数据挖掘KNN算法代码
时间: 2024-04-06 21:26:12 浏览: 23
KNN(K-Nearest Neighbors)算法是一种常用的数据挖掘算法,用于分类和回归问题。下面是一个简单的KNN算法的代码示例:
```python
import numpy as np
from collections import Counter
def euclidean_distance(x1, x2):
return np.sqrt(np.sum((x1 - x2) ** 2))
class KNN:
def __init__(self, k=3):
self.k = k
def fit(self, X, y):
self.X_train = X
self.y_train = y
def predict(self, X):
y_pred = [self._predict(x) for x in X]
return np.array(y_pred)
def _predict(self, x):
distances = [euclidean_distance(x, x_train) for x_train in self.X_train]
k_indices = np.argsort(distances)[:self.k]
k_nearest_labels = [self.y_train[i] for i in k_indices]
most_common = Counter(k_nearest_labels).most_common(1)
return most_common[0][0]
```
这段代码实现了一个简单的KNN算法,其中包括了计算欧氏距离的函数`euclidean_distance`和KNN类。KNN类中的`fit`方法用于训练模型,`predict`方法用于预测新样本的标签。