Question

我写了以下代码

import cvxpy
import numpy as np

def missingMat(A, mask):##true and false for known and missing entries
    M=np.array(A)
    for i in range(0, mask.shape[0]):
        for j in range(0, mask.shape[1]):
            if mask[i][j]==False:
                M[i][j]=float("nan")
    return M

def constraintsFun(X, missing_mask, S, error_tolerance):
        ok_mask = ~missing_mask
        masked_X = cvxpy.mul_elemwise(ok_mask, X)
        masked_S = cvxpy.mul_elemwise(ok_mask, S)
        abs_diff = cvxpy.abs(masked_S - masked_X)
        close_to_data =(abs_diff <= 0.0001)
        constraints = [close_to_data]
        return constraints

def create_objective(m, n):
        S=cvxpy.Semidef(m,n)
        norm = cvxpy.norm(S, "nuc")
        objective = cvxpy.Minimize(norm)
        return S, objective

def solve(X, missing_mask):
        m, n = X.shape
        S, objective = create_objective(m, n)
        constraints = constraintsFun(X=X, missing_mask=missing_mask, S=S, error_tolerance=0.0001)
        problem = cvxpy.Problem(objective, constraints)
        solverLis=cvxpy.installed_solvers()
        for solv in solverLis:
            try:
                problem.solve(solver=solv)##.................error line
            except:
                print "did not solve",  solv
        print S.value
        return problem, S        

A=np.random.uniform(low=1, high=15, size=(8,8))
mask = np.round(np.random.rand(A.shape[0], A.shape[1]))
mask=(mask >= 1)
M=missingMat(A, mask)
prob, S=solve(M, mask)

我试图了解如何使用cvxpy来处理矩阵完成问题。

我有一个矩阵M，缺少与mask矩阵对应的条目。它被最小化到S的核范数，并且约束对应于在一定容差内的S和M的mask - True条目的匹配。

有人可以帮助我找到我的代码或逻辑中的错误吗？我无法找到solve()的正确用法。我试图通过S.value获取缺失值。

min _S || S || _* s.t.已知条目的S = M

cvxpy solve属性返回none

0 个答案: