Mobility model alpha parameter regression from data #15
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paulvarkey
approved these changes
Oct 4, 2024
notebooks/radp_library.py
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| return ue_tracks_generation | ||
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| def plot_ue_tracks(df) -> None: |
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| def plot_ue_tracks(df) -> None: | |
| def plot_ue_tracks(df: pd.DataFrame) -> None: |
notebooks/radp_library.py
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| start_idx = end_idx | ||
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| def plot_ue_tracks_side_by_side(df1, df2): |
notebooks/radp_library.py
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| plt.show() | ||
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| def plot_ue_tracks_on_axis(df, ax, title): |
notebooks/radp_library.py
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| return group | ||
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| def preprocess_ue_data(df): |
notebooks/radp_library.py
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| return df | ||
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| def get_predicted_alpha(data, alpha0): |
| return np.array([v_t_next, theta_t_next]) | ||
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| def residual_vector( |
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| def residual_vector( | |
| def _residual_vector( |
| return t_array, t_next_array, velocity_mean, variance, rng | ||
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| def next( |
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| def next( | |
| def _next( |
| ) | ||
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| class TestParameterFunctions(unittest.TestCase): |
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test for actual values, since rng (and seed) is under user control
| @@ -0,0 +1,340 @@ | |||
| { | |||
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- generate alpha0 closer to cell where it is first used
- update params with re-computed alpha1
radp/digital_twin/requirements.txt
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| @@ -5,3 +5,4 @@ pyarrow==13.0.0 | |||
| scikit-learn==1.2.1 | |||
| torch==2.0.0 | |||
| torchvision==0.15.1 | |||
| scipy | |||
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see if you can find the actual installed version and freeze that
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Updated the codes according to the PR review comments. |
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In the last commit, we added the seed value as a user input to maintain the reproducibility of the predicted alpha. |
paulvarkey
reviewed
Oct 11, 2024
notebooks/radp_library.py
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| return df | ||
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| def get_predicted_alpha(data: pd.DataFrame, alpha0: float,seed: int) -> float: |
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"""This is the main API entry point for alpha prediction, given UE tracks.
@param data : provide columns
"""
paulvarkey
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Summary
This pull request updates are aimed at refining the parameter regression functionality and enhancing the RADP library's mobility model handling as per previous PR changes. Key changes include the transformation of the parameter regression class into callable API service functions, updates to the mobility helper functions within the RADP library, and the introduction of a notebook that demonstrates these new features.
Changes Made
Mobility Model Parameter Regression:
RADP Library Enhancements:
Mobility Notebook:
alpha.Unit Test