diff options
| -rw-r--r-- | bin/Proof_Of_Concept_PELT.py | 89 |
1 files changed, 64 insertions, 25 deletions
diff --git a/bin/Proof_Of_Concept_PELT.py b/bin/Proof_Of_Concept_PELT.py index bcbd53e..dde99d8 100644 --- a/bin/Proof_Of_Concept_PELT.py +++ b/bin/Proof_Of_Concept_PELT.py @@ -3,7 +3,7 @@ import time import sys import getopt import re -from multiprocessing import Pool, Manager +from multiprocessing import Pool, Manager, cpu_count from kneed import KneeLocator from sklearn.cluster import AgglomerativeClustering import matplotlib.pyplot as plt @@ -86,7 +86,7 @@ def calc_pelt(signal, penalty, model="l1", jump=5, min_dist=2, plotting=False): return bkps print_error("No Penalty specified.") - sys.exit() + sys.exit(-1) def calculate_penalty_value(signal, model="l1", jump=5, min_dist=2, range_min=0, range_max=50, @@ -220,7 +220,7 @@ def calculate_penalty_value(signal, model="l1", jump=5, min_dist=2, range_min=0, print_error("With the current thresh-hold S=" + str(S) + " it is not possible to select a penalty value.") - sys.exit() + sys.exit(-1) # very short benchmark yielded approx. 1/3 of speed compared to solution with sorting @@ -405,7 +405,7 @@ if __name__ == '__main__': opt_min_dist = None opt_range_min = None opt_range_max = None - opt_num_processes = None + opt_num_processes = cpu_count() opt_refresh_delay = None opt_refresh_thresh = None opt_S = None @@ -422,7 +422,7 @@ if __name__ == '__main__': if 'filename' not in opt: print_error("No file specified!") - sys.exit(2) + sys.exit(-1) else: opt_filename = opt['filename'] if 'v' in opt: @@ -435,70 +435,70 @@ if __name__ == '__main__': opt_jump = int(opt['jump']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'min_dist' in opt: try: opt_min_dist = int(opt['min_dist']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'range_min' in opt: try: opt_range_min = int(opt['range_min']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'range_max' in opt: try: opt_range_max = int(opt['range_max']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'num_processes' in opt: try: opt_num_processes = int(opt['num_processes']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'refresh_delay' in opt: try: opt_refresh_delay = int(opt['refresh_delay']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'refresh_thresh' in opt: try: opt_refresh_thresh = int(opt['refresh_thresh']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'S' in opt: try: opt_S = float(opt['S']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'pen_override' in opt: try: opt_pen_override = int(opt['pen_override']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'pen_modifier' in opt: try: opt_pen_modifier = float(opt['pen_modifier']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) if 'refinement_thresh' in opt: try: opt_refinement_thresh = int(opt['refinement_thresh']) except ValueError as verr: print(verr, file=sys.stderr) - sys.exit(2) + sys.exit(-1) except getopt.GetoptError as err: print(err, file=sys.stderr) - sys.exit(2) + sys.exit(-1) # OPENING DATA if ".json" in opt_filename: @@ -623,8 +623,8 @@ if __name__ == '__main__': # plt.show() # TODO: Problem: Der Algorithmus nummeriert die Zustände nicht immer gleich... also bspw.: # mal ist das tatsächliche Transmit mit 1 belabelt und mal mit 3 - cluster_labels_list.append(cluster.labels_) - num_cluster_list.append(cluster.n_clusters_) + cluster_labels_list.append((num_trace, cluster.labels_)) + num_cluster_list.append((num_trace, cluster.n_clusters_)) i = i + 1 else: print_info("Discarding measurement No. " + str(num_trace) + " because it " @@ -640,18 +640,55 @@ if __name__ == '__main__': + " Measurements for refinement. " "Others did not recognize number of states correctly.") # TODO: DEBUG Kram - sys.exit() + sys.exit(0) else: print_info("Used all available measurements.") - num_states = np.argmax(np.bincount(num_cluster_list)) + num_states = np.argmax(np.bincount([elem[1] for elem in num_cluster_list])) + avg_per_state_list = [None] * len(cluster_labels_list) + used_clusters = 0 + for number, (num_trace, labels) in enumerate(cluster_labels_list): + if num_cluster_list[number][1] == num_states: + avg_per_state = [0] * num_states + count_per_state = [0] * num_states + raw_states = raw_states_list[num_trace] + for num_label, label in enumerate(labels): + count_per_state[label] = count_per_state[label] + 1 + avg_per_state[label] = avg_per_state[label] + raw_states[num_label][2] + for i,_ in enumerate(avg_per_state): + avg_per_state[i] = avg_per_state[i] / count_per_state[i] + avg_per_state_list[number] = avg_per_state + used_clusters = used_clusters + 1 + + # flattend version for clustering: + values_to_cluster = np.zeros((num_states * used_clusters, 2)) + index = 0 + for avg_per_state in avg_per_state_list: + if None not in avg_per_state: + for avg in avg_per_state: + values_to_cluster[index][0] = avg + values_to_cluster[index][1] = 0 + index = index + 1 + # plt.scatter(values_to_cluster[:, 0], values_to_cluster[:, 1]) + # plt.show() + cluster = AgglomerativeClustering(n_clusters=num_states) + cluster.fit_predict(values_to_cluster) + # HIER WEITER: + # Aktuell hast du hier ein plattes Array mit labels. Jetzt also das wieder auf die + # ursprünglichen Labels abbilden, die dann verändern mit den hier gefundenen Labels. + # Alle identischen Zustände haben identische Labels. Dann vllt bei resulting + # sequence ausgeben, wie groß die übereinstimmung bei der Stateabfolge ist. + resulting_sequence = [None] * num_raw_states i = 0 for x in resulting_sequence: j = 0 test_list = [] - for arr in cluster_labels_list: - if num_cluster_list[j] != num_states: + for arr in [elem[1] for elem in cluster_labels_list]: + if num_cluster_list[j][1] != num_states: + # hopefully this does not happen regularly + print_info("Discarding measurement " + str(j) + + " because the clustering yielded not matching results.") j = j + 1 else: test_list.append(arr[i]) @@ -659,6 +696,7 @@ if __name__ == '__main__': resulting_sequence[i] = np.argmax(np.bincount(test_list)) i = i + 1 print(resulting_sequence) + sys.exit() elif ".tar" in opt_filename: # open with dfatool @@ -670,11 +708,12 @@ if __name__ == '__main__': print_info("Preprocessing file. Depending on its size, this could take a while.") preprocessed_data = raw_data.get_preprocessed_data() print_info("File fully preprocessed") - # TODO: Mal schauen, wie ich das mache. Erstmal nur mit json + print_error("Not implemented yet. Please generate .json files first with dfatool and use" + " those.") else: print_error("Unknown dataformat") - sys.exit(2) + sys.exit(-1) # print(tx_data[1]['parameter']) # # parse json to array for PELT |