#86 Logging Variables

通过使用一些高级Ruby概念,可以轻松地记录所有变量。本篇介绍了一种方法,可以在Rails应用程序中利用logger来输出当前作用域内的所有局部变量和实例变量。
Have you ever wanted to easily log all variables? Now you can by using some advanced Ruby concepts as shown in this episode.
# models/product.rb
logger.debug_variables(binding)

# config/initializers/logger_additions.rb
logger = ActiveRecord::Base.logger
def logger.debug_variables(bind)
vars = eval('local_variables + instance_variables', bind)
vars.each do |var|
debug "#{var} = #{eval(var, bind).inspect}"
end
end
user = mysql # pid-file = /var/run/mysqld/mysqld.pid # socket = /var/run/mysqld/mysqld.sock port = 8306 # datadir = /var/lib/mysql # If MySQL is running as a replication slave, this should be # changed. Ref https://dev.mysql.com/doc/refman/8.0/en/server-system-variables.html#sysvar_tmpdir # tmpdir = /tmp # # Instead of skip-networking the default is now to listen only on # localhost which is more compatible and is not less secure. #bind-address = 127.0.0.1 mysqlx-bind-address = 127.0.0.1 # # * Fine Tuning # key_buffer_size = 16M # max_allowed_packet = 64M # thread_stack = 256K # thread_cache_size = -1 # This replaces the startup script and checks MyISAM tables if needed # the first time they are touched myisam-recover-options = BACKUP # max_connections = 151 # table_open_cache = 4000 # # * Logging and Replication # # Both location gets rotated by the cronjob. # # Log all queries # Be aware that this log type is a performance killer. general_log_file = /var/log/mysql/query.log general_log = 0 # # Error log - should be very few entries. # log_error = /var/log/mysql/error.log # # Here you can see queries with especially long duration # slow_query_log = 1 # slow_query_log_file = /var/log/mysql/mysql-slow.log # long_query_time = 2 # log-queries-not-using-indexes # # The following can be used as easy to replay backup logs or for replication. # note: if you are setting up a replication slave, see README.Debian about # other settings you may need to change. server-id = 2 log_bin = /var/log/mysql/mysql-bin.log relay_bin = /var/log/mysql/relay-bin.log # binlog_expire_logs_seconds = 2592000 max_binlog_size = 100M # binlog_do_db = include_database_name # binlog_ignore_db = include_database_name transaction-isolation=READ-COMMITTED 从MySQL这么配置行吗
06-30
提供了基于BP(Back Propagation)神经网络结合PID(比例-积分-微分)控制策略的Simulink仿真模型。该模型旨在实现对杨艺所著论文《基于S函数的BP神经网络PID控制器及Simulink仿真》中的理论进行实践验证。在Matlab 2016b环境下开发,经过测试,确保能够正常运行,适合学习和研究神经网络在控制系统中的应用。 特点 集成BP神经网络:模型中集成了BP神经网络用于提升PID控制器的性能,使之能更好地适应复杂控制环境。 PID控制优化:利用神经网络的自学习能力,对传统的PID控制算法进行了智能调整,提高控制精度和稳定性。 S函数应用:展示了如何在Simulink中通过S函数嵌入MATLAB代码,实现BP神经网络的定制化逻辑。 兼容性说明:虽然开发于Matlab 2016b,但理论上兼容后续版本,可能会需要调整少量配置以适配不同版本的Matlab。 使用指南 环境要求:确保你的电脑上安装有Matlab 2016b或更高版本。 模型加载: 下载本仓库到本地。 在Matlab中打开.slx文件。 运行仿真: 调整模型参数前,请先熟悉各模块功能和输入输出设置。 运行整个模型,观察控制效果。 参数调整: 用户可以自由调节神经网络的层数、节点数以及PID控制器的参数,探索不同的控制性能。 学习和修改: 通过阅读模型中的注释和查阅相关文献,加深对BP神经网络与PID控制结合的理解。 如需修改S函数内的MATLAB代码,建议有一定的MATLAB编程基础。
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