首页 \ 问答 \ 矩阵排序分段故障(Matrix sorting segmentation fault)

矩阵排序分段故障(Matrix sorting segmentation fault)

我需要存储一个矩阵o N个学生有4个字段(数字,1年级,2年级和平均数)。 这可以通过结构来实现,但这不是本练习的目的。 收集数据( read_data )后,必须根据平均值( sort_matrix )对其进行排序。 但是,当插入4个或更多学生时,会发生分段错误。 我一直无法检测到问题的根源。 我错过了什么? 相关代码如下。

void read_data(float **mtx, int *size){
    float num, grade1, grade2;

    while( scanf("%f %f %f", &num, &grade1, &grade2)==3 ){
        (*size)++;
        mtx = (float**)realloc(mtx, (*size)*sizeof(float*) );
        mtx[*size-1] = (float*)malloc(4*sizeof(float));
        mtx[*size-1][0] = num;
        mtx[*size-1][1] = grade1;
        mtx[*size-1][2] = grade2;
        mtx[*size-1][3] = (grade1+grade2)/2;
    }
    printf("Done reading\n");
}


void sort_matrix(float **mtx, int size){
    int i=0, j=0;
    float *aux = NULL;

    for(i=0; i<size-1; i++){
        for(j = 0; j<size-1-i; j++){
            if(mtx[j][3] > mtx[j+1][3]){
                aux = mtx[j];
                mtx[j] = mtx[j+1];
                mtx[j+1]=aux;
            }
        }
    }
    printf("Done sorting\n");
}

int main(void){
    float **mtx =(float**)malloc(0);
    int size=0;

    read_data(mtx, &size);
    sort_matrix(mtx, size);
    print_matrix(mtx, size);
    return 0;

}

编辑:按照下面给出的答案,我发现这个主题被证明是有用的。 结果问题是在没有传递指针地址的情况下改变了mtx的大小。 对于排序功能,不需要传递地址,因为即使mtx数组指向的地址将改变,大小也不会。


I am required to store a matrix o N students with 4 fields (number, grade1, grade2 and average). This could be achieved with a struct, but that is not the purpose of this exercise. Once the data is collected (read_data), it must be sorted according to the average (sort_matrix). However, when more than 4 or more students are inserted, a segmentation fault happens. I have been unable to detect the origin n of the problem. What am I missing? Relevant code is bellow.

void read_data(float **mtx, int *size){
    float num, grade1, grade2;

    while( scanf("%f %f %f", &num, &grade1, &grade2)==3 ){
        (*size)++;
        mtx = (float**)realloc(mtx, (*size)*sizeof(float*) );
        mtx[*size-1] = (float*)malloc(4*sizeof(float));
        mtx[*size-1][0] = num;
        mtx[*size-1][1] = grade1;
        mtx[*size-1][2] = grade2;
        mtx[*size-1][3] = (grade1+grade2)/2;
    }
    printf("Done reading\n");
}


void sort_matrix(float **mtx, int size){
    int i=0, j=0;
    float *aux = NULL;

    for(i=0; i<size-1; i++){
        for(j = 0; j<size-1-i; j++){
            if(mtx[j][3] > mtx[j+1][3]){
                aux = mtx[j];
                mtx[j] = mtx[j+1];
                mtx[j+1]=aux;
            }
        }
    }
    printf("Done sorting\n");
}

int main(void){
    float **mtx =(float**)malloc(0);
    int size=0;

    read_data(mtx, &size);
    sort_matrix(mtx, size);
    print_matrix(mtx, size);
    return 0;

}

EDIT: Following the answers given bellow, I have found this topic which proved to be useful. The problem turned out to be changing the size of mtx without passing the pointer address. For the sorting function, there is no need to pass the address because even though the addresses the mtx array points to will change, the size will not.


原文:https://stackoverflow.com/questions/35439037
更新时间:2022-04-16 07:04

最满意答案

附加列对应于数据帧的索引,并在读取CSV文件后进行聚合。 您可以使用此索引以有效的方式对DF进行切片,选择或排序。

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.html

http://pandas.pydata.org/pandas-docs/stable/indexing.html

如果要避免使用此索引,可以在使用函数pd.to_csv保存数据pd.to_csv时将index标志设置为False 。 此外,您要删除标题并稍后将其聚合,但您可以使用CSV标题来避免此步骤。

sample = pd.read_csv('123.csv', dtype={0:str, 1:str, 2:str, 3:float})
sample.to_csv('output.csv', index= False)

希望能帮助到你 :)


The additional column corresponds to the index of the dataframe and is aggregated once you read the CSV file. You can use this index to slice, select or sort your DF in an effective manner.

http://pandas.pydata.org/pandas-docs/stable/generated/pandas.Index.html

http://pandas.pydata.org/pandas-docs/stable/indexing.html

If you want to avoid this index, you can set the index flag to False when you save your dataframe with the function pd.to_csv. Also, you are removing the header and aggregating it later, but you can use the header of the CSV to avoid this step.

sample = pd.read_csv('123.csv', dtype={0:str, 1:str, 2:str, 3:float})
sample.to_csv('output.csv', index= False)

Hope it helps :)

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