
        -

      • TypeError – if at least one of the values in kwargs is not callable

        • +

      • TypeError – if at least one of the values in kwargs is not callable

      • unspecified – any exception thrown by the constructor of problem or the deep copy of prob or kwargs

      @@ -467,7 +467,7 @@

      Contents

      Get the decorator for the function called fname.

      This method will return a copy of the decorator that has been registered upon construction for the function called fname. If no decorator for fname has been specified during -construction, None will be returned.

      +construction, None will be returned.

      >>> from pygmo import decorator_problem, problem, rosenbrock
 >>> def f_decor(orig_fitness_function):
 ...     def new_fitness_function(self, dv):
@@ -483,14 +483,14 @@ 
       Contents
      Parameters
      -

      fname (str) – the name of the function whose decorator will be returned

      +

      fname (str) – the name of the function whose decorator will be returned

      Returns
      -

      a copy of the decorator registered for fname, or None if no decorator for fname has been registered

      +

      a copy of the decorator registered for fname, or None if no decorator for fname has been registered

      Raises
        -

      • TypeError – if fname is not a string

        • +

      • TypeError – if fname is not a string

      • unspecified – any exception thrown by the deep copying of the decorator for fname

      @@ -516,7 +516,7 @@

      Contents

      -class pygmo.constant_arguments(prob, fixed_arguments: List[Optional[float]])#
      +class pygmo.constant_arguments(prob, fixed_arguments: List[Optional[float]])#

      Meta problem that sets some arguments of the original problem to constants

      New in version 2.19.

      @@ -540,7 +540,7 @@

      Contents

      Parameters

      • prob – a problem or a user-defined problem, either C++ or Python (if -prob is None, a null_problem will be used in its stead)

        • +prob is None, a null_problem will be used in its stead)

      • fixed_arguments – a list of values, one for each dimension of the wrapped problem. Each value should be either a float, if the argument should be fixed to this value, or None, if it should remain free

        • @@ -548,9 +548,9 @@

        Contents

      Raises
        -

      • ValueError – if the lengths of fixed_arguments differs from the number of dimensions of the wrapped problem

        • -

      • ValueError – if any of the fixed arguments violate the bounds of the wrapped problem

        • -

      • ValueError – if a problem with nix() > 0 is passed

        • +

      • ValueError – if the lengths of fixed_arguments differs from the number of dimensions of the wrapped problem

        • +

      • ValueError – if any of the fixed arguments violate the bounds of the wrapped problem

        • +

      • ValueError – if a problem with nix() > 0 is passed

      • unspecified – any exception thrown by the constructor of problem or the deep copy of prob

      @@ -558,7 +558,7 @@

      Contents

      -get_full_x(x) → List[float]#
      +get_full_x(x) → List[float]#

      Get the full x for a given x of lower dimension

      @@ -575,14 +575,14 @@

      Contents

      Parameters
        -

      • nobj (int) – the number of objectives

        • -

      • nec (int) – the number of equality constraints

        • -

      • nic (int) – the number of inequality constraintsctives

        • +

      • nobj (int) – the number of objectives

        • +

      • nec (int) – the number of equality constraints

        • +

      • nic (int) – the number of inequality constraintsctives

      Raises
        -

      • ValueError – if nobj, nec, nic are negative or greater than an implementation-defined value or if nobj is zero

        • +

      • ValueError – if nobj, nec, nic are negative or greater than an implementation-defined value or if nobj is zero

      • unspecified – any exception thrown by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

      @@ -601,14 +601,14 @@

      Contents

      Parameters
        -

      • prob_id (int) – problem id (one of [1..30])

        • -

      • dim (int) – number of dimensions (one of [2, 10, 20, 30, 50, 100])

        • +

      • prob_id (int) – problem id (one of [1..30])

        • +

      • dim (int) – number of dimensions (one of [2, 10, 20, 30, 50, 100])

      Raises
        -

      • OverflowError – if dim or prob_id are negative or greater than an implementation-defined value

        • -

      • ValueError – if prob_id is not in [1..28] or if dim is not in [2, 10, 20, 30, 50, 100] or if dim is 2 and prob_id is in [17,18,19,20,21,22,29,30]

        • +

      • OverflowError – if dim or prob_id are negative or greater than an implementation-defined value

        • +

      • ValueError – if prob_id is not in [1..28] or if dim is not in [2, 10, 20, 30, 50, 100] or if dim is 2 and prob_id is in [17,18,19,20,21,22,29,30]

      @@ -623,14 +623,14 @@

      Contents

      Parameters
        -

      • prob_id (int) – problem id (one of [1..28])

        • -

      • dim (int) – number of dimensions (one of [2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])

        • +

      • prob_id (int) – problem id (one of [1..28])

        • +

      • dim (int) – number of dimensions (one of [2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])

      Raises
        -

      • OverflowError – if dim or prob_id are negative or greater than an implementation-defined value

        • -

      • ValueError – if prob_id is not in [1..28] or if dim is not in [2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

        • +

      • OverflowError – if dim or prob_id are negative or greater than an implementation-defined value

        • +

      • ValueError – if prob_id is not in [1..28] or if dim is not in [2, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

      @@ -644,12 +644,12 @@

      Contents

      The CEC 2006 problem suite (continuous, constrained, single-objective problems)

      Parameters
      -

      prob_id (int) – problem id (one of [1..24])

      +

      prob_id (int) – problem id (one of [1..24])

      Raises
        -

      • OverflowError – if prob_id is negative or greater than an implementation-defined value

        • -

      • ValueError – if prob_id is not in [1..24]

        • +

      • OverflowError – if prob_id is negative or greater than an implementation-defined value

        • +

      • ValueError – if prob_id is not in [1..24]

      @@ -678,15 +678,15 @@

      Contents

      Parameters
        -

      • prob_id (int) – problem id (one of [1..10])

        • -

      • is_constrained (bool) – selects the constrained version of the problems

        • -

      • dim (int) – problem dimension

        • +

      • prob_id (int) – problem id (one of [1..10])

        • +

      • is_constrained (bool) – selects the constrained version of the problems

        • +

      • dim (int) – problem dimension

      Raises
        -

      • OverflowError – if prob_id or dim are negative or greater than an implementation-defined value

        • -

      • ValueError – if prob_id is not in [1..10] or if dim is zero

        • +

      • OverflowError – if prob_id or dim are negative or greater than an implementation-defined value

        • +

      • ValueError – if prob_id is not in [1..10] or if dim is zero

      @@ -700,12 +700,12 @@

      Contents

      The Rosenbrock problem.

      Parameters
      -

      dim (int) – problem dimension

      +

      dim (int) – problem dimension

      Raises
        -

      • OverflowError – if dim is negative or greater than an implementation-defined value

        • -

      • ValueError – if dim is less than 2

        • +

      • OverflowError – if dim is negative or greater than an implementation-defined value

        • +

      • ValueError – if dim is less than 2

      @@ -756,14 +756,14 @@

      Contents

      Parameters
        -

      • dim_c (int) – MINLP continuous dimension

        • -

      • dim_i (int) – MINLP integer dimension

        • +

      • dim_c (int) – MINLP continuous dimension

        • +

      • dim_i (int) – MINLP integer dimension

      Raises
        -

      • OverflowError – if dim_c / dim_i is negative or greater than an implementation-defined value

        • -

      • ValueError – if dim_c + dim_i is less than 1

        • +

      • OverflowError – if dim_c / dim_i is negative or greater than an implementation-defined value

        • +

      • ValueError – if dim_c + dim_i is less than 1

      @@ -876,7 +876,7 @@

      Contents

      the distance (or average distance) from the Pareto front

      Return type
      -

      float

      +

      float

      See also the docs of the C++ class p_distance()

      @@ -892,16 +892,16 @@

      Contents

      Parameters
        -

      • prob_id (int) – DTLZ problem id

        • -

      • dim (int) – problem dimension

        • -

      • fdim (int) – number of objectives

        • -

      • alpha (int) – controls density of solutions (used only by DTLZ4)

        • +

      • prob_id (int) – DTLZ problem id

        • +

      • dim (int) – problem dimension

        • +

      • fdim (int) – number of objectives

        • +

      • alpha (int) – controls density of solutions (used only by DTLZ4)

      Raises
        -

      • OverflowError – if prob_id, dim, fdim or alpha are negative or greater than an implementation-defined value

        • -

      • ValueError – if prob_id is not in [1..7], fdim is smaller than 2, dim is smaller or equal to fdim.

        • +

      • OverflowError – if prob_id, dim, fdim or alpha are negative or greater than an implementation-defined value

        • +

      • ValueError – if prob_id is not in [1..7], fdim is smaller than 2, dim is smaller or equal to fdim.

      @@ -924,7 +924,7 @@

      Contents

      the distance (or average distance) from the Pareto front

      Return type
      -

      float

      +

      float

      See also the docs of the C++ class p_distance()

      @@ -947,7 +947,7 @@

      Contents

      the current axes instance on the current figure

      Raises
      -

      ValueError – if pop does not contain a DTLZ problem (veryfied by its name only) or if comp is not of length 3

      +

      ValueError – if pop does not contain a DTLZ problem (veryfied by its name only) or if comp is not of length 3

      Examples

      @@ -1010,10 +1010,10 @@

      Contents

      constrained optimization.” (1999). http://hdl.handle.net/11104/0123965

      Parameters
      -

      dim (int) – problem dimension

      +

      dim (int) – problem dimension

      Raises
      -

      OverflowError – if dim is negative or greater than an implementation-defined value

      +

      OverflowError – if dim is negative or greater than an implementation-defined value

      See also the docs of the C++ class pagmo::luksan_vlcek1.

      @@ -1031,13 +1031,13 @@

      Contents

      Parameters

      • prob – a user-defined problem (either Python or C++), or an instance of problem -(if prob is None, a null_problem will be used in its stead)

        • +(if prob is None, a null_problem will be used in its stead)

      • translation (array-like object) – an array containing the translation to be applied

      Raises
        -

      • ValueError – if the length of translation is not equal to the dimension of prob

        • +

      • ValueError – if the length of translation is not equal to the dimension of prob

      • unspecified – any exception thrown by: * the constructor of pygmo.problem, @@ -1066,7 +1066,7 @@

        Contents

        property translation#

        Translation vector.

        -

        This read-only property contains an array of float representing the translation vector used in the +

        This read-only property contains an array of float representing the translation vector used in the construction of this problem.

        Returns
        @@ -1122,17 +1122,17 @@

        Contents

        Parameters

        • prob – a user-defined problem (either Python or C++), or an instance of problem -(if prob is None, a null_problem will be used in its stead)

          • +(if prob is None, a null_problem will be used in its stead)

        • weight (array-like object) – the vector of weights \(\boldsymbol \lambda\)

        • z (array-like object) – the reference point \(\mathbf z^*\)

          • -

        • method (str) – a string containing the decomposition method chosen

          • -

        • adapt_ideal (bool) – when True, the reference point is adapted at each fitness evaluation +

        • method (str) – a string containing the decomposition method chosen

          • +

        • adapt_ideal (bool) – when True, the reference point is adapted at each fitness evaluation to be the ideal point

        Raises
          -

        • ValueError – if either: +

        • ValueError – if either: * prob is single objective or constrained, * method is not one of ['weighted', 'tchebycheff', 'bi'], @@ -1234,14 +1234,14 @@

          Contents

          Parameters

          • prob – a problem or a user-defined problem, either C++ or Python (if -prob is None, a null_problem will be used in its stead)

            • -

          • method (str) – a string containing the unconstrain method chosen, one of ['death penalty', 'kuri', 'weighted', 'ignore_c', 'ignore_o']

            • +prob is None, a null_problem will be used in its stead)

            +

          • method (str) – a string containing the unconstrain method chosen, one of ['death penalty', 'kuri', 'weighted', 'ignore_c', 'ignore_o']

          • weights (array-like object) – the vector of weights to be used if the method chosen is 'weighted'

          Raises
            -

          • ValueError – if either: +

          • ValueError – if either: * prob is unconstrained, * method is not one of ['death penalty', 'kuri', 'weighted', 'ignore_c', 'ignore_o'], @@ -1280,16 +1280,16 @@

            Contents

            Parameters
              -

            • prob_id (int) – WFG problem id

              • -

            • dim_dvs (int) – decision vector size

              • -

            • dim_obj (int) – number of objectives

              • -

            • dim_k (int) – position parameter

              • +

            • prob_id (int) – WFG problem id

              • +

            • dim_dvs (int) – decision vector size

              • +

            • dim_obj (int) – number of objectives

              • +

            • dim_k (int) – position parameter

            Raises
              -

            • OverflowError – if prob_id, dim_dvs, dim_obj or dim_k are negative or greater than an implementation-defined value

              • -

            • ValueError – if prob_id is not in [1, …, 9], dim_dvs is smaller than 1, dim_obj is smaller than 2, dim_k is +

            • OverflowError – if prob_id, dim_dvs, dim_obj or dim_k are negative or greater than an implementation-defined value

              • +

            • ValueError – if prob_id is not in [1, …, 9], dim_dvs is smaller than 1, dim_obj is smaller than 2, dim_k is smaller than 1 or bigger or equal to dim_dvs or if dim_k*mod(*dim_obj-1) is different than zero. Also, when prob_id equals to 2 or 3, if (dim_dvs-dim_k)mod(2) is different than zero.

            diff --git a/r_policies.html b/r_policies.html index fc6dc26a..6adb87de 100644 --- a/r_policies.html +++ b/r_policies.html @@ -426,13 +426,13 @@

            Contents

            See also the docs of the C++ class pagmo::fair_replace.

            Parameters
            -

            rate (int, float) – the desired migration rate

            +

            rate (int, float) – the desired migration rate

            Raises
              -

            • ValueError – if the supplied fractional migration rate is not finite +

            • ValueError – if the supplied fractional migration rate is not finite or not in the \(\left[0,1\right]\) range

              • -

            • TypeError – if rate is not an instance of int or float

              • +

            • TypeError – if rate is not an instance of int or float

            • unspecified – any exception raised by the invoked C++ constructor

            diff --git a/r_policy.html b/r_policy.html index c057d262..3be46d16 100644 --- a/r_policy.html +++ b/r_policy.html @@ -451,7 +451,7 @@

            Replacement policyRaises
              -

            • NotImplementedError – if udrp does not implement the mandatory methods detailed above

              • +

            • NotImplementedError – if udrp does not implement the mandatory methods detailed above

            • unspecified – any exception thrown by methods of the UDRP invoked during construction, the deep copy of the UDRP, the constructor of the underlying C++ class, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function @@ -476,25 +476,25 @@

              Replacement policyr_policy instance. The behaviour of this function depends on the value -of t (which must be a type) and on the type of the internal UDRP:

              +of t (which must be a type) and on the type of the internal UDRP:

              • if the type of the UDRP is t, then a reference to the UDRP will be returned (this mirrors the behaviour of the corresponding C++ method pagmo::r_policy::extract()),

                • -

              • if t is object and the UDRP is a Python object (as opposed to an +

              • if t is object and the UDRP is a Python object (as opposed to an exposed C++ replacement policy), then a reference to the UDRP will be returned (this allows to extract a Python UDRP without knowing its type),

                • -

              • otherwise, None will be returned.

                • +

              • otherwise, None will be returned.

              Parameters
              -

              t (type) – the type of the user-defined replacement policy to extract

              +

              t (type) – the type of the user-defined replacement policy to extract

              Returns
              -

              a reference to the internal user-defined replacement policy, or None if the extraction fails

              +

              a reference to the internal user-defined replacement policy, or None if the extraction fails

              Raises
              -

              TypeError – if t is not a type

              +

              TypeError – if t is not a type

            @@ -510,7 +510,7 @@

            Replacement policy

            extra info about the UDRP

            Return type
            -

            str

            +

            str

            Raises

            unspecified – any exception thrown by the get_extra_info() method of the UDRP

            @@ -529,7 +529,7 @@

            Replacement policy

            the name of the replacement policy

            Return type
            -

            str

            +

            str

        @@ -538,17 +538,17 @@

        Replacement policy is_(t)#

        Check the type of the user-defined replacement policy.

        -

        This method returns False if extract(t) returns -None, and True otherwise.

        +

        This method returns False if extract(t) returns +None, and True otherwise.

        Parameters
        -

        t (type) – the type that will be compared to the type of the UDRP

        +

        t (type) – the type that will be compared to the type of the UDRP

        Returns

        whether the UDRP is of type t or not

        Return type
        -

        bool

        +

        bool

        Raises

        unspecified – any exception thrown by extract()

        @@ -581,28 +581,28 @@

        Replacement policy
        Parameters
          -

        • inds (tuple) – the original group of individuals

          • -

        • nx (int) – the dimension of the problem inds and mig refer to

          • -

        • nix (int) – the integral dimension of the problem inds and mig refer to

          • -

        • nobj (int) – the number of objectives of the problem inds and mig refer to

          • -

        • nec (int) – the number of equality constraints of the problem inds and mig refer to

          • -

        • nic (int) – the number of inequality constraints of the problem inds and mig refer to

          • +

        • inds (tuple) – the original group of individuals

          • +

        • nx (int) – the dimension of the problem inds and mig refer to

          • +

        • nix (int) – the integral dimension of the problem inds and mig refer to

          • +

        • nobj (int) – the number of objectives of the problem inds and mig refer to

          • +

        • nec (int) – the number of equality constraints of the problem inds and mig refer to

          • +

        • nic (int) – the number of inequality constraints of the problem inds and mig refer to

        • tol (array-like object) – the vector of constraints tolerances of the problem inds and mig refer to

          • -

        • mig (tuple) – the group of migrants

          • +

        • mig (tuple) – the group of migrants

        Returns

        a new set of individuals resulting from replacing individuals in inds with individuals from mig

        Return type
        -

        tuple

        +

        tuple

        Raises
          -

        • RuntimeError – if the object returned by a pythonic UDRP is not iterable, or it is an iterable +

        • RuntimeError – if the object returned by a pythonic UDRP is not iterable, or it is an iterable whose number of elements is not exactly 3, or if the invocation of the replace() method of the UDRP raises an exception

          • -

        • ValueError – if inds, mig or the return value are not consistent with the problem properties, +

        • ValueError – if inds, mig or the return value are not consistent with the problem properties, or the ID, decision and fitness vectors in inds, mig or the return value have inconsistent sizes, or the problem properties are invalid (e.g., nobj is zero, nix > nx, etc.)

        • unspecified – any exception raised by failures at the intersection diff --git a/s_policies.html b/s_policies.html index bff045e5..c7661304 100644 --- a/s_policies.html +++ b/s_policies.html @@ -425,13 +425,13 @@

          Contents

          See also the docs of the C++ class pagmo::select_best.

          Parameters
          -

          rate (int, float) – the desired migration rate

          +

          rate (int, float) – the desired migration rate

          Raises
            -

          • ValueError – if the supplied fractional migration rate is not finite +

          • ValueError – if the supplied fractional migration rate is not finite or not in the \(\left[0,1\right]\) range

            • -

          • TypeError – if rate is not an instance of int or float

            • +

          • TypeError – if rate is not an instance of int or float

          • unspecified – any exception raised by the invoked C++ constructor

          diff --git a/s_policy.html b/s_policy.html index 285588ca..d7b3ff8a 100644 --- a/s_policy.html +++ b/s_policy.html @@ -446,7 +446,7 @@

          Selection policyRaises
            -

          • NotImplementedError – if udsp does not implement the mandatory methods detailed above

            • +

          • NotImplementedError – if udsp does not implement the mandatory methods detailed above

          • unspecified – any exception thrown by methods of the UDSP invoked during construction, the deep copy of the UDSP, the constructor of the underlying C++ class, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function @@ -471,25 +471,25 @@

            Selection policys_policy instance. The behaviour of this function depends on the value -of t (which must be a type) and on the type of the internal UDSP:

            +of t (which must be a type) and on the type of the internal UDSP:

            • if the type of the UDSP is t, then a reference to the UDSP will be returned (this mirrors the behaviour of the corresponding C++ method pagmo::s_policy::extract()),

              • -

            • if t is object and the UDSP is a Python object (as opposed to an +

            • if t is object and the UDSP is a Python object (as opposed to an exposed C++ selection policy), then a reference to the UDSP will be returned (this allows to extract a Python UDSP without knowing its type),

              • -

            • otherwise, None will be returned.

              • +

            • otherwise, None will be returned.

            Parameters
            -

            t (type) – the type of the user-defined selection policy to extract

            +

            t (type) – the type of the user-defined selection policy to extract

            Returns
            -

            a reference to the internal user-defined selection policy, or None if the extraction fails

            +

            a reference to the internal user-defined selection policy, or None if the extraction fails

            Raises
            -

            TypeError – if t is not a type

            +

            TypeError – if t is not a type

          @@ -505,7 +505,7 @@

          Selection policy

          extra info about the UDSP

        Return type
        -

        str

        +

        str

        Raises

        unspecified – any exception thrown by the get_extra_info() method of the UDSP

        @@ -524,7 +524,7 @@

        Selection policy

        the name of the selection policy

        Return type
        -

        str

        +

        str

      @@ -533,17 +533,17 @@

      Selection policy is_(t)#

      Check the type of the user-defined selection policy.

      -

      This method returns False if extract(t) returns -None, and True otherwise.

      +

      This method returns False if extract(t) returns +None, and True otherwise.

      Parameters
      -

      t (type) – the type that will be compared to the type of the UDSP

      +

      t (type) – the type that will be compared to the type of the UDSP

      Returns

      whether the UDSP is of type t or not

      Return type
      -

      bool

      +

      bool

      Raises

      unspecified – any exception thrown by extract()

      @@ -575,12 +575,12 @@

      Selection policy
      Parameters
        -

      • inds (tuple) – the original group of individuals

        • -

      • nx (int) – the dimension of the problem inds refers to

        • -

      • nix (int) – the integral dimension of the problem inds refers to

        • -

      • nobj (int) – the number of objectives of the problem inds refers to

        • -

      • nec (int) – the number of equality constraints of the problem inds refers to

        • -

      • nic (int) – the number of inequality constraints of the problem inds refers to

        • +

      • inds (tuple) – the original group of individuals

        • +

      • nx (int) – the dimension of the problem inds refers to

        • +

      • nix (int) – the integral dimension of the problem inds refers to

        • +

      • nobj (int) – the number of objectives of the problem inds refers to

        • +

      • nec (int) – the number of equality constraints of the problem inds refers to

        • +

      • nic (int) – the number of inequality constraints of the problem inds refers to

      • tol (array-like object) – the vector of constraints tolerances of the problem inds refers to

      @@ -588,14 +588,14 @@

      Selection policy

      a new set of individuals resulting from selecting individuals in inds.

      Return type
      -

      tuple

      +

      tuple

      Raises
        -

      • RuntimeError – if the object returned by a pythonic UDSP is not iterable, or it is an iterable +

      • RuntimeError – if the object returned by a pythonic UDSP is not iterable, or it is an iterable whose number of elements is not exactly 3, or if the invocation of the select() method of the UDSP raises an exception

        • -

      • ValueError – if inds or the return value are not consistent with the problem properties, +

      • ValueError – if inds or the return value are not consistent with the problem properties, or the ID, decision and fitness vectors in inds or the return value have inconsistent sizes, or the problem properties are invalid (e.g., nobj is zero, nix > nx, etc.)

      • unspecified – any exception raised by failures at the intersection diff --git a/searchindex.js b/searchindex.js index cb6b0f6c..e09e62aa 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ 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class","List of algorithms","API reference","Archipelago class","Batch fitness evaluator","List of batch evaluators","Changelog","Constrained optimization utilities","Credits","Generic optimization utilities","Generic optimization utilities","Gradient and Hessians utilities","Hypervolume utilities","pygmo","Installation","Island class","List of islands","Known issues","Miscellanea","Multi-objective optimization utilities","Capabilities","Plotting utilities","Population class","Problem class","List of problems","List of replacement policies","Replacement policy","List of selection policies","Selection policy","List of topologies","Topology","Benchmarking Extended Ant Colony Optimization on CEC2006 problems","Participating to the CEC2013 Competition","Comparing algorithms: the case of xNES and CMA-ES","Coding a User Defined Island","Coding a User Defined Problem with constraints (NLP)","Coding a User Defined Problem with an integer part (MINLP)","Coding a User Defined Problem with 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\ No newline at end of file diff --git a/topologies.html b/topologies.html index e98db201..60251ca7 100644 --- a/topologies.html +++ b/topologies.html @@ -416,14 +416,14 @@

        Contents

        Parameters
          -

        • n (int) – the desired number of vertices

          • -

        • w (float) – the weight of the edges

          • +

        • n (int) – the desired number of vertices

          • +

        • w (float) – the weight of the edges

        Raises
          -

        • TypeError – if n is negative or too large

          • -

        • ValueError – if w is not in the \(\left[0, 1\right]\) range

          • +

        • TypeError – if n is negative or too large

          • +

        • ValueError – if w is not in the \(\left[0, 1\right]\) range

        @@ -435,15 +435,15 @@

        Contents

        Parameters
          -

        • i (int) – the first vertex index

          • -

        • j (int) – the second vertex index

          • -

        • w (float) – the edge’s weight

          • +

        • i (int) – the first vertex index

          • +

        • j (int) – the second vertex index

          • +

        • w (float) – the edge’s weight

        Raises
          -

        • TypeError – if i or j are negative or too large

          • -

        • ValueError – if i or j are not smaller than the number of vertices, i and j are already adjacent, or +

        • TypeError – if i or j are negative or too large

          • +

        • ValueError – if i or j are not smaller than the number of vertices, i and j are already adjacent, or if w is not in the \(\left[0, 1\right]\) range

        @@ -466,20 +466,20 @@

        Contents

        Parameters
          -

        • i (int) – the first vertex index

          • -

        • j (int) – the second vertex index

          • +

        • i (int) – the first vertex index

          • +

        • j (int) – the second vertex index

        Returns
        -

        True if i and j are adjacent, False otherwise

        +

        True if i and j are adjacent, False otherwise

        Return type
        -

        bool

        +

        bool

        Raises
          -

        • TypeError – if i or j are negative or too large

          • -

        • ValueError – if i or j are not smaller than the number of vertices

          • +

        • TypeError – if i or j are negative or too large

          • +

        • ValueError – if i or j are not smaller than the number of vertices

        @@ -495,20 +495,20 @@

        Contents

        Parameters
          -

        • i (int) – the source vertex index

          • -

        • j (int) – the destination vertex index

          • +

        • i (int) – the source vertex index

          • +

        • j (int) – the destination vertex index

        Returns

        the weight of the edge connecting i to j

        Return type
        -

        float

        +

        float

        Raises
          -

        • TypeError – if i or j are negative or too large

          • -

        • ValueError – if either i or j are not smaller than the number of vertices, or +

        • TypeError – if i or j are negative or too large

          • +

        • ValueError – if either i or j are not smaller than the number of vertices, or i and j are not adjacent

        @@ -523,7 +523,7 @@

        Contents

        the weight w used in the construction of this topology

        Return type
        -

        float

        +

        float

      @@ -536,7 +536,7 @@

      Contents

      the number of vertices in the topology

      Return type
      -

      int

      +

      int

      @@ -549,14 +549,14 @@

      Contents

      Parameters
        -

      • i (int) – the first vertex index

        • -

      • j (int) – the second vertex index

        • +

      • i (int) – the first vertex index

        • +

      • j (int) – the second vertex index

      Raises
        -

      • TypeError – if i or j are negative or too large

        • -

      • ValueError – if i or j are not smaller than the number of vertices, or i and j are not adjacent

        • +

      • TypeError – if i or j are negative or too large

        • +

      • ValueError – if i or j are not smaller than the number of vertices, or i and j are not adjacent

      @@ -568,10 +568,10 @@

      Contents

      This method will set the weights of all edges in the topology to w.

      Parameters
      -

      w (float) – the edges’ weight

      +

      w (float) – the edges’ weight

      Raises
      -

      ValueError – if w is not in the \(\left[0, 1\right]\) range

      +

      ValueError – if w is not in the \(\left[0, 1\right]\) range

      @@ -584,15 +584,15 @@

      Contents

      Parameters
        -

      • i (int) – the first vertex index

        • -

      • j (int) – the second vertex index

        • -

      • w (float) – the desired weight

        • +

      • i (int) – the first vertex index

        • +

      • j (int) – the second vertex index

        • +

      • w (float) – the desired weight

      Raises
        -

      • TypeError – if i or j are negative or too large

        • -

      • ValueError – if i or j are not smaller than the number of vertices, i and j are not adjacent, or +

      • TypeError – if i or j are negative or too large

        • +

      • ValueError – if i or j are not smaller than the number of vertices, i and j are not adjacent, or if w is not in the \(\left[0, 1\right]\) range

      @@ -613,14 +613,14 @@

      Contents

      Parameters
        -

      • n (int) – the desired number of vertices

        • -

      • w (float) – the weight of the edges

        • +

      • n (int) – the desired number of vertices

        • +

      • w (float) – the weight of the edges

      Raises
        -

      • TypeError – if n is negative or too large

        • -

      • ValueError – if w is not in the \(\left[0, 1\right]\) range

        • +

      • TypeError – if n is negative or too large

        • +

      • ValueError – if w is not in the \(\left[0, 1\right]\) range

      @@ -632,7 +632,7 @@

      Contents

      the weight w used in the construction of this topology

      Return type
      -

      float

      +

      float

    @@ -645,7 +645,7 @@

    Contents

    the number of vertices in the topology

    Return type
    -

    int

    +

    int

    @@ -663,15 +663,15 @@

    Contents

    -

    Construction from None will initialise a topology +

    Construction from None will initialise a topology without vertices or edges.

    -

    Construction from a networkx.DiGraph will initialise +

    Construction from a networkx.DiGraph will initialise a topology whose vertices and edges are described by the input graph. All the edges of the input graph must have -a float attribute called weight whose value +a float attribute called weight whose value is in the \(\left[0 , 1\right]\) range.

    When t is a topology or a UDT, the constructor will attempt to fetch the NetworkX @@ -686,7 +686,7 @@

    Contents

    Raises
      -

    • ValueError – if the edges of the input networkx.DiGraph +

    • ValueError – if the edges of the input networkx.DiGraph do not all have a weight attribute, or if any edge weight is outside the \(\left[ 0, 1 \right]\) range

    • unspecified – any exception thrown by pygmo.topology.to_networkx(), or by @@ -702,15 +702,15 @@

      Contents

      Parameters
        -

      • i (int) – the first vertex index

        • -

      • j (int) – the second vertex index

        • -

      • w (float) – the edge’s weight

        • +

      • i (int) – the first vertex index

        • +

      • j (int) – the second vertex index

        • +

      • w (float) – the edge’s weight

      Raises
        -

      • TypeError – if i or j are negative or too large

        • -

      • ValueError – if i or j are not smaller than the number of vertices, i and j are already adjacent, or +

      • TypeError – if i or j are negative or too large

        • +

      • ValueError – if i or j are not smaller than the number of vertices, i and j are already adjacent, or if w is not in the \(\left[0, 1\right]\) range

      @@ -733,20 +733,20 @@

      Contents

      Parameters
        -

      • i (int) – the first vertex index

        • -

      • j (int) – the second vertex index

        • +

      • i (int) – the first vertex index

        • +

      • j (int) – the second vertex index

      Returns
      -

      True if i and j are adjacent, False otherwise

      +

      True if i and j are adjacent, False otherwise

      Return type
      -

      bool

      +

      bool

      Raises
        -

      • TypeError – if i or j are negative or too large

        • -

      • ValueError – if i or j are not smaller than the number of vertices

        • +

      • TypeError – if i or j are negative or too large

        • +

      • ValueError – if i or j are not smaller than the number of vertices

      @@ -762,20 +762,20 @@

      Contents

      Parameters
        -

      • i (int) – the source vertex index

        • -

      • j (int) – the destination vertex index

        • +

      • i (int) – the source vertex index

        • +

      • j (int) – the destination vertex index

      Returns

      the weight of the edge connecting i to j

      Return type
      -

      float

      +

      float

      Raises
        -

      • TypeError – if i or j are negative or too large

        • -

      • ValueError – if either i or j are not smaller than the number of vertices, or +

      • TypeError – if i or j are negative or too large

        • +

      • ValueError – if either i or j are not smaller than the number of vertices, or i and j are not adjacent

      @@ -790,7 +790,7 @@

      Contents

      the number of vertices in the topology

      Return type
      -

      int

      +

      int

    @@ -803,14 +803,14 @@

    Contents

    Parameters
      -

    • i (int) – the first vertex index

      • -

    • j (int) – the second vertex index

      • +

    • i (int) – the first vertex index

      • +

    • j (int) – the second vertex index

    Raises
      -

    • TypeError – if i or j are negative or too large

      • -

    • ValueError – if i or j are not smaller than the number of vertices, or i and j are not adjacent

      • +

    • TypeError – if i or j are negative or too large

      • +

    • ValueError – if i or j are not smaller than the number of vertices, or i and j are not adjacent

    @@ -822,10 +822,10 @@

    Contents

    This method will set the weights of all edges in the topology to w.

    Parameters
    -

    w (float) – the edges’ weight

    +

    w (float) – the edges’ weight

    Raises
    -

    ValueError – if w is not in the \(\left[0, 1\right]\) range

    +

    ValueError – if w is not in the \(\left[0, 1\right]\) range

    @@ -838,15 +838,15 @@

    Contents

    Parameters
      -

    • i (int) – the first vertex index

      • -

    • j (int) – the second vertex index

      • -

    • w (float) – the desired weight

      • +

    • i (int) – the first vertex index

      • +

    • j (int) – the second vertex index

      • +

    • w (float) – the desired weight

    Raises
      -

    • TypeError – if i or j are negative or too large

      • -

    • ValueError – if i or j are not smaller than the number of vertices, i and j are not adjacent, or +

    • TypeError – if i or j are negative or too large

      • +

    • ValueError – if i or j are not smaller than the number of vertices, i and j are not adjacent, or if w is not in the \(\left[0, 1\right]\) range

    diff --git a/topology.html b/topology.html index e1e58cea..ac5b2225 100644 --- a/topology.html +++ b/topology.html @@ -451,7 +451,7 @@

    TopologyRaises
      -

    • NotImplementedError – if udt does not implement the mandatory methods detailed above

      • +

    • NotImplementedError – if udt does not implement the mandatory methods detailed above

    • unspecified – any exception thrown by methods of the UDT invoked during construction, the deep copy of the UDT, the constructor of the underlying C++ class, or failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function @@ -476,25 +476,25 @@

      Topologytopology instance. The behaviour of this function depends on the value -of t (which must be a type) and on the type of the internal UDT:

      +of t (which must be a type) and on the type of the internal UDT:

      • if the type of the UDT is t, then a reference to the UDT will be returned (this mirrors the behaviour of the corresponding C++ method pagmo::topology::extract()),

        • -

      • if t is object and the UDT is a Python object (as opposed to an +

      • if t is object and the UDT is a Python object (as opposed to an exposed C++ topology), then a reference to the UDT will be returned (this allows to extract a Python UDT without knowing its type),

        • -

      • otherwise, None will be returned.

        • +

      • otherwise, None will be returned.

      Parameters
      -

      t (type) – the type of the user-defined topology to extract

      +

      t (type) – the type of the user-defined topology to extract

      Returns
      -

      a reference to the internal user-defined topology, or None if the extraction fails

      +

      a reference to the internal user-defined topology, or None if the extraction fails

      Raises
      -

      TypeError – if t is not a type

      +

      TypeError – if t is not a type

      Examples

      @@ -534,7 +534,7 @@

      Topologyget_connections() method of the UDT.

      Parameters
      -

      n (int) – the index of the vertex whose incoming connections’ details will be returned

      +

      n (int) – the index of the vertex whose incoming connections’ details will be returned

      Returns

      a pair of arrays describing n’s incoming connections

      @@ -544,10 +544,10 @@

      TopologyRaises
        -

      • RuntimeError – if the object returned by a pythonic UDT is not iterable, or it is an iterable +

      • RuntimeError – if the object returned by a pythonic UDT is not iterable, or it is an iterable whose number of elements is not exactly 2, or if the invocation of the get_connections() method of the UDT raises an exception

        • -

      • ValueError – if the sizes of the returned arrays differ, or if any element of the second +

      • ValueError – if the sizes of the returned arrays differ, or if any element of the second array is not in the \([0.,1.]\) range

      • unspecified – any exception raised by failures at the intersection between C++ and Python (e.g., type conversion errors, mismatched function signatures, etc.)

        • @@ -567,7 +567,7 @@

        Topology

        extra info about the UDT

      Return type
      -

      str

      +

      str

      Raises

      unspecified – any exception thrown by the get_extra_info() method of the UDT

      @@ -586,7 +586,7 @@

      Topology

      the topology’s name

      Return type
      -

      str

      +

      str

    @@ -595,17 +595,17 @@

    Topology is_(t)#

    Check the type of the user-defined topology.

    -

    This method returns False if extract(t) returns -None, and True otherwise.

    +

    This method returns False if extract(t) returns +None, and True otherwise.

    Parameters
    -

    t (type) – the type that will be compared to the type of the UDT

    +

    t (type) – the type that will be compared to the type of the UDT

    Returns

    whether the UDT is of type t or not

    Return type
    -

    bool

    +

    bool

    Raises

    unspecified – any exception thrown by extract()

    @@ -622,11 +622,11 @@

    Topology
    Parameters
    -

    n (int) – the number of times the push_back() method of the UDT will be invoked

    +

    n (int) – the number of times the push_back() method of the UDT will be invoked

    Raises
      -

    • OverflowError – if n is negative or too large

      • +

    • OverflowError – if n is negative or too large

    • unspecified – any exception thrown by the push_back() method of the UDT

    @@ -643,7 +643,7 @@

    Topologyto_networkx() method, then this method will invoke it and return its output. Otherwise, an error will be raised.

    This method is meant to export a representation of the current state of the topology -as a NetworkX graph object. The returned object must be a networkx.DiGraph +as a NetworkX graph object. The returned object must be a networkx.DiGraph in which the edges have a weight attribute represented as a floating-point value.

    Note that this method will strip away all node attributes and edge attributes other than weight from the graph returned by the UDT. It will also redefine the nodes @@ -655,13 +655,13 @@

    Topology

    a graph representation of the UDT

    Return type
    -

    networkx.DiGraph

    +

    networkx.DiGraph

    Raises
      -

    • NotImplementedError – if the UDT does not provide a to_networkx() method

      • -

    • TypeError – if the object returned by the UDT is not a networkx.DiGraph

      • -

    • ValueError – if the edges of the returned graph do not all have a weight attribute

      • +

    • NotImplementedError – if the UDT does not provide a to_networkx() method

      • +

    • TypeError – if the object returned by the UDT is not a networkx.DiGraph

      • +

    • ValueError – if the edges of the returned graph do not all have a weight attribute

    • unspecified – any exception thrown by the to_networkx() method of the UDT

    diff --git a/tutorials/udp_meta_decorator.html b/tutorials/udp_meta_decorator.html index b43e67ea..4b84f2fa 100644 --- a/tutorials/udp_meta_decorator.html +++ b/tutorials/udp_meta_decorator.html @@ -480,7 +480,7 @@

    Hello, decorated world!decorator_problem object (self) and the decision vector (dv), and returns a fitness vector computed via orig_fitness_function(). The call to the original fitness function is bracketed between a couple of lines of code that measure -the elapsed runtime via Python’s time.monotonic() function.

    +the elapsed runtime via Python’s time.monotonic() function.

    We can now construct a decorated Rosenbrock problem:

    >>> drb = pg.problem(pg.decorator_problem(rb, fitness_decorator=f_decor))
    @@ -553,7 +553,7 @@

    Logging fitness evaluationshasattr(self, "dv_log") will be False because, initially, the decorated problem does not contain any logging structure. The decorated fitness function will then proceed to add to the problem a 1-element -list called dv_log containing the current decision vector dv;

    +list called dv_log containing the current decision vector dv;

  • on subsequent calls of the decorated fitness function, the current decision vector dv will be appended to the dv_log list.

    • @@ -583,7 +583,7 @@

    Logging fitness evaluationslogging module rather than use an ad-hoc logging structure, and perhaps one may want +on Python’s logging module rather than use an ad-hoc logging structure, and perhaps one may want to log other information as well (e.g., the fitness vector).