def get_sampen(arr, i):
sampen = []
for data in arr:
sampen.append(data[i])
return sampen2(normalize_data(sampen))
def sampEntropy(rawEMGSignal):
"""
Parameters
----------
rawEMGSignal : ndarray
an epoch of raw emg-signal
Returns
-------
feature_value : float
sample entropy calculated from the rawEMGSignal for the parameters m = 2 and r = 0.2 * std
"""
copy_rawEMGSignal = copy.copy(rawEMGSignal) # shallow copy
copy_feature = sampen2(normalize_data(list(copy_rawEMGSignal)))[2][1]
if copy_feature is None:
feature_value = 0
else:
feature_value = copy_feature
return feature_value
testing_folder_names = [
"jeremie10/", "jeremie11/", "kaan10/", "kaan11/", "ariel7/", "ariel9/",
"ariel12/", "ariel13/", "ariel14/"
]
train_test_folder_label = [
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
1, 1, 1, 1, 1, 1, 1
]
train_data = collect_data(training_folder_names)
test_data = collect_data(testing_folder_names)
print("training model...")
model = svm.SVC(kernel="rbf", C=100, gamma='scale')
model.fit(train_data, train_test_folder_label)
prediction = model.predict(test_data)
print("Prediction: ")
print(prediction)
true_positive, false_positive = get_performance_of_prediction(
prediction, [0, 0, 0, 0, 1, 1, 1, 1, 1])
print("True positives: " + str(true_positive) + " over 3")
print("False positives: " + str(false_positive))
x = [2, 3, 4, 5, 'NaN', 'NaN']
print(normalize_data(x))
pickle.dump(model, open("svm.p", "wb"))
def test_normalize_data(self):
data = [2.0, 3.0, 4.0]
normalized_data = normalize_data(data)
self.assertEqual(ceil(pvariance(normalized_data)), 1.0)
self.assertEqual(mean(normalized_data), 0.0)
def test_normalize_data(self):
data = [2.0, 3.0, 4.0]
normalized_data = normalize_data(data)
self.assertEqual(ceil(pvariance(normalized_data)), 1.0)
self.assertEqual(mean(normalized_data), 0.0)