Minimally Adequate Teacher(MAT)算法相关代码
时间: 2023-12-19 09:06:16 浏览: 25
MAT算法是一个基于有限数据集的机器学习算法,其主要目标是在不知道真实标签的情况下,最小化错误率。以下是MAT算法的相关代码:
```python
import numpy as np
class MAT:
def __init__(self, num_features, num_classes):
self.num_features = num_features
self.num_classes = num_classes
self.weights = np.zeros((self.num_features, self.num_classes))
def train(self, X, y):
for i in range(X.shape[0]):
x = X[i]
label = y[i]
prediction = self.predict(x)
if prediction != label:
self.weights[x][prediction] -= 1
self.weights[x][label] += 1
def predict(self, x):
scores = np.dot(x, self.weights)
return np.argmax(scores)
```
在MAT算法中,我们使用一个权重矩阵来存储每个特征和类别的权重。在训练过程中,我们遍历每个样本并计算其预测值。如果预测错误,则将相关权重进行相应的调整。在预测过程中,我们计算每个类别的得分并返回具有最高得分的类别。