1.K近邻一句话概括：近朱者赤，近墨者黑 + 投票选举
话不多说，上代码
2.用sklearn生成数据集
import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_blobs from sklearn.model_selection import train_test_split
n_samples = 5000 n_bins = 3 # use 3 bins for calibration_curve as we have 3 clusters here
centers = [(-1, -1), (5, 5)] X, y = make_blobs(n_samples=n_samples, n_features=2, cluster_std=1.0, centers=centers, shuffle=False, random_state=42) y[:n_samples // 2] = 0 y[n_samples // 2:] = 1 sample_weight = np.random.RandomState(42).rand(y.shape[0]) X_train, X_test, y_train, y_test, sw_train, sw_test = \ train_test_split(X, y, sample_weight, test_size=0.1, random_state=42)
3.分类--KNN
k = 500 sum1 = 0 def result(dist,k): index = dist.argsort()#获取排序后的数组下标 index = index[:k] out = y_train[index].tolist() return out.count(0) < k - out.count(0) for i in range(len(X_test)): dist = distance(X_test[i], X_train) sum1 = sum1 + np.equal(y_test[i],result(dist,k))#y_train[dist.index(min(dist))]) print(np.float(sum1/len(X_test)))
4.画图显示
plt.scatter(X_train[:,0],X_train[:,1],c=y_train) plt.scatter(X_test[:,0],X_test[:,1],c='b') plt.show()
5.拓展--KD数存储与搜索
class Node: def __init__(self, data, lchild = None, rchild = None): self.data = data self.lchild = lchild self.rchild = rchild def create(self, dataSet, depth): # 创建kd树，返回根结点 if (len(dataSet) > 0): m, n = np.shape(dataSet) # 求出样本行，列 midIndex = m / 2 # 中间数的索引位置 axis = depth % n # 判断以哪个轴划分数据，对应书中算法3.2（2）公式j() sortedDataSet = self.sort(dataSet, axis) # 进行排序 node = Node(sortedDataSet[midIndex]) # 将节点数据域设置为中位数，具体参考下书本 # print sortedDataSet[midIndex] leftDataSet = sortedDataSet[: midIndex] # 将中位数的左边创建2个副本 rightDataSet = sortedDataSet[midIndex + 1:] print(leftDataSet) print(rightDataSet) node.lchild = self.create(leftDataSet, depth + 1) # 将中位数左边样本传入来递归创建树 node.rchild = self.create(rightDataSet, depth + 1) return node else: return None def sort(self, dataSet, axis): # 采用冒泡排序，利用aixs作为轴进行划分 sortDataSet = dataSet[:] # 由于不能破坏原样本，此处建立一个副本 m, n = np.shape(sortDataSet) for i in range(m): for j in range(0, m - i - 1): if (sortDataSet[j][axis] > sortDataSet[j + 1][axis]): temp = sortDataSet[j] sortDataSet[j] = sortDataSet[j + 1] sortDataSet[j + 1] = temp print(sortDataSet) return sortDataSet def preOrder(self, node): if node != None: print("tttt->%s" % node.data) self.preOrder(node.lchild) self.preOrder(node.rchild) #kd树搜索 def search(self, tree, x): # 搜索 self.nearestPoint = None # 保存最近的点 self.nearestValue = 0 # 保存最近的值 def travel(node, depth=0): # 递归搜索 if node != None: # 递归终止条件 n = len(x) # 特征数 axis = depth % n # 计算轴 if x[axis] < node.data[axis]: # 如果数据小于结点，则往左结点找 travel(node.lchild, depth + 1) else: travel(node.rchild, depth + 1) # 以下是递归完毕，对应算法3.3(3) distNodeAndX = self.dist(x, node.data) # 目标和节点的距离判断 if (self.nearestPoint == None): # 确定当前点，更新最近的点和最近的值，对应算法3.3(3)(a) self.nearestPoint = node.data self.nearestValue = distNodeAndX elif (self.nearestValue > distNodeAndX): self.nearestPoint = node.data self.nearestValue = distNodeAndX print(node.data, depth, self.nearestValue, node.data[axis], x[axis]) if (abs(x[axis] - node.data[axis]) <= self.nearestValue): # 确定是否需要去子节点的区域去找（圆的判断），对应算法3.3(3)(b) if x[axis] < node.data[axis]: travel(node.rchild, depth + 1) else: travel(node.lchild, depth + 1) travel(tree) return self.nearestPoint def dist(self, x1, x2): # 欧式距离的计算 return ((np.array(x1) - np.array(x2)) ** 2).sum() ** 0.5