def hedonic_value(self, novelty):
'''Given the agent's desired novelty, how good the novelty value is.
Not used if *desired_novelty*=-1
'''
lmax = gaus_pdf(self.desired_novelty, self.desired_novelty, 4)
pdf = gaus_pdf(novelty, self.desired_novelty, 4)
return pdf / lmax
def _make_estimate(self, addr, features):
vals = np.zeros(self.num_of_features)
for i in range(self.num_of_features):
vals[i] = gaus_pdf(features[i], self.centroids[addr][i],
self.std) / self.max
estimate = np.sum(self.sgd_weights[addr] * vals)
return estimate, vals
def __init__(self,
addrs,
num_of_features,
std,
centroid_rate=200,
weight_rate=0.2):
self.centroid_rate = centroid_rate
self.weight_rate = weight_rate
self.num_of_features = num_of_features
self.std = std
self.sgd_weights = {}
self.centroids = {}
for addr in addrs:
self.sgd_weights[addr] = np.array([0.5] * num_of_features)
self.centroids[addr] = np.array([0.5] * num_of_features)
self.max = gaus_pdf(1, 1, std)
def _map_11(self, mean, std, value):
lmax = gaus_pdf(mean, mean, std)
pdf = gaus_pdf(value, mean, std)
return -1.0 + 2 * (pdf / lmax)
def _map1_1(self, mean, std, value):
lmax = gaus_pdf(mean, mean, std)
pdf = gaus_pdf(value, mean, std)
return 1.0 - 2 * (pdf / lmax)
def _map01(self, mean, std, value):
lmax = gaus_pdf(mean, mean, std)
pdf = gaus_pdf(value, mean, std)
return pdf / lmax
def hedonic_value(self, value, desired_value):
lmax = gaus_pdf(desired_value, desired_value, self.hedonic_std)
pdf = gaus_pdf(value, desired_value, self.hedonic_std)
return pdf / lmax
