“features”的搜索结果

Extract features from 1D signals using signalTimeFeatureExtractor, signalFrequencyFeatureExtractor, and signalTimeFrequencyFeatureExtractor. Use when computing time-domain features (amplitude, energy, shape factors), frequency-domain features (spectral location, power, bandwidth, PSD), or time-frequency features (spectral shape, instantaneous, ridges, wavelet, EMD-derived) on a per-frame basis. Use when the user asks to "extract features", "compute spectral features", "build a feature table for a classifier", "get per-frame statistics", "run feature extraction on this signal", or describes a vibration / biosignal / radar / sensor signal needing features for downstream ML or analysis. Includes optional GPU acceleration via canUseGPU and gpuArray. Does not cover filter design, audio-specific feature extraction (use audioFeatureExtractor in Audio Toolbox instead), batch dataset orchestration, or 2D / image features.

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Define and prioritize features with strategic traceability during PRD v0.3 Commercial Model. Triggers on requests to define features, prioritize capabilities, scope MVP, map features to pricing tiers, identify parity vs. delta features, or when user asks "what features do we build?", "what's in MVP?", "which features matter?", "feature priority", "parity features", "what's our delta?". Consumes KPI- (Outcome Definition), BR- (Pricing Model, Moat), and CFD- (Market Moat Analysis) from v0.3. Outputs FEA- entries with strategic traceability and BR-FEA- governance rules. Feeds v0.4 User Journeys.

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Use SAELens to train and analyze sparse autoencoders, which separate neural network activity into features that may represent recognizable concepts. Explore pretrained features, evaluate reconstruction quality, and investigate how individual features influence language model outputs.

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• Exploring features with a pretrained SAE
• Training and evaluating a custom SAE
• Studying overlapping concepts in model activity

Use SAELens to train and analyze sparse autoencoders, which break model activations into a small set of interpretable features. Explore pretrained features, evaluate reconstruction quality, troubleshoot training, and test how individual features influence generated text.

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• Exploring features in a pretrained SAE
• Training an SAE for a selected model layer
• Investigating inactive features and reconstruction errors

Use SAELens to train and analyze sparse autoencoders, which break model activations into a small set of interpretable features. Explore pretrained features, evaluate reconstruction quality, troubleshoot training, and test how individual features influence generated text.

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• Exploring features in a pretrained SAE
• Training an SAE for a selected model layer
• Investigating inactive features and reconstruction errors

Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.

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Turn pending roadmap features into requirements, technical designs, and implementation tasks. Independent features run in parallel, while prerequisite features finish first. A final review checks shared data, interfaces, naming, and responsibility boundaries across specifications.

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• Generate specifications for multiple roadmap features
• Plan specifications around feature dependencies
• Check shared data and interfaces across specifications

Add or troubleshoot Table v9 features such as sorting, filtering, pagination, selection, merged cells, and column layout. Guidance helps check feature registration, dependencies, and row-processing settings, with references selected for the task.

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• Adding sorting, filtering, and pagination
• Investigating missing table APIs or state
• Configuring row selection, cell selection, or merged cells

Use when you need to refactor Java code to adopt modern Java features (Java 8+) — including migrating anonymous classes to lambdas, replacing Iterator loops with Stream API, adopting Optional for null safety, switching from legacy Date/Calendar to java.time, using collection factory methods, applying text blocks, var inference, or leveraging Java 25 features like flexible constructor bodies and module import declarations. This should trigger for requests such as Review Java code for modern Java development; Apply best practices for modern Java development in Java code; Modernize Java code with records pattern matching or switch expressions; Replace legacy idioms with modern Java features; Adopt Java 8+ language features safely. Part of Plinth Toolkit

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Guides the creation of detailed agile feature documentation from an existing epic. Use when the user wants to split an epic into feature files, derive features with scope and acceptance criteria, or plan feature documentation for stakeholders or engineering. This should trigger for requests such as Create features from an epic; Split epic into features; Feature files from epic; Derive features from epic; Break down an agile epic into deliverable features. Part of Plinth Toolkit

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Compare project documentation with existing code to identify missing core features. Rank the gaps and create GitHub issues with practical specifications, implementation plans, acceptance criteria, effort estimates, and dependencies.

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• Find documented features missing from the implementation
• Prioritize gaps in core product functionality
• Turn feature gaps into actionable GitHub issues

Explore features for a new product through product management, design, and engineering perspectives. Generate five ideas from each viewpoint, then select five priorities with reasoning and assumptions to test before developing the concept further.

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• Exploring features for a startup idea
• Comparing ideas from three professional perspectives
• Choosing five ideas to validate first

Review how you use Superset and identify unused features that fit your workflow. Get a short list of suggestions, learn about each option, and set up selected features such as scheduled agents, parallel workspaces, or shared tasks.

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• Find unused Superset features worth exploring
• Set up scheduled agent tasks
• Start parallel work in separate workspaces

Create requirements, designs, and task lists for multiple roadmap features in dependency order. Independent features run in parallel when supported, followed by checks that shared data, interfaces, naming, and work boundaries agree across specifications.

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• Prepare specifications for multiple planned features
• Sequence specification work by feature dependencies
• Check shared data and interfaces across specifications

Generate requirements, technical designs, and implementation tasks for pending roadmap features in Cascade. Features are processed one at a time in dependency order, followed by a review of consistency and responsibility boundaries across specifications.

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• Generate specifications for multiple features in Cascade
• Process feature specifications in dependency order
• Check shared data and interfaces across specifications

Extract battery features for degradation analysis and health monitoring in MATLAB. Covers cycling test features, differential curves (IC/DV/DT), and measurement statistics. Use when working with battery cycling data, SOH estimation, RUL prediction, or any battery test data analysis in MATLAB. Triggers on battery* functions such as batteryTestDataParser, batteryTestFeatureExtractor, batteryMeasurementFeatures, batteryDifferentialCurves.

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Bash 5.3 release features and improvements with practical examples. Use when working with Bash 5.3 features, new command substitution, GLOBSORT, loadable builtins, or when user asks about Bash 5.3 changes, new features, or version-specific capabilities.

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Bash 5.2 release features and improvements with practical examples. Use when working with Bash 5.2 features, variable handling enhancements, readline improvements, or when user asks about Bash 5.2 changes, new features, or version-specific capabilities.

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Bash 5.1 release features and improvements with practical examples. Use when working with Bash 5.1 features, epoch time variables, redirection enhancements, or when user asks about Bash 5.1 changes, new features, or version-specific capabilities.

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使用REA分析已发布的应用、二进制文件、软件包和固件，解释功能并比较版本差异。结合反编译与运行时观察，区分证据、推断和未知事项。

• 调查已发布应用的功能实现
• 比较不同版本的应用行为
• 分析二进制文件和随附资源

审查现有代码的一个部分，从设计决策记录、实现惯例、缺失功能和技术选型四个方面整理问题，并推进计划、测试、修复及再次审查。

• 审查接手的现有代码
• 在开发途中引入ECC流程
• 添加功能前重新评估模块

在开发前检验产品方向，梳理是否推进、首次使用的障碍与功能优先级，并将依据、风险和下一步行动整理成产品简报或路线图。

• 将模糊想法整理成产品简报
• 确定待开发功能的优先级
• 发布前检查首次使用流程

使用稀疏自编码器将语言模型内部的激活分解为少量特征，研究模型学到的概念。涵盖预训练SAE分析、自定义训练、重建质量评估，以及利用特征调整输出的实验。

• 使用预训练SAE研究特征
• 针对特定模型层训练SAE
• 比较不同输入文本的特征响应

创建包含产品介绍、功能、客户评价或标志、可选价格及注册引导的SaaS介绍页，遵循DESIGN.md的配色、字体和布局规则，输出单个HTML文件。

• 创建SaaS产品介绍页
• 遵循现有设计规范
• 集中展示功能、客户反馈和价格