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  2. Dask (software) - Wikipedia

    en.wikipedia.org/wiki/Dask_(software)

    Dask is an open-source Python library for parallel computing.Dask [1] scales Python code from multi-core local machines to large distributed clusters in the cloud. Dask provides a familiar user interface by mirroring the APIs of other libraries in the PyData ecosystem including: Pandas, scikit-learn and NumPy.

  3. scikit-learn - Wikipedia

    en.wikipedia.org/wiki/Scikit-learn

    scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...

  4. Marker-and-cell method - Wikipedia

    en.wikipedia.org/wiki/Marker-and-Cell_method

    The marker-and-cell method is commonly used in computer graphics to discretize functions for fluid and other simulations. [1] It was developed by Francis Harlow and his collaborators at the Los Alamos National Laboratory .

  5. pandas (software) - Wikipedia

    en.wikipedia.org/wiki/Pandas_(software)

    [4]: 114 A DataFrame is a 2-dimensional data structure of rows and columns, similar to a spreadsheet, and analogous to a Python dictionary mapping column names (keys) to Series (values), with each Series sharing an index. [4]: 115 DataFrames can be concatenated together or "merged" on columns or indices in a manner similar to joins in SQL.

  6. Feature scaling - Wikipedia

    en.wikipedia.org/wiki/Feature_scaling

    The effect of z-score normalization on k-means clustering. 4 gaussian clusters of points are generated, then squashed along the y-axis, and a = clustering was computed. . Without normalization, the clusters were arranged along the x-axis, since it is the axis with most of varia

  7. Latin hypercube sampling - Wikipedia

    en.wikipedia.org/wiki/Latin_hypercube_sampling

    Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution.The sampling method is often used to construct computer experiments or for Monte Carlo integration.

  8. Self-organizing map - Wikipedia

    en.wikipedia.org/wiki/Self-organizing_map

    The input data was a table with a row for each member of Congress, and columns for certain votes containing each member's yes/no/abstain vote. The SOM algorithm arranged these members in a two-dimensional grid placing similar members closer together. The first plot shows the grouping when the data are split into two clusters.

  9. Row- and column-major order - Wikipedia

    en.wikipedia.org/wiki/Row-_and_column-major_order

    Even though the row is indicated by the first index and the column by the second index, no grouping order between the dimensions is implied by this. The choice of how to group and order the indices, either by row-major or column-major methods, is thus a matter of convention. The same terminology can be applied to even higher dimensional arrays.