Compute the average similarity matrix for a list of similarity matrices. Parameters ---------- sm_list : List[pandas.DataFrame] The list of similarity matrices to compute the average for. Returns ---------- average_matrix : pandas.DataFrame The average
(sm_list: List[pd.DataFrame])
| 120 | |
| 121 | |
| 122 | def mean_tmfg(sm_list: List[pd.DataFrame]) -> pd.DataFrame: |
| 123 | """ |
| 124 | Compute the average similarity matrix for a list of similarity matrices. |
| 125 | |
| 126 | Parameters |
| 127 | ---------- |
| 128 | sm_list : List[pandas.DataFrame] |
| 129 | The list of similarity matrices to compute the average for. |
| 130 | |
| 131 | Returns |
| 132 | ---------- |
| 133 | average_matrix : pandas.DataFrame |
| 134 | The average similarity matrix. |
| 135 | """ |
| 136 | |
| 137 | # Stack the matrices along a new axis (axis=0) |
| 138 | stacked_matrices = np.stack(sm_list, axis=0) |
| 139 | |
| 140 | # Calculate the entry-wise average along the new axis |
| 141 | average_matrix = np.mean(stacked_matrices, axis=0) |
| 142 | np.fill_diagonal(average_matrix, 0) |
| 143 | |
| 144 | average_matrix = pd.DataFrame(average_matrix) |
| 145 | |
| 146 | ''' |
| 147 | plt.figure(figsize=(10, 8)) # Optional: Adjusts the size of the figure |
| 148 | sns.heatmap(average_matrix, annot=True, fmt=".2f", cmap='coolwarm', square=True, linewidths=.5) |
| 149 | plt.title("Correlation Matrix Heatmap") |
| 150 | plt.show() |
| 151 | ''' |
| 152 | |
| 153 | return average_matrix |
| 154 | |
| 155 | |
| 156 | def extract_components( |