added examples for mi and entropy of multidim vars

.vscode/launch.json

@@ -0,0 +1,16 @@
+{
+    // Use IntelliSense to learn about possible attributes.
+    // Hover to view descriptions of existing attributes.
+    // For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
+    "version": "0.2.0",
+    "configurations": [
+        {
+            "name": "Python Debugger: Current File",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${file}",
+            "console": "integratedTerminal",
+            "justMyCode": false
+        }
+    ]
+}

examples/it/plot_entropy_high_dimensional.py

@@ -29,14 +29,14 @@
 
 # list of estimators to compare
 metrics = {
-    "GCMI": get_entropy("gcmi", biascorrect=False),
+    "GCMI": get_entropy("gc", biascorrect=False),
     "KNN-3": get_entropy("knn", k=3),
     "KNN-10": get_entropy("knn", k=10),
     "Kernel": get_entropy("kernel"),
 }
 
 # number of samples to simulate data
-n_samples = np.geomspace(20, 1000, 10).astype(int)
+n_samples = np.geomspace(100, 10000, 10).astype(int)
 
 # number of repetitions to estimate the percentile interval
 n_repeat = 10
@@ -152,4 +152,3 @@ def compute_true_entropy(cov_matrix):
 )
 fig.tight_layout()
 plt.show()
-print("Done")

examples/it/plot_mi_high_dimensional.py

@@ -33,13 +33,13 @@
 
 # list of estimators to compare
 metrics = {
-    "GCMI": get_mi("gcmi", biascorrect=False),
+    "GCMI": get_mi("gc", biascorrect=False),
     "KNN-3": get_mi("knn", k=3),
     "KNN-10": get_mi("knn", k=10),
 }
 
 # number of samples to simulate data
-n_samples = np.geomspace(20, 1000, 10).astype(int)
+n_samples = np.geomspace(1000, 10000, 10).astype(int)
 
 # number of repetitions to estimate the percentile interval
 n_repeat = 10