[英]C - values is struct pointer are not set after pointer is returned from function
我正在嘗試將我在 Java 中編寫的 kd 樹上的 knn(k 最近鄰搜索)移植到 C。
Java output,如預期:
Nearest to Key: 6.0,5.0,4.0
Key:6.0,5.0,4.0,min distance:0.0
Key:5.0,4.0,3.0,min distance:3.0
Key:7.0,6.0,5.0,min distance:3.0
Key:4.0,3.0,2.0,min distance:12.0
Key:3.0,2.0,1.0,min distance:27.0
Java 代碼,class(這是一個快速實現,只是為了在我開始我的端口之前讓算法工作):
class kd_tree {
public int DEFAULT_NUMBER_OF_DIMENSIONS = 1;
static int current_number_of_data_points = 0;
static int current_global_median = 0;
kd_tree left;
kd_tree right;
float[] data;
private int k_dimensions;
float distance_to_neighbor;
}
Java knn方法:
/*===========================================================================
Function knn, knn algorithm using kd-tree & minimumNeighbor function.
Description:
==========================================================*/
public static kd_tree[] knn( kd_tree root
, float[] data_point
, int depth
, int k_dimension
, int number_of_nearest_neighbors) {
kd_tree[] all_nearests = new kd_tree[current_number_of_data_points];
kd_tree[] n_nearests = new kd_tree[current_number_of_data_points];
if (root != null) {
int nearests_counter = 0;
/* now lets traverse the tree inorder & calculate distances from the
query point based on Morris Traversal algorithm that does not
use any stacks or no recursion */
kd_tree cur = root;
kd_tree pre;
while (cur != null) {
if (cur.left == null) {
/* debugging System.out.println(cur.data[0]);
calculate distance */
cur.distance_to_neighbor = n_dimensional_euclidean(data_point, cur.data);
all_nearests[nearests_counter] = cur;
nearests_counter++;
cur = cur.right; // move to next right node
} else { // has a left subtree
pre = cur.left;
while (pre.right != null && pre.right != cur) { // find rightmost
pre = pre.right;
}
/* Make current as the right child of its inorder
predecessor */
if (pre.right == null) {
pre.right = cur;
cur = cur.left;
} else {
pre.right = null;
// debugging printf("%d ", current->data);
// calculate distance
cur.distance_to_neighbor = n_dimensional_euclidean( data_point, cur.data);
all_nearests[nearests_counter] = cur;
nearests_counter++;
cur = cur.right;
}
}
}//end while
// base cases
// sort from least to greatest
insertion_sort_based_on_distance(all_nearests);
// return on specified number_of_nearest_neighbors
for (int i = 0; i < number_of_nearest_neighbors; i++) {
n_nearests[i]=all_nearests[i];
}
}
return n_nearests;
}
相關的 C 代碼片段:
#include <stdlib.h>
#ifndef KDTREE_H
#define KDTREE_H
/*
* Representation of a kd tree
*/
typedef struct tree_ {
struct tree* left;
struct tree* right;
float* info;
float distance_to_neighbor;
} tree;
// pre-allocated tree nodes array
static tree tree_space[KD_TREE_HEAP_SIZE];
// the knn algorithm will require memory space upto tree size
static tree* processing_space [KD_TREE_HEAP_SIZE];
tree* knn( tree* root
, float data_point[]
, int depth
, const int k_dimensions
, int number_of_nearest_neighbors
, int total_number_of_elements
, tree* n_nearests);
相關knn實現,kdtree.c:
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <float.h>
#include "kdtree.h"
#include "sorting_utility.h"
static int current_number_of_kd_tree_nodes = 0;
static int current_number_of_data_points = 0;
/*===========================================================================
Function knn, knn algorithm using kd-tree.
Description:
==========================================================*/
tree* knn( tree* root
, float data_point[]
, int depth
, const int k_dimensions
, int number_of_nearest_neighbors
, tree* n_nearests)
{
tree all_nearests[current_number_of_kd_tree_nodes];
tree* source_data_end_ptr = NULL;
tree* source_data_start_ptr = NULL;
tree* destination_data_start_ptr = NULL;
tree* destination_data_end_ptr = NULL;
if(NULL != root && NULL != n_nearests)
{
int nearests_counter = 0;
// now lets traverse the tree inorder & calculate distances
// from the query point
tree* cur = root;
tree* pre;
while(NULL != cur)
{
if(NULL == cur->left)
{
cur->distance_to_neighbor = n_dimensional_euclidean(data_point, cur->info);
processing_space[nearests_counter] = *cur;
nearests_counter++;
cur = cur->right; // move to next right node
}
else
{
pre = cur->left;
while(pre->right != NULL && pre->right != cur)
{ // find rightmost
pre = pre->right;
}
/* Make current as the right child of its inorder predecessor */
if(pre->right == NULL)
{
pre->right = cur;
cur = cur->left;
}
else
{
pre->right = NULL;
// calculate distance
cur->distance_to_neighbor = n_dimensional_euclidean(data_point, cur->info);
processing_space[nearests_counter] = *cur;
nearests_counter++;
cur = cur->right;
}
}
} // end while
// JUST FOR DEBUGGING START
printf ("***For debugging before sort:\n");
for(int i = 0; i < current_number_of_kd_tree_nodes; i++)
{
printf("{");
for(int c = 0; c < k_dimensions; c++)
{
if(NULL != processing_space[i].info)
{
printf("%f,", processing_space[i].info[c]);
}
else
{
break;
}
} // end for
printf("} ");
printf("min_distance=%f\n", processing_space[i].distance_to_neighbor);
} // end for
// JUST FOR DEBUGGING END
/* copy relevant range up current_number_of_kd_tree_nodes before sort,
* in order to avoid sorting beyond range that does not have any data */
source_data_start_ptr = &processing_space[0];
destination_data_start_ptr = &all_nearests[0];
destination_data_end_ptr = &all_nearests[current_number_of_kd_tree_nodes - 1];
while(destination_data_start_ptr <= destination_data_end_ptr)
{
*destination_data_start_ptr = *source_data_start_ptr;
source_data_start_ptr++;
destination_data_start_ptr++;
}
// sort based on distance from query point
quick_sort(all_nearests, 0, current_number_of_kd_tree_nodes - 1);
// JUST FOR DEBUGGING START
printf("***For debugging after sort\n");
for (int i = 0; i < current_number_of_kd_tree_nodes; i++)
{
printf("{");
for (int c = 0; c < k_dimensions; c++)
{
if (NULL != all_nearests[i].info)
{
printf ("%f,", all_nearests[i].info[c]);
}
else
{
break;
}
} // end for
printf("} ");
printf("min_distance=%f\n", all_nearests[i].distance_to_neighbor);
} // end for
// JUST FOR DEBUGGING END
/* copy only the n_nearest & ignore/ do NOT copy any empty tree nodes */
// reuse pointers
destination_data_end_ptr = &n_nearests[number_of_nearest_neighbors - 1];
source_data_end_ptr = &all_nearests[current_number_of_kd_tree_nodes - 1];
source_data_start_ptr = all_nearests;
int counter = 0;
while(counter < number_of_nearest_neighbors && source_data_start_ptr < source_data_end_ptr)
{
// do NOT copy any empty tree nodes
if(source_data_start_ptr != NULL && source_data_start_ptr->info != NULL)
{
/* ATTENTION: i checked with debugger & values for
(distance_to_neighbor,info,left,right were not zeroed or empty */
float distance_to_neighbor = source_data_start_ptr->distance_to_neighbor;
float* info = source_data_start_ptr->info;
tree* left = source_data_start_ptr->left;
tree* right = source_data_start_ptr->right;
n_nearests[counter].distance_to_neighbor = distance_to_neighbor;
n_nearests[counter].info = info;
n_nearests[counter].left = left;
n_nearests[counter].right = right;
counter++;
}
source_data_start_ptr++;
}
}
else
{
printf("Error, knn input parameter error");
}
return n_nearests;
} // end function
main.c 的相關片段:
#include<kdtree.h>
int main()
{
const int query_size = 10;
const int k_dimensions = 3;
printf("knn (k nearest neighboor)\n:");
tree* n_nearests[query_size];
printf("%Nearest to Key: {%f,%f,%f}\n", query_size,query_point[0], query_point[1], query_point[2]);
knn(root, query_point, 0, k_dimensions, query_size, KD_TREE_HEAP_SIZE, n_nearests);
// print n nearest neighbors
tree* tree_ptr = &n_nearests[0];
for(int i = 0; i <query_size; i++)
{
if(NULL != tree_ptr->info)
{
printf("Key={");
for(int c=0; c < k_dimensions; c++)
{
printf("%d,", tree_ptr->info[c]);
}
printf("} ");
printf("%f min distance\n", tree_ptr->distance_to_neighbor);
}
}
return 0;
} // end main
Output 由 C 版本:
knn (k nearest neighboor)
:5 Nearest neighbors to Key: {6.000000,5.000000,4.000000} are:
***For debugging before sort:
{-1.000000,-2.000000,-3.000000,} min_distance=49.000000
{0.000000,-1.000000,-2.000000,} min_distance=36.000000
{1.000000,0.000000,-1.000000,} min_distance=25.000000
{2.000000,1.000000,0.000000,} min_distance=16.000000
{3.000000,2.000000,1.000000,} min_distance=9.000000
{4.000000,3.000000,2.000000,} min_distance=4.000000
{5.000000,4.000000,3.000000,} min_distance=1.000000
{6.000000,5.000000,4.000000,} min_distance=0.000000
{7.000000,6.000000,5.000000,} min_distance=1.000000
{14.000000,13.000000,12.000000,} min_distance=64.000000
{15.000000,14.000000,13.000000,} min_distance=81.000000
{16.000000,15.000000,14.000000,} min_distance=100.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
***For debugging after sort
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{6.000000,5.000000,4.000000,} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{} min_distance=0.000000
{5.000000,4.000000,3.000000,} min_distance=1.000000
{7.000000,6.000000,5.000000,} min_distance=1.000000
{4.000000,3.000000,2.000000,} min_distance=4.000000
{3.000000,2.000000,1.000000,} min_distance=9.000000
{2.000000,1.000000,0.000000,} min_distance=16.000000
{1.000000,0.000000,-1.000000,} min_distance=25.000000
{0.000000,-1.000000,-2.000000,} min_distance=36.000000
{-1.000000,-2.000000,-3.000000,} min_distance=49.000000
{14.000000,13.000000,12.000000,} min_distance=64.000000
{15.000000,14.000000,13.000000,} min_distance=81.000000
{16.000000,15.000000,14.000000,} min_distance=100.000000
**After function return:**
Key={0,0,0,} 0.000000 min distance
Key={0,0,0,} 0.000000 min distance
Key={0,0,0,} 0.000000 min distance
Key={0,0,0,} 0.000000 min distance
Key={0,0,0,} 0.000000 min distance
我已經在 Java 和 C 版本中測試了插入、中序遍歷、搜索、刪除和找到最小鄰居,它們按預期工作。
在 C 版本中,knn function 在 function 返回后返回零值?
為什么在主 function 中調用后結構中的值為零(請參閱標題為“function 返回后”的 output 區域?
希望別人能發現一些明顯的東西,真的很絕望地拔掉我的頭發。
有多個問題:
n_nearest
在main
中定義為指向tree
對象的指針數組。 但是您將knn
的參數定義為指向實際tree
對象數組的指針,這是不兼容的。 該參數應定義為tree **n_nearest
或tree* n_nearest[]
,它是指向指針數組的指針。 然后,您應該只存儲對樹元素的引用,而不是復制值。 同樣, all_nearests
應該定義為指針數組,就像在 java 中一樣,而不是存儲樹節點副本的對象數組。gcc -Wall -Werror
或clang -Weverything -Werror
是節省無數時間的好開始。kd_tree
可以在 C 中定義為typedef tree *kd_tree;
但是 C 程序員發現將指針隱藏在 typedef 后面會令人困惑,其中 java 程序員可以毫無問題地操縱除標量值之外的所有內容的指針,因為 ZA3CBC3F9D0CE2F2C1554E1B671D71D71D9CZ 不包含指向實際對象的指針。processing_space
,則可以避免動態分配臨時數組all_nearests
,該數組從 java 的堆和 C 的堆棧中分配。 請注意,在這種情況下,您不需要傳遞total_number_of_elements
,因為processing_space
被假定大到足以容納對所有樹節點的引用。n_nearest
和all_nearests
分配為大小為current_number_of_data_points
的kd_tree
對象的 arrays ,其中許多將是null
main
中的循環不會增加tree_ptr
,因此它總是打印n_nearests
的第一個元素。這是使用指向結構的指針 arrays 的修改版本,更接近 java 版本:
#ifndef KDTREE_H
#define KDTREE_H
/*
* Representation of a kd tree
*/
typedef struct tree {
struct tree* left;
struct tree* right;
float* info;
float distance_to_neighbor;
} tree;
// pre-allocated tree nodes array
extern tree tree_space[KD_TREE_HEAP_SIZE];
// the knn algorithm will require memory space upto tree size
extern tree* processing_space[KD_TREE_HEAP_SIZE];
// return the number of closest neighbors, <= number_of_nearest_neighbors
int knn(tree* root,
float data_point[],
int depth,
const int k_dimensions,
int number_of_nearest_neighbors,
int total_number_of_elements,
tree* n_nearests);
knn
的實現:
#include <stdio.h>
#include <stdlib.h>
#include "kdtree.h"
static int current_number_of_kd_tree_nodes = 0;
static int current_number_of_data_points = 0;
static int tree_compare_distance(const void *a, const void *b) {
tree* ap = *(tree* const *)a;
tree* bp = *(tree* const *)b;
return ((ap->distance_to_neighbor > bp->distance_to_neighbor) -
(ap->distance_to_neighbor < bp->distance_to_neighbor));
}
static void quick_sort_based_on_distance(tree** array, size_t size) {
qsort(array, size, sizeof(*array), tree_compare_distance);
}
/*===========================================================================
Function knn, knn algorithm using kd-tree.
Description:
==========================================================*/
int knn(tree* root,
float data_point[],
int depth,
const int k_dimensions,
int number_of_nearest_neighbors,
tree** n_nearests)
{
/* use the static processing space to avoid defining a potentially huge
array on the stack */
tree** all_nearests = processing_space;
if (root != NULL && n_nearests != NULL) {
int nearests_counter = 0;
// now lets traverse the tree inorder & calculate distances
// from the query point
tree* cur = root;
tree* pre;
while (cur != NULL) {
if (cur->left == NULL) {
// calculate distance
cur->distance_to_neighbor = n_dimensional_euclidean(data_point, cur->info);
all_nearests[nearests_counter] = cur;
nearests_counter++;
cur = cur->right; // move to next right node
} else { // has a left subtree
pre = cur->left;
while (pre->right != NULL && pre->right != cur) { // find rightmost
pre = pre->right;
}
/* Make current as the right child of its inorder predecessor */
if (pre->right == NULL) {
pre->right = cur;
cur = cur->left;
} else {
pre->right = NULL;
// calculate distance
cur->distance_to_neighbor = n_dimensional_euclidean(data_point, cur->info);
all_nearests[nearests_counter] = cur;
nearests_counter++;
cur = cur->right;
}
}
}
// JUST FOR DEBUGGING START
printf ("***For debugging before sort:\n");
for (int i = 0; i < nearests_counter; i++) {
printf("{");
for (int c = 0; c < k_dimensions; c++) {
printf("%f,", all_nearests[i]->info[c]);
}
printf("} ");
printf("min_distance=%f\n", all_nearests[i]->distance_to_neighbor);
}
// JUST FOR DEBUGGING END
// sort based on distance from query point
quick_sort_based_on_distance(all_nearests, nearests_counter);
// JUST FOR DEBUGGING START
printf("***For debugging after sort\n");
for (int i = 0; i < nearests_counter; i++) {
printf("{");
for (int c = 0; c < k_dimensions; c++) {
printf("%f,", all_nearests[i]->info[c]);
}
printf("} ");
printf("min_distance=%f\n", all_nearests[i]->distance_to_neighbor);
}
// JUST FOR DEBUGGING END
// return only specified number_of_nearest_neighbors
// yet do not return elements beyond nearest_counter
if (number_of_nearest_neighbors > nearest_counter)
number_of_nearest_neighbors = nearest_counter;
for (int i = 0; i < number_of_nearest_neighbors; i++) {
n_nearests[i] = all_nearests[i];
}
return number_of_nearest_neighbors;
} else {
printf("Error, knn input parameter error");
return -1;
}
}
The main code is modified too:
```c
#include <stdio.h>
#include "kdtree.h"
int main() {
const int query_size = 10;
const int k_dimensions = 3;
// ...
// get the values and the query point
// ...
printf("knn (k nearest neighboor)\n:");
tree* n_nearests[query_size];
printf("%d nearest neighbors to Key: {%f,%f,%f}\n", query_size,
query_point[0], query_point[1], query_point[2]);
int result_size = knn(root, query_point, 0, k_dimensions, query_size, n_nearests);
// print the computed nearest neighbors
for (int i = 0; i < result_size; i++) {
tree* tree_ptr = n_nearests[i];
if (tree_ptr->info != NULL) {
printf("Key={");
for (int c = 0; c < k_dimensions; c++) {
printf("%s%d", "," + !c, tree_ptr->info[c]);
}
printf("} ");
printf("%f min distance\n", tree_ptr->distance_to_neighbor);
}
}
return 0;
}
為什么在主 function 中調用后結構中的值為零(請參閱標題為“function 返回后”的 output 區域?
您的代碼中有一些省略,但可能是因為current_number_of_kd_tree_nodes
設置為 0?
如果您只需要“K-最近鄰”實現,請查看現有問題。
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