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fix: NumPy 2.4+ compatibility in feature selection fitting paths #11

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fix: NumPy 2.4+ compatibility in feature selection fitting paths #11

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21 changes: 9 additions & 12 deletions src/fastl2lir/fastl2lir.py
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Original file line number Diff line number Diff line change
Expand Up @@ -274,9 +274,8 @@ def __sub_fit(self, X, Y, alpha=0, n_feat=0, use_all_features=True, dtype=np.flo
I = I[0:n_feat]
I = np.hstack((I, X.shape[1]-1))
W0_sub = (W0.ravel()[(I + (I * W0.shape[1]).reshape((-1, 1))).ravel()]).reshape(I.size, I.size)
Wb = np.linalg.solve(W0_sub, W1[index_outputDim][I].reshape(-1, 1))
for index_selectedDim in range(n_feat):
W[index_outputDim, I[index_selectedDim]] = Wb[index_selectedDim]
Wb = np.linalg.solve(W0_sub, W1[index_outputDim, I])
W[index_outputDim, I[:-1]] = Wb[:-1]
b[0, index_outputDim] = Wb[-1]
W = W.T
else:
Expand All @@ -287,9 +286,8 @@ def __sub_fit(self, X, Y, alpha=0, n_feat=0, use_all_features=True, dtype=np.flo
I = I[0:n_feat]
I = np.hstack((I, X.shape[1]-1))
W0_sub = (W0.ravel()[(I + (I * W0.shape[1]).reshape((-1,1))).ravel()]).reshape(I.size, I.size)
Wb = np.linalg.solve(W0_sub, W1[index_outputDim][I].reshape(-1,1))
for index_selectedDim in range(n_feat):
W[index_outputDim, I[index_selectedDim]] = Wb[index_selectedDim]
Wb = np.linalg.solve(W0_sub, W1[index_outputDim, I])
W[index_outputDim, I[:-1]] = Wb[:-1]
b[0, index_outputDim] = Wb[-1]
W = W.T

Expand All @@ -310,7 +308,7 @@ def __sub_fit_save_select_feat(
# Prepare the matixes to save.
W = np.zeros((Y.shape[1], X.shape[1]), dtype=dtype) # feature size x voxel size
b = np.zeros((1, Y.shape[1]), dtype=dtype) # feautre size
S = np.zeros((Y.shape[1], X.shape[1]), dtype=np.bool) # feature size x voxel size
S = np.zeros((Y.shape[1], X.shape[1]), dtype=np.bool_) # feature size x voxel size

if not (pv.major > 3 or (pv.major == 3 and pv.minor >= 5)):
raise RuntimeError('Python version requires 3.5 or more.')
Expand Down Expand Up @@ -343,13 +341,12 @@ def __sub_fit_save_select_feat(
# Fit
newX = np.hstack((newX, np.ones((newX.shape[0], 1), dtype=dtype))) # Add one column to rightmost column
W0 = np.matmul(newX.T, newX) + alpha * np.eye(newX.shape[1], dtype=dtype)
W1 = np.matmul(selY.ravel(), newX).reshape(-1,1)
Wb = np.linalg.solve(W0, W1)
for index_selectedDim in range(n_feat):
W[index_outputDim, I[index_selectedDim]] = Wb[index_selectedDim]
rhs = np.matmul(selY.ravel(), newX)
Wb = np.linalg.solve(W0, rhs)
W[index_outputDim, I] = Wb[:-1]
b[0, index_outputDim] = Wb[-1]
W = W.T
S = np.asarray(S.T, dtype=np.bool) # Transpose and convert to bool type
S = np.asarray(S.T, dtype=np.bool_) # Transpose and convert to bool type

return W, b, S

Expand Down