commit | 06b5e4d741d9db2cdcd99b0cc10cc476b5c98668 | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Sat Dec 12 16:05:01 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Mon Dec 21 14:11:47 2020 +0100 |
tree | 36aa01d8ec33dcf59b1ee80bf5903d3564c9a2ac | |
parent | fd178aa36f02daea0372e4aab6b8f77711bc7a98 [diff] |
[SYSTEMDS-2757] PCA Transpose(Predict) and Inverse This commit adds functions for PCA transpose and inverse. to enable transposing on unseen data, from training and to inverse the pca transpose, to an approximation of the original data.
Overview: SystemDS is a versatile system for the end-to-end data science lifecycle from data integration, cleaning, and feature engineering, over efficient, local and distributed ML model training, to deployment and serving. To this end, we aim to provide a stack of declarative languages with R-like syntax for (1) the different tasks of the data-science lifecycle, and (2) users with different expertise. These high-level scripts are compiled into hybrid execution plans of local, in-memory CPU and GPU operations, as well as distributed operations on Apache Spark. In contrast to existing systems - that either provide homogeneous tensors or 2D Datasets - and in order to serve the entire data science lifecycle, the underlying data model are DataTensors, i.e., tensors (multi-dimensional arrays) whose first dimension may have a heterogeneous and nested schema.
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Documentation: SystemDS Documentation
Python Documentation Python SystemDS Documentation
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Status and Build: SystemDS is renamed from SystemML which is an Apache Top Level Project. To build from source visit SystemDS Install from source