Skip to content

⚡️ Speed up method DiscreteDP.to_product_form by 9% #45

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
codeflash-ai wants to merge 1 commit into
base: main
Choose a base branch
from
Open

⚡️ Speed up method DiscreteDP.to_product_form by 9% #45

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
12 changes: 10 additions & 2 deletions quantecon/markov/ddp.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -119,6 +119,7 @@
_fill_dense_Q, _s_wise_max_argmax, _s_wise_max, _find_indices,
_has_sorted_sa_indices, _generate_a_indptr
)
from numba import njit


class DiscreteDP:
Expand Down Expand Up @@ -370,7 +371,8 @@ def __init__(self, R, Q, beta, s_indices=None, a_indices=None):
def s_wise_max(vals, out=None, out_argmax=None):
"""
Return the vector max_a vals(s, a), where vals is represented
by a 1-dimensional ndarray of shape (self.num_sa_pairs,).
by a 2-dimensional ndarray of shape (n, m). Stored in out,
which must be of length self.num_states.
out and out_argmax must be of length self.num_states; dtype of
out_argmax must be int.

Expand Down Expand Up @@ -525,7 +527,7 @@ def to_product_form(self):
ns = self.num_states
na = self.a_indices.max() + 1
R = np.full((ns, na), -np.inf)
R[self.s_indices, self.a_indices] = self.R
_assign_sa_rewards(self.s_indices, self.a_indices, self.R, R)
Q = np.zeros((ns, na, ns))
if self._sparse:
_fill_dense_Q(self.s_indices, self.a_indices,
Expand Down Expand Up @@ -1078,3 +1080,9 @@ def backward_induction(ddp, T, v_term=None):
ddp.bellman_operator(vs[t, :], Tv=vs[t-1, :], sigma=sigmas[t-1, :])

return vs, sigmas


@njit(cache=True)
def _assign_sa_rewards(s_indices, a_indices, R_flat, Rmat):
for i in range(R_flat.shape[0]):
Rmat[s_indices[i], a_indices[i]] = R_flat[i]
4 changes: 2 additions & 2 deletions quantecon/markov/utilities.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,7 @@

"""
import numpy as np
from numba import jit
from numba import njit, jit


@jit(nopython=True, cache=True)
Expand Down Expand Up @@ -52,7 +52,7 @@ def sa_indices(num_states, num_actions):
return s_indices, a_indices


@jit(nopython=True, cache=True)
@njit(cache=True)
def _fill_dense_Q(s_indices, a_indices, Q_in, Q_out):
L = Q_in.shape[0]
for i in range(L):
Expand Down