78 lines
850 B
Python
78 lines
850 B
Python
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import tensorflow as tf
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from tensorflow import keras
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import numpy as np
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import os
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import sys
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from data import data
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fns=[f"runs/{zw}" for zw in os.listdir("runs")]
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fs=[np.load(fn) for fn in fns if os.path.isfile(fn)]
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x=fs[0]["x"]
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ds=[f["d"] for f in fs]
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ps=[f["p"][:,0] for f in fs]
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ds=np.array(ds)
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ps=np.array(ps)
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d=np.sqrt(np.mean(np.square(ds),axis=0))
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np.savez_compressed("merged",x=x,ds=ds,ps=ps,d=d)
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print(np.corrcoef(ps))
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sx=[(xx,dd) for xx,dd in zip(x,d)]
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sx.sort(key=lambda x:x[1])
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sx=[xx for xx,dd in sx]
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sx=np.array(sx)
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from plt import plt
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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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plt.savefig(f"imgs/recombine.png")
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#plt.plot(sx[:,0],sx[:,1],'.')
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plt.how()
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