忆阻器数字识别python
时间: 2023-08-27 13:06:52 浏览: 177
以下是使用Python实现忆阻器数字识别的一个例子:
1. 导入必要的库和模块
```python
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
```
2. 加载手写数字数据集
```python
digits = load_digits()
X = digits.data
y = digits.target
```
3. 对数据集进行预处理
```python
# 将特征值进行归一化
X -= X.min()
X /= X.max()
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
```
4. 定义忆阻器模型
```python
class Memristor(object):
def __init__(self, n_inputs, n_memristors, eta=0.01, n_iter=100):
self.n_inputs = n_inputs
self.n_memristors = n_memristors
self.eta = eta
self.n_iter = n_iter
self.W = np.random.randn(self.n_inputs, self.n_memristors)
def _sigmoid(self, x):
return 1 / (1 + np.exp(-x))
def _update_weights(self, x):
for i in range(self.n_memristors):
z_i = x.dot(self.W[:, i])
y_i = self._sigmoid(z_i)
delta_W = self.eta * np.outer(x, y_i - self.W[:, i])
self.W[:, i] += delta_W
def fit(self, X):
for i in range(self.n_iter):
for j in range(X.shape[0]):
self._update_weights(X[j])
def predict(self, X):
y_pred = []
for i in range(X.shape[0]):
z = X[i].dot(self.W)
y = self._sigmoid(z)
y_pred.append(np.argmax(y))
return y_pred
```
5. 训练模型并进行预测
```python
n_inputs = X_train.shape[1]
n_memristors = 50
memristor = Memristor(n_inputs, n_memristors)
memristor.fit(X_train)
y_pred = memristor.predict(X_test)
```
6. 计算准确率
```python
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
```
可以通过调整模型参数和添加其他特征进行优化。
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