55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
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import numpy as np
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from tensorflow import keras
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from mu import *
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from n2ulayer import ulayer
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from loss import loss
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def choosenext(given,possble):
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"""given is a list of scores. possble is a list of list of scores. We want to find the combination of elements in possble that has the lowest correlation to given"""
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opt=len(possble)
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np.random.shuffle(possble)
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possble=np.transpose(possble)
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given=np.expand_dims(given,axis=1)
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#print("given",given.shape)
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#print("possble",possble.shape)
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#print(loss(given,possble,K=np))
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#exit()
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inp=keras.layers.Input(shape=possble.shape[1:])
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q=inp
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#q=ulayer(opt,0,1)(q)
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q=partr(q,1,opt,ulayer)
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model=keras.models.Model(inputs=inp,outputs=q)
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model.compile(loss=loss,optimizer=keras.optimizers.Adam(lr=0.001))
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model.summary()
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model.fit(possble,given,
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batch_size=32,
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epochs=100,
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verbose=1,
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validation_split=0.0,#that stuff cant overfit
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shuffle=True,
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callbacks=[keras.callbacks.EarlyStopping(monitor='loss',patience=10,restore_best_weights=True)])
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return model.predict(possble)
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if __name__=="__main__":
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f=np.load("merged.npz")
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x=f["ps"]
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given=x[0]
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possble=x[1:5]
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choosenext(given,possble)
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