def setBayesModel(vector,normFilelengh,spamFilelengh): labels=array([1]*spamFilelengh + [0] *normFilelengh) print('w值:',vector) print('c值:',labels) model=MultinomialNB model.fit(vector,labels) return model if __name__=='__main__': model=setBayesModel(vector,normFilelengh,spamFilelengh) print('贝叶斯模型',model)
时间: 2023-05-19 12:06:01 浏览: 35
这是一个朴素贝叶斯分类器的代码,用于分类垃圾邮件和正常邮件。其中,vector是邮件的特征向量,normFilelengh和spamFilelengh分别是正常邮件和垃圾邮件的数量。函数返回一个训练好的朴素贝叶斯模型。在主函数中,调用setBayesModel函数并打印出训练好的模型。
相关问题
将以下python代码转化为c++版本。import math import cv2 import numpy as np import os thre1=10 thre2=-10 r=60 ang =0 def select_point(image,ang): #根据遥杆方向确定跟踪点坐标 sinA=math.sin(ang) cosA=math.cos(ang) dirBaseX=int(cosA1000) disBaseY=int(-sinA1000) dirValMax=-1000000000 for i in range(len(image)): for j in range(len(image[0])): if image[i][j]==255: dirVal=idisBaseY+jdirBaseX if dirVal>dirValMax: rstRow=i rstCol=j dirValMax=dirVal return [rstCol,rstRow] sequence_path = "./images/" save_path="./out/" for file in os.listdir(sequence_path): filename=os.path.join(sequence_path, file) image=cv2.imread(filename, 0) image=cv2.blur(image,(3,3)) img=np.zeros((len(image), len(image[0])),np.uint8) for i in range(r,len(image)-r): for j in range(r,len(image[0])-r): shizi_1=( int(image[i][j])-int(image[i-r][j])>thre1 and int(image[i][j])-int(image[i][j-r])>thre1 and (int(image[i][j])-int(image[i+r][j])>thre1) and int(image[i][j])-int(image[i][j+r])>thre1 ) xieshizi_1=( int(image[i][j])-int(image[i-r][j-r])<thre2 and int(image[i][j])-int(image[i+r][j-r])<thre2 and int(image[i][j])-int(image[i-r][j+r])<thre2 and int(image[i][j])-int(image[i+r][j+r])<thre2 ) if (shizi_1 or xieshizi_1): img[i][j]=255 else: img[i][j] =0 retval, labels, stats, centroids = cv2.connectedComponentsWithStats(img, connectivity=8) maxVal = 0 index = 0 for i in range(1, len(stats)): if stats[i][4] > maxVal: maxVal = stats[i][4] index = i #x,y,h,w s for i in range(len(labels)): for j in range(len(labels[0])): if labels[i][j]==index: labels[i][j]=255 else: labels[i][j] = 0 img2=np.array(labels) target_x,target_y=select_point(img2,ang) print("跟踪点坐标:{}".format((target_x,target_y))) cv2.imwrite(os.path.join(save_path, file), cv2.circle(image,(int(target_x),int(target_y)),5,(255,255,0),2))
以下是将给定的Python代码转化为C++版本的示例:
```cpp
#include <iostream>
#include <cmath>
#include <opencv2/opencv.hpp>
int thre1 = 10;
int thre2 = -10;
int r = 60;
double ang = 0;
std::vector<int> select_point(cv::Mat image, double ang) {
double sinA = std::sin(ang);
double cosA = std::cos(ang);
int dirBaseX = static_cast<int>(cosA * 1000);
int disBaseY = static_cast<int>(-sinA * 1000);
int dirValMax = -1000000000;
int rstRow = 0, rstCol = 0;
for (int i = 0; i < image.rows; i++) {
for (int j = 0; j < image.cols; j++) {
if (image.at<uchar>(i, j) == 255) {
int dirVal = i * disBaseY + j * dirBaseX;
if (dirVal > dirValMax) {
rstRow = i;
rstCol = j;
dirValMax = dirVal;
}
}
}
}
return { rstCol, rstRow };
}
int main() {
std::string sequence_path = "./images/";
std::string save_path = "./out/";
for (const auto& file : std::filesystem::directory_iterator(sequence_path)) {
std::string filename = file.path().string();
cv::Mat image = cv::imread(filename, cv::IMREAD_GRAYSCALE);
cv::blur(image, image, cv::Size(3, 3));
cv::Mat img(image.rows, image.cols, CV_8UC1, cv::Scalar(0));
for (int i = r; i < image.rows - r; i++) {
for (int j = r; j < image.cols - r; j++) {
bool shizi_1 = (image.at<uchar>(i, j) - image.at<uchar>(i - r, j) > thre1 &&
image.at<uchar>(i, j) - image.at<uchar>(i, j - r) > thre1 &&
image.at<uchar>(i, j) - image.at<uchar>(i + r, j) > thre1 &&
image.at<uchar>(i, j) - image.at<uchar>(i, j + r) > thre1);
bool xieshizi_1 = (image.at<uchar>(i, j) - image.at<uchar>(i - r, j - r) < thre2 &&
image.at<uchar>(i, j) - image.at<uchar>(i + r, j - r) < thre2 &&
image.at<uchar>(i, j) - image.at<uchar>(i - r, j + r) < thre2 &&
image.at<uchar>(i, j) - image.at<uchar>(i + r, j + r) < thre2);
if (shizi_1 || xieshizi_1) {
img.at<uchar>(i, j) = 255;
}
else {
img.at<uchar>(i, j) = 0;
}
}
}
cv::Mat labels, stats, centroids;
int retval = cv::connectedComponentsWithStats(img, labels, stats, centroids, 8);
int maxVal = 0;
int index = 0;
for (int i = 1; i < stats.rows; i++) {
if (stats.at<int>(i, cv::CC_STAT_AREA) > maxVal) {
maxVal = stats.at<int>(i, cv::CC_STAT_AREA);
index = i;
}
}
for (int i = 0; i < labels.rows; i++) {
for (int j = 0; j < labels.cols; j++) {
if (labels.at<int>(i, j) == index) {
labels.at<int>(i, j) = 255;
}
else {
labels.at<int>(i, j) = 0;
}
}
}
cv::Mat img2(labels.rows, labels.cols, CV_8UC1);
labels.convertTo(img2, CV_8UC1);
std::vector<int> target = select_point(img2, ang);
int target_x = target[0];
int target_y = target[1];
std::cout << "跟踪点坐标:" << target_x << "," << target_y << std::endl;
cv::circle(image, cv::Point(target_x, target_y), 5, cv::Scalar(255, 255, 0), 2);
cv::imwrite(save_path + file.path().filename().string(), image);
}
return 0;
}
```
请注意,C++代码中的OpenCV库版本可能需要根据您的环境进行调整,并且可能需要包含适当的头文件和链接适当的库。此外,文件系统操作需要使用C++17标准。
import osimport jiebaimport numpy as npfrom sklearn.naive_bayes import MultinomialNBfrom sklearn.metrics import accuracy_score# 定义常量data_dir = './data'stopwords_path = './stopwords.txt'category_names = ['文学', '教育', '计算机', '医学', '体育']# 加载停用词stopwords = set()with open(stopwords_path, 'r', encoding='utf-8') as f: for line in f: stopwords.add(line.strip())# 加载语料库,构建训练集和测试集train_data = []train_labels = []test_data = []test_labels = []for i, category_name in enumerate(category_names): category_dir = os.path.join(data_dir, category_name) file_names = os.listdir(category_dir) for j, file_name in enumerate(file_names): with open(os.path.join(category_dir, file_name), 'r', encoding='utf-8') as f: content = f.read() words = [word for word in jieba.cut(content) if word not in stopwords] if j < 3: test_data.append(words) test_labels.append(i) else: train_data.append(words) train_labels.append(i)# 构建词汇表vocab = set()for document in train_data: vocab |= set(document)vocab = list(vocab)vocab.sort()# 构建文档向量def document2vector(document, vocab): vector = np.zeros(len(vocab)) for word in document: if word in vocab: vector[vocab.index(word)] += 1 return vectortrain_vectors = np.array([document2vector(document, vocab) for document in train_data])test_vectors = np.array([document2vector(document, vocab) for document in test_data])# 训练朴素贝叶斯分类器clf = MultinomialNB()clf.fit(train_vectors, train_labels)# 测试分类器predicted_labels = clf.predict(test_vectors)# 评估分类器accuracy = accuracy_score(test_labels, predicted_labels)print('Accuracy:', accuracy)
这段代码实现了一个基于朴素贝叶斯分类器的文本分类器,主要分为以下几个步骤:
1. 加载停用词文件,并存储在一个集合中。
2. 加载语料库,并将每个文档进行分词,去除停用词,并将前三个文档作为测试集,其余的作为训练集。
3. 构建词汇表,将训练集中的所有词语去重后按字典序排序后存储在一个列表中。
4. 将每个文档转化为词向量,其长度为词汇表的长度,每个位置表示对应词汇在该文档中出现的次数。
5. 使用训练集训练一个朴素贝叶斯分类器,并使用测试集测试该分类器的准确率。
需要注意的是,该代码中使用的是多项式朴素贝叶斯分类器(MultinomialNB),因此假设每个词汇的出现次数是服从多项式分布的。同时,该代码中使用的是中文分词工具jieba,因此需要确保该工具已被正确安装。
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