64 lines
1.3 KiB
Python
64 lines
1.3 KiB
Python
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import numpy as np
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from choosenext import choosenext
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from plt import plt
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def draw(p,dat):
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mp=np.mean(p)
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d=np.square(p-mp)
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sx=[(xx,dd) for xx,dd in zip(dat,d)]
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sx.sort(key=lambda x:x[1])
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sx=[zw[0] for zw in sx]
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sx=np.array(sx)
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col1=[1.0,0.0,0.0]
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col2=[0.0,1.0,0.0]
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col1,col2=np.array(col1),np.array(col2)
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ln=len(sx)
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cols=[col1*(i/ln)+col2*(1-i/ln) for i in range(ln)]
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plt.scatter(sx[:,0],sx[:,1],c=cols)
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def combine(a,b):
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a=1+(a-np.mean(a))/np.std(a)
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b=1+(b-np.mean(b))/np.std(b)
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toc=np.concatenate((np.expand_dims(a,axis=1),np.expand_dims(b,axis=1)),axis=1)
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toc=np.sqrt(np.mean(toc**2,axis=1))
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return toc
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if __name__=="__main__":
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f=np.load("merged.npz")
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dat=f["x"]
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x=f["ps"]
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np.random.shuffle(x)
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given=x[0]
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possble=x[1:5]
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ac=choosenext(given,possble)
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nextbest=ac[:,0]
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remainder=ac[:,1:]
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for row in np.transpose(ac):
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print(np.corrcoef(given,row)[0,1])
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#as you see: the correlation is the lowest for the first row.
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#so lets combine it
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updated=combine(given,nextbest)
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draw(given,dat)
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plt.savefig("before.png")
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plt.show()
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draw(nextbest,dat)
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plt.savefig("suggestion.png")
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plt.show()
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draw(updated,dat)
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plt.savefig("updated.png")
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plt.show()
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