from sklearn.metrics import r2_score from mpl_toolkits.mplot3d import Axes3D from Signal import Signal from model_list import model # These constants are also defined in the Signal module # Don't change here unless you also change them there numTrials = 1000 nstep = 100 timelength = 100 trainFrac = .7 # Calls the module Signal with the initialization parameters # then simulates using the initialized model sig = Signal(numTrials, nstep, timelength, trainFrac) uArray, yArray, corrArray, conArray, taus, kps, thetas, train, test = sig.simulate( ) # This may need to be implemented iteratively to determine if stacking # the three different parameters has any significant impact on predicting the # value of any of the 3 parameters xDatas = [yArray, yArray, (yArray - np.mean(yArray)) / np.std(yArray) - uArray] yDatas = [taus, kps, thetas] m = model(nstep) modelList = [] uSum = np.sum(uArray, axis=0) plt.figure() plt.plot(np.linspace(0, 100, 100), uSum) for j in range(0, len(xDatas)):
