fixed mi knn estimator by adding compute_mi_knn

.gitignore

@@ -110,6 +110,10 @@ ENV/
 env.bak/
 venv.bak/
 
+# Pipenv
+Pipfile
+Pipfile.lock
+
 # Spyder project settings
 .spyderproject
 .spyproject

hoi/core/mi.py

@@ -2,6 +2,7 @@
 
 import jax
 import jax.numpy as jnp
+from jax.scipy.special import digamma as psi
 
 from .entropies import get_entropy
 
@@ -28,11 +29,14 @@ def get_mi(method="gcmi", **kwargs):
         Function to compute mutual information on variables of shapes
         (n_features, n_samples)
     """
-    # get the entropy unction
-    _entropy = get_entropy(method=method, **kwargs)
+    if method == "knn":
+        return partial(compute_mi_knn, **kwargs)
+    else:
+        # get the entropy function
+        _entropy = get_entropy(method=method, **kwargs)
 
-    # wrap the mi function with it
-    return partial(compute_mi, entropy_fcn=_entropy)
+        # wrap the mi function with it
+        return partial(compute_mi, entropy_fcn=_entropy)
 
 
 ###############################################################################
@@ -57,7 +61,7 @@ def compute_mi_comb(inputs, comb, mi=None):
 
 ###############################################################################
 ###############################################################################
-#                                 OTHERS
+#                         GENERAL MUTUAL INFORMATION
 ###############################################################################
 ###############################################################################
 
@@ -87,3 +91,70 @@ def compute_mi(x, y, entropy_fcn=None):
         - entropy_fcn(jnp.concatenate((x, y), axis=0))
     )
     return mi
+
+
+###############################################################################
+###############################################################################
+#                         KNN MUTUAL INFORMATION
+###############################################################################
+###############################################################################
+
+
+@partial(jax.jit, static_argnums=(2,))
+def n_neighbours(xy, idx, k=1):
+    """Return number of neighbours for each point based on kth neighbour."""
+    xi, x = xy[0][:, [idx]], xy[0]
+    yi, y = xy[1][:, [idx]], xy[1]
+
+    # compute euclidian distance from xi to all points in x (same y)
+    eucl_xi = jnp.sqrt(jnp.sum((xi - x) ** 2, axis=0))
+    eucl_yi = jnp.sqrt(jnp.sum((yi - y) ** 2, axis=0))
+
+    # distance in space (XxY) is the maximum distance.
+    max_dist_xy = jnp.maximum(eucl_xi, eucl_yi)
+    # indices to the closest points in the (XxY) space.
+    closest_points = jnp.argsort(max_dist_xy)
+    # the kth neighbour is at index k (ignoring the point itself)
+    # distance to the k-th neighbor for each point
+    dist_k = max_dist_xy[closest_points[k]]
+    # don't include the `i`th point itself in nx and ny
+    nx = (eucl_xi < dist_k).sum() - 1
+    ny = (eucl_yi < dist_k).sum() - 1
+
+    return xy, (nx, ny)
+
+
+@partial(jax.jit, static_argnums=(2,))
+def compute_mi_knn(x, y, k: int = 1) -> jnp.array:
+    """Mutual information using the KSG estimator.
+
+    First algorithm proposed in Kraskov et al., Estimating mutual information,
+    Phy rev, 2004.
+
+    Parameters
+    ----------
+    x, y : array_like
+        Input data of shape (n_features, n_samples).
+    k : int
+        Number of nearest neighbors to consider for the KSG estimator.
+
+    Returns
+    -------
+    mi : float
+        Floating value describing the mutual-information between x and y.
+    """
+    # n_samples
+    n = float(x.shape[1])
+
+    _n_neighbours = partial(n_neighbours, k=k)
+    # get number of neighbors for each point in XxY space
+    _, n_neighbors = jax.lax.scan(
+        _n_neighbours, (x, y), jnp.arange(int(n)).astype(int)
+    )
+    nx = n_neighbors[0]
+    ny = n_neighbors[1]
+
+    psi_mean = jnp.sum((psi(nx + 1) + psi(ny + 1)) / n)
+
+    mi = psi(k) - psi_mean + psi(n)
+    return mi / jnp.log(2)

pyproject.toml

@@ -1,2 +1,5 @@
 [tool.black]
+line-length = 79
+
+[tool.ruff]
 line-length = 79