贝叶斯代码
时间: 2023-07-02 08:23:19 浏览: 38
以下是一个简单的贝叶斯分类器的 Python 代码:
```python
import numpy as np
class NaiveBayes:
def fit(self, X, y):
n_samples, n_features = X.shape
self.classes = np.unique(y)
n_classes = len(self.classes)
# 初始化平均值、方差和先验概率
self.mean = np.zeros((n_classes, n_features), dtype=np.float64)
self.var = np.zeros((n_classes, n_features), dtype=np.float64)
self.prior = np.zeros(n_classes, dtype=np.float64)
for c in self.classes:
X_c = X[c==y]
self.mean[c,:] = X_c.mean(axis=0)
self.var[c,:] = X_c.var(axis=0)
self.prior[c] = X_c.shape[0] / float(n_samples)
def predict(self, X):
y_pred = [self._predict(x) for x in X]
return y_pred
def _predict(self, x):
posteriors = []
for idx, c in enumerate(self.classes):
prior = np.log(self.prior[idx])
posterior = np.sum(np.log(self._pdf(idx, x)))
posterior = prior + posterior
posteriors.append(posterior)
return self.classes[np.argmax(posteriors)]
def _pdf(self, class_idx, x):
mean = self.mean[class_idx]
var = self.var[class_idx]
numerator = np.exp(-((x-mean)**2) / (2 * var))
denominator = np.sqrt(2 * np.pi * var)
return numerator / denominator
```
可以使用 `fit` 方法来拟合训练数据,在 `predict` 方法中使用训练好的模型来进行预测。此外, `_pdf` 方法用于计算高斯分布的概率密度函数。