def linear_regression(points):
if Tracker.distance(points[0], meanPoint(points)) < 10:
return [0, 1]
num_pts = len(points)
xc = points[:, [0]]
yc = points[:, [1]]
ones = np.ones((num_pts, 1))
X = np.concatenate((ones, xc), axis=1)
X_intermediary = np.dot(X.T, X)
Y_intermediary = np.dot(X.T, yc)
R = np.dot(inv(X_intermediary), Y_intermediary)
k = R[1][0]
last_2 = points[-2:]
first_x = last_2[0][0]
second_x = last_2[1][0]
if first_x < second_x:
return [1, k]
else:
return [-1, -k]
def linear_regression(points):
if Tracker.distance(points[0], meanPoint(points)) < 10:
return [0, 1]
num_pts = len(points)
xc = points[:, [0]]
yc = points[:, [1]]
ones = np.ones((num_pts, 1))
X = np.concatenate((ones, xc), axis=1)
X_intermediary = np.dot(X.T, X)
Y_intermediary = np.dot(X.T, yc)
R = np.dot(inv(X_intermediary), Y_intermediary)
k = R[1][0]
last_2 = points[-2:]
first_x = last_2[0][0]
second_x = last_2[1][0]
if first_x < second_x:
return [1, k]
else:
return [-1, -k]
