LabeledPoint(y[i], X[i])

本文介绍了Apache Spark中LabeledPoint的概念及使用方法,包括如何创建带有正标签和密集特征向量的标记点,以及带有负标签和稀疏特征向量的标记点。

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复制来自http://spark.apache.org/docs/latest/mllib-data-types.html
A labeled point is represented by LabeledPoint.

Refer to the LabeledPoint Python docs for more details on the API.

from pyspark.mllib.linalg import SparseVector
from pyspark.mllib.regression import LabeledPoint

# Create a labeled point with a positive label and a dense feature vector.创建带有正标签和密集特征向量的标记点。
pos = LabeledPoint(1.0, [1.0, 0.0, 3.0])

# Create a labeled point with a negative label and a sparse feature vector.
neg = LabeledPoint(0.0, SparseVector(3, [0, 2], [1.0, 3.0]))
file_version=3DMine_2009 1,7,0,1.00,1.00,1.00,0,0.5,0,Continuous,0624XK5交接 2,7,0,1.00,1.00,1.00,0,0.5,0,Continuous,0624XK9交接 3,7,0,1.00,1.00,1.00,0,0.5,0,Continuous,1 4,7,0,1.00,1.00,1.00,0,0.5,0,Continuous,范围线 5,7,0,1.00,1.00,1.00,0,0.5,0,Continuous,分界线 0,0,0,0,0,0,0,0, 3,8786919.71000,451915.69700,1308.81500,hp1474,Auto 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786863.21200,451888.23900,1290.13700,hp3051,Auto 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786835.08600,451964.11300,1287.88000,hp3105,Auto 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786859.85500,451993.22600,1299.00000,hp1547,Auto 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8787060.76762,452008.51951,1359.65897,G2031, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786935.94300,451877.72700,1300.95200,hp2697,Auto 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786855.12800,451878.27500,1287.72100,hp3082,Auto 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786939.61900,453272.14200,1329.74000,354,\ 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8787283.88900,451588.94000,1274.78400,L6, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786658.72600,452779.73200,1337.58800,1565, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8787506.41900,451681.15100,1289.04400,dx95, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786855.87800,452698.13100,1394.94100,96, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786886.35500,453211.79000,1308.14400,w3500, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8787476.57500,451240.70300,1352.02400,B31, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8787107.63100,451954.35400,1348.64100,48边收方26, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 3,8786901.11600,453220.09100,1315.57100,w2073, 0,0.000,0.000,0.000,0,,1.000,-1.00,-1.00,-1.00,7 end_of_model_file DbSText,5, 452163.674,8786623.549,1358.100,2.00,-230.04,4,-1.00,-1.00,-1.00,0,,1.0,1 txt.shx 1.000,0.000,0.000,1.000,0.000 1358 DbSText,5, 452465.387,8786597.842,1356.100,2.00,-349.87,4,-1.00,-1.00,-1.00,0,,1.0,1 txt.shx 1.000,0.000,0.000,1.000,0.000 1356 如何区分数据类型
最新发布
07-05
### 数据解析与类型识别的实现方案 在C++中解析具有特定分组标识(如`0,0.000,0.000,0.000`)的文本数据,并根据文件版本(例如`file_version=3DMine_2009`)区分不同数据类型,可以采用以下技术路线: #### 1. 文件读取与行处理 使用`std::ifstream`逐行读取文件内容。每行数据通过`std::istringstream`和`std::getline`按逗号分割为字段。此方法适用于CSV格式的数据解析[^4]。 #### 2. 分组标识判断 定义一个函数用于检测当前行是否是分组标识。例如,若前三项为`0`或`0.000`,则认为该行为分组标记。具体实现如下: ```cpp bool isGroupSeparator(const std::vector<std::string>& fields) { return fields.size() >= 4 && fields[0] == "0" && (fields[1] == "0.000" || fields[1] == "0") && (fields[2] == "0.000" || fields[2] == "0") && (fields[3] == "0.000" || fields[3] == "0"); } ``` #### 3. 版本信息识别 在文件开头或其他固定位置查找版本信息。例如,若某行以`file_version=`开头,则提取其值并存储为字符串变量。随后可根据该变量选择不同的数据结构或解析逻辑。 #### 4. 数据分类与存储 将非分组标识行的数据按其类型分类。例如,若某行为`3,x,y,z,...`,则可将其归类为点云数据;若包含标签如`B14`、`A1`等,则可能表示特征点或控制点。可以定义多个结构体来分别表示不同类型的数据: ```cpp struct PointData { double x, y, z; }; struct LabeledPoint { double x, y, z; std::string label; }; ``` 根据版本信息动态选择合适的数据结构进行解析和存储。 #### 5. 示例代码:完整解析流程 ```cpp #include <iostream> #include <fstream> #include <sstream> #include <vector> #include <string> #include <map> // 判断是否为分组标识 bool isGroupSeparator(const std::vector<std::string>& fields) { return fields.size() >= 4 && fields[0] == "0" && (fields[1] == "0.000" || fields[1] == "0") && (fields[2] == "0.000" || fields[2] == "0") && (fields[3] == "0.000" || fields[3] == "0"); } // 提取版本信息 std::string extractFileVersion(std::ifstream& file) { std::string line; while (std::getline(file, line)) { if (line.find("file_version=") != std::string::npos) { return line.substr(line.find('=') + 1); } } file.seekg(0); // 重置文件指针 return ""; } int main() { std::ifstream file("data.txt"); std::string line; std::string version = extractFileVersion(file); std::vector<std::vector<std::string>> currentGroup; std::vector<std::vector<std::vector<std::string>>> allGroups; while (std::getline(file, line)) { std::istringstream ss(line); std::vector<std::string> fields; std::string field; while (std::getline(ss, field, ',')) { fields.push_back(field); } if (isGroupSeparator(fields)) { if (!currentGroup.empty()) { allGroups.push_back(currentGroup); currentGroup.clear(); } } else { currentGroup.push_back(fields); } } if (!currentGroup.empty()) { allGroups.push_back(currentGroup); } // 根据版本信息选择数据处理方式 for (size_t i = 0; i < allGroups.size(); ++i) { std::cout << "Group " << i + 1 << ":\n"; for (const auto& row : allGroups[i]) { if (version == "3DMine_2009") { // 处理特定版本的数据结构 if (row.size() >= 4 && row[0] == "3") { PointData pd; pd.x = std::stod(row[1]); pd.y = std::stod(row[2]); pd.z = std::stod(row[3]); std::cout << "Point: (" << pd.x << ", " << pd.y << ", " << pd.z << ")\n"; } } else if (row.size() > 4 && !row[4].empty()) { LabeledPoint lp; lp.x = std::stod(row[1]); lp.y = std::stod(row[2]); lp.z = std::stod(row[3]); lp.label = row[4]; std::cout << "Labeled Point: " << lp.label << " (" << lp.x << ", " << lp.y << ", " << lp.z << ")\n"; } } std::cout << "-------------------\n"; } return 0; } ``` --- ### 数据结构设计与优化建议 - **灵活的数据模型**:使用联合(`union`)或变体类型(如`std::variant`)支持多种数据结构共存。 - **版本映射机制**:建立版本与数据类型的映射表,便于扩展新版本的数据解析规则。 - **异常处理**:引入`try-catch`块捕获转换错误(如`std::stod`失败),增强程序鲁棒性。 - **性能优化**:对于大规模数据集,考虑使用内存池或自定义缓冲区减少频繁的内存分配开销[^4]。 ---
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