np.random.rand(1, train_data.shape[1]).reshape((1, train_data.shape[1], 1)) 预测状态 = model.predict(new_interaction) print(f"系统的预测状态：

$SOL 🔺$🔻 $XRP 🔻€🔺


#BTC ~200M (10-15%)#AUSA24 #BitcoinOrg #BigDataAI 🔺 $BTC VS https://chatgpt.com/ activeness.social 🌍 x.com/i/grok-Microsoft-$doge-vs-$BTC 🔻🟦🟥 🔺Binance乌克兰 / binanceus #oprussia binancerussia / binancechina **金砖国家** opchina // 石油危机 // 制裁 /// LSTM
$XRP 🔻🌍🔺这是一个用 #Python 实现基本算法分析的程序示例，用于模拟石油和天然气领域的混乱管理系统。它使用神经网络来分析交互并评估系统的技术状态。

python

复制代码

import numpy as np import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM # 生成随机数据以模拟交互 def generate_synthetic_data(samples=1000, features=10): np.random.seed(42) return np.random.rand(samples, features), np.random.rand(samples, 1) # 准备数据 data, labels = generate_synthetic_data() train_data, test_data = data[:800], data[800:] train_labels, test_labels = labels[:800], labels[800:] # 建立神经网络以进行分析 model = Sequential([ LSTM(64, input_shape=(train_data.shape[1], 1), return_sequences=True), LSTM(32), Dense(16, activation='relu'), Dense(1, activation='sigmoid') ]) # 编译模型 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # 训练模型 train_data_reshaped = train_data.reshape((train_data.shape[0], train_data.shape[1], 1)) test_data_reshaped = test_data.reshape((test_data.shape[0], test_data.shape[1], 1)) model.fit(train_data_reshaped, train_labels, epochs=10, batch_size=32, validation_data=(test_data_reshaped, test_labels)) # 评估模型 loss,