聚类(Clustering):hierarchical clustering 层次聚类及其应用

clustering实现：

from numpy import *
import math
#基于matrix计算的pakage#定义树形结构图的结点，当再往上两则分为一类时则将其连接起来
#用面向对象(class)的方法实现此次的代码:
class cluster_node:def __init__ (self,vec,left=None,right=None,distance=0.0,id=None,count=1):#初始化的函数，每次实例化这个类的时候都会自动调用次函数(同java中的构造函数,self同java中的this)'''vec:传入的数据为numpy array，每一行就是一个vec；left:左节点；right=右节点；distance：结点之间距离id:给节点定义一个名字，算节点平均值时要区分哪些节点包含在哪count：节点计数'''self.vec = vecself.left = leftself.right = rightself.distance = distanceself.id = idself.count = count #only used for weighted average #找出最近的两点的距离:
def L2dist(v1,v2):return math.sqrt(sum((v1-v2)**2)) #2维的两点(向量计算)的直线距离,向量为坐标形式，一个坐标包含x，y两个数值def L1dist(v1,v2): #计算一维的距离时使用return sum(abs(v1-v2))# def Chi2dist(v1,v2):#     return sqrt(sum((v1-v2)**2))#实现 HC的具体树状结构
def hcluster(features,distance=L2dist):'''features:类型为numpy arraydistance:默认为2D距离'''distances = {} #用来储存所有distance,注意字典distances和函数distance的区别currentid = -1 #初始化实例的clusterid#最开始每一个单独的数据就是一个单独的cluster，对应的就是feature(array/matrix)中的每一行==> # clusters are initially just the individual rowsclust = [cluster_node(array(features[i]),id=i) for i in range(len(features))]#注意这里array赋值属性的用法和for循环的使用方法'''features的每一组[]里的数据对应一个点，每一个点都赋值一个类别i,调用cluster_node'''#进行分类，知道所有分类个数为1时结束循环：while len(clust)>1:lowestpair = (0,1)#初始化以(0,1)为最近的组closest = distance(clust[0].vec,clust[1].vec)#首先取前两个向量,只有两个向量，他们的距离也就是最小的(调用了L2dist计算最小距离)# loop through every pair looking for the smallest distancefor i in range(len(clust)):for j in range(len(clust)):# distances is the cache of distance calculationif (clust[i].id,clust[j].id) not in distances:distances[clust[i].id,clust[j].id] = distance(clust[i].vec,clust[j].vec)'''将i,j两点的距离计算并传到字典distances中'''d = distances[(clust[i].id,clust[j].id)]if d < closest: #比较出最小距离,赋值给closest,同时更新最近距离的对组closest = dlowestpair = (i,j) # calculate the average of the two clusters.有多种定义分类的方法(最近距离、最远距离、均值、中值),这里取平均距离print(clust[lowestpair[0].vec[0]])mergevec = [(clust[lowestpair[0]].vec[i]+clust[lowestpair[1]].vec[i])/2.0 for i in range(len(clust[0].vec))]# create the new clusternewcluster = cluster_node(array(mergevec),left=clust[lowestpair[0]],right=clust[lowestpair[1]],distance=closest,id=currentid)# cluster ids that weren't in the original set are negative# 不在原始的数据集合中currentid为负数currentid -= 1#每执行完一次结点计算就将之前的结点坐标值删除？del clust[lowestpair[0]]del clust[lowestpair[1]]clust.append(newcluster)return clust[0]# (以上已经建立好树状图了)def extract_cluster(clust,dist): #dist为预设的距离值# extract list of sub-tree clusters from hcluster tree with distance<distcluster = {}if clust.distance<dist:# we have found a cluster subtreereturn [clust]else:# check the right and left branchescl = []cr = []if clust.left != None:#这里可以直接.left？cl = extract_cluster(clust.left,dist=dist)if clust.right != None:cr = extract_cluster(clust.right,dist=dist)return cl+crdef get_cluster_elements(clust):# return ids for elements in a cluster sub-tree# 如果该要求的clust没有子集，那就返回他本身，如果有子集就返回左子集和右子集相加if clust.id>=0:# positive id means that this is a leafreturn clust.idelse:# check the right and left branchescl = []cr = []if clust.left != None:cl = get_cluster_elements(clust.left)if clust.right !=None:cr = get_cluster_elements(clust.right)return cl+crdef printclust(clust,labels=None,n=0):for i in range(n): print(''),if clust.id<0:# negative id means that this is branchprint('-')else:# positive id means that this is an endpointif labels==None: print(clust.id)else: print(labels[clust.id])if clust.left != None: printclust(clust.left,labels=labels,n=n+1)if clust.right !=None: printclust(clust.right,labels=labels,n=n+1)def getheight(clust):# Is this an endpoint? Then the height is just 1if clust.left == None and clust.right ==None: return 1# Otherwise the height is the same of the heights of each branchreturn getheight(clust.left)+getheight(clust.right) #为什么还要加上getheight这个函数？def getdepth(clust):  #深度是？# The distance of an endpoint is 0.0if clust.left == None and clust.right == None: return 0    # The distance of a branch is the greater of its two sides plus its own distance  return max(getdepth(clust.left),getdepth(clust.right))+clust.distance

clustering代码应用:(借用链接：https://blog.csdn.net/weixin_41790863/article/details/81412564 )

from PIL import ImageDraw, Image
import numpy as np
import os
import sysnodeList = []  # 用于存储所有的节点，包含图片节点，与聚类后的节点
distance = {}  # 用于存储所有每两个节点的距离，数据格式{(node1.id,node2.id):30.0,(node2.id,node3.id):40.0}class node:def __init__(self, data):'''每个样本及样本合并后节点的类data：接受两种格式，1、当为字符（string）时，是图片的地址，同时也表示这个节点就是图片2、合并后的类，传入的格式为(leftNode,rightNode) 即当前类表示合并后的新类，而对应的左右节点就是子节点'''self.id = len(nodeList)  # 设置一个ID,以nodeList当然长度为ID,在本例中ID本身没太大用处，只是如果看代码时，有时要看指向时有点用self.parent = None  # 指向合并后的类self.pos = None  # 用于最后绘制节构图使用，赋值时为(x,y,w,h)格式if type(data) == type(""):'''节点为图片'''self.imgData = Image.open(data)self.left = Noneself.right = Noneself.level = 0  # 图片为最终的子节点，所有图片的层级都为0，设置层级是为了最终绘制结构图npTmp = np.array(self.imgData).reshape(-1, 3)  # 将图片数据转化为numpy数据，shape为(高，宽，3)，3为颜色通道npTmp = npTmp.reshape(-1, 3)  # 重新排列，shape为(高*宽，3)self.feature = npTmp.mean(axis=0)  # 计算RGB三个颜色通道均值else:'''节点为合成的新类'''self.imgData = Noneself.left = data[0]self.right = data[1]self.left.parent = selfself.right.parent = selfself.level = max(self.left.level, self.right.level) + 1  # 层级为左右节高层级的级数+1self.feature = (self.left.feature + self.right.feature) / 2  # 两类的合成一类时，就是左右节点的feature相加/2# 计算该类与每个其他类的距离，并存入distancefor x in nodeList:distance[(x, self)] = np.sqrt(np.sum((x.feature - self.feature) ** 2))nodeList.append(self)  # 将本类加入nodeList变量def drawNode(self, img, draw, vLineLenght):# 绘制结构图if self.pos == None: returnif self.left == None:# 如果是图片self.imgData.thumbnail((self.pos[2], self.pos[3])) #thumbnail将图片变小成缩略图img.paste(self.imgData, (self.pos[0], self.pos[1]))draw.line((int(self.pos[0] + self.pos[2] / 2), self.pos[1] - vLineLenght, int(self.pos[0] + self.pos[2] / 2), self.pos[1]), fill=(255, 0, 0))else:# 如果不是图片draw.line((int(self.pos[0]), self.pos[1], int(self.pos[0] + self.pos[2]), self.pos[1]), fill=(255, 0, 0))draw.line((int(self.pos[0] + self.pos[2] / 2), self.pos[1], int(self.pos[0] + self.pos[2] / 2), self.pos[1] - self.pos[3]), fill=(255, 0, 0))def loadImg(path):'''path 图片目录，根据自己存的地方改写'''files = Nonetry:files = os.listdir(path)except:print('未正确读取目录：' + path + ',图片目录，请根据自己存的地方改写,并保证没有hierarchicalResult.jpg,该文件为最后生成文件')return Nonefor i in files:if os.path.splitext(i)[1].lower() == '.jpg' and os.path.splitext(i)[0].lower() != 'hierarchicalresult':fileName = os.path.join(path, i)node(fileName)return os.path.join(path, 'hierarchicalResult.jpg')def getMinDistance():'''从distance中过滤出未分类的结点，并读取最小的距离'''vars = list(filter(lambda x: x[0].parent == None and x[1].parent == None, distance))minDist = vars[0]for x in vars:if minDist == None or distance[x] < distance[minDist]:minDist = xreturn minDistdef createTree():while len(list(filter(lambda x: x.parent == None, nodeList))) > 1:  # 合并到最后时，只有一个类，只要有两个以上未合并，就循环minDist = getMinDistance()# 创建非图片的节点，之所以把[1]做为左节点，因为绘图时的需要，# 在不断的产生非图片节点时，在nodeList的后面的一般是新节点，但绘图时绘在左边node((minDist[1], minDist[0]))return nodeList[-1]  # 最后一个插入的节点就是要节点def run():root = createTree()  # 创建树结构# 一句话的PYTON，实现二叉树的左右根遍历，通过通过遍历，进行排序后，取出图片，做为最底层的打印sortTree = lambda node: ([] if node.left == None else sortTree(node.left)) + ([] if node.right == None else sortTree(node.right)) + [node]treeTmp = sortTree(root)treeTmp = list(filter(lambda x: x.left == None, treeTmp))  # 没有左节点的，即为图片thumbSize = 60  # 缩略图的大小，，在60X60的小格内缩放thumbSpace = 20  # 缩略图间距vLineLenght = 80  # 上下节点，即每个level之间的高度imgWidth = len(treeTmp) * (thumbSize + thumbSpace)imgHeight = (root.level + 1) * vLineLenght + thumbSize + thumbSpace * 2img = Image.new('RGB', (imgWidth, imgHeight), (255, 255, 255))draw = ImageDraw.Draw(img)for item in enumerate(treeTmp):# 为所有图片增加绘图数据x = item[0] * (thumbSize + thumbSpace) + thumbSpace / 2y = imgHeight - thumbSize - thumbSpace / 2 - ((item[1].parent.level - 1) * vLineLenght)w = item[1].imgData.widthh = item[1].imgData.heightif w > h:h = h / w * thumbSizew = thumbSizeelse:w = w / h * thumbSizeh = thumbSizex += (thumbSize - w) / 2item[1].pos = (int(x), int(y), int(w), int(h))item[1].drawNode(img, draw, vLineLenght)for x in range(1, root.level + 1):# 为所有非图片增加绘图的数据items = list(filter(lambda i: i.level == x, nodeList))for item in items:x = item.left.pos[0] + (item.left.pos[2] / 2)w = item.right.pos[0] + (item.right.pos[2] / 2) - xy = item.left.pos[1] - (item.level - item.left.level) * vLineLenghth = ((item.parent.level if item.parent != None else item.level + 1) - item.level) * vLineLenghtitem.pos = (int(x), int(y), int(w), int(h))item.drawNode(img, draw, vLineLenght)img.save(resultFile)resultFile = loadImg(r"G:\Pythonnotes\test\HierarchicalClusterDataset")  # 读取数据，并返回最后结果要存储的文件名，目录根据自己存的位置进行修改
if resultFile != 'None':run()print("结构图生成成功，最终结构图存储于：" + resultFile)