In [1]:

    

from extractMeanStdPSO import *
%load_ext autoreload
%autoreload 2
%matplotlib inline


    

In [5]:

    

fileName = "DOG_WorkingMemory_100_"
dir = "expPSO/DOG_WorkingMemory_100/"
repet= 50
nbEpoch=9900
batch = Batch.initOrLoad(dir,fileName,repet)


    

    




Batch not present. Reading files...
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Saving batch
Batch loaded

In [6]:

    

batch.saveFitnessEvolution()


    

    




saving :  expPSO/DOG_WorkingMemory_100/fitnessEvolution.csv

In [13]:

    

batch.saveBestInd()
del batch


    

    




saving :  expPSO/DOG_WorkingMemory_100/bestInd.pi

In [8]:

    

array = np.loadtxt("expPSO/pso_DOG_competition/fitnessEvolution.csv")
mean = np.mean(array,axis=1)
std = np.std(array,axis=1)/2.0
X = np.arange(nbEpoch)
plt.fill_between(X, mean - std,mean + std,color="green")
plt.plot(X, mean, color="white", lw=1)
plt.xlim(0,nbEpoch)
plt.xlabel("Epoch")
plt.ylabel("Mean RMSE")


    

    
Out[8]:





<matplotlib.text.Text at 0x7f8f0bce8b70>






    

In [8]:

    




    

    




saving :  expPSO/pso_DOG_competitionbestInd.pi

In [9]:

    

bestInd = Batch.loadBestInd(dir+"bestInd.pi")


    

In [10]:

    

def loadParams(paramList):    
    parameters = {}
    for param in paramList:
        parameters[param] = [d[param] for d in bestInd]
    return parameters


    

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params = loadParams(['iExc','iInh','wExc','wInh','h','th','tau'])


    

In [12]:

    

plt.figure(figsize=(10,10))
plt.subplot(221)
plt.plot(params['h'])
plt.plot(params['th'])
plt.subplot(222)
plt.plot(params['iExc'])
plt.plot(params['iInh'])
plt.subplot(223)
plt.plot(params['wExc'])
plt.plot(params['wInh'])
plt.subplot(224)
plt.plot(params['tau'])


    

    
Out[12]:





[<matplotlib.lines.Line2D at 0x7f8f0bb82d30>]






    

In [37]:

    

plt.plot(np.array(params['h'])-np.array(params['th']))


    

    
Out[37]:





[<matplotlib.lines.Line2D at 0x7fc006977ef0>]






    

In [35]:

    

fig, ax = plt.subplots()

mat = []
keys = ['not']
#row must be variable
for k in params.keys():
    mat.append(params[k])
    keys.append(k)
print(keys)
mat = [params[i] for i in params.keys()]
mat = np.array(mat)
print(mat.shape)
corr = np.corrcoef(mat)
plt.imshow(corr,interpolation='None',cmap='RdYlBu_r')
plt.clim(-1,1)
ax.set_xticklabels(keys)
ax.set_yticklabels(keys)
plt.colorbar()


    

    




['not', 'iInh', 'h', 'tau', 'th', 'wInh', 'wExc', 'iExc']
(7, 50)






    
Out[35]:





<matplotlib.colorbar.Colorbar at 0x7f8f1bdb40f0>






    

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