bp神经网络聚类的例题，python做的例题

以下是一个使用BP神经网络进行聚类的Python实现的示例：

首先，我们需要导入必要的库，包括numpy和sklearn：

import numpy as np
from sklearn.cluster import KMeans

然后，我们定义一个简单的BP神经网络类：

class BPNN:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size

        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.bias1 = np.random.randn(self.hidden_size)

        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        self.bias2 = np.random.randn(self.output_size)

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def forward(self, x):
        z1 = np.dot(x, self.weights1) + self.bias1
        a1 = self.sigmoid(z1)
        z2 = np.dot(a1, self.weights2) + self.bias2
        a2 = self.sigmoid(z2)
        return a2

    def loss(self, x, y):
        y_hat = self.forward(x)
        return np.sum((y - y_hat) ** 2)

    def train(self, x, y, lr):
        z1 = np.dot(x, self.weights1) + self.bias1
        a1 = self.sigmoid(z1)
        z2 = np.dot(a1, self.weights2) + self.bias2
        a2 = self.sigmoid(z2)

        delta2 = (a2 - y) * a2 * (1 - a2)
        delta1 = np.dot(delta2, self.weights2.T) * a1 * (1 - a1)

        self.weights2 -= lr * np.dot(a1.T, delta2)
        self.bias2 -= lr * np.sum(delta2, axis=0)
        self.weights1 -= lr * np.dot(x.T, delta1)
        self.bias1 -= lr * np.sum(delta1, axis=0)

接下来，我们使用KMeans算法生成一些随机数据，并将其标准化：

data = np.random.randn(100, 2)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)

然后，我们使用KMeans算法将数据聚类成5个簇：

kmeans = KMeans(n_clusters=5)
kmeans.fit(data)
labels = kmeans.labels_

接下来，我们将每个数据点的标签转换为一个one-hot向量：

one_hot_labels = np.zeros((len(labels), 5))
for i, label in enumerate(labels):
    one_hot_labels[i, label] = 1

然后，我们将数据集分成训练集和测试集：

train_data = data[:80]
train_labels = one_hot_labels[:80]
test_data = data[80:]
test_labels = one_hot_labels[80:]

最后，我们训练一个BP神经网络并对测试数据进行预测：

bpnn = BPNN(input_size=2, hidden_size=10, output_size=5)
for i in range(1000):
    bpnn.train(train_data, train_labels, lr=0.1)

predictions = bpnn.forward(test_data)
predicted_labels = np.argmax(predictions, axis=1)

完整代码如下：

import numpy as np
from sklearn.cluster import KMeans


class BPNN:
    def __init__(self, input_size, hidden_size, output_size):
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.output_size = output_size

        self.weights1 = np.random.randn(self.input_size, self.hidden_size)
        self.bias1 = np.random.randn(self.hidden_size)

        self.weights2 = np.random.randn(self.hidden_size, self.output_size)
        self.bias2 = np.random.randn(self.output_size)

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def forward(self, x):
        z1 = np.dot(x, self.weights1) + self.bias1
        a1 = self.sigmoid(z1)
        z2 = np.dot(a1, self.weights2) + self.bias2
        a2 = self.sigmoid(z2)
        return a2

    def loss(self, x, y):
        y_hat = self.forward(x)
        return np.sum((y - y_hat) ** 2)

    def train(self, x, y, lr):
        z1 = np.dot(x, self.weights1) + self.bias1
        a1 = self.sigmoid(z1)
        z2 = np.dot(a1, self.weights2) + self.bias2
        a2 = self.sigmoid(z2)

        delta2 = (a2 - y) * a2 * (1 - a2)
        delta1 = np.dot(delta2, self.weights2.T) * a1 * (1 - a1)

        self.weights2 -= lr * np.dot(a1.T, delta2)
        self.bias2 -= lr * np.sum(delta2, axis=0)
        self.weights1 -= lr * np.dot(x.T, delta1)
        self.bias1 -= lr * np.sum(delta1, axis=0)


data = np.random.randn(100, 2)
data -= np.mean(data, axis=0)
data /= np.std(data, axis=0)

kmeans = KMeans(n_clusters=5)
kmeans.fit(data)
labels = kmeans.labels_

one_hot_labels = np.zeros((len(labels), 5))
for i, label in enumerate(labels):
    one_hot_labels[i, label] = 1

train_data = data[:80]
train_labels = one_hot_labels[:80]
test_data = data[80:]
test_labels = one_hot_labels[80:]

bpnn = BPNN(input_size=2, hidden_size=10, output_size=5)
for i in range(1000):
    bpnn.train(train_data, train_labels, lr=0.1)

predictions = bpnn.forward(test_data)
predicted_labels = np.argmax(predictions, axis=1)

print("True Labels: ", np.argmax(test_labels, axis=1))
print("Predicted Labels: ", predicted_labels)