josephbima

total_accuracy = 0.0
total_precision = 0.0
total_recall = 0.0

# Iterate over the cv and fit the decision tree using the training set
# https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html

for i, (train_index, test_index) in enumerate(cv.split(X, Y)):
	X_train, X_test = X[train_index], X[test_index]
	y_train, y_test = Y[train_index], Y[test_index]

josephbima

def _compute_mean_features(window):
    """
    Computes the mean x, y and z acceleration over the given window. 
    """
    return np.mean(window, axis=0)


def _compute_std_features(window):
    '''
    Computes the standard deviation of x, y and z acceleration over the given window.