def affparaminv(est):
#!!!pay attention
dt = np.dtype('f8')
q = MA(np.zeros(2,3),dt)
q = linalg.pinv(MA([[est[0,2],est[0,3]],[est[0,4],est[0,5]]])) * MA([[-est[0,0],1.0,0.0],[-est[0,1],0,1.0]])
q=q.flatten(1)
return MA([[q[0,0],q[0,1],q[0,2],q[0,4],q[0,3],q[0,5]]])
def binary(img1, level): img1 = MA(img1) tempIm = img1.flatten(1) for i in range(tempIm.shape[1]): if tempIm[0,i] < level: tempIm[0,i] = 0; else: tempIm[0,i] = 1; tempIm = (numpy.reshape(tempIm,(img1.shape[1],img1.shape[0]))).T return tempIm
def binary(img1, level):
img1 = MA(img1)
#import pdb;pdb.set_trace()
tempIm = img1.flatten(1)
for i in range(tempIm.shape[1]):
if tempIm[0, i] < level:
tempIm[0, i] = 0
else:
tempIm[0, i] = 1
tempIm = (numpy.reshape(tempIm, (img1.shape[1], img.shape[0]))).T
print tempIm
return tempIm
def binary(img1, level): img1 = MA(img1) #import pdb;pdb.set_trace() tempIm = img1.flatten(1) for i in range(tempIm.shape[1]): if tempIm[0,i] < level: tempIm[0,i] = 0; else: tempIm[0,i] = 1; tempIm = (numpy.reshape(tempIm,(img1.shape[1],img.shape[0]))).T print tempIm return tempIm
def superhill_enc(mat: np.matrix, pt: np.matrix, vector: np.array):
shape = pt.shape
pt = pt.flatten()
vector = np.array(vector)
pmat = pt.reshape((-1, 2))
for i in range(pmat.shape[0]):
pmat[i] = hill_encode(mat, pmat[i])
# pmat[i] = (pmat[i] * mat) % 256
if i % 2 == 0:
# print(mat[0, 0], mat[0, 0] * vector[0], vector[0])
mat[0, 0] = mat[0, 0] * vector[0, 0] % 256
mat[0, 1] = mat[0, 1] * vector[0, 1] % 256
else:
mat[1, 0] = mat[1, 0] * vector[0, 0] % 256
mat[1, 1] = mat[1, 1] * vector[0, 1] % 256
return pmat.reshape(shape)
def calculate_final_score(board: np.matrix, drawn_numbers: List[int]):
unmarked_numbers = []
for number in np.nditer(board.flatten()):
if number not in drawn_numbers:
unmarked_numbers.append(number)
return sum(unmarked_numbers) * drawn_numbers[-1]