lightwood | Lightwood is Legos for Machine Learning | Machine Learning library
kandi X-RAY | lightwood Summary
kandi X-RAY | lightwood Summary
Lightwood is an AutoML framework that enables you to generate and customize machine learning pipelines declarative syntax called JSON-AI. Our goal is to make the data science/machine learning (DS/ML) life cycle easier by allowing users to focus on what they want to do their data without needing to write repetitive boilerplate code around machine learning and data preparation. Instead, we enable you to focus on the parts of a model that are truly unique and custom. Lightwood works with a variety of data types such as numbers, dates, categories, tags, text, arrays and various multimedia formats. These data types can be combined together to solve complex problems. We also support a time-series mode for problems that have between-row dependencies. Our JSON-AI syntax allows users to change any and all parts of the models Lightwood automatically generates. The syntax outlines the specifics details in each step of the modeling pipeline. Users may override default values (for example, changing the type of a column) or alternatively, entirely replace steps with their own methods (ex: use a random forest model for a predictor). Lightwood creates a "JSON-AI" object from this syntax which can then be used to automatically generate python code to represent your pipeline. For details on how to generate JSON-AI syntax and how Lightwood works, check out the Lightwood Philosophy.
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Top functions reviewed by kandi - BETA
- Explain prediction of the model
- Add confidence bounds
- Assigns the timeseries to tss
- Format row and global insights
- Transform a timeseries into a DataFrame
- Add next target to the dataframe
- Determine if a row is a prediction
- Calculate the number of processes
- Compute anomaly detection
- Normalizes numbers
- Decode the given tensor
- Train a model
- Calibrate the calibration
- Guess the type of a sequence
- Evaluate the Categorical accuracy
- Calculate the confidence interval accuracy
- Predict the confidence value of x
- Wrapper for mut_mut_method
- R Evaluate the regression accuracy
- Perform the analysis
- Explain prediction
- Analyze block
- Perform validation
- Analyze timeseries
- Encodes the data into a list of floats
- Splits the data
lightwood Key Features
lightwood Examples and Code Snippets
Community Discussions
Trending Discussions on lightwood
QUESTION
I am learning threejs by doing some games. I am able to render all models in the scene and added few lights to and it is perfect now, but when I try to cast and recieve shadows on the plane. The shadows of the objects in scene are not rendering.
I dont understand where I am doing wrong.
Below is the code
Please have a look and help me resolving the issue.
...ANSWER
Answered 2020-Jun-09 at 11:22I've tested your code offline and there are multiple issue and runtime errors:
AmbientLight
does not cast shadows. SettingcastShadow
will produce a runtime error.- You have not configured the shadow frustum for your instance of
DirectionalLight
correctly. Try it with this code:
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install lightwood
Clone lightwood
cd lightwood && pip install -r requirements.txt
Add it to your python path (e.g. by adding export PYTHONPATH='/where/you/cloned/lightwood':$PYTHONPATH as a newline at the end of your ~/.bashrc file)
Check that the unittests are passing by going into the directory where you cloned lightwood and running: python -m unittest discover tests
Install and enable setting sync using github account (if you use multiple machines)
Install pylance (for types) and make sure to disable pyright
Go to Python > Lint: Enabled and disable everything but flake8
Set python.linting.flake8Path to the full path to flake8 (which flake8)
Set Python › Formatting: Provider to autopep8
Add --global-config=<path_to>/lightwood/.flake8 and --experimental to Python › Formatting: Autopep8 Args
Install live share and live share whiteboard
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