In [2]:

    

%pylab inline
%load_ext Cython


    

    




Populating the interactive namespace from numpy and matplotlib

In [5]:

    




    

    




  File "<ipython-input-5-5db33f195660>", line 1
    from Cython_Res
                   ^
SyntaxError: invalid syntax

In [3]:

    

NumRuns = 1
NumGenerations = 10
PopulationSize = 10
MutationRate = 0.01
ExecutionError= 0.001
ReputationAssignmentError = 0.001
PrivateAssessmentError = 0.001
ReputationUpdateProbability = 1.0
RandomSeed =  666
SocialNormMatrix = [[0, 0],[0, 0]]
CostValue = 1
BenefitValue = 5


    

In [6]:

    

%%timeit
sim.run_instance(NumRuns, NumGenerations, PopulationSize, MutationRate,
                 ExecutionError, ReputationAssignmentError,
                 PrivateAssessmentError, ReputationUpdateProbability,
                 RandomSeed, SocialNormMatrix, CostValue, BenefitValue)


    

    




10 loops, best of 3: 29.1 ms per loop

In [11]:

    

%%timeit
sim.run_instance(NumRuns, NumGenerations, PopulationSize, MutationRate,
                 ExecutionError, ReputationAssignmentError,
                 PrivateAssessmentError, ReputationUpdateProbability,
                 RandomSeed, SocialNormMatrix, CostValue, BenefitValue)


    

    




10 loops, best of 3: 28.1 ms per loop

In [12]:

    

%%timeit
for i in range(100):
    np.random.rand() < 0.001


    

    




10000 loops, best of 3: 22.3 µs per loop

In [14]:

    

%%timeit
ans = np.random.rand(100) < 0.001


    

    




The slowest run took 11.30 times longer than the fastest. This could mean that an intermediate result is being cached 
100000 loops, best of 3: 3.02 µs per loop

In [15]:

    

ans


    

    
Out[15]:





array([False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False,
       False, False, False, False, False, False, False, False, False, False], dtype=bool)

In [ ]:

    




    

In [76]:

    

my_list_fast = []
for _ in range(1000):
    population_size = 10
    population = [10, 0, 0, 2]
    index_focal, index_other = np.random.choice(range(len(population)), size = 2, replace=False)
    my_list_fast.append(tuple((index_focal, index_other)))


    

In [77]:

    

counter = Counter(my_list_fast)


    

In [78]:

    

counter


    

    
Out[78]:





Counter({(0, 3): 839, (3, 0): 161})

In [79]:

    

%%timeit
my_list  = []
for i in range(1000):
    population_size = 10
    population = [10, 0, 0, 5]
    index_focal= np.random.choice(range(len(population)), 
                         p=population/np.sum(population))
    population[index_focal] -=1
    index_other= np.random.choice(range(len(population)), 
                 p=population/np.sum(population))
    my_list.append(tuple((index_focal, index_other)))


    

    




10 loops, best of 3: 72.7 ms per loop

In [167]:

    

from collections import Counter


    

In [65]:

    

counter = Counter(my_list)


    

In [66]:

    

counter


    

    
Out[66]:





Counter({(0, 0): 436, (0, 3): 232, (3, 0): 233, (3, 3): 99})

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In [ ]:

    




    

In [7]:

    

%%timeit
agent_pairs = [np.random.choice(500, size=2, replace=False)]*500


    

    




1000 loops, best of 3: 283 µs per loop

In [10]:

    

%%timeit
agent_pairs = np.array([np.random.choice(500, size=2, replace=False)]*500)


    

    




1000 loops, best of 3: 911 µs per loop

In [ ]:

    




    

In [30]:

    

from collections import Counter


    

In [31]:

    

Z=5


    

In [6]:

    

%%timeit
for _ in range(10):
    for _ in range(3*np.power(10, 3)):
        agent_pairs = [np.random.choice(Z, size=2, replace=False)]*Z


    

    




1 loop, best of 3: 2.65 s per loop

In [75]:

    

%%timeit
for _ in range(300):
    my_list = [np.random.choice(Z, size=2, replace=False) for _ in range(1000)]


    

    




1 loop, best of 3: 10.3 s per loop

In [71]:

    

%%timeit
my_list = []
for _ in range(1000):
    my_list.append(np.random.choice(Z, size=2, replace=False))


    

    




10 loops, best of 3: 48.1 ms per loop

In [72]:

    

%%timeit
my_list = [None]*1000
for i in range(1000):
    my_list[i] = np.random.choice(Z, size=2, replace=False)


    

    




10 loops, best of 3: 48 ms per loop

In [53]:

    

counter = Counter(my_list)
print(counter)


    

    




Counter({(4, 2): 1000})

In [58]:

    

np.zeros((Z, 2))


    

    
Out[58]:





array([[ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.],
       [ 0.,  0.]])

In [76]:

    

%%timeit
for _ in range(10):
    for _ in range(3*np.power(10, 3)):
        agent_pairs = np.random.choice(Z, size=2, replace=False)


    

    




1 loop, best of 3: 1.48 s per loop

In [ ]:

    




    

In [36]:

    

%reload_ext Cython
import pyximport
pyximport.install(setup_args={"include_dirs":np.get_include()},
                  reload_support=True)
import SimulationInstance as sim
import Cython_resources.simulation_instance as sim_c
%reload_ext Cython_resources.simulation_instance


    

In [42]:

    

runs = 1
generations = 100
population_size = 20
mutation_rate = 0.0
execution_error = 0.0
reputation_assignment_error = 0.0
private_assessment_error = 0.0
reputation_update_prob = 1.0
socialnorm = np.array([[1, 0], [0, 1]])
cost = 1
benefit = 5


    

In [43]:

    

%%timeit
sim.simulate(runs, generations, population_size, mutation_rate,
             execution_error, reputation_assignment_error,
             private_assessment_error, reputation_update_prob,
             socialnorm, cost, benefit)


    

    




1 loop, best of 3: 4.39 s per loop

In [44]:

    

%%timeit
sim_c.run_instance(runs, generations, population_size, mutation_rate,
                execution_error, reputation_assignment_error,
                private_assessment_error, reputation_update_prob,
                socialnorm, cost, benefit)


    

    




1 loop, best of 3: 3.63 s per loop

In [ ]: