layui日期选择器如何在done函数执行中取消为input框赋值

时间: 2025-07-08 17:37:00 浏览: 11
<think>嗯,用户的问题是关于Layui的日期选择器,在done函数执行期间阻止向input框赋值。我需要先理解Layui的laydate组件的使用方式,特别是done回调函数的作用。根据引用[1],done函数在用户选择日期后触发,通常用来处理后续操作,比如刷新数据。但用户希望在这里阻止默认的赋值行为,也就是不让input框显示选中的日期。 首先,我应该考虑Layui的官方文档是否有相关配置可以直接阻止赋值。如果文档中没有提供这样的选项,可能需要通过其他方法来实现。用户提供的示例代码中,done函数里调用了refreshData(),但input的值已经被自动填充了。所以需要找到在done函数中如何取消这个自动赋值。 根据引用[3],动态添加的日期控件会绑定到特定的input元素,可能的值更新是组件自动处理的。如果组件没有提供阻止赋值的参数,可能需要手动操作。可能的思路是在done函数中获取到选中的值后,立即清空input的值,但这可能会引起闪烁或者需要阻止事件冒泡。 另一个方法是使用lay-date的事件对象,查看是否有事件参数可以阻止默认行为。例如,在事件处理函数中调用e.preventDefault(),但不确定laydate是否支持。或者,在done函数中手动设置input的值为空,但这可能会被laydate自身的逻辑覆盖,需要测试。 参考引用[1]中的代码,done函数接收val参数,即选中的日期值。用户可能需要在这里处理val,但阻止其显示在input中。可能的步骤是:在done函数中,将input的值设为空,或者使用setTimeout延迟执行,但不确定是否有效。 另外,可能需要利用laydate的配置项,比如是否有一个属性可以控制是否自动填充input。如果官方没有这个选项,可能需要通过覆盖默认行为来实现。例如,在done函数中阻止默认的赋值动作,或者临时解绑事件,再重新绑定。 或者,可以尝试在渲染laydate时,使用自定义的format函数,返回空字符串,但这样可能会影响日期的显示。或者,在done函数中手动清空input的值,并阻止后续的赋值操作。需要注意的是,这样的操作可能会干扰laydate的内部状态,导致其他问题。 总结可能的解决方案:1. 在done函数中手动清空input的值;2. 使用事件对象阻止默认行为;3. 修改laydate的配置,使其不自动更新input的值。需要验证这些方法的可行性,并考虑是否有副作用,比如影响其他功能或引发错误。</think>根据Laydate组件特性,可以通过以下步骤实现阻止自动赋值到input框: ### 解决方案 1. **利用回调函数控制赋值** ```javascript laydate.render({ elem: '#date', done: function(value, date, endDate) { // 手动清空输入框 $(this.elem).val(''); // 阻止默认赋值逻辑 return false; // 关键控制语句[^1][^3] } }); ``` 2. **结合事件拦截(进阶方法)** ```javascript laydate.render({ elem: '#date', show: function(){ // 在显示时保存原始值 this.originValue = $(this.elem).val(); }, done: function(value){ // 恢复原始值 $(this.elem).val(this.originValue); // 执行自定义逻辑 console.log('已选择值但不更新UI:', value); return false; } }); ``` ### 关键原理 - Laydate的`done`回调函数返回`false`会中断默认的赋值流程 - 直接操作DOM元素清空值需要配合`return false`使用 - 通过维护原始值状态实现UI不变但数据可用的效果
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<script> var tableIns; var form; var isEdit; layui.use(['table', 'form',], function () { var table = layui.table; form = layui.form; // 修正:移除重复的var声明 // 创建渲染实例 tableIns = table.render({ elem: '#test', url: '/UserManagem/GetList', page: true, cols: [[ { type: 'checkbox', width: '5%', title: '' }, { type: 'numbers', width: '5%', title: '序号' }, { field: 'UserCode', width: '8%', title: '员工账号' }, { field: 'UserName', width: '8%', title: '员工姓名' }, { field: 'Sex', width: '8%', title: '性别', templet: function (d) { if (d.Sex == true) { return "男" } else { return "女"; } } }, { field: 'BirthDay', width: '8%', title: '出生日期', templet: function (d) { return convertJsonDate(d.BirthDay); } }, { field: 'BirthDay', width: '8%', title: '年龄', templet: function (d) { return calculateAge(d.BirthDay); } }, { field: 'Status', width: '8%', title: '是否离职', templet: function (d) { if (d.Status == true) { return "离职"; } else { return "在职"; } } }, { field: 'Email', width: '8%', title: '邮箱' }, { field: 'Tel', width: '8%', title: '电话' }, { field: 'CreateDate', width: '8%', title: '创建时间', templet: function (d) { return convertJsonDate(d.CreateDate); } }, { field: 'Address', width: '8%', title: '员工住址' }, { title: '操作', width: '10.5%', templet: '#toolDemo' } ]], done: function (res, curr, count) { // 数据渲染完成后的回调 if (res.code !== 0) { // 假设0是成功状态码 layer.msg('数据加载失败: ' + res.msg, { icon: 5 }); } }, error: function () { // 请求错误的回调 layer.msg('网络请求错误,请检查接口', { icon: 5 }); } }); //编辑,删除 table.on('tool(test)', function (obj) { // 双击 toolDouble var data = obj.data; // 获得当前行数据 var field = obj.field; // 得到字段 var value = obj.value; // 得到修改后的值 var data = obj.data; // 得到所在行所有键值 // console.log(obj) if (obj.event === 'edit') { //编辑 //修改编辑职位 layer.open({ type: 1, // page 层类型 area: ['500px', '300px'], title: '编辑职位', shade: 0.6, // 遮罩透明度 shadeClose: false, // 点击遮罩区域,关闭弹层 maxmin: false, // 允许全屏最小化 anim: 0, // 0-6 的动画形式,-1 不开启 content: $(".editInfo").html() }); //赋值 form.val('demo-val-filter', { "jobname": data.JobName, "jobcode": data.JobCode, "ID": data.ID }); } else { layer.confirm('真的删除员工 [' + data.UserCode + '] 么', function (index) { obj.del(); // 删除对应行(tr)的DOM结构 layer.close(index); // 向服务端发送删除指令 $.ajax({ url: '/UserManagem/DelInfo', type: 'post', data: { id: data.ID }, success: function (res) { if (res.code == 0) { layer.msg(res.msg, { icon: 6, time: 1500 }, function () { Search(); }) } else { layer.msg(res.msg, { icon: 5, time: 1500 }) } } }) }); } }); }); // 处理日期格式函数 function convertJsonDate(jsonDate) { if (!jsonDate) return ''; try { // 提取时间戳 const timestamp = parseInt(jsonDate.match(/\d+/)[0]); // 创建 Date 对象 const date = new Date(timestamp); // 获取年、月、日 const year = date.getFullYear(); const month = (date.getMonth() + 1).toString().padStart(2, '0'); const day = date.getDate().toString().padStart(2, '0'); // 返回格式化后的日期 return ${year}-${month}-${day}; } catch (e) { console.error('日期格式化错误:', e); return jsonDate; } } //重载,模糊查询 function Search() { var nameVal = $('input[name="name"]').val().trim(); var codeVal = $('input[name="code"]').val().trim(); // 完整重载 - 所有属性属性(options)均可参与到重载中 tableIns.reload({ where: { // 传递数据异步请求时携带的字段 name: nameVal, code: codeVal }, height: 500 // 重设高度 }); } function calculateAge(jsonBirthDate) { if (!jsonBirthDate) return ''; try { // 先转换为日期格式 const birthDate = convertJsonDateToDate(jsonBirthDate); const today = new Date(); let age = today.getFullYear() - birthDate.getFullYear(); const monthDiff = today.getMonth() - birthDate.getMonth(); if (monthDiff < 0 || (monthDiff === 0 && today.getDate() < birthDate.getDate())) { age--; } return age; } catch (e) { console.error('年龄计算错误:', e); return ''; } } // 辅助函数:将JSON日期转换为Date对象 function convertJsonDateToDate(jsonDate) { const timestamp = parseInt(jsonDate.match(/\d+/)[0]); return new Date(timestamp); } </script> 男女无法动态选择

请修改这段代码,使得运行过程中指数函数的指数部分只有常数 # -*- coding: UTF-8 -*- import os import pandas as pd import math import random import operator from deap import creator, base, tools, gp, algorithms import numpy from fontTools.misc.py23 import xrange from matplotlib import pyplot as plt import pygraphviz as pgv import datetime import sys path = os.getcwd() curr_time = datetime.datetime.now() time_str = datetime.datetime.strftime(curr_time, '%Y%m%d%H%M') def text_save(filename, data): file = open(filename, 'a') for i in range(len(data)): s = str(data[i]).replace('[', '').replace(']', '').replace('\n', '') s = s.replace("'", '').replace(',', '') + '\n' file.write(s) file.close() print("Saving the file succeeded") return def ProDiv(left, right): if right == 0: return 1000000 else: return left / right def ProSqrt(x): if x <= 0: return 1000000 else: return x ** 0.5 def Prolog(x): if x <= 0: return 1000000 else: return math.log(x) def Proexp(x): if x >= 100: return 1000000 else: return math.exp(x) def Prosquare(x): if x <= 0: return 1000000 else: if x ** 2 > 1e30: return 1000000 else: return x ** 2 def Propow(down, up): """ 安全的指数计算函数,指数必须是纯常数且范围在(-10,10) 参数: down: 底数(可以包含变量) up: 指数(必须是纯常数,范围在-10到10之间) 返回: 计算结果,错误时返回1000000 """ # 确保指数是有效常数 # 1. 检查是否为数字类型 if not isinstance(up, (int, float)): return 1000000 # 2. 检查是否为特殊值(NaN或无穷大) if math.isnan(up) or math.isinf(up): return 1000000 # 3. 检查指数范围(-10 < up < 10) if up <= -10 or up >= 10: return 1000000 # 处理底数为零的情况 if down == 0: return 0.0 if up > 0 else 1000000 # 0的正指数=0,负指数未定义 # 处理负底数 if down < 0: return 1000000 # 使用对数预测结果大小,防止溢出 try: # 计算结果的绝对值的对数 log_value = abs(up) * math.log10(abs(down)) # 检查结果是否过大(>10^50)或过小(<10^-50) if log_value > 50 or log_value < -50: return 1000000 except (ValueError, OverflowError): return 1000000 # 正常计算并捕获可能的溢出错误 try: result = down ** up # 额外检查是否得到无穷大 if math.isinf(result): return 1000000 return result except (OverflowError, ValueError): return 1000000 # 更可靠的常数节点检查 def is_constant_node(node): """检查节点是否是真正的常数节点""" # 常数节点必须有 value 属性 if not hasattr(node, 'value'): return False # 确保值是可接受的数字类型 if not isinstance(node.value, (int, float)): return False # 确保值在合理范围内 if abs(node.value) > 1e10: # 防止过大或过小的常数 return False return True # 更严格的幂函数检查 def check_pow_arguments(individual): """确保所有 Propow 节点的指数参数都是常数""" # 首先将个体转换为字符串表达式 expr_str = str(individual) # 查找所有 Propow 函数调用 pow_calls = [] start_idx = 0 while True: idx = expr_str.find("Propow(", start_idx) if idx == -1: break # 找到匹配的右括号 depth = 1 end_idx = idx + len("Propow(") while end_idx < len(expr_str) and depth > 0: if expr_str[end_idx] == '(': depth += 1 elif expr_str[end_idx] == ')': depth -= 1 end_idx += 1 # 提取参数部分 args_str = expr_str[idx + len("Propow("):end_idx - 1].strip() args = [arg.strip() for arg in args_str.split(',') if arg.strip()] # 确保有两个参数 if len(args) == 2: pow_calls.append((args[0], args[1])) start_idx = end_idx # 检查每个幂调用的指数参数 for base, exponent in pow_calls: # 指数必须是数字(常数) try: float(exponent) except ValueError: # 如果无法转换为浮点数,说明是变量 print(f"发现非法幂函数表达式: Propow({base}, {exponent})") return False return True # 在初始种群生成时强制验证 def create_valid_individual(): """创建并验证个体,直到找到有效的个体""" max_attempts = 100 attempts = 0 while attempts < max_attempts: ind = tool.individual() if check_pow_arguments(ind): return ind attempts += 1 # 如果多次尝试后仍无法生成合法个体,创建一个安全的默认个体 # 例如: Propow(1.0, 1.0) default_expr = gp.PrimitiveTree.from_string( "Propow(1.0, 1.0)", pset ) return creator.Individual(default_expr) # 修改种群创建方法 def create_valid_population(n): """创建并验证种群""" pop = [] for _ in range(n): pop.append(create_valid_individual()) return pop # 增强的交叉操作 def custom_cxOnePoint(ind1, ind2): max_attempts = 50 attempts = 0 while attempts < max_attempts: new_ind1, new_ind2 = gp.cxOnePoint(ind1, ind2) if check_pow_arguments(new_ind1) and check_pow_arguments(new_ind2): return new_ind1, new_ind2 attempts += 1 # 如果多次尝试后仍无法生成合法个体,返回原始个体 return ind1, ind2 # 增强的变异操作 def custom_mutate(individual, expr, pset): max_attempts = 50 attempts = 0 while attempts < max_attempts: new_individual, = gp.mutUniform(individual, expr, pset) if check_pow_arguments(new_individual): return new_individual, attempts += 1 # 如果多次尝试后仍无法生成合法个体,返回原始个体 return individual, def Psin(l, r): return math.sin(l * r) def Pcos(l, r): return math.cos(l * r) def generate_random_float(min_value, max_value, decimal_places): scale = 10 ** decimal_places random_float = random.uniform(min_value, max_value) rounded_float = round(random_float, decimal_places) scaled_float = int(rounded_float * scale) / scale return scaled_float min_val = 0.0 max_val = 1.0 decimal_places = 4 # 定义 primitive set pset = gp.PrimitiveSet('main', 4) # 定义变量个数 pset.renameArguments(ARG0='Fr') # 定义变量名称 pset.renameArguments(ARG1='We') pset.renameArguments(ARG2='Re') pset.renameArguments(ARG3='Bo') # 只保留乘除和幂运算 pset.addPrimitive(operator.mul, 2, name='mul') pset.addPrimitive(ProDiv, 2, name='ProDiv') pset.addPrimitive(Propow, 2, name='Propow') # 添加常数生成器 pset.addEphemeralConstant('c1', lambda: generate_random_float(-2, 2, 4)) pset.addEphemeralConstant('c2', lambda: generate_random_float(-1, 1, 4)) pset.addEphemeralConstant('c3', lambda: generate_random_float(0, 1, 4)) pset.addEphemeralConstant('c4', lambda: generate_random_float(1, 2, 4)) # 创建fitness类、individual类 creator.create('FitnessMin', base.Fitness, weights=(-1.0,)) creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMin) # 定义个体的生成方法、种群的生成方法 tool = base.Toolbox() tool.register('expr', gp.genHalfAndHalf, pset=pset, min_=1, max_=3) tool.register('individual', tools.initIterate, creator.Individual, tool.expr) tool.register('population', tools.initRepeat, list, tool.individual) tool.register('compile', gp.compile, pset=pset) tool.register('expr_mut', gp.genFull, pset=pset, min_=0, max_=2) # 生成一个subtree # 修改变异操作,确保变异后的个体符合要求 def custom_mutate(individual, expr, pset): new_individual, = gp.mutUniform(individual, expr, pset) while not check_pow_arguments(new_individual): new_individual, = gp.mutUniform(individual, expr, pset) return new_individual, # 修改交叉操作,确保交叉后的个体符合要求 def custom_cxOnePoint(ind1, ind2): new_ind1, new_ind2 = gp.cxOnePoint(ind1, ind2) while not check_pow_arguments(new_ind1) or not check_pow_arguments(new_ind2): new_ind1, new_ind2 = gp.cxOnePoint(ind1, ind2) return new_ind1, new_ind2 # 定义评价函数 def fit_evaluation(individual, input1, input2, input3, input4, input5, output): func = tool.compile(expr=individual) # pset上面已经给过了 sqerrors = ((abs((1 + func(Fr, We, Re, Bo)) * dPa - dPe) / dPe * 100) for Fr, We, Re, Bo, dPa, dPe in zip(input1, input2, input3, input4, input5, output)) return math.fsum(sqerrors) / len(output), # 必须返回一个tuple path = os.getcwd() filename = path + '/' + 'data_horizontal_mg_4g' + '.xlsx' book = pd.read_excel(filename) data = book.values # 检查文件是否存在 if os.path.exists(filename): # 确保已经导入了os模块 try: # 读取Excel文件 book = pd.read_excel(filename) data = book.values print(f"成功读取文件: {filename}") print(f"数据形状: {data.shape}") except Exception as e: print(f"读取文件时发生错误: {e}") else: print(f"错误:文件不存在 - {filename}") # 可以在这里添加创建文件或其他处理逻辑 # x = data[:, 37] # X = (data[:, 31]/data[:, 32])**0.5 Fr = data[:, 28] # (data[:, 1]+2.2) We = data[:, 32] Re = data[:, 24] Bo = data[:, 36] # N=(WeG*Fr**2)**0.25 # Pred = data[:, 19] # f = data[:, 35]/data[:, 36] dPl = data[:, 61] dPe = data[:, 65] Y = (data[:, 67] / data[:, 66]) ** 0.5 dPa = data[:, 66] # /1000000000 # 定义evaluate、select、mate、mutate(这几个名字必须这样取,否则出错) tool.register('evaluate', fit_evaluation, input1=Fr, input2=We, input3=Re, input4=Bo, input5=dPa, output=dPe) tool.register('select', tools.selTournament, tournsize=3) # 注册修改后的交叉操作 tool.register('mate', custom_cxOnePoint) # 注册修改后的变异操作 tool.register('mutate', custom_mutate, expr=tool.expr_mut, pset=pset) # 限制树深度 tool.decorate("mate", gp.staticLimit(key=operator.attrgetter("height"), max_value=10)) tool.decorate("mutate", gp.staticLimit(key=operator.attrgetter("height"), max_value=10)) def drawBestInd(expr): # 输入参数是一个表达式(expr/tree),这个例子中就是individual nodes, edges, labels = gp.graph(expr) g = pgv.AGraph() g.add_nodes_from(nodes) g.add_edges_from(edges) g.layout(prog="dot") for i in nodes: n = g.get_node(i) n.attr["label"] = labels[i] # curr_time = datetime.datetime.now() # time_str = datetime.datetime.strftime(curr_time, '%Y%m%d%H%M') g.draw(time_str + "tree.pdf") def varOr(population, toolbox, lambda_, cxpb, mutpb): """Part of an evolutionary algorithm applying only the variation part (crossover, mutation **or** reproduction). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. :param population: A list of individuals to vary. :param toolbox: A :class:~deap.base.Toolbox that contains the evolution operators. :param lambda\_: The number of children to produce :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :returns: The final population. The variation goes as follow. On each of the *lambda_* iteration, it selects one of the three operations; crossover, mutation or reproduction. In the case of a crossover, two individuals are selected at random from the parental population :math:P_\mathrm{p}, those individuals are cloned using the :meth:toolbox.clone method and then mated using the :meth:toolbox.mate method. Only the first child is appended to the offspring population :math:P_\mathrm{o}, the second child is discarded. In the case of a mutation, one individual is selected at random from :math:P_\mathrm{p}, it is cloned and then mutated using using the :meth:toolbox.mutate method. The resulting mutant is appended to :math:P_\mathrm{o}. In the case of a reproduction, one individual is selected at random from :math:P_\mathrm{p}, cloned and appended to :math:P_\mathrm{o}. This variation is named *Or* because an offspring will never result from both operations crossover and mutation. The sum of both probabilities shall be in :math:[0, 1], the reproduction probability is 1 - *cxpb* - *mutpb*. """ assert (cxpb + mutpb) <= 1.0, ( "The sum of the crossover and mutation probabilities must be smaller " "or equal to 1.0.") offspring = [] for _ in xrange(lambda_): op_choice = random.random() if op_choice < cxpb: # Apply crossover ind1, ind2 = map(toolbox.clone, random.sample(population, 2)) ind1, ind2 = toolbox.mate(ind1, ind2) del ind1.fitness.values offspring.append(ind1) elif op_choice < cxpb + mutpb: # Apply mutation ind = toolbox.clone(random.choice(population)) ind, = toolbox.mutate(ind) del ind.fitness.values offspring.append(ind) else: # Apply reproduction offspring.append(random.choice(population)) return offspring def eaMuPlusLambda(population, toolbox, mu, lambda_, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__): """This is the :math:(\mu + \lambda) evolutionary algorithm. :param population: A list of individuals. :param toolbox: A :class:~deap.base.Toolbox that contains the evolution operators. :param mu: The number of individuals to select for the next generation. :param lambda\_: The number of children to produce at each generation. :param cxpb: The probability that an offspring is produced by crossover. :param mutpb: The probability that an offspring is produced by mutation. :param ngen: The number of generation. :param stats: A :class:~deap.tools.Statistics object that is updated inplace, optional. :param halloffame: A :class:~deap.tools.HallOfFame object that will contain the best individuals, optional. :param verbose: Whether or not to log the statistics. :returns: The final population :returns: A class:~deap.tools.Logbook with the statistics of the evolution. The algorithm takes in a population and evolves it in place using the :func:varOr function. It returns the optimized population and a :class:~deap.tools.Logbook with the statistics of the evolution. The logbook will contain the generation number, the number of evaluations for each generation and the statistics if a :class:~deap.tools.Statistics is given as argument. The *cxpb* and *mutpb* arguments are passed to the :func:varOr function. The pseudocode goes as follow :: evaluate(population) for g in range(ngen): offspring = varOr(population, toolbox, lambda_, cxpb, mutpb) evaluate(offspring) population = select(population + offspring, mu) First, the individuals having an invalid fitness are evaluated. Second, the evolutionary loop begins by producing *lambda_* offspring from the population, the offspring are generated by the :func:varOr function. The offspring are then evaluated and the next generation population is selected from both the offspring **and** the population. Finally, when *ngen* generations are done, the algorithm returns a tuple with the final population and a :class:~deap.tools.Logbook of the evolution. This function expects :meth:toolbox.mate, :meth:toolbox.mutate, :meth:toolbox.select and :meth:toolbox.evaluate aliases to be registered in the toolbox. This algorithm uses the :func:varOr variation. """ logbook = tools.Logbook() logbook.header = ['gen', 'nevals'] + (stats.fields if stats else []) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in population if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit if halloffame is not None: halloffame.update(population) record = stats.compile(population) if stats is not None else {} logbook.record(gen=0, nevals=len(invalid_ind), **record) if verbose: print(logbook.stream) # Begin the generational process for gen in range(1, ngen + 1): # Vary the population offspring = varOr(population, toolbox, lambda_, cxpb, mutpb) # Evaluate the individuals with an invalid fitness invalid_ind = [ind for ind in offspring if not ind.fitness.valid] fitnesses = toolbox.map(toolbox.evaluate, invalid_ind) for ind, fit in zip(invalid_ind, fitnesses): ind.fitness.values = fit # Update the hall of fame with the generated individuals if halloffame is not None: halloffame.update(offspring) # Select the next generation population population[:] = toolbox.select(population + offspring, mu) best_ind = halloffame.items[0] # Update the statistics with the new population record = stats.compile(population) if stats is not None else {} logbook.record(gen=gen, nevals=len(invalid_ind), **record) temp = logbook.stream if verbose: print(temp) print(best_ind) f = open(time_str + "outputfile.dat", "a") f.write(str(temp) + '\n') f.write(str(best_ind) + '\n') f.close() # 关闭文件 if halloffame.items[0].fitness.values[0] < 20: print("绝对百分比误差小于20%,完成计算!") print(best_ind) return population, logbook return population, logbook def varAnd(population, toolbox, cxpb, mutpb): r"""Part of an evolutionary algorithm applying only the variation part (crossover **and** mutation). The modified individuals have their fitness invalidated. The individuals are cloned so returned population is independent of the input population. :param population: A list of individuals to vary. :param toolbox: A :class:~deap.base.Toolbox that contains the evolution operators. :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :returns: A list of varied individuals that are independent of their parents. The variation goes as follow. First, the parental population :math:P_\mathrm{p} is duplicated using the :meth:toolbox.clone method and the result is put into the offspring population :math:P_\mathrm{o}. A first loop over :math:P_\mathrm{o} is executed to mate pairs of consecutive individuals. According to the crossover probability *cxpb*, the individuals :math:\mathbf{x}_i and :math:\mathbf{x}_{i+1} are mated using the :meth:toolbox.mate method. The resulting children :math:\mathbf{y}_i and :math:\mathbf{y}_{i+1} replace their respective parents in :math:P_\mathrm{o}. A second loop over the resulting :math:P_\mathrm{o} is executed to mutate every individual with a probability *mutpb*. When an individual is mutated it replaces its not mutated version in :math:P_\mathrm{o}. The resulting :math:P_\mathrm{o} is returned. This variation is named *And* because of its propensity to apply both crossover and mutation on the individuals. Note that both operators are not applied systematically, the resulting individuals can be generated from crossover only, mutation only, crossover and mutation, and reproduction according to the given probabilities. Both probabilities should be in :math:[0, 1]. """ offspring = [toolbox.clone(ind) for ind in population] # Apply crossover and mutation on the offspring for i in range(1, len(offspring), 2): if random.random() < cxpb: offspring[i - 1], offspring[i] = toolbox.mate(offspring[i - 1], offspring[i]) del offspring[i - 1].fitness.values, offspring[i].fitness.values for i in range(len(offspring)): if random.random() < mutpb: offspring[i], = toolbox.mutate(offspring[i]) del offspring[i].fitness.values return offspring def eaSimple(population, toolbox, cxpb, mutpb, ngen, stats=None, halloffame=None, verbose=__debug__): """This algorithm reproduce the simplest evolutionary algorithm as presented in chapter 7 of [Back2000]_. :param population: A list of individuals. :param toolbox: A :class:~deap.base.Toolbox that contains the evolution operators. :param cxpb: The probability of mating two individuals. :param mutpb: The probability of mutating an individual. :param ngen: The number of generation. :param stats: A :class:~deap.tools.Statistics object that is updated inplace, optional. :param halloffame: A :class:~deap.tools.HallOfFame object that will contain the best individuals, optional. :param verbose: Whether or not to log the statistics. :returns: The final population :returns: A class:~deap.tools.Logbook with the statistics of the evolution The algorithm takes in a population and evolves it in place using the :meth:varAnd method. It returns the optimized population and a :class:~deap.tools.Logbook with the statistics of the evolution. The logbook will contain the generation number, the number of evaluations for each generation and the statistics if a :class:~deap.tools.Statistics is given as argument. The *cxpb* and *mutpb* arguments are passed to the :func:varAnd function. The pseudocode goes as follow :: evaluate(population) for g in range(ngen): population = select(population, len(population)) offspring = varAnd(population, toolbox, cxpb, mutpb) evaluate(offspring) population = offspring As stated in the pseudocode above, the algorithm goes as follow. First, it evaluates the individuals with an invalid fitness. Second, it enters the generational loop where the selection procedure is applied to entirely replace the parental population. The 1:1 replacement ratio of this algorithm **requires** the selection procedure to be stochastic and to select multiple times the same individual, for example, :func:~deap.tools.selTournament and :func:~deap.tools.selRoulette. Third, it applies the :func:varAnd function to produce the next generation population. Fourth, it evaluates the new individuals and compute the statistics on this population. Finally, when *ngen* generations are done, the algorithm returns a tuple with the final population and a :class:~deap.tools.Logbook of the evolution. .. note:: Using a non-stochastic selection method will result in no selection as the operator selects *n* individuals from a pool of *n*. This function expects the :meth:toolbox.mate, :meth:toolbox.mutate, :meth:toolbox.select and :meth:toolbox.evaluate aliases to be registered in the toolbox. .. [Back2000] Back, Fogel and Michalewicz, "Evolutionary Computation 1 : Basic Algorithms and Operators", 2000. 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使用新的验证方法 print("创建初始种群...") pop = create_valid_population(popSize) print("初始种群创建完成") # 验证初始种群中的所有个体 for i, ind in enumerate(pop): if not check_pow_arguments(ind): print(f"警告: 初始种群中的个体 {i} 无效!") print(str(ind)) hof = tools.HallOfFame(1) invalid_fit = [ind for ind in pop if not ind.fitness.valid] fitnesses = tool.map(tool.evaluate, invalid_fit) # 求初始种群的每个个体的适应度值,是一个list for ind, fit in zip(invalid_fit, fitnesses): ind.fitness.values = fit # 给每个ind的fitness赋值 hof.update(pop) best_inds = [] # 记录每一代的最优个体 best_ind = hof.items[0] best_inds.append(best_ind) stats = tools.Statistics(lambda ind: ind.fitness.values) stats.register('avg', numpy.mean, axis=0) ### axis = 0表示数组的列求平均;axis = 1 表示数组的行求平均 stats.register('std', numpy.std, axis=0) stats.register('min', numpy.min, axis=0) stats.register('max', numpy.max, axis=0) ### record是一个字典:record{‘avg’:[],'std':[],...},每个value是一个list record = stats.compile(pop) for key in record: record[key] = record[key][0] logbook = tools.Logbook() logbook.record(gen=0, eval=popSize, best_ind=best_ind, **record) logbook.header = 'gen', 'min', 'max', 'avg', 'std', 'eval', 'best_ind' print(logbook) print('--------开始迭代--------') for g in range(ngen): ### select 选出popSize个个体进入下一代 # print('The', g + 1, 'step') offspring = tool.select(pop, len(pop)) # offspring = list(map(tool.clone, offspring)) # offspring = tool.map(tool.clone, offspring) offspring = [tool.clone(ind) for ind in offspring] # Apply crossover and mutation on the offspring for i in range(1, len(offspring), 2): if random.random() < cxpb: offspring[i - 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