使此C数组处理代码更像python(甚至numpy)
作者:互联网
我正在努力使自己的Python达到惊人的列表处理能力(最终达到numpy).我正在将我编写的一些C代码转换为python.
我有一个文本数据文件,其中第一行是标题,然后每个奇数行是我的输入数据,每个偶数行是我的输出数据.所有数据空间分开.我很奇怪,我设法使用嵌套列表推导将所有数据读入列表.很棒的东西.
with open('data.txt', 'r') as f:
# get all lines as a list of strings
lines = list(f)
# convert header row to list of ints and get info
header = map(int, lines[0].split(' '))
num_samples = header[0]
input_dim = header[1]
output_dim = header[2]
del header
# bad ass list comprehensions
inputs = [[float(x) for x in l.split()] for l in lines[1::2]]
outputs = [[float(x) for x in l.split()] for l in lines[2::2]]
del x, l, lines
然后,我想生成一个新列表,其中每个元素都是对应的输入输出对的函数.我不知道如何使用任何特定于python的优化来做到这一点.这是C风格的python:
# calculate position
pos_list = [];
pos_y = 0
for i in range(num_samples):
pantilt = outputs[i];
target = inputs[i];
if(pantilt[0] > 90):
pantilt[0] -=180
pantilt[1] *= -1
elif pantilt[0] < -90:
pantilt[0] += 180
pantilt[1] *= -1
tan_pan = math.tan(math.radians(pantilt[0]))
tan_tilt = math.tan(math.radians(pantilt[1]))
pos = [0, pos_y, 0]
pos[2] = tan_tilt * (target[1] - pos[1]) / math.sqrt(tan_pan * tan_pan + 1)
pos[0] = pos[2] * tan_pan
pos[0] += target[0]
pos[2] += target[2]
pos_list.append(pos)
del pantilt, target, tan_pan, tan_tilt, pos, pos_y
我试图通过理解或地图来做到这一点,但不知道如何:
>为pos_list数组的每个元素从两个不同的列表(输入和输出)中绘制
>将算法的主体放在理解中.它必须是一个单独的函数还是为此使用lambda的一种时髦方式?
>甚至可以完全不使用循环来执行此操作,只需将其粘贴在numpy中并对整个对象进行矢量化处理?
解决方法:
一种使用boolean-indexing/mask
的矢量化方法-
import numpy as np
def mask_vectorized(inputs,outputs,pos_y):
# Create a copy of outputs array for editing purposes
pantilt_2d = outputs[:,:2].copy()
# Get mask correspindig to IF conditional statements in original code
mask_col0_lt = pantilt_2d[:,0]<-90
mask_col0_gt = pantilt_2d[:,0]>90
# Edit the first column as per the statements in original code
pantilt_2d[:,0][mask_col0_gt] -= 180
pantilt_2d[:,0][mask_col0_lt] += 180
# Edit the second column as per the statements in original code
pantilt_2d[ mask_col0_lt | mask_col0_gt,1] *= -1
# Get vectorized tan_pan and tan_tilt
tan_pan_tilt = np.tan(np.radians(pantilt_2d))
# Vectorized calculation for: "tan_tilt * (target[1] .." from original code
V = (tan_pan_tilt[:,1]*(inputs[:,1] - pos_y))/np.sqrt((tan_pan_tilt[:,0]**2)+1)
# Setup output numpy array
pos_array_vectorized = np.empty((num_samples,3))
# Put in values into columns of output array
pos_array_vectorized[:,0] = inputs[:,0] + tan_pan_tilt[:,0]*V
pos_array_vectorized[:,1] = pos_y
pos_array_vectorized[:,2] = inputs[:,2] + V
# Convert to list, if so desired for the final output
# (keeping as numpy array could boost up the performance further)
return pos_array_vectorized.tolist()
运行时测试
In [415]: # Parameters and setup input arrays
...: num_samples = 1000
...: outputs = np.random.randint(-180,180,(num_samples,5))
...: inputs = np.random.rand(num_samples,6)
...: pos_y = 3.4
...:
In [416]: %timeit original(inputs,outputs,pos_y)
100 loops, best of 3: 2.44 ms per loop
In [417]: %timeit mask_vectorized(inputs,outputs,pos_y)
10000 loops, best of 3: 181 µs per loop
标签:vectorization,c-3,arrays,python,numpy 来源: https://codeday.me/bug/20191120/2042375.html