def find_eigenvector(A, tolerance=0.00001):
guess = [1 for __ in A]
while True:
result = matrix_operate(A, guess)
length = magnitude(result)
next_guess = scalar_multiply(1 / length, result)
if distance(guess, next_guess) < tolerance:
return next_guess, length # eigenvector, eigenvalue
guess = next_guess
def find_eigenvector(A, tolerance=0.00001):
guess = [1 for __ in A]
while True:
result = matrix_operate(A, guess)
length = magnitude(result)
next_guess = scalar_multiply(1/length, result)
if distance(guess, next_guess) < tolerance:
return next_guess, length # eigenvector, eigenvalue
guess = next_guess
def find_eigenvector(A, tolerance=0.00001):
guess = [1 for __ in A]
while True:
# 计算结果向量
result = matrix_operate(A, guess)
# 向量的模
length = magnitude(result)
# 下一个向量,标量(1/length)和向量(result)的乘法,
next_guess = scalar_multiply(1/length, result)
# 两个向量的距离小于某个阙值则返回更新后的向量和向量的模
if distance(guess, next_guess) < tolerance:
return next_guess, length # eigenvector, eigenvalue
guess = next_guess
def direction(w):
mag = magnitude(w)
return [w_i / mag for w_i in w]
C = la.vector_sum([A, B])
print("A and B summary = ", C)
C = la.scalar_multiply(10, A)
print("10 * A = ", C)
C = la.vector_mean([A, B])
print("A and B mean = ", C)
C = la.dot(A, B)
print("A dot B = ", C)
C = la.sum_of_squares(A)
print("A^2's summary = ", C)
C = la.magnitude(A)
print("A's magnitude = ", C)
C = la.distance(A, B)
print("A's distance = ", C)
print()
print("*** matrix ......")
M = [[1, 2, 3], [5, 6, 7], [3, 6, 9]]
print("M = ", M)
shape = la.shape(M)
print("M's shape = ", shape)
row_1 = la.get_row(M, 1)
print("M[1,:] = ", row_1)
def direction(w: Vector) -> Vector:
""" Find direction vector"""
mag = magnitude(w)
return [w_i / mag for w_i in w]
def direction(w):
mag = magnitude(w)
return [w_i / mag for w_i in w]
def test_magnitude(self):
self.assertEqual(5, magnitude([4, 3]))