I have two 2D numpy arrays a(N,D) b(M,D) and I want to fill a third array c(M,D*N) which is a function of a and b. If N= 2 and D=3 I want c to be the following:
c[:,0]=b[:,0]*np.std(b[:,0])+a[0,0]
c[:,1]=b[:,1]*np.std(b[:,1])+a[0,1]
c[:,2]=b[:,2]*np.std(b[:,2])+a[0,2]
c[:,3]=b[:,0]*np.std(b[:,0])+a[1,0]
c[:,4]=b[:,1]*np.std(b[:,1])+a[1,1]
c[:,5]=b[:,2]*np.std(b[:,2])+a[1,2]
How can I fill c using a loop (for, while)?
Here's a vectorized way leveraging broadcasting, meant for performance efficiency -
bs = b*np.std(b,axis=0,keepdims=True)
c_out = (bs[:,None,:]+a).reshape(len(b),-1)
Sample run -
In [43]: N,M,D = 2,4,3
...: np.random.seed(0)
...: a = np.random.rand(N,D)
...: b = np.random.rand(M,D)
...: c = np.zeros((M,D*N))
...:
...: c[:,0]=b[:,0]*np.std(b[:,0])+a[0,0]
...: c[:,1]=b[:,1]*np.std(b[:,1])+a[0,1]
...: c[:,2]=b[:,2]*np.std(b[:,2])+a[0,2]
...:
...: c[:,3]=b[:,0]*np.std(b[:,0])+a[1,0]
...: c[:,4]=b[:,1]*np.std(b[:,1])+a[1,1]
...: c[:,5]=b[:,2]*np.std(b[:,2])+a[1,2]
In [44]: c
Out[44]:
array([[0.63, 1.05, 0.93, 0.62, 0.75, 0.98],
[0.62, 1.01, 0.78, 0.61, 0.72, 0.83],
[0.65, 1.06, 0.63, 0.64, 0.77, 0.67],
[0.56, 0.72, 0.89, 0.56, 0.43, 0.93]])
In [45]: bs = b*np.std(b,axis=0,keepdims=True)
...: c_out = (bs[:,None,:]+a).reshape(len(b),-1)
In [46]: c_out
Out[46]:
array([[0.63, 1.05, 0.93, 0.62, 0.75, 0.98],
[0.62, 1.01, 0.78, 0.61, 0.72, 0.83],
[0.65, 1.06, 0.63, 0.64, 0.77, 0.67],
[0.56, 0.72, 0.89, 0.56, 0.43, 0.93]])
