Lesson 75 Uncomfortable shoes 不舒适的鞋子

女士询问店家是否有像她姐姐在美国购买的时髦鞋款,店家解释说那种鞋去年和前年流行,但今年已经过时,并展示了当前流行的款式。

    Listen to the tape then answer this question. What's wrong with the fashionable shoes?

  听录音,然后回答问题。这些时髦的鞋有什么毛病?

  LADY:           Do you have any shoes like these?

  SHOP ASSISTANT: What size?

  LADY:           Black.

  SHOP ASSISTANT: I'm sorry. We don't have any.

  LADY:           But my sister bought this pair last month.

  SHOP ASSISTANT: Did she buy them here?

  LADY:           No, she bought them in the U.S.

  SHOP ASSISTANT: We had some shoes like those a month ago,but we don't have any now.

  LADY:           Can you get a pair for me, please?

  SHOP ASSISTANT: I'm afraid that I can't. They were in fashion last year and the year before last. But they're not in fashion this year.

  SHOP ASSISTANT: These shoes are in fashion now.

  LADY:           They look very uncomfortable.

  SHOP ASSISTANT: They are very uncomfortable. But women always wear uncomfortable shoes!

 

  New Word and expressions 生词和短语

  ago adv. 以前

  buy(bough)

  v.   买

  pair n.   双,对

  fashion n.  (服装的)流行式样

  uncomfortable adj. 不舒服的

  wear v.   穿着

 

  参考译文

  女  士:像这样的鞋子你们有吗?

  售货员:什么尺码的?

  女  士:5号的。

  售货员:什么颜色?

  女  士:黑的售货员:对不起,我们没有。

  女  士:但是,我姐姐上个月买到了这样的一双。

  售货员:她是在这儿买的吗?

  女  士:不。她是在美国买的。

  售货员:一个月前我们有这要的鞋。

  但是现在没有了。

  女  士:您能为我找一双吗?

  售货员:恐怕不行。这鞋在去年和前年时兴,而今年已不流行了。

  售货员:现在流行的是这种鞋子。

  女  士:这种鞋子看上去很不舒适。

  售货员:的确很不舒适。可是女人们总是穿不舒适的鞋子!

### 确定理论在程序中的应用与实现方法 确定理论是一种用于处理确定性问题的数学工具,其核心在于通过分布函数来描述和量化确定性。以下将详细介绍确定理论在程序中的实现方法,并结合具体示例进行说明。 #### 1. 分布函数的定义与计算 在确定理论中,分布函数是描述确定变量的关键。通常需要先定义一个确定变量及其分布函数。例如,假设一个确定变量 \( X \) 的分布函数为 \( \Phi(x) \),可以通过编程语言实现其定义和计算[^3]。 ```python def uncertain_distribution(x): # 示例:定义一个简单的分布函数 Φ(x) if x < 0: return 0 elif 0 <= x <= 1: return x ** 2 else: return 1 ``` #### 2. 确定变量的生成 为了在程序中模拟确定变量的行为,可以基于分布函数生成随机样本。这一步骤通常需要结合数值方法或逆变换法来实现[^1]。 ```python import numpy as np def generate_uncertain_variable(num_samples=1000): # 使用逆变换法生成确定变量的样本 samples = np.random.rand(num_samples) uncertain_values = [uncertain_distribution_inverse(s) for s in samples] return uncertain_values def uncertain_distribution_inverse(p): # 反函数:从分布函数值 p 推导出对应的 x 值 if 0 <= p <= 1: return np.sqrt(p) else: raise ValueError("p 必须在 [0, 1] 范围内") ``` #### 3. 确定推理的应用 确定理论常用于专家系统、信息处理等领域。以下是一个基于可信度方法的简单推理示例,展示如何根据初始证据和规则推导结论[^2]。 ```python class UncertaintyReasoning: def __init__(self, evidence, rules): self.evidence = evidence # 初始证据 self.rules = rules # 推理规则 def infer(self): conclusions = [] for rule in self.rules: condition, conclusion, confidence = rule if condition in self.evidence: conclusions.append((conclusion, confidence)) return conclusions # 示例规则 rules = [ ("天气晴朗", "适合外出", 0.9), ("天气下雨", "适合外出", 0.8) ] # 示例证据 evidence = ["天气晴朗"] # 推理过程 reasoner = UncertaintyReasoning(evidence, rules) result = reasoner.infer() print(result) # 输出:[('适合外出', 0.9)] ``` #### 4. 模糊推理的实现 模糊推理是确定理论的重要分支之一,广泛应用于控制领域。以下是一个简单的模糊推理示例。 ```python from skfuzzy import control as ctrl # 定义输入和输出变量 temperature = ctrl.Antecedent(np.arange(0, 51, 1), 'temperature') humidity = ctrl.Antecedent(np.arange(0, 101, 1), 'humidity') comfort = ctrl.Consequent(np.arange(0, 11, 1), 'comfort') # 定义隶属函数 temperature['cold'] = fuzz.trimf(temperature.universe, [0, 0, 25]) temperature['warm'] = fuzz.trimf(temperature.universe, [20, 25, 30]) humidity['dry'] = fuzz.trimf(humidity.universe, [0, 0, 50]) humidity['wet'] = fuzz.trimf(humidity.universe, [50, 100, 100]) comfort['uncomfortable'] = fuzz.trimf(comfort.universe, [0, 0, 5]) comfort['comfortable'] = fuzz.trimf(comfort.universe, [5, 10, 10]) # 定义规则 rule1 = ctrl.Rule(temperature['cold'] & humidity['dry'], comfort['uncomfortable']) rule2 = ctrl.Rule(temperature['warm'] & humidity['wet'], comfort['comfortable']) # 创建控制系统 comfort_ctrl = ctrl.ControlSystem([rule1, rule2]) comfort_sim = ctrl.ControlSystemSimulation(comfort_ctrl) # 输入值 comfort_sim.input['temperature'] = 22 comfort_sim.input['humidity'] = 60 # 计算输出 comfort_sim.compute() print(comfort_sim.output['comfort']) # 输出舒适度值 ``` ###
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