Python手工设计贝叶斯分类器代码
时间: 2024-06-20 14:02:20 浏览: 8
在Python中,手动实现贝叶斯分类器需要理解基本的概率论和贝叶斯定理,并利用这些概念来构建一个简单的分类器。贝叶斯分类器的核心思想是根据先验概率和条件概率来预测给定样本的类别。下面是一个基础的手工实现朴素贝叶斯分类器的例子,使用的是高斯朴素贝叶斯模型:
```python
import numpy as np
class NaiveBayesClassifier:
def __init__(self):
self.classes = []
self.class_priors = {}
self.mean = {}
self.variance = {}
def train(self, X, y):
self.classes = np.unique(y)
for c in self.classes:
X_c = X[y == c]
self.class_priors[c] = len(X_c) / len(y)
self.mean[c] = X_c.mean(axis=0)
self.variance[c] = X_c.var(axis=0)
def predict(self, X_test):
predictions = []
for x in X_test:
posterior = {}
for c in self.classes:
likelihood = self.gaussian_likelihood(x, self.mean[c], self.variance[c])
prior = self.class_priors[c]
posterior[c] = likelihood * prior
max_prob_class = max(posterior, key=posterior.get)
predictions.append(max_prob_class)
return predictions
def gaussian_likelihood(self, x, mean, variance):
normalizer = (2 * np.pi * variance)**-0.5
return normalizer * np.exp(-0.5 * np.linalg.norm((x - mean)**2, axis=1) / variance)
# 使用示例
X_train = ... # 训练数据
y_train = ... # 训练标签
clf = NaiveBayesClassifier()
clf.train(X_train, y_train)
X_test = ... # 测试数据
predictions = clf.predict(X_test)
```
在这个例子中,我们首先定义了一个`NaiveBayesClassifier`类,然后实现了训练(`train`)和预测(`predict`)方法。训练方法计算每个类别的先验概率和特征的均值和方差。预测方法则使用高斯分布(朴素贝叶斯假设特征独立)计算每个类别的后验概率。