unique(arr) 返回 arr 中惟一的元素 in1d(a1, a2) a1中的元素是否都在a2中存在?比如,在调仓换股中,如果当前持仓都在买入计划中,则不需要执行调仓 intersect1d 两个数组的交集 setdiff1d(a1, a2) 相当于 set(a1) - set(a2),留下只在a1中存在的元素 setxor1d(a1, a2) 相当于 set(a1) +
optimal_arm : float The arm ID with the largest expected reward. """ # 返回最优奖励和最优臂 return self.best_ev, self.best_arm # 定义一个私有方法,用于选择特定臂的奖励 def _pull(self, arm_id, context): # 获取特定臂的奖励 payoffs = self.payoffs[arm_id] # 获取特定臂的奖励概率 pr...
random.randint(0, 2, 5) # 假设array的形状(shape)相同和一个误差容限(tolerance) equal = np.allclose(A,B) print(equal) # 检查形状和元素值,没有误差容限(值必须完全相等) equal = np.array_equal(A,B) print(equal) 43. 把数组变为只读 (★★☆) (提示: flags.writeable) 代码语言:javascript ...
B)print(equal)# Checking both the shape and the element values, no tolerance (values have to be exactly equal)equal = np.array_equal(A,B)print(equal)
print(np.unique(I)) 67. 思考如何求一个四维数组最后两个轴的数据和(★★★) (提示: sum(axis=(-2,-1))) A = np.random.randint(0,10,(3,4,3,4)) 传递一个元组(numpy 1.7.0) sum = A.sum(axis=(-2,-1)) print(sum) 将最后两个维度压缩为一个 ...
A=np.random.randint(0,2,5)B=np.random.randint(0,2,5)# Assuming identical shapeofthe arrays and a toleranceforthe comparisonofvalues equal=np.allclose(A,B)print(equal)# Checking both the shape and the element values,notolerance(values have to be exactly equal)equal=np.array_equal(A,B)...
print(np.unique(I)) 67. 思考如何求一个四维数组最后两个轴的数据和(★★★) (提示: sum(axis=(-2,-1)))A = np.random.randint(0,10,(3,4,3,4)) # 传递一个元组(numpy 1.7.0) sum = A.sum(axis=(-2,-1)) print(sum) # 将最后两个维度压缩为一个 ...
]# -- Options for Epub output ---# Bibliographic Dublin Core info.epub_title = project# The unique identifier of the text. This can be a ISBN number# or the project homepage.## epub_identifier = ''# A unique identification for the text.## epub_uid = ''# A list of files that...
n = len(np.unique(F)) print(np.unique(I)) 67. 思考如何求一个四维数组最后两个轴的数据和(★★★) (提示: sum(axis=(-2,-1))) A = np.random.randint(0,10,(3,4,3,4)) # 传递一个元组(numpy 1.7.0) sum = A.sum(axis=(-2,-1)) ...
Rougier faces = np.random.randint(0,100,(10,3)) F = np.roll(faces.repeat(2,axis=1),-1,axis=1) F = F.reshape(len(F)*3,2) F = np.sort(F,axis=1) G = F.view( dtype=[('p0',F.dtype),('p1',F.dtype)] ) G = np.unique(G) print(G)...