如何解决AttributeError: ''str'' 对象没有属性 ''dot''？

我正在使用 qndiag 库来尝试找到 2 个给定矩阵的对角线。

github 在这里：qndiag libray

我正在使用这个 Python 脚本来计算这 2 个尽可能封闭的对角线：

import os,sys
import numpy as np
from qndiag import qndiag

# dimension
m=7
# number of matrices
n=2

# Load spectro and WL+GCph+XC
FISH_GCsp = np.loadtxt(''Fisher_GCsp_flat.txt'')
FISH_XC = np.loadtxt(''Fisher_XC_GCph_WL_flat.txt'')

# Marginalizing over uncommon parameters between the two matrices
COV_GCsp_first = np.linalg.inv(FISH_GCsp)
COV_XC_first = np.linalg.inv(FISH_XC)
COV_GCsp = COV_GCsp_first[0:m,0:m]
COV_XC = COV_XC_first[0:m,0:m]
# Invert to get Fisher matrix
FISH_sp = np.linalg.inv(COV_GCsp);
FISH_xc = np.linalg.inv(COV_XC);
# Drawing a random set of commuting matrices
C=np.zeros((n,m,m));
C[0,:,:] = FISH_sp
C[1,:] = FISH_xc

[D,B] = qndiag(C,''max_iter'',1000,''tol'',1e-3);

# Print expected diagonal matrices
B*C[0,:]*B.T
B*C[1,:]*B.T

给定我的 2 个矩阵 7x7 FISH_sp 和 FISH_xc，我得到了一个错误：

approximate_joint_diagonalization_qndiag/qndiag/qndiag/qndiag.py",line 90,in qndiag
    D = transform_set(B,C)

和关注

 approximate_joint_diagonalization_qndiag/qndiag/qndiag/qndiag.py",line 151,in transform_set
    op[i] = M.dot(d.dot(M.T))
AttributeError: ''str'' object has no attribute ''dot''

这里是相关的function transform_set：

def transform_set(M,D,diag_only=False):
    n,p,_ = D.shape
    if not diag_only:
        op = np.zeros((n,p))
        for i,d in enumerate(D):
            op[i] = M.dot(d.dot(M.T))

以及在我的初始化中调用的主函数：

def qndiag(C,B0=None,weights=None,max_iter=1000,tol=1e-6,lambda_min=1e-4,max_ls_tries=10,diag_only=False,return_B_list=False,verbose=False):
    """Joint diagonalization of matrices using the quasi-Newton method


    Parameters
    ----------
    C : array-like,shape (n_samples,n_features,n_features)
        Set of matrices to be jointly diagonalized. C[0] is the first matrix,etc...

    B0 : None | array-like,shape (n_features,n_features)
        Initial point for the algorithm. If None,a whitener is used.

    weights : None | array-like,)
        Weights for each matrix in the loss:
        L = sum(weights * KL(C,C'')) / sum(weights).
        No weighting (weights = 1) by default.

    max_iter : int,optional
        Maximum number of iterations to perform.

    tol : float,optional
        A positive scalar giving the tolerance at which the
        algorithm is considered to have converged. The algorithm stops when
        |gradient| < tol.

    lambda_min : float,optional
        A positive regularization scalar. Each eigenvalue of the Hessian
        approximation below lambda_min is set to lambda_min.

    max_ls_tries : int,optional
        Maximum number of line-search tries to perform.

    diag_only : bool,optional
        If true,the line search is done by computing only the diagonals of the
        dataset. The dataset is then computed after the line search.
        Taking diag_only = True might be faster than diag_only=False
        when the matrices are large (n_features > 200)

    return_B_list : bool,optional
        Chooses whether or not to return the list of iterates.

    verbose : bool,optional
        Prints informations about the state of the algorithm if True.

    Returns
    -------
    D : array-like,n_features)

       Set of matrices jointly diagonalized

    B : array,n_features)
        Estimated joint diagonalizer matrix.

    infos : dict
        Dictionnary of monitoring informations,containing the times,gradient norms and objective values.

    References
    ----------
    P. Ablin,J.F. Cardoso and A. Gramfort. Beyond Pham''s algorithm
    for joint diagonalization. Proc. ESANN 2019.
    https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2019-119.pdf
    https://hal.archives-ouvertes.fr/hal-01936887v1
    https://arxiv.org/abs/1811.11433
    """
    t0 = time()
    n_samples,_ = C.shape
    if B0 is None:
        C_mean = np.mean(C,axis=0)
        d,p = np.linalg.eigh(C_mean)
        B = p.T / np.sqrt(d[:,None])
    else:
        B = B0
    if weights is not None:  # normalize
        weights_ = weights / np.mean(weights)
    else:
        weights_ = None
   D = transform_set(B,C)

为什么点运算符会出现这个错误？它似乎是矩阵产品（如 np.dot），但它的使用方式让我们认为并非如此。

解决方法

parts = (line.split('','') for line in f)

column = (part.strip() for part in parts)

column = ([col.strip() for col in part] for part in parts)

with open("/home/mic/tmp/test.txt", ''rb'') as f:
    reader = csv.reader(f, skipinitialspace=True)
    for object_ in iter_something(reader):
        print(object_)

#!/usr/bin/env python
from __future__ import absolute_import,division,print_function
from itertools import groupby


DATA = [["Test","A","B01",828288,1,7,''C'',5],["Test",''T'',6],171878,3,871963,9,''A'',''G'',1932523,10,4],''X'',667214,14,6]]


def iter_something(rows):
    key_names = [''type'',''name'',''sub_name'',''pos'',''s_type'',''x_type'']
    chr_key_names = [''letter'',''no'']
    for keys,group in groupby(rows,lambda row: row[:6]):
        result = dict(zip(key_names,keys))
        result[''chr''] = [dict(zip(chr_key_names,row[6:])) for row in group]
        yield result


def convert(val):
    constructors = [int,str]
    for c in constructors:
        try:
            return c(val)
        except ValueError:
            pass


def main():
    with open("/home/mic/tmp/test.txt") as f:
        parts = (line.split('','') for line in f)
        column = (part.strip() for part in parts)
        for object_ in iter_something(column):
            print(object_)


if __name__ == ''__main__'':
    main()