def process_data(self, data):
y = data.rangefinder[0].readings
self.mean_readings = weighted_average(self.mean_readings, self.num_samples, y)
yn = y - self.mean_readings
T = outer(yn, yn)
self.cov_readings = weighted_average(self.cov_readings, self.num_samples, T)
self.num_samples += 1
def process_data(self, data):
y = data.sensels
self.mean_sensels = weighted_average(self.mean_sensels, self.num_samples, y)
yn = y - self.mean_sensels
T = outer(yn, yn)
self.cov_sensels = weighted_average(self.cov_sensels, self.num_samples, T)
self.num_samples += 1
def process_data(self, data):
y = data.sensels
y_dot = data.sensels_dot
u = data.commands
y_n = y - self.y_mean
T = outer(u, outer(y_n, y_dot))
self.T = weighted_average(self.T, self.num_samples, T)
self.y_mean = weighted_average(self.y_mean, self.num_samples, y)
self.num_samples += 1
def process_data(self, data):
y = data.optics[0].luminance
# Update mean estimate
self.mean_luminance = weighted_average(self.mean_luminance, self.num_samples, y)
# Subtract the mean
yn = y - self.mean_luminance
# Compute the exterior product of normalized luminance
T = outer(yn, yn)
# Update covariance estimate
self.cov_luminance = weighted_average(self.cov_luminance, self.num_samples, T)
# Keep track of how many we integrated so far
self.num_samples += 1
def process_data(self, data):
y = data.sensels
# y_dot = numpy.sign(data.sensels_dot)
y_dot = data.sensels_dot
u = data.commands
self.y_mean = weighted_average(self.y_mean, self.num_samples, y.mean())
y_n = y - self.y_mean
T = outer(u, outer(y_n, y_dot))
# if self.num_samples > 50:
# delay execution
self.T = weighted_average(self.T, self.num_samples, T)
self.num_samples += 1
def process_data(self, data):
y = data.sensels
y_dot = data.sensels_dot
u = data.commands
T = outer(u, outer(y, y_dot))
self.T = weighted_average(self.T, self.num_samples, T)
self.num_samples += 1
