Not a member of Pastebin yet?
Sign Up,
it unlocks many cool features!
- [[ 0. 0. 0. ..., 0. 0. 0.]
- [ 0. 0. 0. ..., 0. 0. 0.]
- [ 0. 0. 0. ..., 0. 0. 0.]
- [ 0. 0. 0. ..., 0. 0. 0.]
- [ 0. 0. 0. ..., 0. 0. 0.]]
- [ 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.01176471
- 0.07058824 0.07058824 0.07058824 0.49411765 0.53333336 0.68627453
- 0.10196079 0.65098041 1. 0.96862745 0.49803922 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0.11764706 0.14117648 0.36862746
- 0.60392159 0.66666669 0.99215686 0.99215686 0.99215686 0.99215686
- 0.99215686 0.88235295 0.67450982 0.99215686 0.94901961 0.7647059
- 0.25098041 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.19215687
- 0.93333334 0.99215686 0.99215686 0.99215686 0.99215686 0.99215686
- 0.99215686 0.99215686 0.99215686 0.98431373 0.36470589 0.32156864
- 0.32156864 0.21960784 0.15294118 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0.07058824 0.85882354 0.99215686 0.99215686 0.99215686
- 0.99215686 0.99215686 0.7764706 0.71372551 0.96862745 0.94509804
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0.3137255 0.61176473
- 0.41960785 0.99215686 0.99215686 0.80392158 0.04313726 0.
- 0.16862746 0.60392159 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0.05490196 0.00392157 0.60392159 0.99215686 0.35294119 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0.54509807 0.99215686 0.74509805 0.00784314
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0.04313726 0.74509805 0.99215686
- 0.27450982 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.13725491
- 0.94509804 0.88235295 0.627451 0.42352942 0.00392157 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0.31764707 0.94117647 0.99215686 0.99215686 0.46666667 0.09803922
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0.17647059 0.72941178 0.99215686 0.99215686
- 0.58823532 0.10588235 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0.0627451 0.36470589
- 0.98823529 0.99215686 0.73333335 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0.97647059 0.99215686 0.97647059 0.25098041 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0.18039216 0.50980395
- 0.71764708 0.99215686 0.99215686 0.81176472 0.00784314 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0.15294118 0.58039218 0.89803922
- 0.99215686 0.99215686 0.99215686 0.98039216 0.71372551 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0.09411765 0.44705883 0.86666667 0.99215686
- 0.99215686 0.99215686 0.99215686 0.78823531 0.30588236 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0.09019608 0.25882354 0.83529413 0.99215686 0.99215686
- 0.99215686 0.99215686 0.7764706 0.31764707 0.00784314 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0.07058824 0.67058825 0.85882354 0.99215686 0.99215686 0.99215686
- 0.99215686 0.7647059 0.3137255 0.03529412 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0.
- 0.21568628 0.67450982 0.88627452 0.99215686 0.99215686 0.99215686
- 0.99215686 0.95686275 0.52156866 0.04313726 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0.53333336 0.99215686 0.99215686 0.99215686 0.83137256
- 0.52941179 0.51764709 0.0627451 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. 0. 0.
- 0. 0. 0. 0. 0. ]
- Массив_2, пример:
- n=3, m=2
- 0 1 2
- 3 4 5
- N и M - размерности M_1
- M_1[0][0]*M_2[0][0]+M_1[1][0]*M_2[1][0]+...+M_1[N-1][0]*M_2[N-1][0]+
- +M_1[0][1]*M_2[0][1]+M_1[0][1]*M_2[0][1]+...+M_1[N-1][M-1]*M_2[N-1][M-1]
- M_1[0][0]*M_2[1][0]+M_1[1][0]*M_2[2][0]+...+M_1[N-1][0]*M_2[N][0]+
- +M_1[0][1]*M_2[1][1]+M_1[0][1]*M_2[1][1]+...+M_1[N-1][M-1]*M_2[N][M-1]
- def count(self, A, n1, n2, syn_, type_):# подсчет
- result=[]
- i_=0
- #--------------------------------
- # Создаем элементы и зануляем (реализация на мой взгляд не очень)
- result=np.random.random((len(A)-n1+1, len(A[0])-n2+1, len(A.T)))
- result[:]=0
- B=np.random.random((n1, n2, len(A.T)))
- B[:]=0
- #--------------------------------
- for i in range(0, len(A)-n1+1):# проход по строкам
- A_=A[i:i+n1]# "урезаем" массив и берем нужные элементы
- for l in range(0, len(A[i])-n2+1):# проход по столбцам
- for j in range(0, n1):# проход по элементам подаваемым на подсчет в свертку
- B[j]=A_[j][l:l+n2]# "урезаем" массив и берем нужные элементы
- # ++++++++++++
- # умножаем и суммируем (требуется двойная сумма (по двум мерностям),
- # суммируются все нужные элементы (остается N чисел,
- # где N - количество картинок))
- result[i][l]=sum(sum(syn_.dot(B)))
- # ++++++++++++
- return result
Add Comment
Please, Sign In to add comment