[TensorV2] Feature Scaling Functions @open sesame 03/14 11:49 #2495
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📝 TAOS-CI Version: 1.5.20200925. Thank you for submitting PR #2495. Please a submit 1commit/1PR (one commit per one PR) policy to get comments quickly from reviewers. Your PR must pass all verificiation processes of cibot before starting a review process from reviewers. If you are new member to join this project, please read manuals in documentation folder and wiki page. In order to monitor a progress status of your PR in more detail, visit http://ci.nnstreamer.ai/. |
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This pull request adds two new feature scaling functions - standardization and normalization - to the Tensor class. These functions help users preprocess input data before feeding it into models, improving model performance and accuracy. **Changes proposed in this PR:** * Added normalization() function to rescale values to a range between 0 and 1 * Added standardization() function to center data around the mean and scales to a standard deviation of 1 **Self-evaluation:** 1. Build test: [X]Passed [ ]Failed [ ]Skipped 2. Run test: [X]Passed [ ]Failed [ ]Skipped Signed-off-by: Donghyeon Jeong <dhyeon.jeong@samsung.com>
This commit integrated all remaining functions from Tensor class into TensorV2. This includes fill(), setData(), setValueInt(), sin(), and cos(). Signed-off-by: Donghyeon Jeong <dhyeon.jeong@samsung.com>
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This pull request adds two new feature scaling functions - standardization and normalization - to the Tensor class.
These functions help users preprocess input data before feeding it into models, improving model performance and accuracy.
Changes proposed in this PR:
normalization()function to rescale values to a range between 0 and 1standardization()function to center data around the mean and scales to a standard deviation of 1Self-evaluation: