معرفی شرکت ها
OptimizationTestFunctions-1.0.1
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
PyPI package containing collection of optimization test functions and some useful methods for working with them
| ویژگی | مقدار |
|---|---|
| سیستم عامل | OS Independent |
| نام فایل | OptimizationTestFunctions-1.0.1 |
| نام | OptimizationTestFunctions |
| نسخه کتابخانه | 1.0.1 |
| نگهدارنده | ["['Demetry Pascal']"] |
| ایمیل نگهدارنده | [] |
| نویسنده | Demetry Pascal |
| ایمیل نویسنده | qtckpuhdsa@gmail.com |
| آدرس صفحه اصلی | https://github.com/PasaOpasen/OptimizationTestFunctions |
| آدرس اینترنتی | https://pypi.org/project/OptimizationTestFunctions/ |
| مجوز | - |
# Optimization Test Functions
[](https://pypi.org/project/OptimizationTestFunctions/)
Collection of optimization test functions and some useful methods for working with them
```
pip install OptimizationTestFunctions
```
- [Optimization Test Functions](#optimization-test-functions)
- [Test function object](#test-function-object)
- [Available test functions](#available-test-functions)
- [Sphere](#sphere)
- [Ackley](#ackley)
- [AckleyTest](#ackleytest)
- [Rosenbrock](#rosenbrock)
- [Fletcher](#fletcher)
- [Griewank](#griewank)
- [Penalty2](#penalty2)
- [Quartic](#quartic)
- [Rastrigin](#rastrigin)
- [SchwefelDouble](#schwefeldouble)
- [SchwefelMax](#schwefelmax)
- [SchwefelAbs](#schwefelabs)
- [SchwefelSin](#schwefelsin)
- [Stairs](#stairs)
- [Abs](#abs)
- [Michalewicz](#michalewicz)
- [Scheffer](#scheffer)
- [Eggholder](#eggholder)
- [Weierstrass](#weierstrass)
- [Plotting tools](#plotting-tools)
- [Structure](#structure)
- [How to use](#how-to-use)
- [Transformation tools](#transformation-tools)
- [Structure](#structure-1)
- [How to use](#how-to-use-1)
## Test function object
Each test function is the **callable object** of some class with next fields at least:
* `bounds` -- tuple with structure `(xmin, xmax, ymin, ymax)`. It is recommended borders for 3D plotting and 2D optimization for this function
* `x_best` -- global minimum argument of function in `bounds` area as numpy array. If unknown, it's `None`
* `f_best` -- function value at `x_best` if `x_best` exists and `None` otherwise
A lot of function objects need determined `dim` argument in constructor.
U can call these "functions" like usual functions with structure `numpy 1D-array -> float value`.
## Available test functions
Checklist:
* `Sphere(dim, degree = 2)`
* `Ackley(dim)`
* `AckleyTest(dim)`
* `Rosenbrock(dim)`
* `Fletcher(dim, seed = None)`
* `Griewank(dim)`
* `Penalty2(dim, a=5, k=100, m=4)`
* `Quartic(dim)`
* `Rastrigin(dim)`
* `SchwefelDouble(dim)`
* `SchwefelMax(dim)`
* `SchwefelAbs(dim)`
* `SchwefelSin(dim)`
* `Stairs(dim)`
* `Abs(dim)`
* `Michalewicz(m = 10)`
* `Scheffer(dim)`
* `Eggholder(dim)`
* `Weierstrass(dim, a = 0.5, b = 3, kmax = 20)`
U imports them using code:
```python
from OptimizationTestFunctions import Sphere, Ackley, AckleyTest, Rosenbrock, Fletcher, Griewank, Penalty2, Quartic, Rastrigin, SchwefelDouble, SchwefelMax, SchwefelAbs, SchwefelSin, Stairs, Abs, Michalewicz, Scheffer, Eggholder, Weierstrass
```
And plot them using [code](tests/heatmap_test.py)
### Sphere
### Ackley
### AckleyTest
### Rosenbrock
### Fletcher
### Griewank
### Penalty2
### Quartic
### Rastrigin
### SchwefelDouble
### SchwefelMax
### SchwefelAbs
### SchwefelSin
### Stairs
### Abs
### Michalewicz
### Scheffer
### Eggholder
### Weierstrass
## Plotting tools
### Structure
There are `plot_3d` function for 3D-plotting:
```python
plot_3d(func, points_by_dim = 50, title = '', bounds = None, show_best_if_exists = True, save_as = None, cmap = 'twilight', plot_surface = True, plot_heatmap = True)
```
with arguments:
* `func` : **class callable object**;
Object which can be called as function.
* `points_by_dim` : **int**, optional;
points for each dimension of plotting (50x50, 100x100...). The default is 50.
* `title` : **str**, optional;
title of plot with LaTeX notation. The default is ''.
* `bounds` : **tuple**, optional;
space bounds with structure `(xmin, xmax, ymin, ymax)`. The default is None.
* `show_best_if_exists` : **boolean**, optional;
point best solution by arrow if x_best exists. The default is True.
* `save_as` : **str/None**, optional;
file path to save image (None if not needed). The default is None.
* `cmap` : **str**, optional;
color map of plot. The default is `'twilight'`. See another cmaps examples [here](https://github.com/PasaOpasen/Wave-animation-from-svd#heatmaps)
* `plot_surface` : **boolean**, optional;
plot 3D surface. The default is True.
* `plot_heatmap` : **boolean**, optional;
plot 2D heatmap. The default is True.
### How to use
```python
from OptimizationTestFunctions import Fletcher, plot_3d
# dim should be 2 for plotting 3D
dim = 2
# Fletcher is good function depends on random seed!
seed = 1
f1 = Fletcher(dim, seed)
# full available functional of plotting
plot_3d(f1,
points_by_dim = 70,
title = fr"{type(f1).__name__}\ with\ seed = {seed}", # LaTeX formula notation
bounds = None,
show_best_if_exists = True,
save_as = "Fletcher1.png",
cmap = 'twilight',
plot_surface = True,
plot_heatmap = True)
```
```python
# disable arrow
plot_3d(f1,
points_by_dim = 70,
title = fr"{type(f1).__name__}\ with\ seed = {seed}",
bounds = None,
show_best_if_exists = False,
save_as = "Fletcher2.png",
cmap = 'twilight',
plot_surface = True,
plot_heatmap = True)
```
```python
# select another bounds
plot_3d(f1,
points_by_dim = 70,
title = fr"{type(f1).__name__}\ with\ seed = {seed}",
bounds = (-2, 6, -8, 10),
show_best_if_exists = False,
save_as = "Fletcher3.png",
cmap = 'twilight',
plot_surface = True,
plot_heatmap = True)
```
```python
# Create another Fletcher function
seed = 33
f2 = Fletcher(dim, seed)
# use another cmap
plot_3d(f2,
points_by_dim = 70,
title = fr"{type(f1).__name__}\ with\ seed = {seed}",
bounds = None,
show_best_if_exists = False,
save_as = "Fletcher4.png",
cmap = 'inferno',
plot_surface = True,
plot_heatmap = True)
```
```python
# plot only 3D
plot_3d(f2,
points_by_dim = 70,
title = fr"{type(f1).__name__}\ with\ seed = {seed}",
bounds = None,
show_best_if_exists = False,
save_as = "Fletcher5.png",
cmap = 'inferno',
plot_surface = True,
plot_heatmap = False)
```
```python
# plot only heatmap
plot_3d(f2,
points_by_dim = 70,
title = fr"{type(f1).__name__}\ with\ seed = {seed}",
bounds = None,
show_best_if_exists = True,
save_as = "Fletcher6.png",
cmap = 'inferno',
plot_surface = False,
plot_heatmap = True)
```
## Transformation tools
### Structure
`Transformation` object is the callable object like "functions" of this package. It performs next useful transformations:
* parallel transfer (*shift*)
* rotation
* add noises
U can create `Transformation` object using code:
```python
transform = Transformation(transformed_function, shift_step = None, rotation_matrix = None, noise_generator = None, seed = None)
```
where:
* `transformed_function` : **function or class callable object**;
transformed function.
* `shift_step` : **numpy 1D array/None**, optional;
array of shifts by each dimension or `None`. The default is `None`.
* `rotation_matrix` : **2D-array/int/None**, optional;
2D ortogonal rotation matrix or dimension for creating random rotation matrix or `None` if no rotate. The default is `None`.
* `noise_generator` : **function**, optional;
function gets current value and returns value with some noise. The default is `None`.
* `seed` : **int**, optional;
random seed for rotation matrix if needed reproduce. The default is `None`.
U also can create noises by using `Noises` static class.
### How to use
```python
import numpy as np
from OptimizationTestFunctions import Weierstrass, plot_3d, Transformation, Noises
# dim should be 2 for plotting 3D
dim = 2
# Let's create Weierstrass function
f = Weierstrass(dim, a = 0.5, b = 5, kmax = 20)
# show it
plot_3d(f,
points_by_dim = 70,
title = f"{type(f).__name__}",
bounds = None,
show_best_if_exists = True,
save_as = "Trans1.png",
cmap = 'hot',
plot_surface = True,
plot_heatmap = True)
```
```python
# transformation with shift
shifted_func = Transformation(f, shift_step=np.array([3, 4]))
# show it
plot_3d(shifted_func,
points_by_dim = 70,
title = "shifted",
bounds = None,
show_best_if_exists = True,
save_as = "Trans2.png",
cmap = 'hot',
plot_surface = True,
plot_heatmap = True)
```
```python
# transformation with rotation
rotated_func = Transformation(f, rotation_matrix = dim, seed = 2) # random rotation matrix with dim 2
# show it
plot_3d(rotated_func,
points_by_dim = 70,
title = "rotated",
bounds = None,
show_best_if_exists = True,
save_as = "Trans3.png",
cmap = 'hot',
plot_surface = True,
plot_heatmap = True)
```
```python
# transformation with noise
noised_func = Transformation(f, noise_generator = Noises.normal(center = 0, sd = 0.5))
# show it
plot_3d(noised_func,
points_by_dim = 70,
title = "noised",
bounds = None,
show_best_if_exists = True,
save_as = "Trans4.png",
cmap = 'hot',
plot_surface = True,
plot_heatmap = True)
```
```python
# U can specify your noise behavior
def add_noise(current_val):
if current_val > 5:
return 0
return current_val + np.random.random()/10
noised_func = Transformation(f, noise_generator = add_noise)
plot_3d(noised_func,
points_by_dim = 70,
title = "noised",
bounds = None,
show_best_if_exists = True,
save_as = "Trans5.png",
cmap = 'hot',
plot_surface = True,
plot_heatmap = True)
```
```python
# Also u can combine all these transformations
new_func = Transformation(f,
shift_step= np.array([10, -10]),
rotation_matrix = 2, seed = 3,
noise_generator = Noises.uniform(-0.1, 0.5)
)
plot_3d(new_func,
points_by_dim = 70,
title = "mixed",
bounds = None,
show_best_if_exists = True,
save_as = "Trans6.png",
cmap = 'hot',
plot_surface = True,
plot_heatmap = True)
```
نیازمندی
| مقدار | نام |
|---|---|
| - | numpy |
| - | matplotlib |
| - | OppOpPopInit |
نحوه نصب
نصب پکیج whl OptimizationTestFunctions-1.0.1:
pip install OptimizationTestFunctions-1.0.1.whl
نصب پکیج tar.gz OptimizationTestFunctions-1.0.1:
pip install OptimizationTestFunctions-1.0.1.tar.gz