Hyperbolic Geometry

本文探讨了111维舞台上的Poincaré盘模型,介绍了如何通过复数定义超曲面距离,如Poincaré距离,并揭示了在双曲几何中直线视觉扭曲的现象。还通过映射和极限性质展示了距离属性。

111-dimensional stage: (−1,1)(-1,1)(1,1).

If you get a complex stage, you will get Poincaré disc.

I guess its stage is {∣z∣⩽1∣z∈C}\{|z|\leqslant1|z\in\mathbb C\}{z1zC}.

If you define routine of shortest distance straight line, then you will find that straight line is not visually “straight” in hyperbolic geometry.

Structure

Distance

Definition:
d(x1,x2)=∣∫x1x2dx1−x2∣. d(x_1,x_2)=|\int_{x_1}^{x_2}\frac{\text dx}{1-x^2}|. d(x1,x2)=x1x21x2dx.
This is called hyperbolic distance or Poincaré distance.

We can prove it satisfies those 3 axioms of distance.


Properties:

1, Define: ωi=xi−x01−xix0\omega_i=\dfrac{x_i-x_0}{1-x_ix_0}ωi=1xix0xix0.

This mapping keeps order: (x1−x2)(ω1−ω2)>0,x1≠x2(x_1-x_2)(\omega_1-\omega_2)>0,x_1\neq x_2(x1x2)(ω1ω2)>0,x1=x2.

It keeps distance: d(x1,x2)=d(ω1,ω2)d(x_1,x_2)=d(\omega_1,\omega_2)d(x1,x2)=d(ω1,ω2).

2, lim⁡x→±1d(x,x0)=+∞\lim\limits_{x\rightarrow\pm 1}d(x,x_0)=+\inftyx±1limd(x,x0)=+.

个人防护装备实例分割数据集 一、基础信息 • 数据集名称:个人防护装备实例分割数据集 • 图片数量: 训练集:4524张图片 • 训练集:4524张图片 • 分类类别: 手套(Gloves) 头盔(Helmet) 未戴手套(No-Gloves) 未戴头盔(No-Helmet) 未穿鞋(No-Shoes) 未穿背心(No-Vest) 鞋子(Shoes) 背心(Vest) • 手套(Gloves) • 头盔(Helmet) • 未戴手套(No-Gloves) • 未戴头盔(No-Helmet) • 未穿鞋(No-Shoes) • 未穿背心(No-Vest) • 鞋子(Shoes) • 背心(Vest) • 标注格式:YOLO格式,适用于实例分割任务,包含边界框或多边形坐标。 • 数据格式:图片数据,来源于监控或相关场景。 二、适用场景 • 工业安全监控系统开发:用于自动检测工人是否佩戴必要的个人防护装备,提升工作场所安全性,减少工伤风险。 • 智能安防应用:集成到监控系统中,实时分析视频流,识别PPE穿戴状态,辅助安全预警。 • 合规性自动化检查:在建筑、制造等行业,自动检查个人防护装备穿戴合规性,支持企业安全审计。 • 计算机视觉研究:支持实例分割、目标检测等算法在安全领域的创新研究,促进AI模型优化。 三、数据集优势 • 类别全面:覆盖8种常见个人防护装备及其缺失状态,提供丰富的检测场景,确保模型能处理各种实际情况。 • 标注精准:采用YOLO格式,每个实例都经过精细标注,边界框或多边形坐标准确,提升模型训练质量。 • 真实场景数据:数据来源于实际环境,增强模型在真实世界中的泛化能力和实用性。 • 兼容性强:YOLO格式便于与主流深度学习框架(如YOLO、PyTorch等)集成,支持快速部署和实验。
### Hyperbolic API Documentation and Usage Examples Hyperbolic APIs are designed to work with hyperbolic geometry, which is essential in various applications such as graph embedding, natural language processing, and information retrieval systems. These APIs provide functions that facilitate operations within the hyperbolic space. #### Key Features of Hyperbolic APIs The primary features include methods for converting Euclidean data into hyperbolic representations, calculating distances between points in hyperbolic space, and performing transformations on these points[^1]. ```python import numpy as np from hyperbolic_api import PoincareBall # Initialize a point in the Poincaré ball model point_a = np.array([0.5, 0.2]) poincare_ball = PoincareBall() # Calculate distance from origin distance_from_origin = poincare_ball.distance_to_origin(point_a) print(f"Distance from origin: {distance_from_origin}") ``` This code snippet demonstrates how one can initialize a point using the `PoincareBall` class and calculate its distance from the origin in hyperbolic space. The library used here provides an interface similar to popular machine learning libraries like TensorFlow or PyTorch but specialized for hyperbolic computations. For more advanced functionalities, users may explore additional modules provided by this type of API: - **Embedding**: Tools for mapping high-dimensional vectors into lower dimensional hyperbolic spaces. - **Optimization**: Algorithms tailored specifically towards optimizing objectives defined over hyperbolic manifolds. - **Visualization**: Methods to visualize datasets embedded in two or three dimensions while preserving their intrinsic properties under hyperbolic metrics. --related questions-- 1. What specific tasks benefit most from utilizing hyperbolic embeddings? 2. How does optimization differ when working within hyperbolic versus traditional Euclidean geometries? 3. Can you explain some common challenges encountered during implementation involving hyperbolic models?
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值