def _weights(self, dists, index, sigma_sq):
w = np.fromiter((gaussian_kernel(d, sigma_sq)
for d in dists), np.float, len(dists))
# Weight by timestamp of samples to forget old values
max_index = max(index)
wt = np.fromiter((gaussian_kernel(max_index - idx, self.sigma_t_sq)
for idx in index), np.float, len(dists))
w = w * wt
wsum = w.sum()
if wsum == 0:
return 1.0/len(dists)*np.ones((len(dists),))
else:
eps = wsum * 1e-10 / self.dim_x
return np.fromiter((w_i/wsum if w_i > eps else 0.0 for w_i in w), np.float)
def _weights(self, dists, index, sigma_sq):
w = np.fromiter((gaussian_kernel(d, sigma_sq) for d in dists),
np.float, len(dists))
wsum = w.sum()
if wsum == 0:
return 1.0 / len(dists) * np.ones((len(dists), ))
else:
eps = wsum * 1e-10 / self.dim_x
return np.fromiter((w_i / wsum if w_i > eps else 0.0 for w_i in w),
np.float)
def _weights(self, dists, sigma_sq):
w = np.fromiter((gaussian_kernel(d/self.dim_x, sigma_sq)
for d in dists), np.float)
# We eliminate the outliers # TODO : actually reduce w and index
wsum = w.sum()
if wsum == 0:
return 1.0/len(dists)*np.ones((len(dists),))
else:
eps = wsum * 1e-15 / self.dim_x
return np.fromiter((w_i/wsum if w_i > eps else 0.0 for w_i in w), np.float)
def _weights(self, dists, sigma_sq):
w = np.fromiter((gaussian_kernel(d / self.dim_x, sigma_sq)
for d in dists), np.float)
# We eliminate the outliers # TODO : actually reduce w and index
wsum = w.sum()
if wsum == 0:
return 1.0 / len(dists) * np.ones((len(dists), ))
else:
eps = wsum * 1e-15 / self.dim_x
return np.fromiter((w_i / wsum if w_i > eps else 0.0 for w_i in w),
np.float)
def _weights(self, index, dists, sigma_sq, y_desired):
dists = [dist(self.fmodel.dataset.get_y(idx), y_desired)
for idx in index] # could be optimized
w = np.fromiter((gaussian_kernel(d/self.fmodel.dim_y, sigma_sq)
for d in dists), np.float)
# We eliminate the outliers # TODO : actually reduce w and index
wsum = w.sum()
if wsum == 0:
return 1.0/len(dists)*np.ones((len(dists),))
else:
eps = wsum * 1e-15 / self.fmodel.dim_y
return np.fromiter((w_i/wsum if w_i > eps else 0.0 for w_i in w), np.float)
def _weights(self, index, dists, sigma_sq, y_desired):
dists = [
dist(self.fmodel.dataset.get_y(idx), y_desired) for idx in index
] # could be optimized
w = np.fromiter((gaussian_kernel(d / self.fmodel.dim_y, sigma_sq)
for d in dists), np.float)
# We eliminate the outliers # TODO : actually reduce w and index
wsum = w.sum()
if wsum == 0:
return 1.0 / len(dists) * np.ones((len(dists), ))
else:
eps = wsum * 1e-15 / self.fmodel.dim_y
return np.fromiter((w_i / wsum if w_i > eps else 0.0 for w_i in w),
np.float)
