# -*- coding: utf-8 -*-
import keras
import numpy
#from keras.layers.core import Dense, Dropout, Activation 
from keras.optimizers import SGD,Adam
from keras.models import Sequential
from keras.layers import Input, Dense, Dropout
from keras.models import Model

import pandas 
from collections import namedtuple
from sklearn.metrics import accuracy_score as perf
save_tuple= namedtuple("save_tuple",["pred_train","pred_dev","pred_test"])

def train_sae(train,dev,test,hidden_sizes,dropouts=None,in_activation="tanh",out_activation="relu",loss="mse",sgd=None,epochs=500,batch_size=8,verbose=1,patience=20):
     
    
    if dropouts == None:
        dropouts = [ 0 ] * (len(hidden_sizes) +1)

    if sgd == None : 
        sgd = SGD(lr=0.01, decay=0, momentum=0.9)
    xt,xd,xte = train,dev,test
    preds = []
    for h_layer in hidden_sizes :
        input_vect = Input(shape=(xt.shape[1],))
        previous = input_vect
        if dropouts:
            d = dropouts.pop(0)
            if d :
                previous = Dropout(d)(previous)

        h = Dense(h_layer,activation=in_activation)(previous)
        out = Dense(xt.shape[1],activation=in_activation)(h)
        model = Model(input_vect,out)
        model.compile(loss=loss,optimizer=sgd)
        model.fit(xt,xt,nb_epoch=epochs,batch_size=batch_size,callbacks=[keras.callbacks.EarlyStopping(monitor="val_loss",patience=patience,verbose=0)],validation_data=(xd,xd),verbose=0)
        xt,xd,xte=( model.predict(xt),model.predict(xd),model.predict(xte) )
        preds.append((xt,xd,xte))
    return preds