Kmeans是一种简单易用的聚类算法，是少有的会出现在深度学习项目中的传统算法，比如人脸搜索项目、物体检测项目（yolov3中用到了Kmeans进行anchors聚类）等。
一般使用Kmeans会直接调sklearn，如果任务比较复杂，可以通过numpy进行自定义，这里介绍使用Pytorch实现的方式，经测试，通过Pytorch调用GPU之后，能够提高多特征聚类的速度。
代码语言：javascript复制import torch
import time
from tqdm import tqdm
class KMEANS:
def __init__(self, n_clusters=20, max_iter=None, verbose=True,device = torch.device("cpu")):
self.n_cluster = n_clusters
self.n_clusters = n_clusters
self.labels = None
self.dists = None # shape: [x.shape[0],n_cluster]
self.centers = None
self.variation = torch.Tensor([float("Inf")]).to(device)
self.verbose = verbose
self.started = False
self.representative_samples = None
self.max_iter = max_iter
self.count = 0
self.device = device
def fit(self, x):
# 随机选择初始中心点，想更快的收敛速度可以借鉴sklearn中的kmeans 初始化方法
init_row = torch.randint(0, x.shape[0], (self.n_clusters,)).to(self.device)
init_points = x[init_row]
self.centers = init_points
while True:
# 聚类标记
self.nearest_center(x)
# 更新中心点
self.update_center(x)
if self.verbose:
print(self.variation, torch.argmin(self.dists, (0)))
if torch.abs(self.variation) < 1e-3 and self.max_iter is None:
break
elif self.max_iter is not None and self.count == self.max_iter:
break
self.count = 1
self.representative_sample()
def nearest_center(self, x):
labels = torch.empty((x.shape[0],)).long().to(self.device)
dists = torch.empty((0, self.n_clusters)).to(self.device)
for i, sample in enumerate(x):
dist = torch.sum(torch.mul(sample - self.centers, sample - self.centers), (1))
labels[i] = torch.argmin(dist)
dists = torch.cat([dists, dist.unsqueeze(0)], (0))
self.labels = labels
if self.started:
self.variation = torch.sum(self.dists - dists)
self.dists = dists
self.started = True
def update_center(self, x):
centers = torch.empty((0, x.shape[1])).to(self.device)
for i in range(self.n_clusters):
mask = self.labels == i
cluster_samples = x[mask]
centers = torch.cat([centers, torch.mean(cluster_samples, (0)).unsqueeze(0)], (0))
self.centers = centers
def representative_sample(self):
# 查找距离中心点最近的样本，作为聚类的代表样本，更加直观
self.representative_samples = torch.argmin(self.dists, (0))
def time_clock(matrix,device):
a = time.time()
k = KMEANS(max_iter=10,verbose=False,device=device)
k.fit(matrix)
b = time.time()
return (b-a)/k.count
def choose_device(cuda=False):
if cuda:
device = torch.device("cuda:0")
else:
device = torch.device("cpu")
return device
if __name__ == "__main__":
import matplotlib.pyplot as plt
plt.figure()
device = choose_device(False)
cpu_speeds = []
for i in tqdm([20,100,500,2000,8000,20000]):
matrix = torch.rand((10000,i)).to(device)
speed = time_clock(matrix,device)
cpu_speeds.append(speed)
l1, = plt.plot([20,100,500,2000,8000,20000],cpu_speeds,color = 'r',label = 'CPU')
device = choose_device(True)
gpu_speeds = []
for i in tqdm([20, 100, 500, 2000, 8000, 20000]):
matrix = torch.rand((10000, i)).to(device)
speed = time_clock(matrix,device)
gpu_speeds.append(speed)
l2, = plt.plot([20, 100, 500, 2000, 8000, 20000], gpu_speeds, color='g',label = "GPU")
plt.xlabel("num_features")
plt.ylabel("speed(s/iter)")
plt.title("Speed with cuda")
plt.legend(handles = [l1,l2],labels = ['CPU','GPU'],loc='best')
plt.savefig("../result/speed.jpg")cpu和gpu运行的结果对比如下：
可以看到，在特征数<3000的情况下，cpu运行速度更快，但是特征数量超过3000之后，gpu的优势越来越明显。
因为pytorch的矩阵运算接口基本是照着numpy写的，所以numpy的实现方式大概只需要将代码中的torch替换成numpy就可以了。
