from data import data
import numpy as np

x,y,z=data()
x=np.concatenate([np.expand_dims(zw,1) for zw in [x,y,z]],axis=1)


from tensorflow import keras
from mu import *
from n2ulayer import ulayer

from loss import loss2d

dim=int(x.shape[1])
pdim=2

inp=keras.layers.Input(x.shape[1:])
q=inp

q=partr(q,pdim,dim,ulayer)
q=cutdown(q,pdim)

model=keras.models.Model(inp,q)

model.summary()

#opt=keras.optimizers.Adam(lr=0.0001)
#opt=keras.optimizers.Adam(lr=0.001)
opt=keras.optimizers.Adam(lr=0.01)

model.compile(opt,loss=loss2d)

model.fit(x,x,
        epochs=10000,
        shuffle=False,
        validation_split=0.2,
        callbacks=[keras.callbacks.EarlyStopping(patience=250,monitor="loss",restore_best_weights=True)])


mats=[]
for lay in model.layers[1:]:
    if not ("ulayer" in str(type(lay))):continue
    #print(dir(lay))
    #try:
    mats.append(lay.numpify())
    #except:
    #    pass

mat=None
for m in mats:
    if mat is None:
        mat=m
    else:
        mat=np.dot(m,mat)

mat=mat[:pdim]

print(mat)





loss=model.evaluate(x[:800],x[:800])
print(loss)
p=model.predict(x[:800])
import matplotlib.pyplot as plt

plt.plot(p[:,0],p[:,1],".",alpha=0.75)
plt.title(str(loss))
plt.how()