import sys, os
import numpy as np
import data_processing.decode_data as decode_dat
import time
import pickle

import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt

from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.naive_bayes import GaussianNB
from sklearn.externals import joblib

FRAME_RATE = 0.1

PWD = os.getcwd()

def main():

    featlist = "../lists/all_art_ch_htk.list"
    mdl_dir = "./output/02_p1_trained_mdl"
    rdir_a = "/data/isip/exp/tuh_eeg/exp_2382/output/htk"
    odir_a = os.path.join(PWD, "output", "svm", "03_p1_decode")
    ofile_ext_a = "hyp"

    dat_obj = decode_dat.decode(featlist)

    ## load trained model
    #
    loaded_model = joblib.load("svm_mdl.pkl")
    loaded_model.probability = True
    pca_components = 20

    with open("pca_transform.pickle", "rb") as fin:
        pca = pickle.load(fin)

    for i in range(len(dat_obj.flist)):

        dat = dat_obj.next()

        ## change the shape
        #
        dat = np.reshape(dat, (-1, np.shape(dat)[-1]))

        dat = pca.transform(dat)

        ## reshape again to match up with samples
        #
        dat = np.reshape(dat, (-1, dat_obj.sample_win_d , pca_components))
    
        ## remove the window and convert them as feats
        #
        dat = np.reshape(dat, (-1, dat_obj.sample_win_d*pca_components))

        print "decoding file: ", dat_obj.flist[i]

        score = loaded_model.predict_proba(dat)

        ## save the hyp file
        #
        hyp_filename = decode_dat.write_ofile(dat_obj.flist[i], score, rdir_a, odir_a, ofile_ext_a)

        
    print "decoding finished successfully..."
  
if __name__ == "__main__": main()