Algorithm 1.

RAP Learning via Exact Imitation

Input: Λ = Training sequence of sensor event data labeled with activity segments, L = Loss function
Output: Π, the recurrent predictor
1:	for each activity predictor j = 1 to K do
2:	Initialize the set of regression examples 𝒟j = ∅
3:	end for
4:	for each time step i = 1 to N do
5:	for each activity predictor j = 1 to K do
6:	Compute Ψlocal(i) = Φ(λi)
7:	Compute Ψcontext(i, j)
8:	Joint features Ψij = Ψlocal(i) ⊕ Ψcontext(i, j)
9:	Compute best output $y_{ij}^{\ast }\in \Re$ using L
10:	Add regression example (Ψij, $y_{ij}^{\ast }$) to 𝒟j
11:	end for
12:	end for
13:	for each activity predictor j = 1 to K do
14:	Πj =Regression-Learner(𝒟j)
15:	end for
16:	return learned predictor Π = (Π1,Π2, ⋯,ΠK)