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Python implementation algorithm

Project description

=====
slapy
=====
1. install
from pypi:
pip install snowland-algorithm
or
from source code:
download code from https://gitee.com/hoops/snowland-algorithm-python, you can choose a release version
pip install -r requirements.txt
python setup.py install
#. dirs
1. graph
a. dijkstra(v0.0.1+)
#. spfa(v0.0.1+)
#. swarm
a. pso(v0.0.1+)
#. gso(v0.0.2+)
#. fa(v0.0.3+)
#. cso(v0.0.6+)
#. ba(v0.0.6+)
#. sfla(v0.0.6+)
#. bas(v0.0.6+)
#. sa(v0.0.6+)
#. fwa(v0.0.6+)
#. cs(v0.0.7+)
#. bfo(v0.0.7+)
#. quick use
1. import package
>>> from slapy.swarm.package_name import engine_name
2. define the fitness function
example:
>>> fun = lambda x: np.cos(x[0]) + np.sin(x[0]) - x[0] * x[1]
note: arg need to be 1 X n vector
3. run the model
>>> engine = engine_name(your_args)
>>> engine.run()
4. show result
>>> x, y = engine.gbest.chromosome
>>> print('max value', fun(engine.gbest.chromosome))
>>> print('x:', x, 'y:', y)
There is a example for PSO.
>>> def fun(vars):
>>> # fitness function
>>> x, y = vars
>>> if 1 <= x <= 2 * np.pi and 1 <= y <= np.pi:
>>> return np.cos(x) + np.sin(x) - x * y
>>> else:
>>> return -2 - 4 * np.pi ** 2 # return a small float number can not reach

>>> if __name__ == '__main__':
>>> engine = PSOEngine(vmax=0.01, bound=[[1, 2 * np.pi]], min_fitness_value=-1, dim=2, fitness_function=fun, steps=100)
>>> engine.run()
>>> x, y = engine.gbest.chromosome
>>> print('max value', fun(engine.gbest.chromosome))
>>> print('x:', x, 'y:', y)


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