python - 请求 for 循环和列表推导的 NumPy/SciPy 向量化替换
问题描述
我有两个不同的数组处理问题,我想解决 AQAP(Q=quickly),以确保解决方案在我的过程中没有速率限制(使用 NEAT 训练视频游戏机器人)。在一种情况下,我想建立一个惩罚函数来制造更大的柱高,而在另一种情况下,我想奖励建立“具有共同价值的岛屿”。
操作从具有黑色/0 背景的 26 行 x 6 列 numpy 灰度值数组开始。
对于已经实现了一些 numpy 的每个问题,我都有可行的解决方案,但我想推动对两者都采用完全矢量化的方法。
import numpy as np,
from scipy.ndimage.measurements import label as sp_label
from math import ceil
这两个问题都从这样的数组开始:
img= np.array([[ 0., 0., 0., 12., 0., 0.],
[ 0., 0., 0., 14., 0., 0.],
[ 0., 0., 0., 14., 0., 0.],
[ 0., 0., 0., 14., 0., 0.],
[16., 0., 0., 14., 0., 0.],
[16., 0., 0., 12., 0., 0.],
[12., 0., 11., 0., 0., 0.],
[12., 0., 11., 0., 0., 0.],
[16., 0., 15., 0., 15., 0.],
[16., 0., 15., 0., 15., 0.],
[14., 0., 12., 0., 11., 0.],
[14., 0., 12., 0., 11., 0.],
[14., 15., 11., 0., 11., 0.],
[14., 15., 11., 0., 11., 0.],
[13., 16., 12., 0., 13., 0.],
[13., 16., 12., 0., 13., 0.],
[13., 14., 16., 0., 16., 0.],
[13., 14., 16., 0., 16., 0.],
[16., 14., 15., 0., 14., 0.],
[16., 14., 15., 0., 14., 0.],
[14., 16., 14., 0., 11., 0.],
[14., 16., 14., 0., 11., 0.],
[11., 13., 14., 16., 12., 13.],
[11., 13., 14., 16., 12., 13.],
[12., 12., 15., 14., 15., 11.],
[12., 12., 15., 14., 15., 11.]])
第一个(列高)问题目前正在解决:
# define valid connection directions for sp_label
c_valid_conns = np.array((0,1,0,0,1,0,0,1,0,), dtype=np.int).reshape((3,3))
# run the island labeling function sp_label
# c_ncomponents is a simple count of the conected columns in labeled
columns, c_ncomponents = sp_label(img, c_valid_conns)
# calculate out the column lengths
col_lengths = np.array([(columns[columns == n]/n).sum() for n in range(1, c_ncomponents+1)])
col_lengths
给我这个数组:[ 6. 22. 20. 18. 14. 4. 4.]
(如果代码始终忽略不“包含”数组底部的标记区域(行索引 25/-1))
第二个问题涉及屏蔽每个唯一值并计算每个屏蔽数组中的连续体,以获得连续体的大小:
# initial values to start the ball rolling
values = [11, 12, 13, 14, 15, 16]
isle_avgs_i = [1.25, 2, 0, 1,5, 2.25, 1]
# apply filter masks to img to isolate each value
# Could these masks be pushed out into a third array dimension instead?
masks = [(img == g) for g in np.unique(values)]
# define the valid connectivities (8-way) for the sp_label function
m_valid_conns = np.ones((3,3), dtype=np.int)
# initialize islanding lists
# I'd love to do away with these when I no longer need the .append() method)
mask_isle_avgs, isle_avgs = [],[]
# for each mask in the image:
for i, mask in enumerate(masks):
# run the island labeling function sp_label
# m_labeled is the array containing the sequentially labeled islands
# m_ncomponents is a simple count of the islands in m_labeled
m_labeled, m_ncomponents = sp_label(mask, m_valid_conns)
# collect the average (island size-1)s (halving to account for...
# ... y resolution) for each island into mask_isle_avgs list
# I'd like to vectorize this step
mask_isle_avgs.append((sum([ceil((m_labeled[m_labeled == n]/n).sum()/2)-1
for n in range(1, m_ncomponents+1)]))/(m_ncomponents+1))
# add up the mask isle averages for all the islands...
# ... and collect into isle_avgs list
# I'd like to vectorize this step
isle_avgs.append(sum(mask_isle_avgs))
# initialize a difference list for the isle averages (I also want to do away with this step)
d_avgs = []
# evaluate whether isle_avgs is greater for the current frame or the...
# ... previous frame (isle_avgs_i) and append either the current...
# ... element or 0, depending on whether the delta is non-negative
# I want this command vectorized
[d_avgs.append(isle_avgs[j])
if (isle_avgs[j]-isle_avgs_i[j])>=0
else d_avgs.append(0) for j in range(len(isle_avgs))]
d_avgs
给我这个 d_avgs 数组:[0, 0, 0.46785714285714286, 1.8678571428571429, 0, 0]
(如果代码始终忽略不“包含”数组底部的标记区域(行索引 25/-1),则再次提供此数组:
[0, 0, 0.43452380952380953, 1.6345238095238095, 0, 0])
我正在寻找删除任何列表操作和理解并将它们移动到具有相同结果的完全矢量化的 numpy/scipy 实现中。
任何删除这些步骤的帮助将不胜感激。
解决方案
以下是我最终解决此问题的方法:
######## column height penalty calculation ########
# c_ncomponents is a simple count of the conected columns in labeled
columns, c_ncomponents = sp_label(unit_img, c_valid_conns)
# print(columns)
# throw out the falling block with .isin(x,x[-1]) combined with...
# the mask nonzero(x)
drop_falling = np.isin(columns, columns[-1][np.nonzero(columns[-1])])
col_hts = drop_falling.sum(axis=0)
# print(f'col_hts {col_hts}')
# calculate differentials for the (grounded) column heights
d_col_hts = np.sum(col_hts - col_hts_i)
# print(f'col_hts {col_hts} - col_hts_i {col_hts_i} ===> d_col_hts {d_col_hts}')
# set col_hts_i to current col_hts for next evaluation
col_hts_i = col_hts
# calculate penalty/bonus function
# col_pen = (col_hts**4 - 3**4).sum()
col_pen = np.where(d_col_hts > 0, (col_hts**4 - 3**4), 0).sum()
#
# if col_pen !=0:
# print(f'col_pen: {col_pen}')
######## end column height penalty calculation ########
######## color island bonus calculation ########
# mask the unit_img to remove the falling block
isle_img = drop_falling * unit_img
# print(isle_img)
# broadcast the game board to add a layer for each color
isle_imgs = np.broadcast_to(isle_img,(7,*isle_img.shape))
# define a mask to discriminate on color in each layer
isle_masked = isle_imgs*[isle_imgs==ind_grid[0]]
# reshape the array to return to 3 dimensions
isle_masked = isle_masked.reshape(isle_imgs.shape)
# generate the isle labels
isle_labels, isle_ncomps = sp_label(isle_masked, i_valid_conns)
# determine the island sizes (via return_counts) for all the unique labels
isle_inds, isle_sizes = np.unique(isle_labels, return_counts=True)
# zero out isle_sizes[0] to remove spike for background (500+ for near empty board)
isle_sizes[0] = 0
# evaluate difference to determine whether bonus applies
if isle_sizes_i.sum() != isle_sizes.sum():
# calculate bonus for all island sizes ater throwing away the 0 count
isle_bonus = (isle_sizes**3).sum()
else:
isle_bonus = 0
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