class ANFIS: def __init__(self, n_mf, n_outputs, problem=None):怎么引用ANFIS
时间: 2023-11-27 07:54:24 浏览: 27
如果ANFIS类已经在你的代码中定义好了,你可以通过实例化该类来使用它的方法和属性。例如:
```
anfis_model = ANFIS(n_mf=5, n_outputs=1)
```
这将创建一个名为`anfis_model`的ANFIS对象,其中`n_mf`参数设置为5,`n_outputs`参数设置为1。
然后,你可以调用该对象的方法和属性。例如,要训练该模型,你可以调用`train`方法:
```
anfis_model.train(X_train, y_train, epochs=100)
```
这将使用`X_train`和`y_train`训练模型,将训练轮数设置为100。
相关问题
用ANFIS代码预测三分类数据
ANFIS(自适应网络模糊推理系统)是一种基于模糊逻辑和神经网络的混合模型,可以用于回归和分类问题。下面是一个用于预测三分类数据的简单 ANFIS 代码:
```python
import numpy as np
import skfuzzy as fuzz
from sklearn.datasets import make_classification
from sklearn.metrics import accuracy_score
# 生成三分类数据
X, y = make_classification(n_samples=1000, n_features=4, n_classes=3)
# 数据归一化
X_norm = np.zeros_like(X)
for i in range(X.shape[1]):
X_norm[:, i] = fuzz.interp_membership(X[:, i], X[:, i], [np.min(X[:, i]), np.max(X[:, i])])
# 随机划分训练集和测试集
idx = np.arange(X.shape[0])
np.random.shuffle(idx)
train_idx = idx[:int(0.8 * X.shape[0])]
test_idx = idx[int(0.8 * X.shape[0]):]
X_train, y_train = X_norm[train_idx], y[train_idx]
X_test, y_test = X_norm[test_idx], y[test_idx]
# 定义 ANFIS 模型
class ANFIS:
def __init__(self, n_input, n_output, n_mf):
self.n_input = n_input
self.n_output = n_output
self.n_mf = n_mf
self.mf_params = np.zeros((n_input, n_mf))
self.output_params = np.zeros((n_mf + 1, n_output))
def membership_function(self, x, params):
mf = fuzz.trimf(x, params)
return mf
def rule_evaluation(self, x):
rule_out = np.zeros((x.shape[0], self.n_mf ** self.n_input))
for i in range(x.shape[0]):
rule = np.zeros((self.n_mf, self.n_input))
for j in range(self.n_input):
rule[:, j] = self.membership_function(x[i, j], self.mf_params[j, :])
rule = np.meshgrid(*rule)
rule = np.stack(rule, axis=-1)
rule = rule.reshape((-1, self.n_input))
rule_out[i, :] = np.min(rule, axis=1)
return rule_out
def output_evaluation(self, rule_out):
output = np.zeros((rule_out.shape[0], self.n_output))
for i in range(self.n_output):
output[:, i] = np.sum(rule_out * self.output_params[1:, i], axis=1) + self.output_params[0, i]
return output
def fit(self, x, y, lr=0.1, epochs=100):
for epoch in range(epochs):
rule_out = self.rule_evaluation(x)
output = self.output_evaluation(rule_out)
error = y - output
delta_output = error
delta_params = np.zeros_like(self.output_params)
for i in range(self.n_output):
delta_params[0, i] = np.sum(delta_output[:, i])
for j in range(self.n_mf + 1):
delta_params[j, i] = np.sum(delta_output[:, i] * rule_out[:, j])
self.output_params += lr * delta_params / x.shape[0]
delta_rule_out = np.zeros_like(rule_out)
for i in range(self.n_input):
for j in range(self.n_mf):
mf = self.membership_function(x[:, i], self.mf_params[i, :])
delta_rule_out += delta_output[:, :] * mf[:, j, np.newaxis]
self.mf_params[i, j] += lr * np.sum(delta_output[:, :] * rule_out[:, j, np.newaxis] *
(x[:, i, np.newaxis] - self.mf_params[i, j]), axis=0) / \
np.sum(delta_output[:, :] * rule_out[:, j, np.newaxis], axis=0)
if epoch % 10 == 0:
print('Epoch: %d, Train Loss: %.4f' % (epoch, np.mean(np.abs(error))))
def predict(self, x):
rule_out = self.rule_evaluation(x)
output = self.output_evaluation(rule_out)
return np.argmax(output, axis=1)
# 训练 ANFIS 模型
model = ANFIS(n_input=X_train.shape[1], n_output=3, n_mf=3)
model.fit(X_train, y_train, lr=0.1, epochs=100)
# 在测试集上进行预测
y_pred = model.predict(X_test)
print('Test Accuracy: %.4f' % accuracy_score(y_test, y_pred))
```
在这个例子中,我们首先生成了一个随机的三分类数据集,然后对数据进行归一化处理。接着,我们将数据随机划分为训练集和测试集,并定义了一个 ANFIS 类来构建模型。在训练过程中,我们使用了批量梯度下降算法来更新 ANFIS 模型的参数,直到达到指定的迭代次数。最后,我们在测试集上进行了预测,并计算了模型的准确率。
ANFIS代码
ANFIS(Adaptive Neuro Fuzzy Inference System)是一种结合神经网络和模糊逻辑的模型,可以用于分类、回归等问题。以下是一个简单的 ANFIS 的 Python 代码示例:
```python
import numpy as np
class ANFIS:
def __init__(self, n_inputs, n_rules, learning_rate=0.1):
self.n = n_inputs
self.m = n_rules
self.lr = learning_rate
self.rules = np.random.rand(n_rules, n_inputs + 1)
def forward(self, X):
alpha = np.zeros((len(X), self.m))
for i in range(self.m):
alpha[:, i] = np.exp(-np.sum((X - self.rules[i, :-1])**2, axis=1) / (2 * self.rules[i, -1]**2))
w = alpha / np.sum(alpha, axis=1, keepdims=True)
y = np.sum(w * X, axis=1)
return y
def backward(self, X, y_true):
y_pred = self.forward(X)
error = y_true - y_pred
alpha = np.zeros((len(X), self.m))
for i in range(self.m):
alpha[:, i] = np.exp(-np.sum((X - self.rules[i, :-1])**2, axis=1) / (2 * self.rules[i, -1]**2))
w = alpha / np.sum(alpha, axis=1, keepdims=True)
d_alpha = w * (1 - w) * np.sum((X - self.rules[:, :-1]) * (error.reshape(-1, 1)), axis=2)
d_sigma = w * (1 - w) * np.sum((X - self.rules[:, :-1])**2 * (error.reshape(-1, 1)**2), axis=2) / self.rules[:, -1].reshape(-1, 1)**3
d_rules = np.concatenate((d_alpha, d_sigma.reshape(-1, 1)), axis=1)
self.rules += self.lr * np.sum(d_rules, axis=0)
def train(self, X, y, epochs):
for i in range(epochs):
self.backward(X, y)
def predict(self, X):
return self.forward(X)
```
使用 ANFIS 模型时,需要指定输入特征的数量 `n_inputs` 和规则数量 `n_rules`,以及学习率 `learning_rate`。在训练过程中,可以使用 `train` 方法进行多轮迭代。最终可以使用 `predict` 方法对新的数据进行预测。具体使用时,可以参考以下示例:
```python
# 构造数据
X = np.random.rand(100, 2)
y = np.sin(X[:, 0]) + np.cos(X[:, 1])
# 创建 ANFIS 模型
anfis = ANFIS(n_inputs=2, n_rules=10, learning_rate=0.1)
# 训练模型
anfis.train(X, y, epochs=100)
# 预测新数据
X_new = np.random.rand(5, 2)
y_pred = anfis.predict(X_new)
```
当然,这只是一个简单的示例,实际使用中可能需要更多的调参和优化。