import json
import keras
import numpy as np
import scipy.io as sio
import scipy.stats as sst
import load
import network
import util
def predict(record):
ecg = load.load_ecg(record +".mat")
preproc = util.load(".")
x = preproc.process_x([ecg])
params = json.load(open("config.json"))
params.update({
"compile" : False,
"input_shape": [None, 1],
"num_categories": len(preproc.classes)
})
model = network.build_network(**params)
model.load_weights('model.hdf5')
probs = model.predict(x)
prediction = sst.mode(np.argmax(probs, axis=2).squeeze())[0][0]
return preproc.int_to_class[prediction]
if __name__ == '__main__':
import sys
print predict(sys.argv[1])
