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Showing 101 - 120 results of 1,273 for search '(( algorithm python function ) OR ( algorithm allows function ))*', query time: 0.50s Refine Results
  1. 101
    Code snippets to add user-specific custom functionality in PyNoetic.

    Code snippets to add user-specific custom functionality in PyNoetic. by Gursimran Singh (575288)

    Published 2025
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  2. 102
    Python implementation from Symplectic decomposition from submatrix determinants

    Python implementation from Symplectic decomposition from submatrix determinants by Jason L. Pereira (11598632)

    Published 2021
    “…Python implementation of the algorithm and demonstration of how to use the functions.…”
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  3. 103
    The SGLA comparison data of and ℛ<sub>ℏ,1</sub>(ƻ) with ℏ = 3 at <i>α</i>∈{0.1,0.7} and <i>β</i> = 0.5 in Example 2.

    The SGLA comparison data of and ℛ<sub>ℏ,1</sub>(ƻ) with ℏ = 3 at <i>α</i>∈{0.1,0.7} and <i>β</i> = 0.5 in Example 2. by Hind Sweis (18880638)

    Published 2024
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  4. 104
    Fig 1 -

    Fig 1 - by Hind Sweis (18880638)

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  5. 105
    Fig 4 -

    Fig 4 - by Hind Sweis (18880638)

    Published 2024
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  6. 106
    Fig 2 -

    Fig 2 - by Hind Sweis (18880638)

    Published 2024
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  7. 107
    The SGLA comparison data of and ℛ<sub>ℏ,2</sub>(ƻ) with ℏ = 3 at <i>α</i> = 0.5 and <i>β</i>∈{0.3,0.8} in Example 1.

    The SGLA comparison data of and ℛ<sub>ℏ,2</sub>(ƻ) with ℏ = 3 at <i>α</i> = 0.5 and <i>β</i>∈{0.3,0.8} in Example 1. by Hind Sweis (18880638)

    Published 2024
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  8. 108
    The SGLA comparison data of and ℛ<sub>ℏ,1</sub>(ƻ) with ℏ = 3 at <i>α</i> = 0.5 and <i>β</i>∈{0.3,0.8} in Example 1.

    The SGLA comparison data of and ℛ<sub>ℏ,1</sub>(ƻ) with ℏ = 3 at <i>α</i> = 0.5 and <i>β</i>∈{0.3,0.8} in Example 1. by Hind Sweis (18880638)

    Published 2024
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  9. 109
    The SGLA comparison data of and ℛ<sub>ℏ,1</sub>(ƻ) with ℏ = 3 at <i>α</i>∈{0.1,0.7} and <i>β =</i> 0.5 in Example 2.

    The SGLA comparison data of and ℛ<sub>ℏ,1</sub>(ƻ) with ℏ = 3 at <i>α</i>∈{0.1,0.7} and <i>β =</i> 0.5 in Example 2. by Hind Sweis (18880638)

    Published 2024
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  10. 110
    Fig 3 -

    Fig 3 - by Hind Sweis (18880638)

    Published 2024
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  11. 111
    Mean size of stripes as a function of <i>α</i>.

    Mean size of stripes as a function of <i>α</i>. by Pratik Mullick (12851011)

    Published 2022
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  12. 112
    Mean crossing time as a function of <i>α</i>.

    Mean crossing time as a function of <i>α</i>. by Pratik Mullick (12851011)

    Published 2022
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  13. 113
    GraSPy: an Open Source Python Package for Statistical Connectomics

    GraSPy: an Open Source Python Package for Statistical Connectomics by Benjamin Pedigo (6580352)

    Published 2019
    “…We developed GraSPy, an open-source Python toolkit for statistical inference on graphs. …”
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  14. 114
    Modular architecture design of PyNoetic showing all its constituent functions.

    Modular architecture design of PyNoetic showing all its constituent functions. by Gursimran Singh (575288)

    Published 2025
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  15. 115
    Comparison between simulations for all algorithms and for all cases (1, 2 and 3).

    Comparison between simulations for all algorithms and for all cases (1, 2 and 3). by Rutinaldo Aguiar Nascimento (14259594)

    Published 2022
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  16. 116
    List of Abbreviations

    List of Abbreviations by Gursimran Singh (575288)

    Published 2025
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  17. 117
    Adaptive H-REMD simulation algorithm.

    Adaptive H-REMD simulation algorithm. by Danial Pourjafar-Dehkordi (7165523)

    Published 2021
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  18. 118
    The results of ICA performed using PyNoetic.

    The results of ICA performed using PyNoetic. by Gursimran Singh (575288)

    Published 2025
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  19. 119
    The workflow used to analyze imbalanced and balanced sequences.

    The workflow used to analyze imbalanced and balanced sequences. by Solomon Shiferaw Beyene (9143178)

    Published 2020
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  20. 120
    Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN).

    Performances of Naïve Bayes (NB), Multilayer Perceptron (MLP), Random Forest (RF), Gradient Boosting (GB), Support Vector Machine (SVM) and K- Nearest Neighbors (KNN). by Solomon Shiferaw Beyene (9143178)

    Published 2020
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