ENH: cluster: reimplement the update-step of K-means in Cython

The current implementation of update-step in `vq.kmeans` and `vq.kmeans2` is inefficient due to unnecessary copying and high cache-miss rate (I guess).

I calculate the sums of obs in each cluster first and then divide them by the numbers of obs. It should be more efficient. And the benchmark shows that the speedup seems to be significant for low dimensional data.

```
old:                        new:
                            
30000 x 100, k = 10         30000 x 100, k = 10
--float64: 15504.6652ms     --float64: 12687.4250ms
--float32: 6647.2686ms      --float32: 5272.2000ms
                            
10000 x 1, k = 3            10000 x 1, k = 3
--float64: 96.9102ms        --float64: 41.6186ms
--float32: 99.7274ms        --float32: 41.9904ms
                            
100000 x 3, k = 4           100000 x 3, k = 4
--float64: 1396.6506ms      --float64: 529.0992ms
--float32: 1028.3450ms      --float32: 431.0248ms
                            
10000 x 30, k = 10          10000 x 30, k = 10
--float64: 1164.1506ms      --float64: 752.1930ms
--float32: 992.7966ms       --float32: 658.1064ms
                            
1000 x 3, k = 4             1000 x 3, k = 4
--float64: 30.8582ms        --float64: 9.8542ms
--float32: 31.3026ms        --float32: 12.4894ms
```
Could use `calloc` and `free` instead of a numpy array, although you would need to check for allocation success. I don't know how much overhead a numpy array adds here.