numpy支持矩阵减向量（每行减去某个向量） 列的情况可以考虑转置。
R实现：
library(class)
attach(iris)
train <- iris[,1:2]
y <- as.numeric(Species)
x <- train
fit <- knn(x, x, y)
print(1 - sum(fit==y)/length(y))
library(class)
attach(iris)
train <- iris[,1:2]
y <- as.numeric(Species)
x <- train
fit <- knn(x, x, y)
print(1 - sum(fit==y)/length(y))
利用ndarray的诸多特性，可以实现非常简短的（DIY）KNN：
import numpy as np from sklearn.datasets import load_iris iris = load_iris() #print iris.DESCR X = iris.data[:,1:3] y = iris.target def predict_y(x, k = 1): distance_array = np.linalg.norm(X - x, axis = 1) # the increase distance indexs distance_indexs = np.argsort(distance_array)[1:k + 1] y_unique_counts = np.unique(y[distance_indexs], return_counts = True) return y_unique_counts[0][np.argmax(y_unique_counts[1])] predict = map(predict_y, X) error_num = 0 for i in range(len(predict)): if predict[i] != y[i]: error_num += 1 print "error_error :" print float(error_num) / len(predict)