import numpy as np from lib.timer import Timer import breakout_detection import runners.data_loader as data_loader import matplotlib.pyplot as plt SAMPLE_FILE_PATH = '../data/demo7.csv' if __name__ == '__main__': sw = Timer() data = data_loader.load_data(SAMPLE_FILE_PATH) sw.start() edm_multi = breakout_detection.EdmMulti() max_snp = max(max(data.values), 1) # Z = [x/float(max_snp) for x in data.values] Z = [x for x in data.values] edm_multi.evaluate(Z, min_size=24, beta=0.001, degree=1) print(sw.elapsed(f'data length: {len(data.values)}, using time:')) plt.plot(np.asarray(data.index).tolist(), Z) result = edm_multi.getLoc() print(result) for i in result: plt.axvline(np.asarray(data.index).tolist()[i], color='#FF4E24') # plt.plot(np.asarray(data.index).tolist()[i], np.asarray(data.values).tolist()[i], 'ro') plt.show()
if i % (100) == 0: dataSaver.add({ 'ephoch': j, 'iter': i, 'totLoss': loss, 'fastConvergeEntropy': fcLoss, 'stableConvergeEntropy': stLoss, 'reward': reward }) print( 'ephoc: ', j, '\titer: ', i, '\tloss: ', roundDec(loss), '\treward: ', roundDec(reward), '\ttimeElapsed: ', timer.elapsed(step=( i + j * (mnist.train_size // config.batch_size))), '\tfastConvergeEntropy :', roundDec(fcLoss), '\tstableConvergeEntropy :', roundDec(stLoss), '\tremaining: ', timer.left()) if j % (5) == 0: print('Tot Time Elapsed: ', timer.elpasedTot(), ' after ', j, ' steps') if ((j % (25) == 0) & (j != 0)): print( '------------------ Saving Session ------------------') saver.save(sess, modelSavePath) print('------------------ Training Completed ------------------') print('Tot Time Elapsed ', timer.elpasedTot())