def __init__(self, modelname):
self.svm_classifier = SVMClassifier()
self.svm_classifier.loadSvmModel("../SVM_models/"+modelname)
self.g_means = gm.GMeans()
class HeartBeatClassifier(object):
def __init__(self, modelname):
self.svm_classifier = SVMClassifier()
self.svm_classifier.loadSvmModel("../SVM_models/"+modelname)
self.g_means = gm.GMeans()
def classify(self, package_number=101):
# number_of_clusters = 2
normalized_data = self.getQrsComplexDataFromFile(package_number, 'ConvertedQRSRawData.txt')
# print('TEST OF IMPORT:', normalized_data[0])
# print('TEST OF TO_NDARRAY CONVERSION:', normalized_data[0].to_ndarray())
centroids, labels_dict = self.g_means.cluster_data(normalized_data)
K = max(labels_dict.values()) + 1
self.update_data_list(normalized_data, labels_dict)
self.svm_classifier.predict(normalized_data)
return K
@staticmethod
def update_data_list(data_list, labels_dict):
for i, element in enumerate(data_list):
element.class_id = labels_dict[i]
@staticmethod
def array_to_qrs_data(data_array, labels_dict):
# THIS METHOD IS DEPRECATED. I thought that we'll pass data in numpy.ndarrays but it's not the case.
qrs_data_list = []
for i, element in enumerate(data_array):
qrs = QRSData(element)
qrs.class_id = labels_dict[i]
qrs_data_list.append(qrs)
return qrs_data_list
def getQrsComplexDataFromFile(self, package_number, filename):
dir_to_cpp_signals = os.path.dirname(os.path.dirname(__file__))
dir_to_cpp_signals = os.path.join('..', 'ReferencyjneDane', str(package_number))
filename = os.path.join(dir_to_cpp_signals, filename)
with open(filename, "r") as ins:
qrs_data_list = [QRSData([float(x) for x in line.split()]) for line in ins]
min_max_scaler = MinMaxScaler()
r_peak_values = [item.r_peak_value for item in qrs_data_list]
p_onset_values = [item.p_onset_val for item in qrs_data_list]
p_peak_values = [item.p_peak_val for item in qrs_data_list]
p_end_values = [item.p_end_val for item in qrs_data_list]
qrs_onset_values = [item.qrs_onset_val for item in qrs_data_list]
qrs_end_values = [item.qrs_end_val for item in qrs_data_list]
t_peak_values = [item.t_peak_val for item in qrs_data_list]
t_end_values = [item.t_end_val for item in qrs_data_list]
scaled_r_peak_values = min_max_scaler.fit_transform(r_peak_values)
scaled_p_onset_values = min_max_scaler.fit_transform(p_onset_values)
scaled_p_peak_values = min_max_scaler.fit_transform(p_peak_values)
scaled_p_end_values = min_max_scaler.fit_transform(p_end_values)
scaled_qrs_onset_values = min_max_scaler.fit_transform(qrs_onset_values)
scaled_qrs_end_values = min_max_scaler.fit_transform(qrs_end_values)
scaled_t_peak_values = min_max_scaler.fit_transform(t_peak_values)
scaled_t_end_values = min_max_scaler.fit_transform(t_end_values)
data = []
for i in range(len(qrs_data_list)):
tmp = QRSData([qrs_data_list[i].r_peak,
scaled_r_peak_values[i],
qrs_data_list[i].rr_pre_interval,
qrs_data_list[i].rr_post_interval,
qrs_data_list[i].p_onset,
scaled_p_onset_values[i],
qrs_data_list[i].p_peak,
scaled_p_peak_values[i],
qrs_data_list[i].p_end,
scaled_p_end_values[i],
qrs_data_list[i].qrs_onset,
scaled_qrs_onset_values[i],
qrs_data_list[i].qrs_end,
scaled_qrs_end_values[i],
qrs_data_list[i].t_peak,
scaled_t_peak_values[i],
qrs_data_list[i].t_end,
scaled_t_end_values[i]
])
data.append(tmp)
return data
def run(self):
pass
