c/c++语言开发共享使这个C数组处理代码更加python（甚至numpy）

一种使用boolean-indexing/mask 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 

假设您将文件读入列表，如下所示：

 lines = open('data.txt', 'r').readlines() 

标题是这样的：

 lines[0] 

偶数行是：

 even = lines[1:][::2] 

奇怪的是：

 odd = lines[2:][::2] 

现在，您可以使用以下两个列表中的itertools.izip创建列表：

 itertools.izip(even, odd) 

这是一种类似于列表的东西（你可以循环它，或者只是在它周围写一个list( ... )以使它成为一个真正的列表），其每个条目都是一对输入输出数据。

如果有人偶然发现同一个问题，这里有四种基于Ami建议的变化（函数do1，do1b，do2，do3）

对于那些好奇的人来说，这里有基准测试（我有大约1000个输入输出数据对。可能基本上更多的数据基准会变化更多）

….

 def load_file(filename = 'Sharpy_7.txt'): global file_data, num_samples, input_dim, output_dim with open(filename, 'r') as f: # get all lines as a list of strings file_data = list(f) # convert header row to list of ints and get info header = map(int, file_data[0].split(' ')) num_samples = header[0] input_dim = header[1] output_dim = header[2] f.close() def calc_pos2(d): target = d[0] pantilt = d[1] 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, 0, 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] return pos def calc_pos(target, pantilt): 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, 0, 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] return pos def calc_stats(): global pos_array, pos_avg, pos_std pos_array = np.asarray(pos_list) pos_avg = np.mean(pos_array, 0) pos_std = np.std(pos_array, 0) # map on itertools.izip def do3(): global pos_list # bad ass list comprehensions target_list = [[float(x) for x in l.split()] for l in file_data[1::2]] pantilt_list = [[float(x) for x in l.split()] for l in file_data[2::2]] # calculate position pos_list = map(calc_pos2, itertools.izip(target_list, pantilt_list)) # list comprehension on itertools.izip def do2(): global pos_list # bad ass list comprehensions target_list = [[float(x) for x in l.split()] for l in file_data[1::2]] pantilt_list = [[float(x) for x in l.split()] for l in file_data[2::2]] # calculate position pos_list = [calc_pos(d[0], d[1]) for d in itertools.izip(target_list, pantilt_list)] # for loop with function call def do1b(): global pos_list # bad ass list comprehensions target_list = [[float(x) for x in l.split()] for l in file_data[1::2]] pantilt_list = [[float(x) for x in l.split()] for l in file_data[2::2]] # calculate position pos_list = []; for i in range(num_samples): pos_list.append(calc_pos(target_list[i], pantilt_list[i])) # for loop with unrolled algorithm def do1(): global pos_list # bad ass list comprehensions target_list = [[float(x) for x in l.split()] for l in file_data[1::2]] pantilt_list = [[float(x) for x in l.split()] for l in file_data[2::2]] # calculate position pos_list = []; for i in range(num_samples): pantilt = pantilt_list[i]; target = target_list[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, 0, 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)