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        "author": "Saptarshi Bej, Narek Davtyan",
        "author_email": "davtyannarek@hotmail.com",
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        "classifiers": [
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
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        "description": "# LoRAS\nLocalized Randomized Affine Shadowsampling (LoRAS) oversampling technique\n\n## Installation\nThe latest version is available on PyPi and installable with the command: `pip install loras`\n\n## Usage\nThere is just one method `fit_resample(maj_class_points, min_class_points, k, num_shadow_points, list_sigma_f, num_generated_points, num_aff_comb, random_state=42)`\n\nThere are two mandatory inputs:  \n\n- `maj_class_points` : Majority class parent data points which is a non-empty list containing numpy arrays acting as points\n- `min_class_points` : Minority class parent data points which is a non-empty list containing numpy arrays acting as points   \n\nThere are also optional parameters:\n\n- `k` : Number of nearest neighbours to be considered per parent data point (default value: `8 if len(min_class_points)<100 else 30`)\n- `num_shadow_points` : Number of generated shadowsamples per parent data point (default value: `ceil(2*num_aff_comb / k)`)\n- `list_sigma_f` : List of standard deviations for normal distributions for adding noise to each feature (default value: `[0.005, ... , 0.005]`)\n- `num_generated_points` : Number of shadow points to be chosen for a random affine combination (default value: `ceil((len(maj_class_points) + len(min_class_points)) / len(min_class_points))`)\n- `num_aff_comb` : Number of generated LoRAS points for each nearest neighbours group (default value: `min_class_points.shape[1]`)   \n\nOutput:\n\n - `min_class_points::oversampled_set` : Concatenation of original data points and oversampled ones\n\n\n",
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        "maintainer": "",
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        "name": "loras",
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