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y = np.empty_like(x)
for i in range(100):
y[i,:] = np.linalg.solve(A[i,:,:], x[i,:])
y = linalg.solve(A,x)
y = linalg.solve(A,x,axis=0)
y = linalg.solve(A,x,axes=[[1,2],1])
y = linalg.solve(A.T, x.T)
y = linalg.solve(A.T, x).T
y = linalg.solve(A, x[None,:,:])
y = linalg.solve(A,x[:,:,None])
y = linalg.solve(A,x[:,:,None])[:,:,0]
y = linalg.solve(A[:,:,:,None],x[:,None,None,:])
y = linalg.solve(A.transpose([1,2,0]),x[:,:,None]).T
A = np.ones((10,20,30,40))
i = np.array([1,2,3])
j = np.array([[0],[1]])
B = A[:,i,j,:]
Forwarded from Alexander S
Forwarded from Reliable ML
