class KNearestNeighbor(object): def __init__(self): pass def train(self, X, y): self.X_train = X self.y_train = y def predict(self, X, k=1): num_test = X.shape[0] num_train = self.X_train.shape[0] dists = np.zeros((num_test, num_train)) d1 = -2 * np.dot(X, self.X_train.T) d2 = np.sum(np.square(X), axis=1, keepdims=True) d3 = np.sum(np.square(self.X_train), axis=1) dist = np.sqrt(d1 + d2 + d3) y_pred = np.zeros(num_test) for i in range(num_test): dist_k_min = np.argsort(dist[i])[:k] y_kclose = self.y_train[dist_k_min] y_pred[i] = np.argmax(np.bincount(y_kclose.tolist())) return y_pred注释每一行代码
时间: 2023-11-20 17:05:20 浏览: 54
这是一个KNN(K-Nearest Neighbor)算法的Python实现,具体注释如下:
```python
class KNearestNeighbor(object):
def __init__(self):
pass
def train(self, X, y):
self.X_train = X # 训练数据集
self.y_train = y # 训练数据集对应的标签
def predict(self, X, k=1):
num_test = X.shape[0] # 测试数据集数量
num_train = self.X_train.shape[0] # 训练数据集数量
dists = np.zeros((num_test, num_train)) # 初始化距离矩阵
# 计算欧氏距离
d1 = -2 * np.dot(X, self.X_train.T)
d2 = np.sum(np.square(X), axis=1, keepdims=True)
d3 = np.sum(np.square(self.X_train), axis=1)
dist = np.sqrt(d1 + d2 + d3)
y_pred = np.zeros(num_test) # 初始化预测结果
for i in range(num_test):
# 找到距离最近的k个训练数据点的索引
dist_k_min = np.argsort(dist[i])[:k]
# 找到这k个训练数据点对应的标签
y_kclose = self.y_train[dist_k_min]
# 在k个标签中找到出现次数最多的标签,作为预测结果
y_pred[i] = np.argmax(np.bincount(y_kclose.tolist()))
return y_pred
```
KNN算法是一种比较简单的分类算法,主要步骤包括以下几点:
1. 计算测试数据集与训练数据集之间的距离(通常使用欧氏距离);
2. 找到距离最近的k个训练数据点,这k个数据点对应的标签就是预测结果;
3. 在k个标签中找到出现次数最多的标签,作为最终的预测结果。