fix alpha for iterative methods

bsi_zoo/estimators.py

@@ -207,7 +207,7 @@ def _solve_reweighted_lasso(
             n_positions = L_w.shape[1] // n_orient
             lc = np.empty(n_positions)
             for j in range(n_positions):
-                L_j = L_w[:, (j * n_orient): ((j + 1) * n_orient)]
+                L_j = L_w[:, (j * n_orient) : ((j + 1) * n_orient)]
                 lc[j] = np.linalg.norm(np.dot(L_j.T, L_j), ord=2)
             coef_, active_set, _ = _mixed_norm_solver_bcd(
                 y,
@@ -530,15 +530,18 @@ def gprime(w):
 
     return x
 
+
 def norm_l2inf(A, n_orient, copy=True):
     from math import sqrt
+
     """L2-inf norm."""
     if A.size == 0:
         return 0.0
     if copy:
         A = A.copy()
     return sqrt(np.max(groups_norm2(A, n_orient)))
 
+
 def iterative_L1(L, y, alpha=0.2, n_orient=1, max_iter=1000, max_iter_reweighting=10):
     """Iterative Type-I estimator with L1 regularizer.
 
@@ -586,20 +589,20 @@ def gprime(w):
         grp_norms = np.sqrt(groups_norm2(w.copy(), n_orient))
         return np.repeat(grp_norms, n_orient).ravel() + eps
 
-    if n_orient==1:
+    if n_orient == 1:
         alpha_max = abs(L.T.dot(y)).max() / len(L)
-    else:      
+    else:
         n_dip_per_pos = 3
         alpha_max = norm_l2inf(np.dot(L.T, y), n_dip_per_pos)
-        
+
     alpha = alpha * alpha_max
 
-    eigen_fields, sing, eigen_leads = _safe_svd(L, full_matrices=False)
+    # eigen_fields, sing, eigen_leads = _safe_svd(L, full_matrices=False)
 
     # y->M
     # L->gain
     x = _solve_reweighted_lasso(
-        eigen_leads, y, alpha, n_orient, weights, max_iter, max_iter_reweighting, gprime
+        L, y, alpha, n_orient, weights, max_iter, max_iter_reweighting, gprime
     )
 
     return x
@@ -617,6 +620,7 @@ def iterative_L2(L, y, alpha=0.2, n_orient=1, max_iter=1000, max_iter_reweightin
     for solving the following problem:
         x^(k+1) <-- argmin_x ||y - Lx||^2_Fro + alpha * sum_i w_i^(k)|x_i|
 
+
     Parameters
     ----------
     L : array, shape (n_sensors, n_sources)
@@ -651,7 +655,12 @@ def gprime(w):
         grp_norm2 = groups_norm2(w.copy(), n_orient)
         return np.repeat(grp_norm2, n_orient).ravel() + eps
 
-    alpha_max = abs(L.T.dot(y)).max() / len(L)
+    if n_orient == 1:
+        alpha_max = abs(L.T.dot(y)).max() / len(L)
+    else:
+        n_dip_per_pos = 3
+        alpha_max = norm_l2inf(np.dot(L.T, y), n_dip_per_pos)
+
     alpha = alpha * alpha_max
 
     x = _solve_reweighted_lasso(
@@ -710,7 +719,12 @@ def g(w):
     def gprime(w):
         return 2.0 * np.repeat(g(w), n_orient).ravel()
 
-    alpha_max = abs(L.T.dot(y)).max() / len(L)
+    if n_orient == 1:
+        alpha_max = abs(L.T.dot(y)).max() / len(L)
+    else:
+        n_dip_per_pos = 3
+        alpha_max = norm_l2inf(np.dot(L.T, y), n_dip_per_pos)
+
     alpha = alpha * alpha_max
 
     x = _solve_reweighted_lasso(
@@ -795,7 +809,12 @@ def iterative_L1_typeII(
     n_sensors, n_sources = L.shape
     weights = np.ones(n_sources)
 
-    alpha_max = abs(L.T.dot(y)).max() / len(L)
+    if n_orient == 1:
+        alpha_max = abs(L.T.dot(y)).max() / len(L)
+    else:
+        n_dip_per_pos = 3
+        alpha_max = norm_l2inf(np.dot(L.T, y), n_dip_per_pos)
+
     alpha = alpha * alpha_max
 
     if isinstance(cov, float):
@@ -894,7 +913,12 @@ def iterative_L2_typeII(
     n_sensors, n_sources = L.shape
     weights = np.ones(n_sources)
 
-    alpha_max = abs(L.T.dot(y)).max() / len(L)
+    if n_orient == 1:
+        alpha_max = abs(L.T.dot(y)).max() / len(L)
+    else:
+        n_dip_per_pos = 3
+        alpha_max = norm_l2inf(np.dot(L.T, y), n_dip_per_pos)
+
     alpha = alpha * alpha_max
 
     if isinstance(cov, float):