def create_P(W: np.matrix):
P = np.full_like(W, -1, dtype=np.int32)
for index, x in np.ndenumerate(W):
i, j = index
if i == j or W.item(index) == np.inf or x == 0:
continue
P.itemset(index, i + 1) # NOT 0 INDEXED!
return P
def getScale(cls, m: np.matrix) -> list:
scale = [
math.sqrt(m.item(0, 0)**2 + m.item(0, 1)**2 + m.item(0, 2)**2),
math.sqrt(m.item(1, 0)**2 + m.item(1, 1)**2 + m.item(1, 2)**2),
math.sqrt(m.item(2, 0)**2 + m.item(2, 1)**2 + m.item(2, 2)**2)
]
return scale
def get_chosen_cell(grid: numpy.matrix, points: list) -> list:
print(points)
#FIXME: Only support one cell now
assert len(points) == 2
begin = points[0]
end = points[1]
ret = []
for row in range(begin[0], end[0]):
ret += grid[row].flat
#FIXME: 假定至少有一排
for col in range(0, end[1] + 1):
ret.append(grid.item(end[0], col))
#过滤掉 None
ret = [s for s in ret if s != None]
print("After check")
print(ret)
return ret
def getTranslation(cls, m: np.matrix) -> [float, float, float]:
'''
Gets the translation of the transform in the 4th row of the matrix.
'''
return [m.item(3, 0), m.item(3, 1), m.item(3, 2)]
def function(x: np.matrix):
x1 = x.item(0)
x2 = x.item(1)
return np.matrix([[100 * (x1**2 - x2)**2 + (x1 - 1)**2]])
def hessian(x: np.matrix):
x1 = x.item(0)
x2 = x.item(1)
return np.matrix([[1200 * x1**2 - 400 * x2 + 2, -400 * x1],
[-400 * x1, 200 * x2]])
def gradient(x: np.matrix):
x1 = x.item(0)
x2 = x.item(1)
return np.matrix([[2 * (200 * x1 * (x1**2 - x2) + x1 - 1)],
[-200 * (x1**2 - x2)]])
def way_dist(A: np.matrix, B: np.matrix) -> int:
whole = 0
for i in range(A.shape[0]):
whole += (A.item(i, 0)) - B.item((i, 0)) ** 2
return whole