def segment_heuristically(self, sensors, reference_signal, label=None):
p = PeakAnalysis(self.view)
peaks = p.get_peaks(reference_signal)
timestamps = []
for peak_position in peaks:
timestamps.append(sensors[0].timestamp[peak_position])
if label:
label_timestamps = label.timestamp
labels = label.label
closer_labels = []
closer_timestamps = []
for i in range(0, len(label_timestamps)):
min_dist = label_timestamps[len(label_timestamps) -
1] - label_timestamps[0]
closer_label = None
closer_timestamp = None
for heuristic_timestamp in timestamps:
dist = sqrt(
np.square(label_timestamps[i] - heuristic_timestamp))
if dist < min_dist:
min_dist = dist
closer_label = labels[i]
closer_timestamp = heuristic_timestamp
closer_labels.append(closer_label)
closer_timestamps.append(closer_timestamp)
self.segment(sensors, closer_timestamps, closer_labels)
else:
self.segment(sensors, timestamps)
def segment_heuristically(self, sensors, reference_signal, label=None):
p = PeakAnalysis(self.view)
peaks = p.get_peaks(reference_signal)
timestamps = []
for peak_position in peaks:
timestamps.append(sensors[0].timestamp[peak_position])
if label:
label_timestamps = label.timestamp
labels = label.label
closer_labels = []
closer_timestamps = []
for i in range(0, len(label_timestamps)):
min_dist = label_timestamps[len(label_timestamps) - 1] - label_timestamps[0]
closer_label = None
closer_timestamp = None
for heuristic_timestamp in timestamps:
dist = sqrt(np.square(label_timestamps[i] - heuristic_timestamp))
if dist < min_dist:
min_dist = dist
closer_label = labels[i]
closer_timestamp = heuristic_timestamp
closer_labels.append(closer_label)
closer_timestamps.append(closer_timestamp)
self.segment(sensors, closer_timestamps, closer_labels)
else:
self.segment(sensors, timestamps)
def get_statistical_features(self, data):
p = PeakAnalysis(self.view)
min_value = np.amin(data)
max_value = np.amax(data)
root_mean_square = UMath.get_root_mean_square(data)
peaks_number = np.mean(len(p.get_peaks(data)))
crest_factor = np.median(p.get_peak_to_average_ratios(data))
skewness = stats.skew(data, False)
kurtosis = stats.kurtosis(data, False)
variance = np.var(data)
return [min_value, max_value, root_mean_square, peaks_number, crest_factor, skewness, kurtosis, variance]
def get_statistical_features(self, data):
p = PeakAnalysis(self.view)
min_value = np.amin(data)
max_value = np.amax(data)
root_mean_square = UMath.get_root_mean_square(data)
peaks_number = np.mean(len(p.get_peaks(data)))
crest_factor = np.median(p.get_peak_to_average_ratios(data))
skewness = stats.skew(data, False)
kurtosis = stats.kurtosis(data, False)
variance = np.var(data)
return [
min_value, max_value, root_mean_square, peaks_number, crest_factor,
skewness, kurtosis, variance
]
