test_example_lengths = np.load(old_data_path+'test_example_lengths_102012.npy')
detection_array = np.zeros((test_example_lengths.shape[0],
test_example_lengths.max() + 2),dtype=np.float32)
linear_filter,c = et.construct_linear_filter(aar_template,
clipped_bgd)
# need to state the syllable we are working with
syllable = np.array(['aa','r'])
detection_array,example_start_end_times, detection_lengths = gtrd.get_detection_scores(data_path+'Test/',
detection_array,
syllable,
linear_filter,c,verbose=True)
np.save(tmp_data_path+'detection_array_aar_1.npy',detection_array)
out = open(tmp_data_path+'example_start_end_times_aar_1.pkl','wb')
cPickle.dump(example_start_end_times,out)
out.close()
out = open(tmp_data_path+'detection_lengths_aar_1.pkl','wb')
cPickle.dump(detection_lengths,out)
out.close()
import template_speech_rec.roc_functions as rf
window_start,window_end = rf.get_auto_syllable_window(aar_template)
num_mix = 1
aar_parts_template, aar_registered = et.register_templates_time_zero(syllable_examples,min_prob=.01)
np.save(cur_tmp_data_path+'parts_aar_template_%d.npy' % num_mix,aar_parts_template)
detection_array = np.zeros((parts_test_example_lengths.shape[0],
parts_test_example_lengths.max() + 2),dtype=np.float32)
linear_filter,c = et.construct_linear_filter(aar_parts_template,
waliji_bgd)
for i in xrange(num_mix):
np.save(cur_tmp_data_path+'linear_filter_aar_%d_%d.npy'%(num_mix,i),linear_filter)
np.save(cur_tmp_data_path+'c_aar_%d_%d.npy'%(num_mix,i),np.array(c))
syllable = np.array(['aa','r'])
detection_array,parts_example_start_end_times, parts_detection_lengths = gtrd.get_detection_scores(test_data_path,
detection_array,
syllable,
linear_filter,c,
log_part_blocks=log_part_blocks,
log_invpart_blocks=log_invpart_blocks,
verbose=True)
np.save(cur_tmp_data_path+'parts_detection_array_aar_%d.npy' % num_mix,detection_array)
window_start = -2
window_end = 2
max_detect_vals = rf.get_max_detection_in_syllable_windows(detection_array,
parts_example_start_end_times,
parts_detection_lengths,
window_start,
window_end)
np.save(cur_tmp_data_path+'parts_max_detect_vals_aar_%d.npy' % num_mix,max_detect_vals)
num_mix = 1
max_detect_vals = np.load(cur_tmp_data_path+'parts_max_detect_vals_aar_%d.npy' % num_mix)
detection_array = np.load(cur_tmp_data_path+'parts_detection_array_aar_%d.npy' % num_mix)
