In [1]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

from JSAnimation.IPython_display import display_animation


    

In [3]:

    

import pyximport; pyximport.install(setup_args={"include_dirs":numpy.get_include()})
import paramless as pmain
import paramless_cython as pm


    

In [4]:

    

def target_function(x):
    return x**2.0


    

In [5]:

    

x = np.linspace(-1.0, 1.0, 25)  
target = target_function(x)


    

In [6]:

    

plot(x, target_function(x))


    

    
Out[6]:





[<matplotlib.lines.Line2D at 0x7fd407ffe410>]






    

In [7]:

    

initial_surface = np.zeros_like(target)
population = {1:20}
definitions = {1:initial_surface}
fitness_function = pm.ModelDistanceFitnessFunction(target)
mutator = pm.PointMutator(0.001)
evolver = pm.WrightFisherEvolver(fitness_function, mutator)
pm.setup(777)


    

In [11]:

    

ans, series = pmain.modelEvolve(population, definitions, evolver, iterations=250000, return_time_series=True, seed=777)


    

In [12]:

    

plot(x,ans)
plot(x,target)
plt.xlim((-1,1))
plt.ylim((0,1))


    

    
Out[12]:





(0, 1)






    

In [13]:

    

ani = pmain.create_video_from_time_series(series, target_surface=target, domain=x, filename='./model_parabola_hard.mp4', approximate_number_of_frames=50, record_every=5000)
display_animation(ani)


    

    
Out[13]:








    
    

    
    

    –
    
    
    
    
    
    
    
    +
  

     Once 
     Loop 
     Reflect 
  

In [31]:

    

domain = np.linspace(-1.0, 1.0, 1000)  
target = target_function(domain)
initial_surface = np.zeros_like(target)
population = {1:20}
definitions = {1:initial_surface}
iterations= 50000
mutator = pm.GaussianMutator(0.01, domain, 0.05, 0)
fitness_function = pm.ModelDistanceFitnessFunction(target)
evolver = pm.WrightFisherEvolver(fitness_function, mutator)
pm.setup(777)
ans_soft, series_soft = pmain.modelEvolve(population, definitions, evolver, iterations=iterations, return_time_series=True, seed=777)


    

In [32]:

    

plot(domain,ans_soft)
plot(domain,target)
plt.xlim((-1,1))
plt.ylim((0,1))


    

    
Out[32]:





(0, 1)






    

In [33]:

    

num_frames = 50
frame_every = iterations / num_frames
animation_soft = pmain.create_video_from_time_series(series_soft, target_surface=target, domain=domain, filename='./wfmodel_parabola_soft.mp4', approximate_number_of_frames=num_frames, record_every=frame_every)
display_animation(animation_soft)


    

    
Out[33]:








    
    

    
    

    –
    
    
    
    
    
    
    
    +
  

     Once 
     Loop 
     Reflect 
  

In [34]:

    

domain = np.linspace(-1.0, 1.0, 1000)  
target = sin(domain*domain*domain)


    

In [35]:

    

plot(domain, target)


    

    
Out[35]:





[<matplotlib.lines.Line2D at 0x7fd407b1ab90>]






    

In [38]:

    

initial_surface = np.zeros_like(target)
population = {1:20}
definitions = {1:initial_surface}
iterations= 20000
mutator = pm.GaussianMutator(0.01, domain, 0.05)
fitness_function = pm.ModelDistanceFitnessFunction(target)
evolver = pm.WrightFisherEvolver(fitness_function, mutator)
pm.setup(777)
ans_sin, series_sin = pmain.modelEvolve(population, definitions, evolver, iterations=iterations, return_time_series=True, seed=777)


    

In [39]:

    

plot(domain, ans_sin)


    

    
Out[39]:





[<matplotlib.lines.Line2D at 0x7fd407a76dd0>]






    

In [41]:

    

animation_sin = pmain.create_video_from_time_series(series_sin, target_surface=target, domain=domain, filename='./wfmodel_continuous.mp4', approximate_number_of_frames=50, record_every=200)
display_animation(animation_sin)


    

    
Out[41]:








    
    

    
    

    –
    
    
    
    
    
    
    
    +
  

     Once 
     Loop 
     Reflect 
  

In [42]:

    

def discontinous_target(x):
    if (x < 0.0):
        return -0.5
    return 0.5


    

In [43]:

    

target = np.array([discontinous_target(x) for x in domain])


    

In [44]:

    

plot(domain, target)


    

    
Out[44]:





[<matplotlib.lines.Line2D at 0x7fd4073fd1d0>]






    

In [45]:

    

initial_surface = np.zeros_like(target)
population = {1:20}
definitions = {1:initial_surface}
iterations= 10000
mutator = pm.GaussianMutator(0.01, domain, 0.05)
fitness_function = pm.ModelDistanceFitnessFunction(target)
evolver = pm.WrightFisherEvolver(fitness_function, mutator)
pm.setup(777)
ans_dis, series_dis = pmain.modelEvolve(population, definitions, evolver, iterations=iterations, return_time_series=True, seed=777)


    

In [46]:

    

plot(domain, ans_dis)


    

    
Out[46]:





[<matplotlib.lines.Line2D at 0x7fd40720bad0>]






    

In [47]:

    

animation_dis = pmain.create_video_from_time_series(series_dis, target_surface=target, domain=domain, filename='./wfmodel_discontinuous.mp4', approximate_number_of_frames=50, record_every=100)
display_animation(animation_dis)


    

    
Out[47]:








    
    

    
    

    –
    
    
    
    
    
    
    
    +
  

     Once 
     Loop 
     Reflect 
  

In [ ]: