k近邻算法python代码

下面是一个简单的k近邻算法的Python代码示例：

from math import sqrt

# 计算两个点之间的欧几里得距离
def euclidean_distance(point1, point2):
    distance = 0.0
    for i in range(len(point1)):
        distance += (point1[i] - point2[i]) ** 2
    return sqrt(distance)

# 找到k个最近邻居
def get_neighbors(train_set, test_instance, k):
    distances = []
    for train_instance in train_set:
        dist = euclidean_distance(test_instance, train_instance)
        distances.append((train_instance, dist))
    distances.sort(key=lambda x: x[1])
    neighbors = []
    for i in range(k):
        neighbors.append(distances[i][0])
    return neighbors

# 预测测试实例的类别
def predict_classification(train_set, test_instance, k):
    neighbors = get_neighbors(train_set, test_instance, k)
    output_values = [instance[-1] for instance in neighbors]
    prediction = max(set(output_values), key=output_values.count)
    return prediction

这个代码可以用于处理带有数值属性的数据集。你需要将数据集分成训练集和测试集，并将每个实例表示为一个包含数值属性和类别的列表。例如，对于一个含有3个属性和1个类别标签的数据集，可以表示为一个列表的列表：

dataset = [[2.7810836,2.550537003,0],
	[1.465489372,2.362125076,0],
	[3.396561688,4.400293529,0],
	[1.38807019,1.850220317,0],
	[3.06407232,3.005305973,0],
	[7.627531214,2.759262235,1],
	[5.332441248,2.088626775,1],
	[6.922596716,1.77106367,1],
	[8.675418651,-0.242068655,1],
	[7.673756466,3.508563011,1]]

对于每个测试实例，可以使用`predict_classification`函数预测它的类别。例如，对于测试实例`[5.0, 2.0]`，可以这样预测：

k = 3
prediction = predict_classification(dataset, [5.0, 2.0], k)
print('Prediction:', prediction)

输出结果应该为`Prediction: 1`。