9计算1+4+7+...+301，写出算法的程序.（for和while两种做法）

使用for循环的算法程序:

sum = 0
for i in range(1, 302, 3):
    sum += i
print("1+4+7+...+301的和为：", sum)

使用while循环的算法程序:

sum = 0
i = 1
while i <= 301:
    sum += i
    i += 3
print("1+4+7+...+301的和为：", sum)

两个程序的输出结果都为：

1+4+7+...+301的和为： 15351

相关问题

你用SORT和deep SORT两种算法分别实现多目标行人的跟踪，并写出示例代码

好的，下面分别介绍使用 SORT 和 Deep SORT 两种算法实现多目标行人跟踪的示例代码。

首先介绍使用 SORT 算法实现多目标行人跟踪的示例代码：

import numpy as np
from filterpy.kalman import KalmanFilter
from scipy.optimize import linear_sum_assignment

class SORT:
    def __init__(self):
        self.dt = 1.0
        self.lamb = 100
        self.sigma_l = 10
        self.sigma_h = 1.0
        self.sigma_iou = 0.3
        self.min_hits = 3
        self.max_age = 20
        self.trackers_list = []
        self.frame_count = 0

    def __dist(self, bbox1, bbox2):
        x1, y1, w1, h1 = bbox1
        x2, y2, w2, h2 = bbox2
        c1 = np.array([x1 + w1 / 2.0, y1 + h1 / 2.0])
        c2 = np.array([x2 + w2 / 2.0, y2 + h2 / 2.0])
        return np.sqrt(np.sum(np.square(c1 - c2)))

    def __linear_assignment(self, cost_matrix):
        row_ind, col_ind = linear_sum_assignment(cost_matrix)
        return row_ind, col_ind

    def __get_cost_matrix(self, detections, trackers):
        cost_matrix = np.zeros((len(detections), len(trackers)), dtype=np.float32)
        for i, detection in enumerate(detections):
            for j, tracker in enumerate(trackers):
                cost_matrix[i, j] = self.__dist(detection, tracker.predict())
        return cost_matrix

    def __update_tracker(self, tracker, detection):
        tracker.kf.update(detection)
        tracker.hits += 1
        tracker.no_losses = 0

    def __create_tracker(self, detection):
        tracker = KalmanFilter(dim_x=7, dim_z=4)
        tracker.x[:4] = detection
        tracker.x[4:] = 0.0
        tracker.P[4:, 4:] *= 1000.0
        tracker.P *= 10.0
        tracker.R = np.diag([self.sigma_l ** 2, self.sigma_l ** 2,
                             self.sigma_h ** 2, self.sigma_h ** 2])
        tracker.Q[:4, :4] *= 0.01
        tracker.Q[4:, 4:] *= 0.01
        tracker.Q[4:, 4:] *= self.dt
        tracker.H = np.zeros((4, 7))
        tracker.H[:4, :4] = np.eye(4)
        tracker.hits = 1
        tracker.no_losses = 0
        return tracker

    def __remove_trackers(self):
        self.trackers_list = [tracker for tracker in self.trackers_list if tracker.no_losses <= self.max_age]

    def __remove_duplicate_trackers(self):
        for tracker1 in self.trackers_list:
            for tracker2 in self.trackers_list:
                if tracker1 == tracker2:
                    continue
                if self.__iou(tracker1.predict(), tracker2.predict()) > self.sigma_iou:
                    if tracker1.hits >= tracker2.hits:
                        tracker2.no_losses = self.max_age + 1
                    else:
                        tracker1.no_losses = self.max_age + 1

    def __ious(self, bboxes1, bboxes2):
        ious = np.zeros((len(bboxes1), len(bboxes2)), dtype=np.float32)
        for i, bbox1 in enumerate(bboxes1):
            for j, bbox2 in enumerate(bboxes2):
                ious[i, j] = self.__iou(bbox1, bbox2)
        return ious

    def __iou(self, bbox1, bbox2):
        x1, y1, w1, h1 = bbox1
        x2, y2, w2, h2 = bbox2
        xi1 = max(x1, x2)
        yi1 = max(y1, y2)
        xi2 = min(x1 + w1, x2 + w2)
        yi2 = min(y1 + h1, y2 + h2)
        inter_area = max(xi2 - xi1, 0) * max(yi2 - yi1, 0)
        union_area = w1 * h1 + w2 * h2 - inter_area
        return inter_area / union_area

    def update(self, detections):
        self.frame_count += 1

        for tracker in self.trackers_list:
            self.__update_tracker(tracker, np.array([0, 0, 0, 0]))

        cost_matrix = self.__get_cost_matrix(detections, self.trackers_list)

        row_ind, col_ind = self.__linear_assignment(cost_matrix)

        matched = set()
        for i, j in zip(row_ind, col_ind):
            if cost_matrix[i, j] > self.lamb:
                continue
            tracker = self.trackers_list[j]
            detection = detections[i]
            self.__update_tracker(tracker, detection)
            matched.add(j)

        unmatched_trackers = set(range(len(self.trackers_list))) - matched
        for j in unmatched_trackers:
            tracker = self.trackers_list[j]
            tracker.no_losses += 1

        unmatched_detections = set(range(len(detections))) - set(row_ind)
        for i in unmatched_detections:
            detection = detections[i]
            tracker = self.__create_tracker(detection)
            self.trackers_list.append(tracker)

        self.__remove_trackers()
        self.__remove_duplicate_trackers()

        return self.trackers_list

使用 SORT 算法跟踪多目标行人的示例代码：

import cv2
import numpy as np
from sort import SORT

def main():
    cap = cv2.VideoCapture("test.mp4")
    tracker = SORT()
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        detections = [[100, 100, 50, 100], [200, 200, 100, 200]]
        trackers = tracker.update(detections)
        for tracker in trackers:
            bbox = tracker.predict()
            x, y, w, h = bbox
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.imshow("frame", frame)
        if cv2.waitKey(1) == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

接着介绍使用 Deep SORT 算法实现多目标行人跟踪的示例代码：

import numpy as np
from filterpy.kalman import KalmanFilter
from scipy.optimize import linear_sum_assignment

class DeepSORT:
    def __init__(self):
        self.dt = 1.0
        self.lamb = 100
        self.sigma_l = 10
        self.sigma_h = 1.0
        self.sigma_iou = 0.3
        self.min_hits = 3
        self.max_age = 20
        self.trackers_list = []
        self.frame_count = 0

    def __dist(self, bbox1, bbox2):
        x1, y1, w1, h1 = bbox1
        x2, y2, w2, h2 = bbox2
        c1 = np.array([x1 + w1 / 2.0, y1 + h1 / 2.0])
        c2 = np.array([x2 + w2 / 2.0, y2 + h2 / 2.0])
        return np.sqrt(np.sum(np.square(c1 - c2)))

    def __linear_assignment(self, cost_matrix):
        row_ind, col_ind = linear_sum_assignment(cost_matrix)
        return row_ind, col_ind

    def __get_cost_matrix(self, detections, trackers):
        cost_matrix = np.zeros((len(detections), len(trackers)), dtype=np.float32)
        for i, detection in enumerate(detections):
            for j, tracker in enumerate(trackers):
                cost_matrix[i, j] = self.__dist(detection, tracker.predict())
        return cost_matrix

    def __update_tracker(self, tracker, detection):
        tracker.kf.update(detection)
        tracker.hits += 1
        tracker.no_losses = 0

    def __create_tracker(self, detection):
        tracker = KalmanFilter(dim_x=7, dim_z=4)
        tracker.x[:4] = detection
        tracker.x[4:] = 0.0
        tracker.P[4:, 4:] *= 1000.0
        tracker.P *= 10.0
        tracker.R = np.diag([self.sigma_l ** 2, self.sigma_l ** 2,
                             self.sigma_h ** 2, self.sigma_h ** 2])
        tracker.Q[:4, :4] *= 0.01
        tracker.Q[4:, 4:] *= 0.01
        tracker.Q[4:, 4:] *= self.dt
        tracker.H = np.zeros((4, 7))
        tracker.H[:4, :4] = np.eye(4)
        tracker.hits = 1
        tracker.no_losses = 0
        return tracker

    def __remove_trackers(self):
        self.trackers_list = [tracker for tracker in self.trackers_list if tracker.no_losses <= self.max_age]

    def __remove_duplicate_trackers(self):
        for tracker1 in self.trackers_list:
            for tracker2 in self.trackers_list:
                if tracker1 == tracker2:
                    continue
                if self.__iou(tracker1.predict(), tracker2.predict()) > self.sigma_iou:
                    if tracker1.hits >= tracker2.hits:
                        tracker2.no_losses = self.max_age + 1
                    else:
                        tracker1.no_losses = self.max_age + 1

    def __ious(self, bboxes1, bboxes2):
        ious = np.zeros((len(bboxes1), len(bboxes2)), dtype=np.float32)
        for i, bbox1 in enumerate(bboxes1):
            for j, bbox2 in enumerate(bboxes2):
                ious[i, j] = self.__iou(bbox1, bbox2)
        return ious

    def __iou(self, bbox1, bbox2):
        x1, y1, w1, h1 = bbox1
        x2, y2, w2, h2 = bbox2
        xi1 = max(x1, x2)
        yi1 = max(y1, y2)
        xi2 = min(x1 + w1, x2 + w2)
        yi2 = min(y1 + h1, y2 + h2)
        inter_area = max(xi2 - xi1, 0) * max(yi2 - yi1, 0)
        union_area = w1 * h1 + w2 * h2 - inter_area
        return inter_area / union_area

    def update(self, detections, features):
        self.frame_count += 1

        for tracker in self.trackers_list:
            self.__update_tracker(tracker, np.array([0, 0, 0, 0]))

        cost_matrix = self.__get_cost_matrix(detections, self.trackers_list)

        row_ind, col_ind = self.__linear_assignment(cost_matrix)

        matched = set()
        for i, j in zip(row_ind, col_ind):
            if cost_matrix[i, j] > self.lamb:
                continue
            tracker = self.trackers_list[j]
            detection = detections[i]
            feature = features[i]
            tracker.features.append(feature)
            self.__update_tracker(tracker, detection)
            matched.add(j)

        unmatched_trackers = set(range(len(self.trackers_list))) - matched
        for j in unmatched_trackers:
            tracker = self.trackers_list[j]
            tracker.no_losses += 1

        unmatched_detections = set(range(len(detections))) - set(row_ind)
        for i in unmatched_detections:
            detection = detections[i]
            feature = features[i]
            tracker = self.__create_tracker(detection)
            tracker.features.append(feature)
            self.trackers_list.append(tracker)

        self.__remove_trackers()
        self.__remove_duplicate_trackers()

        return self.trackers_list

使用 Deep SORT 算法跟踪多目标行人的示例代码：

import cv2
import numpy as np
from deep_sort import DeepSORT

def main():
    cap = cv2.VideoCapture("test.mp4")
    deepsort = DeepSORT()
    while True:
        ret, frame = cap.read()
        if not ret:
            break
        detections = [[100, 100, 50, 100], [200, 200, 100, 200]]
        features = np.random.rand(len(detections), 128)
        trackers = deepsort.update(detections, features)
        for tracker in trackers:
            bbox = tracker.predict()
            x, y, w, h = bbox
            cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 2)
        cv2.imshow("frame", frame)
        if cv2.waitKey(1) == ord('q'):
            break
    cap.release()
    cv2.destroyAllWindows()

if __name__ == '__main__':
    main()

以上就是使用 SORT 和 Deep SORT 两种算法实现多目标行人跟踪的示例代码。

用C语言写一个B+Tree算法

### 回答1：
好的，下面是一个冒泡排序的JavaScript代码实现：

// 定义一个函数用于实现冒泡排序
function bubbleSort(arr) {
  // 定义一个变量用于表示是否发生交换的标志
  let swapped;
  // 循环遍历数组中的每一个元素
  for (let i = 0; i < arr.length; i++) {
    // 初始化标志变量为 false
    swapped = false;
    // 内层循环，用于比较相邻的两个元素并交换位置
    for (let j = 0; j < arr.length - i - 1; j++) {
      // 如果前一个元素比后一个元素大，则交换它们的位置
      if (arr[j] > arr[j + 1  

### 回答2：
B-Tree（B树）是一种平衡的多路查找树，适用于大规模数据存储和索引管理。我们可以使用C语言来实现B-Tree算法，以下是一个简单的B树实现示例：

#include <stdio.h>
#include <stdlib.h>

#define MAX_KEYS 3

typedef struct BTreeNode {
int *keys; // 所有键的数组
struct BTreeNode **children; // 所有子节点的指针数组
int num_keys; // 当前节点的键的数量
int is_leaf; // 标志节点是否为叶子节点
} BTreeNode;

// 创建一个具有空节点的B树
BTreeNode* createBTree() {
BTreeNode* root = (BTreeNode*)malloc(sizeof(BTreeNode));
root->keys = (int*)malloc(sizeof(int) * (MAX_KEYS + 1));
root->children = (BTreeNode**)malloc(sizeof(BTreeNode*) * (MAX_KEYS + 2));
root->num_keys = 0;
root->is_leaf = 1;
return root;
}

// 查找函数
BTreeNode* search(BTreeNode* root, int key) {
int i = 0;
// 在当前节点中找到key的位置
while (i < root->num_keys && key > root->keys[i]) {
i++;
}
// 如果找到key，则返回当前节点
if (i < root->num_keys && key == root->keys[i]) {
return root;
}
// 如果是叶子节点，则没有找到key
if (root->is_leaf) {
return NULL;
}
// 否则，递归地搜索子节点
return search(root->children[i], key);
}

// 插入函数
void insert(BTreeNode** root, int key) {
BTreeNode* temp = *root;
if (temp->num_keys == MAX_KEYS) {
// 如果当前节点已满，则需要进行分裂
BTreeNode* new_node = (BTreeNode*)malloc(sizeof(BTreeNode));
new_node->keys = (int*)malloc(sizeof(int) * (MAX_KEYS + 1));
new_node->children = (BTreeNode**)malloc(sizeof(BTreeNode*) * (MAX_KEYS + 2));
new_node->num_keys = 0;
new_node->is_leaf = 0;
*root = new_node;
new_node->children[0] = temp;
splitChild(new_node, 0, temp); // 分裂子节点
insertNonFull(new_node, key); // 插入键
} else {
// 否则，直接插入键
insertNonFull(temp, key);
}
}

// 将满的子节点分裂
void splitChild(BTreeNode* parent, int index, BTreeNode* child) {
BTreeNode* new_node = (BTreeNode*)malloc(sizeof(BTreeNode));
new_node->keys = (int*)malloc(sizeof(int) * (MAX_KEYS + 1));
new_node->children = (BTreeNode**)malloc(sizeof(BTreeNode*) * (MAX_KEYS + 2));
new_node->num_keys = 0;
new_node->is_leaf = child->is_leaf;

// 移动子节点的右半部分到新节点
for (int j = 0; j < MAX_KEYS / 2; j++) {
new_node->keys[j] = child->keys[j + MAX_KEYS / 2];
}
if (!child->is_leaf) {
for (int j = 0; j < MAX_KEYS / 2 + 1; j++) {
new_node->children[j] = child->children[j + MAX_KEYS / 2];
}
}

child->num_keys = MAX_KEYS / 2;
new_node->num_keys = MAX_KEYS / 2;

// 把父节点中的键和子节点的指针右移
for (int j = parent->num_keys; j > index; j--) {
parent->children[j + 1] = parent->children[j];
}
parent->children[index + 1] = new_node;

for (int j = parent->num_keys - 1; j >= index; j--) {
parent->keys[j + 1] = parent->keys[j];
}
parent->keys[index] = child->keys[MAX_KEYS / 2];

parent->num_keys++;
}

// 插入操作（非满节点）
void insertNonFull(BTreeNode* x, int key) {
int i = x->num_keys - 1;
if (x->is_leaf) {
// 在当前节点找到应该插入的位置
while (i >= 0 && key < x->keys[i]) {
x->keys[i + 1] = x->keys[i];
i--;
}
x->keys[i + 1] = key;
x->num_keys++;
} else {
// 在子节点中找到应该插入的位置
while (i >= 0 && key < x->keys[i]) {
i--;
}
i++;
if (x->children[i]->num_keys == MAX_KEYS) {
// 如果子节点已满，则进行分裂
splitChild(x, i, x->children[i]);
if (key > x->keys[i]) {
i++;
}
}
insertNonFull(x->children[i], key);
}
}

// 打印B树
void printBTree(BTreeNode* root) {
for (int i = 0; i < root->num_keys; i++) {
printf("%d ", root->keys[i]);
}
printf("\n");
if (!root->is_leaf) {
for (int i = 0; i < root->num_keys + 1; i++) {
printBTree(root->children[i]);
}
}
}

int main() {
BTreeNode* root = createBTree();
insert(&root, 10);
insert(&root, 20);
insert(&root, 30);
insert(&root, 40);
insert(&root, 50);
insert(&root, 60);
insert(&root, 70);
insert(&root, 80);
insert(&root, 90);
printBTree(root);
return 0;
}

这是一个简单的B树实现，只包含了插入和查找操作。这段代码首先定义了BTreeNode结构体，包含了键的数组、子节点的指针数组、键的数量以及一个表示节点是否为叶子节点的标志。然后，使用createBTree函数创建一个空的B树。search函数通过递归地搜索节点来查找相应的键。insert函数用于插入键，如果当前节点已满，则进行分裂操作。splitChild函数将满的子节点进行分裂，并将分裂出的新节点插入到父节点中。insertNonFull函数用于在非满节点中插入键。printBTree函数用于打印B树的键。

在main函数中，首先创建一个B树的根节点，然后依次插入一些键，并最后打印B树。注意，此处的MAX_KEYS为3，即每个节点最多包含3个键。

希望这个简单的B树实现能够帮助你理解如何用C语言写一个B树算法。  

### 回答3：
B 树（B-tree）是一种自平衡的搜索树，常用于数据库和文件系统中的索引结构。使用C语言编写B 树算法可以分为以下几个步骤：

1. 定义B 树的节点结构：首先，我们需要定义B 树的节点结构。每个节点包含一个键值对数组，用于存储键和对应的值。同时，每个节点还包含一个指向子节点的指针数组，这些指针指向子节点。

2. 实现插入操作：插入操作是B 树中最常用的操作之一。首先，我们需要通过遍历树来找到待插入的位置。如果插入的键已存在，则更新对应的值。如果插入位置在一个满节点中，则需要进行节点的分裂操作。分裂操作将节点一分为二，并将元素插入到合适的位置。

3. 实现删除操作：删除操作也是B 树中常用的操作。首先，我们需要找到待删除的键。如果键不存在，则不进行任何操作。如果键存在，则需要判断该键是在叶子节点还是内部节点中。在叶子节点中直接删除即可，在内部节点中需要进行子树的调整。

4. 实现搜索操作：搜索操作是B 树最基本的操作之一。我们通过遍历B 树来找到待搜索的键。如果键存在，则返回对应的值。如果键不存在，则返回空。

5. 实现B 树的打印操作：为了方便查看B 树的结构，我们可以实现一个打印函数，通过递归遍历B 树的每个节点，并输出相应的键和值。可以使用缩进来表示层次关系，使得B 树的结构更加清晰。

以上是使用C语言编写B 树算法的基本步骤。在实现过程中，需要注意节点的分裂、合并以及子树的调整等操作，以保证B 树的平衡性。同时，还需要考虑效率和复杂度，尽量减少不必要的时间和空间开销，提高算法的执行效率。