Welcome to emlearn’s documentation!

Contents:

• emlearn
  • Status
  • License
  • Key features
  • Platform support
  • Projects using emlearn
  • Model support
  • Documentation
    • Other learning resources
  • Installing
  • Usage
    • Simple classifiers
    • Neural net regressor
  • Contributing
  • Contributors
  • Citations
• User Guide
  • 1. Getting started on PC (Linux/MacOS/Windows)
    • 1.1. Prerequisites
    • 1.2. Install scikit-learn
    • 1.3. Install emlearn
    • 1.4. Create model in Python
    • 1.5. Use in C code
    • 1.6. Try it out
    • 1.7. Next
  • 2. Getting started on hardware (Arduino)
    • 2.1. Prerequisites
    • 2.2. Create Arduino project
    • 2.3. Create model in Python
    • 2.4. Copy model files into Arduino
    • 2.5. Install emlearn as Arduino library
    • 2.6. Build and upload the code
    • 2.7. Test it out
    • 2.8. Next
  • 3. Getting started on Zephyr RTOS
    • 3.1. Prerequisites
    • 3.2. Add emlearn to your Zephyr project
    • 3.3. Enable emlearn in your Zephyr project
    • 3.4. Create model in Python
    • 3.5. Example application code
    • 3.6. Build and run in simulator
  • 4. Getting started on browser (WASM/Emscripten)
    • 4.1. Prerequisites
    • 4.2. Install scikit-learn
    • 4.3. Install emlearn
    • 4.4. Create model in Python
    • 4.5. Use in C code for WASM module
    • 4.6. Load WASM module in webpage
    • 4.7. Try it out
    • 4.8. Next
  • 5. Getting started with MicroPython
  • 6. Platform support
    • 6.1. Software frameworks and RTOS
    • 6.2. Microcontroller hardware platforms
    • 6.3. Embedded OS platforms
    • 6.4. Other programming languages
    • 6.5. More
  • 7. Feature extraction
    • 7.1. Time-domain features
    • 7.2. Statistical summaries
    • 7.3. Digital filters
    • 7.4. Spectrum (frequency domain)
    • 7.5. Spectrogram (time-frequency domain)
    • 7.6. Integrating feature extraction
  • 8. Classification
    • 8.1. Applications
    • 8.2. Classification models
    • 8.3. Related
  • 9. Regression
    • 9.1. Applications
    • 9.2. Regression models
    • 9.3. Related
  • 10. Anomaly Detection
    • 10.1. Applications
    • 10.2. Trade-off between False Alarms and Missed Detections
    • 10.3. Selecting anomaly threshold
    • 10.4. Anomaly Detection models
    • 10.5. Outlier detection for handling unknown data
  • 11. Event Detection
    • 11.1. Applications
    • 11.2. Binary or multi-class
    • 11.3. Sliding time windows
    • 11.4. Classification models for Event Detection
    • 11.5. Training with class imbalance
    • 11.6. Trade-off between False Alarms and Missed Detections
    • 11.7. Evaluation metrics
    • 11.8. Frame-wise evaluation
    • 11.9. Event-wise evaluation
  • 12. Model optimization
    • 12.1. Goals for efficient models
    • 12.2. Measuring model costs
  • 13. Tree-based models
    • 13.1. Basic usage
    • 13.2. Probability output
    • 13.3. Regression
    • 13.4. Inference method: Inline vs loadable
    • 13.5. Feature representation
    • 13.6. Hard voting (majority) vs soft voting (proportions)
    • 13.7. Target quantization and leaf-deduplication
    • 13.8. Optimizing model complexity
    • 13.9. Optimization of features
• Examples
• Python API
  • 1. Model conversion
  • Model
    • Model.predict()
    • Model.predict_proba()
    • Model.save()
    • Model.score_samples()
  • convert()
  • 2. Pareto-optimal evaluation
  • find_pareto_front()
  • is_pareto_efficient_simple()
  • plot_pareto_front()
  • 3. Tree evaluation metrics
  • compute_cost_estimate()
  • count_trees()
  • get_tree_estimators()
  • model_size_bytes()
  • model_size_leaves()
  • model_size_nodes()
  • tree_depth_average()
  • tree_depth_difference()
  • check_build_tools()
  • get_program_size()
  • parse_binutils_size_a_output()
  • run_binutils_size()
  • 4. Utilities
  • compile_executable()
  • get_include_dir()
  • 5. C code generation utilities
  • array_declare()
  • array_declare_fixedpoint()
  • assert_valid_identifier()
  • constant()
  • constant_declare()
  • identifier_is_reserved()
  • identifier_is_valid()
  • struct_declare()
  • struct_init()
• C API
  • Tree-based models (C API)
    • EmlTrees
    • eml_trees_predict()
    • eml_trees_regress1()
    • eml_trees_regress()
  • Neural Network (C API)
    • EmlNet
    • eml_net_predict_proba()
    • eml_net_predict()
  • Gaussian Naive Bayes (C API)
    • EmlBayesModel
    • eml_bayes_predict()
  • Distance-based models (C API)
    • EmlEllipticEnvelope
    • eml_elliptic_envelope_predict()
    • eml_mahalanobis_distance_squared()
  • Mixture models (C API)
    • EmlMixtureModel
    • eml_mixture_score()
  • Nearest Neighbors models (C API)
    • EmlNeighborsModel
    • eml_neighbors_predict()
    • eml_neighbors_add_item()
    • EmlNeighborsDistanceItem
  • IIR digital filters (C API)
    • EmlIIR
    • eml_iir_check()
    • eml_iir_filter()
  • Fast Fourier Transform (C API)
    • EmlFFT
    • eml_fft_fill()
    • eml_fft_forward()
  • Audio processing (C API)
    • eml_audio_melspectrogram()
    • eml_audio_melspec()
    • eml_audio_power_spectrogram()
    • eml_sparse_filterbank()
  • Utilities (C API)
    • EmlError
    • _EmlError
    • eml_error_str()
  • Logging (C API)
    • EML_LOG_ENABLE
    • EML_LOG_PRINTF
    • EML_LOG_PRINT_ARRAY
    • EML_LOG_BOOL_STR
    • EML_LOG_BEGIN
    • EML_LOG_END
    • EML_LOG_ADD
    • EML_LOG_ADD_INTEGER
    • EML_LOG_ADD_BOOL
    • EML_LOG_ADD_FLOAT
    • EML_LOG_ADD_ARRAY
• Made with emlearn
  • Projects using emlearn
• The MIT License

Indices and tables

• Index

• Module Index

• Search Page