min_histogram.py 文件源码

def hist_comp(arry, hist, result, index):

    # We have N threads per block
    # And We have one block only

    x = cuda.grid(1)

    R = cuda.shared.array(9, dtype=float64)


    # No of featureVectors
    # array.shape[0] == 9*34
    A = cuda.shared.array(shape=(9,34), dtype=float64)

    # Vecture To Compair
    # hist.shape[0] == BIN_COUNT == 34 ?
    B = cuda.shared.array(34, dtype=float64)
    for i in range(BIN_COUNT):
        B[i] = hist[i]

    A[x] = arry[x]



    cuda.syncthreads()

    # Do Actual Calculations.
    # i.e: kullback_leibler_divergence
    Sum = 0.00
    for i in range(BIN_COUNT):
        a = B[i]
        b = A[x][i]
        Sum += (a * (math.log(a/b) / math.log(2.0)))

    # R Contains the KL-Divergences
    R[x] = Sum
    cuda.syncthreads()

    # These Should be Shared Variables.
    Min = cuda.shared.array(1,dtype=float32)
    mIndex = cuda.shared.array(1,dtype=int8)
    Min = 0.0000000000
    mIndex = 0

    if x == 0:
        Min = R[x]
        mIndex = x

    cuda.syncthreads()
    if R[x] <= Min:
        Min = R[x]
        mIndex = x
    cuda.syncthreads()

    if x == mIndex :
        index=mIndex