python - Numpy, masking and sklearn clustering -


i having issue modifying 3d 2d in order supply bandwidth function mean shift calculation. query db data in 1d array of values , set of idś belong these values - me later identify sources. prior calculation add 1 more dimension ensure calculation go right , of these results being kept in 3d array. need supply 2d containing value , 0 calculating function, having trouble constructing 2d compressed (without thrid value describing id) array that. know how using numpy , not having separate list containing id's?

source array:

[(2.819999933242798, 0.0, 16383)   (3.75, 0.0, 16384)   (3.75, 0.0, 16385)]

array after has been masked:

[(2.819999933242798, 0.0, --)   (3.75, 0.0, --)   (3.75, 0.0, --)]

array needs be:

[(2.819999933242798, 0.0)   (3.75, 0.0)   (3.75, 0.0)]

cheers

you first convert numpy array:

h=[(2.819999933242798, 0.0, 16383), (3.75, 0.0, 16384) , (3.75, 0.0, 16385)] a=np.array(h)

and columns want:

a[:,0:2]

gives:

array([[ 2.81999993,  0.        ],        [ 3.75      ,  0.        ],        [ 3.75      ,  0.        ]])

or a[:,:-1] suggested @brenbarn in comments


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