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home:technologies:topology [2025/01/07 07:01] – [gridds : A Data Science Toolkit for Energy Grid Machine Learning] rahulhome:technologies:topology [2025/01/07 08:05] (current) – [Anomaly detection] rahul
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 https://colab.research.google.com/github/LLNL/gridds/blob/master/docs/source/autoregression.ipynb https://colab.research.google.com/github/LLNL/gridds/blob/master/docs/source/autoregression.ipynb
 +
 +https://colab.research.google.com/github/LLNL/gridds/blob/master/docs/source/autoregression_visualization.ipynb
 +
 +Touched up requirements.txt:
  
 <csv hdr_rows=0 file=:home:technologies:requirements.csv maxlines=10>requirements.txt</csv> <csv hdr_rows=0 file=:home:technologies:requirements.csv maxlines=10>requirements.txt</csv>
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 ''!pip install -r requirements.txt'' ''!pip install -r requirements.txt''
  
 +At least one dependency unmet: Our proposed method uses a data-driven deep learning method called Latent Ordinary Differential Equations (LatentODE) for data imputation, followed by a Bayesian non-parametric method called **distance-dependent Chinese Restaurant Franchise** (dd-CRF) for unsupervised discovery of latent states of the energy grid.
 +
 +<note warning>/content/gridds/experimenter.py in <module>
 +     17 from abc import ABCMeta, abstractmethod
 +     18 import numpy as np
 +---> 19 from ddcrf.run_ddcrf import run_ddcrf
 +     20 import gridds.tools.utils as utils
 +     21 from sklearn import preprocessing
 +
 +ModuleNotFoundError: No module named 'ddcrf'</note>