In [1]:

    

%pylab inline


    

    




Welcome to pylab, a matplotlib-based Python environment [backend: module://IPython.zmq.pylab.backend_inline].
For more information, type 'help(pylab)'.

In [2]:

    

from JSAnimation.IPython_display import display_animation


    

In [3]:

    

%run ../paramless.py


    

In [4]:

    

def target_function(x):
    return x**2.0


    

In [5]:

    

x = np.linspace(-1.0, 1.0, 100)  
target = target_function(x)


    

In [6]:

    

plot(x, target_function(x))


    

    
Out[6]:





[<matplotlib.lines.Line2D at 0x107b43210>]






    

In [7]:

    

initial_surface = np.zeros_like(target)
ans, series = evolve(initial_surface=initial_surface, fitness_function=distance_fitness_function, mutation_function=point_mutation, iterations=1000000, return_time_series=True, seed=777, target_surface=target, mutation_epsilon=0.001, atol=1e-12)


    

In [8]:

    

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


    

    
Out[8]:





(0, 1)






    

In [9]:

    

ani = create_video_from_time_series(series, target_surface=target, domain=x, filename='/Users/garcia/Desktop/x_cuadrado.mp4', approximate_number_of_frames=200, record_every=1000)
display_animation(ani)


    

    
Out[9]:








    
    

    
    

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In [10]:

    

domain = np.linspace(-1.0, 1.0, 1000)  
target = target_function(domain)
initial_surface = np.zeros_like(target)
iterations= 10000
mutation_epsilon=0.01
width=0.05
ans_soft, series_soft = evolve(initial_surface=initial_surface, fitness_function=distance_fitness_function, mutation_function=gaussian_mutation, iterations=iterations, return_time_series=True, seed=777, target_surface=target, mutation_epsilon=mutation_epsilon, domain=domain, width=width, atol=1e-12)


    

In [11]:

    

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


    

    
Out[11]:





(0, 1)






    

In [13]:

    

animation_soft = create_video_from_time_series(series_soft, target_surface=target, domain=domain, filename='/Users/garcia/Desktop/soft.mp4', approximate_number_of_frames=200, record_every=200)
display_animation(animation_soft)


    

    
Out[13]:








    
    

    
    

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In [15]:

    

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


    

In [16]:

    

plot(domain, target)


    

    
Out[16]:





[<matplotlib.lines.Line2D at 0x10f53d610>]






    

In [17]:

    

domain = np.linspace(-1.0, 1.0, 1000)  
target = sin(domain*domain*domain)
initial_surface = np.zeros_like(target)
iterations= 10000
mutation_epsilon=0.01
width=0.05
ans_sin, series_sin = evolve(initial_surface=initial_surface, fitness_function=distance_fitness_function, mutation_function=gaussian_mutation, iterations=iterations, return_time_series=True, seed=777, target_surface=target, mutation_epsilon=mutation_epsilon, domain=domain, width=width, atol=1e-12)


    

In [18]:

    

plot(domain, ans_sin)


    

    
Out[18]:





[<matplotlib.lines.Line2D at 0x10f510610>]






    

In [20]:

    

animation_sin = create_video_from_time_series(series_sin, target_surface=target, domain=domain, filename='/Users/garcia/Desktop/soft_sin.mp4', approximate_number_of_frames=200, record_every=200)
display_animation(animation_sin)


    

    
Out[20]:








    
    

    
    

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In [21]:

    

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


    

In [22]:

    

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


    

In [23]:

    

plot(domain, target)


    

    
Out[23]:





[<matplotlib.lines.Line2D at 0x10d5c6b10>]






    

In [24]:

    

domain = np.linspace(-1.0, 1.0, 1000)  
initial_surface = np.zeros_like(target)
iterations= 10000
mutation_epsilon=0.01
width=0.05
ans_dis, series_dis = evolve(initial_surface=initial_surface, fitness_function=distance_fitness_function, mutation_function=gaussian_mutation, iterations=iterations, return_time_series=True, seed=777, target_surface=target, mutation_epsilon=mutation_epsilon, domain=domain, width=width, atol=1e-12)


    

In [25]:

    

plot(domain, ans_dis)


    

    
Out[25]:





[<matplotlib.lines.Line2D at 0x10cad6290>]






    

In [26]:

    

animation_dis = create_video_from_time_series(series_dis, target_surface=target, domain=domain, filename='/Users/garcia/Desktop/soft_sin.mp4', approximate_number_of_frames=200, record_every=100)
display_animation(animation_dis)


    

    
Out[26]:








    
    

    
    

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