def find_eigenvector(m: Matrix,
tolerance: float = 0.00001) -> Tuple[Vector, float]:
guess = [random.random() for _ in m]
while True:
result = matrix_times_vector(m, guess) # transform guess
norm = magnitude(result) # compute norm
next_guess = [x / norm for x in result] # rescale
if distance(guess, next_guess) < tolerance:
# convergence so return (eigenvector, eigenvalue)
return next_guess, norm
guess = next_guess
def find_eigenvector(m: Matrix,
tolerance: float = 0.00001) -> Tuple[Vector, float]:
guess = [random.random() for _ in m]
while True:
result = matrix_times_vector(m, guess) # transform guess
norm = magnitude(result) # compute norm
next_guess = [x / norm for x in result] # rescale
if distance(guess, next_guess) < tolerance:
# convergence so return (eigenvector, eigenvalue)
return next_guess, norm
guess = next_guess
def find_eigenvector(m: Matrix,
tolerance: float = 0.00001) -> Tuple[Vector, float]:
guess = [random.random() for _ in m]
while True:
result = matrix_times_vector(m,
guess) # przekształcenie wartości losowej
norm = magnitude(result) # wyliczenie współczynnika normalizującego
next_guess = [x / norm for x in result] # przeskalowanie
if distance(guess, next_guess) < tolerance:
# punkt zbieżności, więc zwracamy (wektor własny, wartość własna)
return next_guess, norm
guess = next_guess
def direction(w: Vector) -> Vector:
mag = magnitude(w)
return [w_i / mag for w_i in w]
def direction(w: Vector) -> Vector:
mag = magnitude(w)
return [w_i / mag for w_i in w]