fpr)
np.save('data/tpr_aar_%d.npy' % num_mix,
tpr)
detection_clusters = rf.get_detect_clusters_threshold_array(max_detect_vals,
detection_array,
np.array(detection_lengths),
C0,C1)
out = open('data/detection_clusters_aar_%d.npy' % num_mix,
'wb')
cPickle.dump(detection_clusters,out)
out.close()
for i in xrange(1,11):
if not np.any(fpr*60 <= i): continue
thresh_idx = (np.arange(fpr.shape[0])[fpr*60 <= i]).min()
FOMS[num_mix].append(tpr[thresh_idx])
pos_cluster_responses,neg_cluster_responses = rf.get_pos_neg_detections(detection_clusters[thresh_idx],detection_array,C1,window_start,window_end,example_start_end_times)
pos_cluster_responses += np.random.randn(*pos_cluster_responses.shape)/1000
neg_cluster_responses += np.random.randn(*neg_cluster_responses.shape)/1000
if pos_cluster_responses.shape[0] > 1:
np.save("data/aar_pos_cluster_responses_%d_%d.npy"% (num_mix,i),pos_cluster_responses)
pos_response_grid, pos_response_points = rf.map_cluster_responses_to_grid(pos_cluster_responses)
rf.display_response_grid("aar_pos_response_grid_%d_%d.png" % (num_mix,i),pos_response_grid,pos_response_points)
if neg_cluster_responses.shape[0] > 1:
np.save("data/aar_neg_cluster_responses_%d_%d.npy"%(num_mix,i),neg_cluster_responses)
neg_response_grid, neg_response_points = rf.map_cluster_responses_to_grid(neg_cluster_responses)
rf.display_response_grid("aar_neg_response_grid_%d_%d.png" % (num_mix,i),neg_response_grid,neg_response_points)
num_clusters = sum( len(cset) for cset in detection_clusters_at_threshold)
num_pos_clusters = 0
num_neg_clusters = 0
'rb')
detection_clusters = cPickle.load(out)
out.close()
out = open('data/example_start_end_times_aar.pkl','rb')
example_start_end_times = cPickle.load(out)
out.close()
tpr = np.load('data/tpr_aar_%d.npy' % num_mix
)
detection_array = np.load('data/detection_array_aar_%d.npy' % num_mix)
C1 = int(33 * 1.5+.5)
window_start = -10
window_end = 10
rf.get_pos_neg_detections(detection_clusters_at_threshold,detection_array,
C1,
window_start,
window_end,
example_start_end_times)
num_clusters = sum( len(cset) for cset in detection_clusters_at_threshold)
num_pos_clusters = 0
num_neg_clusters = 0
pos_clusters = np.zeros((num_clusters,C1))
neg_clusters = np.zeros((num_clusters,C1))
for detect_clusters, detection_row, start_end_times in itertools.izip(detection_clusters_at_threshold,detection_array,example_start_end_times):
for c in detect_clusters:
is_neg = True
for s,e in start_end_times:
