三维点云学习（3）6- 实现K-Means
2024-04-02 21:12:35
三维点云学习（3）6- 实现K-Means
最终效果图
编码流程
调用模块
在E-step 搜寻近邻点时使用了kd-tree的KNN搜索方法
import numpy as np
import random
import matplotlib.pyplot as plt
from resultset import KNNResultSet, RadiusNNResultSet
import kdtree as kdtree定义K-Means
class KMeans(object):# k是分组数；tolerance‘中心点误差’；maxiter是迭代次数def __init(self, n_clusters=2, tolerance=0.0001, max_iter=300):self.k= nclustersself.tolerance = toleranceself.maxiter = maxiter
fit() 对最后中心点的确定
# 进行中心点的确定def fit(self, data):## 屏蔽开始#step1 随机选取 K个数据点 作为聚类的中心self.centers_ = data[random.sample(range(data.shape[0]),self.k_)] #random.sample(list,num)old_centers = np.copy(self.centers_) #存储old_centers#step2 E-Step(expectation)：N个点、K个中心，求N个点到K个中心的nearest-neighbor#kd-tree configleaf_size = 1k = 1 # 结果每个点选取属于自己的类中心for _ in range(self.max_iter_):labels = [[] for i in range(self.k_)] #用于分类所有数据点root = kdtree.kdtree_construction(self.centers_ , leaf_size=leaf_size) #对中心点进行构建kd-treefor i in range(data.shape[0]): #对每一个点在4个中心点中进行 1-NN的搜索result_set = KNNResultSet(capacity=k)query = data[i]kdtree.kdtree_knn_search(root, self.centers_, result_set, query) #返回对应中心点的索引# labels[result_set.output_index].append(data[i])#print(result_set)output_index = result_set.knn_output_index()[0] #获取最邻近点的索引labels[output_index].append(data[i]) #将点放入类中#step3 M-Step(maximization)：更新中心点的位置，把属于同一个类的数据点求一个均值，作为这个类的中心值for i in range(self.k_): #求K类里，每个类的的中心点points = np.array(labels[i])self.centers_[i] = points.mean(axis=0) #取点的均值，作为新的聚类中心# print(points)# print(self.centers_[i])if np.sum(np.abs(self.centers_ - old_centers)) < self.tolerance_ * self.k_: # 如果前后聚类中心的距离相差小于self.tolerance_ * self.k_ 输出breakold_centers = np.copy(self.centers_) #保存旧中心点self.fitted = TruePoint_Show(self.centers_)predict() 计算每个点的类别
#计算每个点的类别def predict(self, pdatas):result = []# 作业2# 屏蔽开始if not self.fitted:print('Unfitter. ')return resultfor point in p_datas:diff = np.linalg.norm(self.centers - point, axis=1) #使用二范数求解每个点对新的聚类中心的距离result.append(np.argmin(diff)) #返回离该点距离最小的聚类中心，标记rnk = 1# 屏蔽结束return result
效果图
输入原始，随机生成的10个点
进行聚类后效果图，类初始化为4
最终输出，每个点对应的聚类中心
完整代码
注意：这里调用了kdtree.py 和 resultset.py,代码在下边
# 文件功能： 实现 K-Means 算法import numpy as np
import random
import matplotlib.pyplot as plt
from result_set import KNNResultSet, RadiusNNResultSet
import kdtree as kdtree# 二维点云显示函数
def Point_Show(point,color):x = []y = []point = np.asarray(point)for i in range(len(point)):x.append(point[i][0])y.append(point[i][1])plt.scatter(x, y,color=color)plt.show()class K_Means(object):# k是分组数；tolerance‘中心点误差’；max_iter是迭代次数def __init__(self, n_clusters=2, tolerance=0.0001, max_iter=300):self.k_ = n_clustersself.tolerance_ = toleranceself.max_iter_ = max_iter# 进行中心点的确定def fit(self, data):## 屏蔽开始#step1 随机选取 K个数据点 作为聚类的中心self.centers_ = data[random.sample(range(data.shape[0]),self.k_)] #random.sample(list,num)old_centers = np.copy(self.centers_) #存储old_centers#step2 E-Step(expectation)：N个点、K个中心，求N个点到K个中心的nearest-neighbor#kd-tree configleaf_size = 1k = 1 # 结果每个点选取属于自己的类中心for _ in range(self.max_iter_):labels = [[] for i in range(self.k_)] #用于分类所有数据点root = kdtree.kdtree_construction(self.centers_ , leaf_size=leaf_size) #对中心点进行构建kd-treefor i in range(data.shape[0]): #对每一个点在4个中心点中进行 1-NN的搜索result_set = KNNResultSet(capacity=k)query = data[i]kdtree.kdtree_knn_search(root, self.centers_, result_set, query) #返回对应中心点的索引# labels[result_set.output_index].append(data[i])#print(result_set)output_index = result_set.knn_output_index()[0] #获取最邻近点的索引labels[output_index].append(data[i]) #将点放入类中#step3 M-Step(maximization)：更新中心点的位置，把属于同一个类的数据点求一个均值，作为这个类的中心值for i in range(self.k_): #求K类里，每个类的的中心点points = np.array(labels[i])self.centers_[i] = points.mean(axis=0) #取点的均值，作为新的聚类中心# print(points)# print(self.centers_[i])if np.sum(np.abs(self.centers_ - old_centers)) < self.tolerance_ * self.k_: # 如果前后聚类中心的距离相差小于self.tolerance_ * self.k_ 输出breakold_centers = np.copy(self.centers_) #保存旧中心点self.fitted = True# 屏蔽结#计算每个点的类别def predict(self, p_datas):result = []# 作业2# 屏蔽开始if not self.fitted:print('Unfitter. ')return resultfor point in p_datas:diff = np.linalg.norm(self.centers_ - point, axis=1) #使用二范数求解每个点对新的聚类中心的距离result.append(np.argmin(diff)) #返回离该点距离最小的聚类中心，标记rnk = 1# 屏蔽结束return resultif __name__ == '__main__':db_size = 10dim = 2#x = np.random.rand(db_size,dim)# x = np.array([[1, 2], [1.5, 1.8], [5, 8], [8, 8], [1, 0.6], [9, 11]])x = np.genfromtxt(r"point.txt",delimiter="").reshape((-1,2))Point_Show(x,color="blue")k_means = K_Means(n_clusters=2) #计算迭代后的中心点k_means.fit(x) #计算每个点属于哪个类cat = k_means.predict(x)print(cat)cluster = [[] for i in range(2)] #用于分类所有数据点for i in range(len(x)):if cat[i] == 0:cluster[0].append(x[i])else:cluster[1].append(x[i])Point_Show(cluster[0],"red")Point_Show(cluster[1], "yellow")result_set.py
import copyclass DistIndex:def __init__(self, distance, index):self.distance = distanceself.index = indexdef __lt__(self, other):return self.distance < other.distanceclass KNNResultSet:def __init__(self, capacity):self.capacity = capacityself.count = 0self.worst_dist = 1e10self.dist_index_list = []self.output_index = []for i in range(capacity):self.dist_index_list.append(DistIndex(self.worst_dist, 0))self.comparison_counter = 0def size(self):return self.countdef full(self):return self.count == self.capacitydef worstDist(self):return self.worst_distdef add_point(self, dist, index):self.comparison_counter += 1if dist > self.worst_dist:returnif self.count < self.capacity:self.count += 1i = self.count - 1while i > 0:if self.dist_index_list[i - 1].distance > dist:self.dist_index_list[i] = copy.deepcopy(self.dist_index_list[i - 1])i -= 1else:breakself.dist_index_list[i].distance = distself.dist_index_list[i].index = indexself.worst_dist = self.dist_index_list[self.capacity - 1].distancedef __str__(self):output = ''for i, dist_index in enumerate(self.dist_index_list):output += '%d - %.2f\n' % (dist_index.index, dist_index.distance)output += 'In total %d comparison operations.' % self.comparison_counterreturn outputdef knn_output_index(self):output = ''for i, dist_index in enumerate(self.dist_index_list):output += '%d - %.2f\n' % (dist_index.index, dist_index.distance)self.output_index.append(dist_index.index)output += 'In total %d comparison operations.' % self.comparison_counterreturn self.output_indexclass RadiusNNResultSet:def __init__(self, radius):self.radius = radiusself.count = 0self.worst_dist = radiusself.dist_index_list = []self.comparison_counter = 0def size(self):return self.countdef worstDist(self):return self.radiusdef add_point(self, dist, index):self.comparison_counter += 1if dist > self.radius:returnself.count += 1self.dist_index_list.append(DistIndex(dist, index))def __str__(self):self.dist_index_list.sort()output = ''for i, dist_index in enumerate(self.dist_index_list):output += '%d - %.2f\n' % (dist_index.index, dist_index.distance)output += 'In total %d neighbors within %f.\nThere are %d comparison operations.' \% (self.count, self.radius, self.comparison_counter)return output数据集point.txt
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