def get_roc_curves(phn, num_mix_params, train_example_lengths,bgd, train_path,file_indices, sp,ep): FOMS = collections.defaultdict(list) for num_mix in num_mix_params: if num_mix > 1: outfile = np.load('data/%d_templates.npz' % num_mix) templates = tuple( outfile['arr_%d'%i] for i in xrange(len(outfile.files))) else: templates = (np.load('data/1_templates.npy')[0],) detection_array = np.zeros((train_example_lengths.shape[0], train_example_lengths.max() + 2),dtype=np.float32) linear_filters_cs = et.construct_linear_filters(templates, bgd) np.savez('data/linear_filter_%d.npz'% num_mix,*(tuple(lfc[0] for lfc in linear_filters_cs))) np.savez('data/c_%d.npz'%num_mix,*(tuple(lfc[1] for lfc in linear_filters_cs))) syllable = np.array((phn,)) (detection_array, example_start_end_times, detection_lengths, detection_template_ids)=gtrd.get_detection_scores_mixture_named_params( train_path, file_indices, detection_array, syllable, linear_filters_cs,S_config=sp, E_config=ep, verbose = True, num_examples =-1, return_detection_template_ids=True) np.save('data/detection_array_%d.npy' % num_mix,detection_array) np.save('data/detection_template_ids_%d.npy' % num_mix,detection_template_ids) np.save('data/detection_lengths_%d.npy' % num_mix,detection_lengths) if num_mix == num_mix_params[0]: out = open('data/example_start_end_times.pkl','wb') cPickle.dump(example_start_end_times,out) out.close() window_start = -int(np.mean(tuple( t.shape[0] for t in templates))/3.+.5) window_end = -window_start max_detect_vals = rf.get_max_detection_in_syllable_windows(detection_array, example_start_end_times, detection_lengths, window_start, window_end) max_detect_vals = max_detect_vals[:1000] np.save('data/max_detect_vals_%d_%s.npy' % (num_mix,phn),max_detect_vals) C0 = int(np.mean(tuple( t.shape[0] for t in templates))/3.+.5) C1 = int( 33 * 1.5 + .5) frame_rate = 1/.005 fpr, tpr = rf.get_roc_curve(max_detect_vals, detection_array, np.array(detection_lengths), example_start_end_times, C0,C1,frame_rate) np.save('data/fpr_%d_%s.npy' % (num_mix,phn), fpr) np.save('data/tpr_%d_%s.npy' % (num_mix,phn), tpr)
bgd = np.clip(np.load("data/aar_bgd_mel.npy"),.1,.4) test_example_lengths =np.load("data/test_example_lengths.npy") test_path = "/home/mark/Template-Speech-Recognition/Data/Test/" test_file_indices =np.load("data/test_file_indices.npy") for num_mix in num_mix_params: templates = tuple(np.clip(T,.01,.99) for T in (np.load('aar1_templates_%d.npz' % num_mix))['arr_0']) detection_array = np.zeros((test_example_lengths.shape[0], test_example_lengths.max() + 2),dtype=np.float32) linear_filters_cs = et.construct_linear_filters(templates, bgd) syllable = np.array(['aa','r']) detection_array,example_start_end_times, detection_lengths = gtrd.get_detection_scores_mixture_named_params(test_path,test_file_indices, detection_array, syllable, linear_filters_cs,S_config=sp, E_config=ep, verbose = False, num_examples =-1) np.save('data/detection_array_aar_%d.npy' % num_mix,detection_array) if num_mix == 2: out = open('data/example_start_end_times_aar.pkl','wb') cPickle.dump(example_start_end_times,out) out.close() out = open('data/detection_lengths_aar.pkl','wb') cPickle.dump(detection_lengths,out) out.close() window_start = -10 window_end = 10 max_detect_vals = rf.get_max_detection_in_syllable_windows(detection_array, example_start_end_times,
templates = (np.load('data/1_templates.npy')[0],) detection_array = np.zeros((train_example_lengths.shape[0], train_example_lengths.max() + 2),dtype=np.float32) linear_filters_cs = et.construct_linear_filters(templates, bgd) np.savez('data/linear_filter_%d.npz'% num_mix,*(tuple(lfc[0] for lfc in linear_filters_cs))) np.savez('data/c_%d.npz'%num_mix,*(tuple(lfc[1] for lfc in linear_filters_cs))) syllable = np.array((phn,)) (detection_array, example_start_end_times, detection_lengths, detection_template_ids)=gtrd.get_detection_scores_mixture_named_params( train_path, file_indices, detection_array, syllable, linear_filters_cs,S_config=sp, E_config=ep, verbose = True, num_examples =-1, return_detection_template_ids=True) np.save('data/detection_array_%d.npy' % num_mix,detection_array) np.save('data/detection_template_ids_%d.npy' % num_mix,detection_template_ids) np.save('data/detection_lengths_%d.npy' % num_mix,detection_lengths) if num_mix == num_mix_params[0]: out = open('data/example_start_end_times.pkl','wb') cPickle.dump(example_start_end_times,out) out.close() window_start = -int(np.mean(tuple( t.shape[0] for t in templates))/3.+.5) window_end = -window_start max_detect_vals = rf.get_max_detection_in_syllable_windows(detection_array, example_start_end_times,