用RBM识别尾矿坝异常模式，给出他的代码

以下是使用Python实现的RBM模型，用于尾矿坝异常模式识别：

首先，需要导入必要的库：

import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

接着，读取尾矿坝数据集，将数据集划分为训练集和测试集：

data = pd.read_csv('tailings_dataset.csv')
X = data.drop(['Class'], axis=1).values
y = data['Class'].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

然后，定义RBM类：

class RBM:
    def __init__(self, n_visible, n_hidden, learning_rate=0.1, batch_size=10, n_epochs=100):
        self.n_visible = n_visible
        self.n_hidden = n_hidden
        self.learning_rate = learning_rate
        self.batch_size = batch_size
        self.n_epochs = n_epochs
        self.W = tf.Variable(tf.random.normal(shape=(n_visible, n_hidden), mean=0.0, stddev=0.01))
        self.v_bias = tf.Variable(tf.zeros(shape=(n_visible, 1)))
        self.h_bias = tf.Variable(tf.zeros(shape=(n_hidden, 1)))
        
    def sigmoid(self, x):
        return 1 / (1 + tf.exp(-x))
    
    def prob_h_given_v(self, v):
        return self.sigmoid(tf.matmul(v, self.W) + self.h_bias)
    
    def prob_v_given_h(self, h):
        return self.sigmoid(tf.matmul(h, tf.transpose(self.W)) + self.v_bias)
    
    def sample_prob(self, probs):
        return tf.nn.relu(tf.sign(probs - tf.random.uniform(shape=tf.shape(probs))))
    
    def gibbs_sampling(self, v):
        h_probs = self.prob_h_given_v(v)
        h_states = self.sample_prob(h_probs)
        v_probs = self.prob_v_given_h(h_states)
        v_states = self.sample_prob(v_probs)
        return [h_states, v_probs, v_states]
    
    def train(self, X):
        n_batches = int(np.ceil(len(X) / self.batch_size))
        X_tf = tf.convert_to_tensor(X, dtype=tf.float32)
        for epoch in range(self.n_epochs):
            for batch in range(n_batches):
                v0 = X_tf[batch * self.batch_size:(batch + 1) * self.batch_size]
                h0_probs = self.prob_h_given_v(v0)
                h0_states = self.sample_prob(h0_probs)
                chain_start = h0_states
                for step in range(1):
                    h_states, v_probs, v_states = self.gibbs_sampling(chain_start)
                    chain_start = v_states
                positive_grad = tf.matmul(tf.transpose(v0), h0_probs)
                negative_grad = tf.matmul(tf.transpose(v_states), h_states)
                W_grad = (positive_grad - negative_grad) / tf.cast(tf.shape(v0)[0], tf.float32)
                vb_grad = tf.reduce_mean(v0 - v_states, axis=0, keepdims=True)
                hb_grad = tf.reduce_mean(h0_probs - h_states, axis=0, keepdims=True)
                self.W.assign_add(self.learning_rate * W_grad)
                self.v_bias.assign_add(self.learning_rate * vb_grad)
                self.h_bias.assign_add(self.learning_rate * hb_grad)

最后，实例化RBM类并进行训练：

rbm = RBM(n_visible=X_train.shape[1], n_hidden=10, learning_rate=0.01, batch_size=20, n_epochs=1000)
rbm.train(X_train)

可以使用训练好的模型进行预测：

h_probs = rbm.prob_h_given_v(X_test)
h_states = rbm.sample_prob(h_probs)
v_probs = rbm.prob_v_given_h(h_states)
v_states = rbm.sample_prob(v_probs)
y_pred = [1 if np.any(x) else 0 for x in v_states]
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy:', accuracy)

完整代码如下所示：