Merge pull request #38 from khalil-research/main

Typo
khalil-research · Jun 14, 2024 · 0261692 · 0261692
2 parents c3c0aec + 317a197
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diff --git a/README.md b/README.md
@@ -41,7 +41,8 @@ The official ``PyEPO`` docs can be found at [https://khalil-research.github.io/P
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/khalil-research/PyEPO/blob/main/notebooks/03%20Training%20and%20Testing.ipynb)**03 Training and Testing:** Train and test different approaches
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/khalil-research/PyEPO/blob/main/notebooks/04%202D%20knapsack%20Solution%20Visualization.ipynb)**04 2D knapsack Solution Visualization:** Visualize solutions for knapsack problem
 - [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/khalil-research/PyEPO/blob/main/notebooks/05%20Warcraft%20Shortest%20Path.ipynb)**05 Warcraft Shortest Path:** Use the Warcraft terrains dateset to train shortest path
-- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/khalil-research/PyEPO/blob/main/notebooks/06%20kNN%20Robust%20Losses.ipynb)**06 kNN Robust Losses:** Use the optDatasetKNN for robust losses
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/khalil-research/PyEPO/blob/main/notebooks/06%20Real-World%20Energy%20Scheduling.ipynb)**06 Real-World Energy Scheduling:** Apply PyEPO to real energy data
+- [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/khalil-research/PyEPO/blob/main/notebooks/07%20kNN%20Robust%20Losses.ipynb)**07 kNN Robust Losses:** Use the optDatasetKNN for robust losses
 
 
 ## Experiments

diff --git a/docs/source/content/examples/function.rst b/docs/source/content/examples/function.rst
@@ -132,7 +132,7 @@ Adaptive Implicit Maximum Likelihood Estimator (AI-MLE) [#f8]_ use the adaptive
 
  import pyepo
 
- aimle = pyepo.func.implicitMLE(optmodel, n_samples=10, sigma=1.0, processes=2)
+ aimle = pyepo.func.adaptiveImplicitMLE(optmodel, n_samples=10, sigma=1.0, processes=2)
 
 
 

diff --git a/notebooks/03 Training and Testing.ipynb b/notebooks/03 Training and Testing.ipynb
diff --git a/notebooks/04 2D knapsack Solution Visualization.ipynb b/notebooks/04 2D knapsack Solution Visualization.ipynb
diff --git a/notebooks/06 Real-World Energy Scheduling.ipynb b/notebooks/06 Real-World Energy Scheduling.ipynb
diff --git a/notebooks/06 kNN Robust Losses.ipynb b/notebooks/06 kNN Robust Losses.ipynb
diff --git a/notebooks/07 kNN Robust Losses.ipynb b/notebooks/07 kNN Robust Losses.ipynb
diff --git a/pkg/pyepo/model/grb/portfolio.py b/pkg/pyepo/model/grb/portfolio.py
@@ -41,7 +41,7 @@ def _getRiskLevel(self, gamma):
  Returns:
  float: risk level
  """
- risk_level = gamma * np.mean(self.cov)
+ risk_level = gamma * np.mean(self.covariance)
  return risk_level
 
  def _getModel(self):