def __init__(self,
num_epochs_between_val,
pred_win_size,
period,
init_x,
init_d,
process_noise_var=1e-6,
# num_extra_win_size=20,
noise_est_win_size=None
):
if len(init_x) < period:
print("Better set estimation start position larger than period")
if noise_est_win_size == None:
self.noise_est_win_size = pred_win_size
else:
self.noise_est_win_size = noise_est_win_size
self.numEpsBtwVal = num_epochs_between_val
self.predWinSize = pred_win_size
self.currentWinSize = min(len(init_x), self.predWinSize)
self.pointPeriod = period # in terms of # of points
self.epochPeriod = period * num_epochs_between_val # in terms of # of epochs
self.weights = np.ones(self.predWinSize) # the weights when doing regression
# KF init
# the # of points for R estimation, if any
# self.RWinSize = num_extra_win_size + pred_win_size
## variance of process noise
self.var_ud = process_noise_var
epochs = (np.arange(len(init_x))+1)*self.numEpsBtwVal
a, b = power_regression(epochs, init_x, np.ones(len(init_x)))
s_est = power_function(epochs, a, b)
# A = np.vstack([epochs, np.ones(len(init_x))]).T
# a, b = np.linalg.lstsq(A, init_x, rcond=-1)[0]
# s_est = a*epochs + b
self.epochs = epochs[len(epochs) - self.currentWinSize:]
self.x = init_x[len(init_x) - self.currentWinSize:]
self.s_est = s_est[len(s_est) - self.currentWinSize:]
# self.d_est = self.x[-1] - self.s_est[-1]
self.d_est = init_d
## updated state/signal estimation
self.sd_est = np.concatenate([self.s_est, np.array([self.d_est])])
## updated covariance estimation
# self.M_est = np.zeros((self.currentWinSize+1,self.currentWinSize+1))
# dev = self.x - self.s_est
# dev_d = np.concatenate([dev, np.zeros(1)]).reshape([-1,1])
# self.M_est = dev_d.dot(dev_d.T)
self.M_est = np.ones((self.currentWinSize+1,self.currentWinSize+1))
# the array that store all x and all s for now
self.all_original_data = np.array(init_x)
self.all_estimates = s_est
def _predicted_state_estimation(self):
self.a, self.b = power_regression(self.epochs, self.s_est, np.ones(self.currentWinSize))
# next q points predicted by current regression line
# self.q_epochs = self.epochs[:self.pointPeriod] + self.currentWinSize * self.numEpsBtwVal
self.q_epochs = np.arange(self.epochs[-1]+self.numEpsBtwVal, self.epochs[-1]+(self.pointPeriod+1)*self.numEpsBtwVal, self.numEpsBtwVal)
# self.sq_pred = power_function(self.q_epochs, self.a, self.b) + self.d_est
self.sq_pred = power_function(self.q_epochs, self.a, self.b) + np.tanh(np.power(self.d_est,2)*(self.q_epochs-self.epochs[-1]))
# next state predition
if self.currentWinSize + self.pointPeriod >= self.predWinSize:
self.s_pred = np.concatenate([self.s_est[len(self.s_est)-(self.predWinSize-self.pointPeriod):], self.sq_pred])
else:
self.s_pred = np.concatenate([self.s_est, self.sq_pred])
self.d_pred = self.d_est
self.sd_pred = np.concatenate([self.s_pred, np.array([self.d_pred])])
def __init__(
self,
num_epochs_between_val,
# pred_win_size,
# period,
init_x,
init_d,
init_epochs,
noise_est_win_size,
process_noise_var=1e-6,
):
self.noise_est_win_size = noise_est_win_size
self.currentWinSize = len(init_x)
self.numEpsBtwVal = num_epochs_between_val
# self.predWinSize = pred_win_size
# self.currentWinSize = min(len(init_x), self.predWinSize)
# self.pointPeriod = period # in terms of # of points
# self.epochPeriod = period * num_epochs_between_val # in terms of # of epochs
# self.weights = np.ones(self.predWinSize) # the weights when doing regression
# KF init
## variance of process noise
self.var_ud = process_noise_var
a, b = power_regression(init_epochs, init_x,
np.ones(self.currentWinSize))
self.s_est = power_function(init_epochs, a, b)
self.epochs = init_epochs
self.x = init_x
self.d_est = init_d
## updated state/signal estimation
self.sd_est = np.concatenate([self.s_est, np.array([self.d_est])])
## updated covariance estimation
self.M_est = np.ones(
(self.currentWinSize + 1, self.currentWinSize + 1))
# the array that store all x and all s for now
self.all_original_data = np.array(init_x)
self.all_estimates = self.s_est
self.next_x = self.x
self.nextWinSize = self.currentWinSize
self.next_epochs = self.epochs
def predictionByCurrent(self, num):
s_queue = list(self.s_est)
epoch_queue = list(self.epochs)
predicts = []
a, b = power_regression(np.array(epoch_queue), np.array(s_queue), np.ones(len(s_queue)))
for i in range(1,num):
# if (i-1) % self.pointPeriod == 0:
# a, b = power_regression(np.array(epoch_queue), np.array(s_queue), np.ones(len(s_queue)))
epoch = self.epochs[-1] + i * self.numEpsBtwVal
if len(epoch_queue) >= self.predWinSize:
epoch_queue.pop(0)
epoch_queue.append(epoch)
# predict = power_function(epoch, a, b) + self.d_est
predict = power_function(epoch, a, b) + np.tanh(np.power(self.d_est,2)*(epoch-self.epochs[-1]))
predicts.append(predict)
if len(s_queue) >= self.predWinSize:
s_queue.pop(0)
s_queue.append(predict)
return np.array(predicts), np.concatenate([self.all_estimates, np.array(predicts)])