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| author | Mario Mulansky <mario.mulansky@gmx.net> | 2015-10-10 20:45:09 +0200 |
|---|---|---|
| committer | Mario Mulansky <mario.mulansky@gmx.net> | 2018-06-02 12:59:43 -0700 |
| commit | 18ea80e2d01e9eb4ceee17219f91098efbcdf67c (patch) | |
| tree | d7819736b059e9885d53c14e28160d6487d93e6c /pyspike/spike_directionality.py | |
| parent | a5e6a12a619cb9528a4cf7f3ef8f082e5eb877c2 (diff) | |
spike sync filtering, cython sim ann
Added function for filtering out events based on a threshold for the spike
sync values. Usefull for focusing on synchronous events during directionality
analysis.
Also added cython version of simulated annealing for performance.
Diffstat (limited to 'pyspike/spike_directionality.py')
| -rw-r--r-- | pyspike/spike_directionality.py | 54 |
1 files changed, 33 insertions, 21 deletions
diff --git a/pyspike/spike_directionality.py b/pyspike/spike_directionality.py index cda7fe3..e1f5f16 100644 --- a/pyspike/spike_directionality.py +++ b/pyspike/spike_directionality.py @@ -242,27 +242,39 @@ def optimal_spike_train_order_from_matrix(D, full_output=False): p = np.arange(N) - T = 2*np.max(D) # starting temperature - T_end = 1E-5 * T # final temperature - alpha = 0.9 # cooling factor - total_iter = 0 - while T > T_end: - iterations = 0 - succ_iter = 0 - while iterations < 100*N and succ_iter < 10*N: - # exchange two rows and cols - ind1 = np.random.randint(N-1) - delta_A = -2*D[p[ind1], p[ind1+1]] - if delta_A > 0.0 or exp(delta_A/T) > np.random.random(): - # swap indices - p[ind1], p[ind1+1] = p[ind1+1], p[ind1] - A += delta_A - succ_iter += 1 - iterations += 1 - total_iter += iterations - T *= alpha # cool down - if succ_iter == 0: - break + T_start = 2*np.max(D) # starting temperature + T_end = 1E-5 * T_start # final temperature + alpha = 0.9 # cooling factor + + from cython.cython_simulated_annealing import sim_ann_cython as sim_ann + + p, A, total_iter = sim_ann(D, T_start, T_end, alpha) + + # T = T_start + # total_iter = 0 + # while T > T_end: + # iterations = 0 + # succ_iter = 0 + # # equilibrate for 100*N steps or 10*N successful steps + # while iterations < 100*N and succ_iter < 10*N: + # # exchange two rows and cols + # ind1 = np.random.randint(N-1) + # if ind1 < N-1: + # ind2 = ind1+1 + # else: # this can never happend + # ind2 = 0 + # delta_A = -2*D[p[ind1], p[ind2]] + # if delta_A > 0.0 or exp(delta_A/T) > np.random.random(): + # # swap indices + # p[ind1], p[ind2] = p[ind2], p[ind1] + # A += delta_A + # succ_iter += 1 + # iterations += 1 + # total_iter += iterations + # T *= alpha # cool down + # if succ_iter == 0: + # break + if full_output: return p, A, total_iter else: |