PHP数据库连接类db_test_class.php功能解析

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资源摘要信息:"db_test_class.rar_class_php db" 知识点一:PHP类的创建与使用 PHP类是面向对象编程(OOP)中的一个核心概念,它用于将数据和功能封装在一起。根据标题和描述中的信息,可以推断出db_test_class.php文件包含了用于连接数据库的PHP类。在PHP中,类通过关键字class来定义,而对象的创建需要使用new关键字。这个类可能包含用于初始化数据库连接的方法,例如构造函数、连接数据库的参数设置以及可能的错误处理方法。类中也可能包含了用于执行SQL语句的函数,如查询、插入、更新和删除数据等。 知识点二:数据库连接 在PHP中,数据库连接通常使用数据库抽象层(DBAL),如PDO(PHP Data Objects)或MySQLi扩展。根据文件名称和描述,这个PHP类很可能封装了用于创建数据库连接的代码。类可能使用了如PDO的构造函数来创建连接实例,包括数据库类型(如MySQL)、主机名、数据库名、用户名和密码等参数。此外,这个类可能还包含了一些实用的方法来执行数据库操作,比如断开连接、执行SQL查询、获取查询结果等。 知识点三:PHP面向对象编程特性 这个PHP类可能会展示面向对象编程的一些关键特性,包括继承、封装和多态。虽然具体类的实现细节未提供,但可以推测该类可能会利用继承来扩展更多的功能,封装隐藏了数据库连接的细节,从而只暴露给用户需要的操作接口。此外,多态性可能通过类的方法重载或接口实现来提供不同的数据库操作功能。 知识点四:资源文件的压缩与解压 提到的资源文件格式为rar,这是一种常见的压缩文件格式。压缩文件可以减小文件大小,方便传输和存储。在开发过程中,将项目文件压缩成rar格式可以方便地进行打包分发。要使用db_test_class.php文件,用户需要先将rar文件解压,然后才能访问和使用其中的PHP类。解压rar文件通常可以使用WinRAR、7-Zip等工具。 知识点五:PHP数据库编程实践 文件名中的"db"表明这是一个关于数据库编程的实践。数据库编程是指使用编程语言来编写能够与数据库系统交互的程序。PHP是一个流行的用于Web开发的脚本语言,它能够与多种数据库系统如MySQL、PostgreSQL、SQLite等进行交互。通过PHP内置的数据库扩展或DBAL,开发者可以编写用于管理数据库数据的代码,执行增删改查(CRUD)操作,以及进行更复杂的数据库操作。 知识点六:PHP文件的组织和管理 在Web开发中,良好的代码组织和管理对于项目的维护和扩展至关重要。db_test_class.php文件的创建和命名表明了开发者对于将数据库连接逻辑封装在一个独立的PHP类中的重视。这样的实践有助于将业务逻辑代码和数据库交互代码分离,遵循了软件开发的单一职责原则。此外,这也表明开发者可能在项目中使用了MVC(模型-视图-控制器)架构模式,其中db_test_class.php可能是模型层的一部分,负责处理数据的获取和存储。

请逐行注释下面的代码:class riscv_instr_base_test extends uvm_test; riscv_instr_gen_config cfg; string test_opts; string asm_file_name = "riscv_asm_test"; riscv_asm_program_gen asm_gen; string instr_seq; int start_idx; uvm_coreservice_t coreservice; uvm_factory factory; uvm_component_utils(riscv_instr_base_test) function new(string name="", uvm_component parent=null); super.new(name, parent); void'($value$plusargs("asm_file_name=%0s", asm_file_name)); void'($value$plusargs("start_idx=%0d", start_idx)); endfunction virtual function void build_phase(uvm_phase phase); super.build_phase(phase); coreservice = uvm_coreservice_t::get(); factory = coreservice.get_factory(); uvm_info(gfn, "Create configuration instance", UVM_LOW) cfg = riscv_instr_gen_config::type_id::create("cfg"); uvm_info(gfn, "Create configuration instance...done", UVM_LOW) uvm_config_db#(riscv_instr_gen_config)::set(null, "*", "instr_cfg", cfg); if(cfg.asm_test_suffix != "") asm_file_name = {asm_file_name, ".", cfg.asm_test_suffix}; // Override the default riscv instruction sequence if($value$plusargs("instr_seq=%0s", instr_seq)) begin factory.set_type_override_by_name("riscv_instr_sequence", instr_seq); end if (riscv_instr_pkg::support_debug_mode) begin factory.set_inst_override_by_name("riscv_asm_program_gen", "riscv_debug_rom_gen", {gfn, ".asm_gen.debug_rom"}); end endfunction function void report_phase(uvm_phase phase); uvm_report_server rs; int error_count; rs = uvm_report_server::get_server(); error_count = rs.get_severity_count(UVM_WARNING) + rs.get_severity_count(UVM_ERROR) + rs.get_severity_count(UVM_FATAL); if (error_count == 0) begin uvm_info("", "TEST PASSED", UVM_NONE); end else begin uvm_info("", "TEST FAILED", UVM_NONE); end uvm_info("", "TEST GENERATION DONE", UVM_NONE); super.report_phase(phase); endfunction virtual function void apply_directed_instr(); endfunction task run_phase(uvm_phase phase); int fd; for(int i = 0; i < cfg.num_of_tests; i++) begin string test_name; randomize_cfg(); riscv_instr::create_instr_list(cfg); riscv_csr_instr::create_csr_filter(cfg); asm_gen = riscv_asm_program_gen::type_id::create("asm_gen", , gfn); asm_gen.cfg = cfg; asm_gen.get_directed_instr_stream(); test_name = $sformatf("%0s_%0d.S", asm_file_name, i+start_idx); apply_directed_instr(); uvm_info(gfn, "All directed instruction is applied", UVM_LOW) asm_gen.gen_program(); asm_gen.gen_test_file(test_name); end endtask virtual function void randomize_cfg(); DV_CHECK_RANDOMIZE_FATAL(cfg); uvm_info(`gfn, $sformatf("riscv_instr_gen_config is randomized:\n%0s", cfg.sprint()), UVM_LOW) endfunction endclass

2023-05-24 上传

class vbase_test extends uvm_test; `uvm_component_utils(vbase_test) env m_env; vseqr m_vseqr; int unsigned simSeed; function new(string name, uvm_component parent); super.new(name, parent); endfunction : new extern function void build_phase (uvm_phase phase); extern function void connect_phase (uvm_phase phase); extern task reset_phase(uvm_phase phase); extern task reset_reg_model(); extern function void end_of_elaboration_phase(uvm_phase phase); extern function void start_of_simulation_phase(uvm_phase phase); extern task main_phase(uvm_phase phase); // report test result extern virtual function void report_phase(uvm_phase phase); endclass : vbase_test function void vbase_test::build_phase (uvm_phase phase); super.build_phase(phase); m_env = env::type_id::create(.name("m_env"), .parent(this)); // virtual sequencer m_vseqr = vseqr::type_id::create(.name("m_vseqr"), .parent(this)); uvm_config_db# (uvm_object_wrapper)::set(this,"m_vseqr.main_phase","default_sequence",vBaseSeq::type_id::get()); //uvm_config_db# (uvm_object_wrapper)::set(this,"m_vseqr.main_phase","default_sequence",vUniBaseSeq#()::type_id::get()); endfunction : build_phase function void vbase_test::connect_phase (uvm_phase phase); m_vseqr.p_rm = m_env.m_reg_model; m_vseqr.i2c_seqr = m_env.m_i2c_agent.m_seqr; endfunction : connect_phase task vbase_test::reset_phase(uvm_phase phase); //`uvm_info(get_type_name(), {"REGISTER MODEL:\n", m_reg_model.sprint()}, UVM_MEDIUM) reset_reg_model(); super.reset_phase(phase); endtask task vbase_test::reset_reg_model(); forever begin wait (tb_top.reset_n == 0); m_env.m_reg_model.reset(); `uvm_info(get_type_name(), "Reseting Complete", UVM_MEDIUM) wait (tb_top.reset_n == 1); end endtask function void vbase_test::end_of_elaboration_phase(uvm_phase phase); int handle; $system("rm -rf TEST_RUNNING"); simSeed = $get_initial_random_seed(); handle = $fopen($psprintf("TEST_RUNNING_%0d",simSeed),"w"); $fclose(handle); handle = $fopen("caseSeed","w"); $fwrite(handle,"%0d",simSeed); $fclose(handle); if($test$plusargs("uvm_tree")) uvm_top.print_topology(); endfunction : end_of_elaboration_phase function void vbase_test::start_of_simulation_phase(uvm_phase phase); `uvm_info(get_type_name(), {"start of simulation for ", get_full_name()}, UVM_HIGH); endfunction : start_of_simulation_phase task vbase_test::main_phase(uvm_phase phase); phase.phase_done.set_drain_time(this, 200ns); endtask : main_phase // report test result function void vbase_test::report_phase(uvm_phase phase); uvm_report_server server; int handle; int unsigned err_num; super.report_phase(phase); server = get_report_server(); err_num = (server.get_severity_count(UVM_ERROR) + server.get_severity_count(UVM_FATAL)); simSeed = $get_initial_random_seed(); $display("\n********************************************************************************************\n"); if (err_num != 0) begin $display("TEST CASE FAILED!!!"); handle = $fopen($psprintf("TEST_FAILED_%0d",simSeed),"w"); end else begin $display("TEST CASE PASSED!!!"); handle = $fopen($psprintf("TEST_PASSED_%0d",simSeed),"w"); end $fclose(handle); $display("\n********************************************************************************************\n"); $system("rm -rf TEST_RUNNING*"); endfunction `endif

2023-07-25 上传

LDAM损失函数pytorch代码如下:class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下:# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) # 读取数据集 dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform) dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test)# 导入数据 train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

2023-05-30 上传
2023-07-13 上传

import numpy as np # 定义神经网络模型 class NeuralNetwork: def __init__(self, input_size, hidden_size, output_size, learning_rate=0.1): # 初始化权重和偏置 self.weights1 = np.random.randn(input_size, hidden_size) self.bias1 = np.zeros((1, hidden_size)) self.weights2 = np.random.randn(hidden_size, output_size) self.bias2 = np.zeros((1, output_size)) # 学习率 self.learning_rate = learning_rate # 前向传播 def forward(self, x): # 第一层 z1 = np.dot(x, self.weights1) + self.bias1 a1 = np.maximum(0, z1) # ReLU激活函数 # 第二层 z2 = np.dot(a1, self.weights2) + self.bias2 return z2, a1 # 训练模型 def train(self, X, y, epochs): for i in range(epochs): # 前向传播,计算预测值和激活值 y_hat, _ = self.forward(X) # 计算损失函数 loss = np.mean((y_hat - y) ** 2) # 反向传播,更新参数 self.backward(X, y, y_hat) # 输出当前状态 print(f"Epoch {i+1}/{epochs}, Loss: {loss}") # 如果损失函数值小于指定值,退出训练 if loss < 0.001: print("训练完成") break # 反向传播 def backward(self, x, y, y_hat): # 计算损失函数的梯度 delta2 = y_hat - y # 计算第二层的参数梯度 dw2 = np.dot(self.a1.T, delta2) db2 = np.sum(delta2, axis=0, keepdims=True) # 计算第一层的参数梯度 delta1 = np.dot(delta2, self.weights2.T) * (self.a1 > 0) dw1 = np.dot(x.T, delta1) db1 = np.sum(delta1, axis=0, keepdims=True) # 更新权重和偏置 self.weights2 -= self.learning_rate * dw2 self.bias2 -= self.learning_rate * db2 self.weights1 -= self.learning_rate * dw1 self.bias1 -= self.learning_rate * db1 # 预测模型 def predict(self, x): y_hat, _ = self.forward(x) return y_hat[0][0] # 用户输入 input_value = input("请输入模型的输入值: ") x_test = np.array([[float(input_value)]]) # 初始化神经网络模型 model = NeuralNetwork(input_size=1, hidden_size=10, output_size=1, learning_rate=0.1) # 训练模型 X_train = np.array([[1], [1.1], [1.2], [2]]) y_train = np.array([[2.21], [2.431], [2.664], [8]]) model.train(X_train, y_train, epochs=1000) # 预测输出值 y_test = model.predict(x_test) print(f"输入值: {x_test[0][0]}, 输出值: {y_test}")

2023-03-21 上传
2023-06-03 上传