commit | 4ff3819c20eb5035749cfd88d32c92848e187207 | [log] [tgz] |
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author | baunsgaard <baunsgaard@tugraz.at> | Thu Oct 29 21:42:44 2020 +0100 |
committer | baunsgaard <baunsgaard@tugraz.at> | Thu Oct 29 21:45:46 2020 +0100 |
tree | 430012bf7cd2f88050743b770fe2e8f9e465d83f | |
parent | 19266fdeffd622c7d10eaeae217d53791e87d9e3 [diff] |
[MINOR] K-means seeded fix This commit change the seeding of k-means to actually work. Since the last change to add the seed, unfortunately, made each initial cluster assign to the same location, making the entire selection and sampling in the beginning invalid. Now the seed are incremented and different for each of the initial values.
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.
Quick Start Install, Quick Start and Hello World
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