from dataclasses import dataclass from breed_health_info import breed_health_info from breed_noise_info import breed_noise_info import traceback @dataclass class UserPreferences: """使用者偏好設定的資料結構""" living_space: str # "apartment", "house_small", "house_large" yard_access: str # "no_yard", "shared_yard", "private_yard" exercise_time: int # minutes per day exercise_type: str # "light_walks", "moderate_activity", "active_training" grooming_commitment: str # "low", "medium", "high" experience_level: str # "beginner", "intermediate", "advanced" time_availability: str # "limited", "moderate", "flexible" has_children: bool children_age: str # "toddler", "school_age", "teenager" noise_tolerance: str # "low", "medium", "high" space_for_play: bool other_pets: bool climate: str # "cold", "moderate", "hot" health_sensitivity: str = "medium" barking_acceptance: str = None training_commitment: str = "medium" # "low", "medium", "high" - 訓練投入程度 living_environment: str = "ground_floor" # "ground_floor", "with_elevator", "walk_up" - 居住環境細節 def __post_init__(self): if self.barking_acceptance is None: self.barking_acceptance = self.noise_tolerance @staticmethod def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float: """計算品種額外加分""" bonus = 0.0 temperament = breed_info.get('Temperament', '').lower() # 1. 壽命加分（最高0.05） try: lifespan = breed_info.get('Lifespan', '10-12 years') years = [int(x) for x in lifespan.split('-')[0].split()[0:1]] longevity_bonus = min(0.05, (max(years) - 10) * 0.01) bonus += longevity_bonus except: pass # 2. 性格特徵加分（最高0.15） positive_traits = { 'friendly': 0.05, 'gentle': 0.05, 'patient': 0.05, 'intelligent': 0.04, 'adaptable': 0.04, 'affectionate': 0.04, 'easy-going': 0.03, 'calm': 0.03 } negative_traits = { 'aggressive': -0.08, 'stubborn': -0.06, 'dominant': -0.06, 'aloof': -0.04, 'nervous': -0.05, 'protective': -0.04 } personality_score = sum(value for trait, value in positive_traits.items() if trait in temperament) personality_score += sum(value for trait, value in negative_traits.items() if trait in temperament) bonus += max(-0.15, min(0.15, personality_score)) # 3. 適應性加分（最高0.1） adaptability_bonus = 0.0 if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment": adaptability_bonus += 0.05 if 'adaptable' in temperament or 'versatile' in temperament: adaptability_bonus += 0.05 bonus += min(0.1, adaptability_bonus) # 4. 家庭相容性（最高0.1） if user_prefs.has_children: family_traits = { 'good with children': 0.06, 'patient': 0.05, 'gentle': 0.05, 'tolerant': 0.04, 'playful': 0.03 } unfriendly_traits = { 'aggressive': -0.08, 'nervous': -0.07, 'protective': -0.06, 'territorial': -0.05 } # 年齡評估 age_adjustments = { 'toddler': {'bonus_mult': 0.7, 'penalty_mult': 1.3}, 'school_age': {'bonus_mult': 1.0, 'penalty_mult': 1.0}, 'teenager': {'bonus_mult': 1.2, 'penalty_mult': 0.8} } adj = age_adjustments.get(user_prefs.children_age, {'bonus_mult': 1.0, 'penalty_mult': 1.0}) family_bonus = sum(value for trait, value in family_traits.items() if trait in temperament) * adj['bonus_mult'] family_penalty = sum(value for trait, value in unfriendly_traits.items() if trait in temperament) * adj['penalty_mult'] bonus += min(0.15, max(-0.2, family_bonus + family_penalty)) # 5. 專門技能加分（最高0.1） skill_bonus = 0.0 special_abilities = { 'working': 0.03, 'herding': 0.03, 'hunting': 0.03, 'tracking': 0.03, 'agility': 0.02 } for ability, value in special_abilities.items(): if ability in temperament.lower(): skill_bonus += value bonus += min(0.1, skill_bonus) # 6. 適應性評估 adaptability_bonus = 0.0 if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment": adaptability_bonus += 0.08 # 小型犬更適合公寓 # 環境適應性評估 if 'adaptable' in temperament or 'versatile' in temperament: if user_prefs.living_space == "apartment": adaptability_bonus += 0.10 # 適應性在公寓環境更重要 else: adaptability_bonus += 0.05 # 其他環境仍有加分 # 氣候適應性 description = breed_info.get('Description', '').lower() climate = user_prefs.climate if climate == 'hot': if 'heat tolerant' in description or 'warm climate' in description: adaptability_bonus += 0.08 elif 'thick coat' in description or 'cold climate' in description: adaptability_bonus -= 0.10 elif climate == 'cold': if 'thick coat' in description or 'cold climate' in description: adaptability_bonus += 0.08 elif 'heat tolerant' in description or 'short coat' in description: adaptability_bonus -= 0.10 bonus += min(0.15, adaptability_bonus) return min(0.5, max(-0.25, bonus)) @staticmethod def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict: """ 計算額外的評估因素，結合品種特性與使用者需求的全面評估系統 1. 多功能性評估 - 品種的多樣化能力 2. 訓練性評估 - 學習和服從能力 3. 能量水平評估 - 活力和運動需求 4. 美容需求評估 - 護理和維護需求 5. 社交需求評估 - 與人互動的需求程度 6. 氣候適應性 - 對環境的適應能力 7. 運動類型匹配 - 與使用者運動習慣的契合度 8. 生活方式適配 - 與使用者日常生活的匹配度 """ factors = { 'versatility': 0.0, # 多功能性 'trainability': 0.0, # 可訓練度 'energy_level': 0.0, # 能量水平 'grooming_needs': 0.0, # 美容需求 'social_needs': 0.0, # 社交需求 'weather_adaptability': 0.0,# 氣候適應性 'exercise_match': 0.0, # 運動匹配度 'lifestyle_fit': 0.0 # 生活方式適配度 } temperament = breed_info.get('Temperament', '').lower() description = breed_info.get('Description', '').lower() size = breed_info.get('Size', 'Medium') # 1. 多功能性評估 - 加強品種用途評估 versatile_traits = { 'intelligent': 0.25, 'adaptable': 0.25, 'trainable': 0.20, 'athletic': 0.15, 'versatile': 0.15 } working_roles = { 'working': 0.20, 'herding': 0.15, 'hunting': 0.15, 'sporting': 0.15, 'companion': 0.10 } # 計算特質分數 trait_score = sum(value for trait, value in versatile_traits.items() if trait in temperament) # 計算角色分數 role_score = sum(value for role, value in working_roles.items() if role in description) # 根據使用者需求調整多功能性評分 purpose_traits = { 'light_walks': ['calm', 'gentle', 'easy-going'], 'moderate_activity': ['adaptable', 'balanced', 'versatile'], 'active_training': ['intelligent', 'trainable', 'working'] } if user_prefs.exercise_type in purpose_traits: matching_traits = sum(1 for trait in purpose_traits[user_prefs.exercise_type] if trait in temperament) trait_score += matching_traits * 0.15 factors['versatility'] = min(1.0, trait_score + role_score) # 2. 訓練性評估 trainable_traits = { 'intelligent': 0.3, 'eager to please': 0.3, 'trainable': 0.2, 'quick learner': 0.2, 'obedient': 0.2 } base_trainability = sum(value for trait, value in trainable_traits.items() if trait in temperament) # 根據使用者經驗調整訓練性評分 experience_multipliers = { 'beginner': 1.2, # 新手更需要容易訓練的狗 'intermediate': 1.0, 'advanced': 0.8 # 專家能處理較難訓練的狗 } factors['trainability'] = min(1.0, base_trainability * experience_multipliers.get(user_prefs.experience_level, 1.0)) # 3. 能量水平評估 exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper() energy_levels = { 'VERY HIGH': { 'score': 1.0, 'min_exercise': 120, 'ideal_exercise': 150 }, 'HIGH': { 'score': 0.8, 'min_exercise': 90, 'ideal_exercise': 120 }, 'MODERATE': { 'score': 0.6, 'min_exercise': 60, 'ideal_exercise': 90 }, 'LOW': { 'score': 0.4, 'min_exercise': 30, 'ideal_exercise': 60 } } breed_energy = energy_levels.get(exercise_needs, energy_levels['MODERATE']) # 計算運動時間匹配度 if user_prefs.exercise_time >= breed_energy['ideal_exercise']: energy_score = breed_energy['score'] else: # 如果運動時間不足，按比例降低分數 deficit_ratio = max(0.4, user_prefs.exercise_time / breed_energy['ideal_exercise']) energy_score = breed_energy['score'] * deficit_ratio factors['energy_level'] = energy_score # 4. 美容需求評估 grooming_needs = breed_info.get('Grooming Needs', 'MODERATE').upper() grooming_levels = { 'HIGH': 1.0, 'MODERATE': 0.6, 'LOW': 0.3 } # 特殊毛髮類型評估 coat_adjustments = 0 if 'long coat' in description: coat_adjustments += 0.2 if 'double coat' in description: coat_adjustments += 0.15 if 'curly' in description: coat_adjustments += 0.15 # 根據使用者承諾度調整 commitment_multipliers = { 'low': 1.5, # 低承諾度時加重美容需求的影響 'medium': 1.0, 'high': 0.8 # 高承諾度時降低美容需求的影響 } base_grooming = grooming_levels.get(grooming_needs, 0.6) + coat_adjustments factors['grooming_needs'] = min(1.0, base_grooming * commitment_multipliers.get(user_prefs.grooming_commitment, 1.0)) # 5. 社交需求評估 social_traits = { 'friendly': 0.25, 'social': 0.25, 'affectionate': 0.20, 'people-oriented': 0.20 } antisocial_traits = { 'independent': -0.20, 'aloof': -0.20, 'reserved': -0.15 } social_score = sum(value for trait, value in social_traits.items() if trait in temperament) antisocial_score = sum(value for trait, value in antisocial_traits.items() if trait in temperament) # 家庭情況調整 if user_prefs.has_children: child_friendly_bonus = 0.2 if 'good with children' in temperament else 0 social_score += child_friendly_bonus factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score)) # 6. 氣候適應性評估 - 更細緻的環境適應評估 climate_traits = { 'cold': { 'positive': ['thick coat', 'winter', 'cold climate'], 'negative': ['short coat', 'heat sensitive'] }, 'hot': { 'positive': ['short coat', 'heat tolerant', 'warm climate'], 'negative': ['thick coat', 'cold climate'] }, 'moderate': { 'positive': ['adaptable', 'all climate'], 'negative': [] } } climate_score = 0.4 # 基礎分數 if user_prefs.climate in climate_traits: # 正面特質加分 climate_score += sum(0.2 for term in climate_traits[user_prefs.climate]['positive'] if term in description) # 負面特質減分 climate_score -= sum(0.2 for term in climate_traits[user_prefs.climate]['negative'] if term in description) factors['weather_adaptability'] = min(1.0, max(0.0, climate_score)) # 7. 運動類型匹配評估 exercise_type_traits = { 'light_walks': ['calm', 'gentle'], 'moderate_activity': ['adaptable', 'balanced'], 'active_training': ['athletic', 'energetic'] } if user_prefs.exercise_type in exercise_type_traits: match_score = sum(0.25 for trait in exercise_type_traits[user_prefs.exercise_type] if trait in temperament) factors['exercise_match'] = min(1.0, match_score + 0.5) # 基礎分0.5 # 8. 生活方式適配評估 lifestyle_score = 0.5 # 基礎分數 # 空間適配 if user_prefs.living_space == 'apartment': if size == 'Small': lifestyle_score += 0.2 elif size == 'Large': lifestyle_score -= 0.2 elif user_prefs.living_space == 'house_large': if size in ['Large', 'Giant']: lifestyle_score += 0.2 # 時間可用性適配 time_availability_bonus = { 'limited': -0.1, 'moderate': 0, 'flexible': 0.1 } lifestyle_score += time_availability_bonus.get(user_prefs.time_availability, 0) factors['lifestyle_fit'] = min(1.0, max(0.0, lifestyle_score)) return factors def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences) -> dict: """計算品種與使用者條件的相容性分數""" try: print(f"Processing breed: {breed_info.get('Breed', 'Unknown')}") print(f"Breed info keys: {breed_info.keys()}") if 'Size' not in breed_info: print("Missing Size information") raise KeyError("Size information missing") def calculate_space_score(size: str, living_space: str, has_yard: bool, exercise_needs: str) -> float: """ 1. 動態的基礎分數矩陣 2. 強化空間品質評估 3. 增加極端情況處理 4. 考慮不同空間組合的協同效應 """ def get_base_score(): # 基礎分數矩陣 - 更極端的分數分配 base_matrix = { "Small": { "apartment": { "no_yard": 0.85, # 小型犬在公寓仍然適合 "shared_yard": 0.90, # 共享院子提供額外活動空間 "private_yard": 0.95 # 私人院子最理想 }, "house_small": { "no_yard": 0.80, "shared_yard": 0.85, "private_yard": 0.90 }, "house_large": { "no_yard": 0.75, "shared_yard": 0.80, "private_yard": 0.85 } }, "Medium": { "apartment": { "no_yard": 0.35, # 中型犬在公寓較受限 "shared_yard": 0.45, "private_yard": 0.55 }, "house_small": { "no_yard": 0.75, "shared_yard": 0.85, "private_yard": 0.90 }, "house_large": { "no_yard": 0.85, "shared_yard": 0.90, "private_yard": 0.95 } }, "Large": { "apartment": { "no_yard": 0.15, # 大型犬在公寓極不適合 "shared_yard": 0.25, "private_yard": 0.35 }, "house_small": { "no_yard": 0.55, "shared_yard": 0.65, "private_yard": 0.75 }, "house_large": { "no_yard": 0.85, "shared_yard": 0.90, "private_yard": 1.0 } }, "Giant": { "apartment": { "no_yard": 0.10, # 巨型犬在公寓基本不適合 "shared_yard": 0.20, "private_yard": 0.30 }, "house_small": { "no_yard": 0.40, "shared_yard": 0.50, "private_yard": 0.60 }, "house_large": { "no_yard": 0.80, "shared_yard": 0.90, "private_yard": 1.0 } } } yard_type = "private_yard" if has_yard else "no_yard" return base_matrix.get(size, base_matrix["Medium"])[living_space][yard_type] def calculate_exercise_adjustment(): # 運動需求對空間評分的影響 exercise_impact = { "Very High": { "apartment": -0.30, # 高運動需求在公寓環境更受限 "house_small": -0.15, "house_large": -0.05 }, "High": { "apartment": -0.25, "house_small": -0.10, "house_large": 0 }, "Moderate": { "apartment": -0.15, "house_small": -0.05, "house_large": 0 }, "Low": { "apartment": 0.10, # 低運動需求反而適合小空間 "house_small": 0.05, "house_large": 0 } } return exercise_impact.get(exercise_needs, exercise_impact["Moderate"])[living_space] def calculate_yard_bonus(): # 院子效益評估更加細緻 if not has_yard: return 0 yard_benefits = { "Giant": { "Very High": 0.25, "High": 0.20, "Moderate": 0.15, "Low": 0.10 }, "Large": { "Very High": 0.20, "High": 0.15, "Moderate": 0.10, "Low": 0.05 }, "Medium": { "Very High": 0.15, "High": 0.10, "Moderate": 0.08, "Low": 0.05 }, "Small": { "Very High": 0.10, "High": 0.08, "Moderate": 0.05, "Low": 0.03 } } size_benefits = yard_benefits.get(size, yard_benefits["Medium"]) return size_benefits.get(exercise_needs, size_benefits["Moderate"]) def apply_extreme_case_adjustments(score): # 處理極端情況 if size == "Giant" and living_space == "apartment": return score * 0.5 # 巨型犬在公寓給予更嚴重的懲罰 if size == "Large" and living_space == "apartment" and exercise_needs == "Very High": return score * 0.6 # 高運動需求的大型犬在公寓更不適合 if size == "Small" and living_space == "house_large" and exercise_needs == "Low": return score * 0.9 # 低運動需求的小型犬在大房子可能過於寬敞 return score # 計算最終分數 base_score = get_base_score() exercise_adj = calculate_exercise_adjustment() yard_bonus = calculate_yard_bonus() # 整合所有評分因素 initial_score = base_score + exercise_adj + yard_bonus # 應用極端情況調整 final_score = apply_extreme_case_adjustments(initial_score) # 確保分數在有效範圍內，但允許更極端的結果 return max(0.05, min(1.0, final_score)) def calculate_exercise_score(breed_needs: str, exercise_time: int, exercise_type: str) -> float: """ 精確評估品種運動需求與使用者運動條件的匹配度 改進重點： 1. 擴大分數範圍到 0.1-1.0 2. 加強運動類型影響 3. 考慮運動強度與時間的綜合效果 4. 更細緻的時間匹配評估 """ exercise_levels = { 'VERY HIGH': { 'min': 120, 'ideal': 150, 'max': 180, 'intensity': 'high', 'sessions': 'multiple', 'preferred_types': ['active_training', 'intensive_exercise'], 'type_weights': { 'active_training': 1.0, 'moderate_activity': 0.6, 'light_walks': 0.3 } }, 'HIGH': { 'min': 90, 'ideal': 120, 'max': 150, 'intensity': 'moderate_high', 'sessions': 'multiple', 'preferred_types': ['active_training', 'moderate_activity'], 'type_weights': { 'active_training': 0.9, 'moderate_activity': 0.8, 'light_walks': 0.4 } }, 'MODERATE HIGH': { 'min': 70, 'ideal': 90, 'max': 120, 'intensity': 'moderate', 'sessions': 'flexible', 'preferred_types': ['moderate_activity', 'active_training'], 'type_weights': { 'active_training': 0.8, 'moderate_activity': 0.9, 'light_walks': 0.5 } }, 'MODERATE': { 'min': 45, 'ideal': 60, 'max': 90, 'intensity': 'moderate', 'sessions': 'flexible', 'preferred_types': ['moderate_activity', 'light_walks'], 'type_weights': { 'active_training': 0.7, 'moderate_activity': 1.0, 'light_walks': 0.8 } }, 'MODERATE LOW': { 'min': 30, 'ideal': 45, 'max': 70, 'intensity': 'light_moderate', 'sessions': 'flexible', 'preferred_types': ['light_walks', 'moderate_activity'], 'type_weights': { 'active_training': 0.6, 'moderate_activity': 0.9, 'light_walks': 1.0 } }, 'LOW': { 'min': 15, 'ideal': 30, 'max': 45, 'intensity': 'light', 'sessions': 'single', 'preferred_types': ['light_walks'], 'type_weights': { 'active_training': 0.5, 'moderate_activity': 0.8, 'light_walks': 1.0 } } } breed_level = exercise_levels.get(breed_needs.upper(), exercise_levels['MODERATE']) # 時間匹配度評估（基礎分數） def calculate_time_score(): if exercise_time >= breed_level['ideal']: if exercise_time > breed_level['max']: # 超出最大值的懲罰更明顯 excess = (exercise_time - breed_level['max']) / 30 return max(0.4, 1.0 - (excess * 0.2)) return 1.0 # 理想範圍內給予滿分 elif exercise_time >= breed_level['min']: # 在最小值和理想值之間使用更陡峭的曲線 progress = (exercise_time - breed_level['min']) / (breed_level['ideal'] - breed_level['min']) return 0.5 + (progress * 0.5) else: # 低於最小值時給予更嚴厲的懲罰 deficit_ratio = exercise_time / breed_level['min'] return max(0.1, deficit_ratio * 0.5) # 運動類型匹配度評估 def calculate_type_score(): type_weight = breed_level['type_weights'].get(exercise_type, 0.5) # 根據運動需求等級調整類型權重 if breed_needs.upper() in ['VERY HIGH', 'HIGH']: if exercise_type == 'light_walks': type_weight *= 0.5 # 高需求品種做輕度運動的懲罰 elif breed_needs.upper() == 'LOW': if exercise_type == 'active_training': type_weight *= 0.7 # 低需求品種做高強度運動的輕微懲罰 return type_weight # 計算最終分數 time_score = calculate_time_score() type_score = calculate_type_score() # 綜合評分，運動時間佔70%，類型佔30% final_score = (time_score * 0.7) + (type_score * 0.3) # 特殊情況調整 if exercise_time < breed_level['min'] * 0.5: # 運動時間嚴重不足 final_score *= 0.5 elif exercise_time > breed_level['max'] * 1.5: # 運動時間過多 final_score *= 0.7 return max(0.1, min(1.0, final_score)) def calculate_grooming_score(breed_needs: str, user_commitment: str, breed_size: str) -> float: """ 計算美容需求分數，強化美容維護需求與使用者承諾度的匹配評估。 這個函數特別注意品種大小對美容工作的影響，以及不同程度的美容需求對時間投入的要求。 """ # 重新設計基礎分數矩陣，讓美容需求的差異更加明顯 base_scores = { "High": { "low": 0.20, # 高需求對低承諾極不合適，顯著降低初始分數 "medium": 0.65, # 中等承諾仍有挑戰 "high": 1.0 # 高承諾最適合 }, "Moderate": { "low": 0.45, # 中等需求對低承諾有困難 "medium": 0.85, # 較好的匹配 "high": 0.95 # 高承諾會有餘力 }, "Low": { "low": 0.90, # 低需求對低承諾很合適 "medium": 0.85, # 略微降低以反映可能過度投入 "high": 0.80 # 可能造成資源浪費 } } # 取得基礎分數 base_score = base_scores.get(breed_needs, base_scores["Moderate"])[user_commitment] # 根據品種大小調整美容工作量 size_adjustments = { "Giant": { "low": -0.35, # 大型犬的美容工作量顯著增加 "medium": -0.20, "high": -0.10 }, "Large": { "low": -0.25, "medium": -0.15, "high": -0.05 }, "Medium": { "low": -0.15, "medium": -0.10, "high": 0 }, "Small": { "low": -0.10, "medium": -0.05, "high": 0 } } # 應用體型調整 size_adjustment = size_adjustments.get(breed_size, size_adjustments["Medium"])[user_commitment] current_score = base_score + size_adjustment # 特殊毛髮類型的額外調整 def get_coat_adjustment(breed_description: str, commitment: str) -> float: """ 評估特殊毛髮類型所需的額外維護工作 """ adjustments = 0 # 長毛品種需要更多維護 if 'long coat' in breed_description.lower(): coat_penalties = { 'low': -0.20, 'medium': -0.15, 'high': -0.05 } adjustments += coat_penalties[commitment] # 雙層毛的品種掉毛量更大 if 'double coat' in breed_description.lower(): double_coat_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } adjustments += double_coat_penalties[commitment] # 捲毛品種需要定期專業修剪 if 'curly' in breed_description.lower(): curly_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } adjustments += curly_penalties[commitment] return adjustments # 季節性考量 def get_seasonal_adjustment(breed_description: str, commitment: str) -> float: """ 評估季節性掉毛對美容需求的影響 """ if 'seasonal shedding' in breed_description.lower(): seasonal_penalties = { 'low': -0.15, 'medium': -0.10, 'high': -0.05 } return seasonal_penalties[commitment] return 0 # 專業美容需求評估 def get_professional_grooming_adjustment(breed_description: str, commitment: str) -> float: """ 評估需要專業美容服務的影響 """ if 'professional grooming' in breed_description.lower(): grooming_penalties = { 'low': -0.20, 'medium': -0.15, 'high': -0.05 } return grooming_penalties[commitment] return 0 # 應用所有額外調整 # 由於這些是示例調整，實際使用時需要根據品種描述信息進行調整 coat_adjustment = get_coat_adjustment("", user_commitment) seasonal_adjustment = get_seasonal_adjustment("", user_commitment) professional_adjustment = get_professional_grooming_adjustment("", user_commitment) final_score = current_score + coat_adjustment + seasonal_adjustment + professional_adjustment # 確保分數在有意義的範圍內，但允許更大的差異 return max(0.1, min(1.0, final_score)) def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float: """ 計算使用者經驗與品種需求的匹配分數，加強經驗等級的影響力 重要改進： 1. 擴大基礎分數差異 2. 加重困難特徵的懲罰 3. 更細緻的品種特性評估 """ # 基礎分數矩陣 - 大幅擴大不同經驗等級的分數差異 base_scores = { "High": { "beginner": 0.15, # 降低起始分，高難度品種對新手幾乎不推薦 "intermediate": 0.65, # 中級玩家仍需謹慎 "advanced": 1.0 # 資深者能完全勝任 }, "Moderate": { "beginner": 0.40, # 適中難度對新手仍具挑戰 "intermediate": 0.85, # 中級玩家較適合 "advanced": 0.95 # 資深者完全勝任 }, "Low": { "beginner": 0.85, # 新手友善品種 "intermediate": 0.90, # 中級玩家幾乎完全勝任 "advanced": 0.85 # 資深者完全勝任 } } # 取得基礎分數 score = base_scores.get(care_level, base_scores["Moderate"])[user_experience] temperament_lower = temperament.lower() temperament_adjustments = 0.0 # 根據經驗等級設定不同的特徵評估標準 if user_experience == "beginner": # 新手不適合的特徵 - 更嚴格的懲罰 difficult_traits = { 'stubborn': -0.30, # 固執性格嚴重影響新手 'independent': -0.25, # 獨立性高的品種不適合新手 'dominant': -0.25, # 支配性強的品種需要經驗處理 'strong-willed': -0.20, # 強勢性格需要技巧管理 'protective': -0.20, # 保護性強需要適當訓練 'aloof': -0.15, # 冷漠性格需要耐心培養 'energetic': -0.15, # 活潑好動需要經驗引導 'aggressive': -0.35 # 攻擊傾向極不適合新手 } # 新手友善的特徵 - 適度的獎勵 easy_traits = { 'gentle': 0.05, # 溫和性格適合新手 'friendly': 0.05, # 友善性格容易相處 'eager to please': 0.08, # 願意服從較容易訓練 'patient': 0.05, # 耐心的特質有助於建立關係 'adaptable': 0.05, # 適應性強較容易照顧 'calm': 0.06 # 冷靜的性格較好掌握 } # 計算特徵調整 for trait, penalty in difficult_traits.items(): if trait in temperament_lower: temperament_adjustments += penalty for trait, bonus in easy_traits.items(): if trait in temperament_lower: temperament_adjustments += bonus # 品種類型特殊評估 if 'terrier' in temperament_lower: temperament_adjustments -= 0.20 # 梗類犬種通常不適合新手 elif 'working' in temperament_lower: temperament_adjustments -= 0.25 # 工作犬需要經驗豐富的主人 elif 'guard' in temperament_lower: temperament_adjustments -= 0.25 # 護衛犬需要專業訓練 elif user_experience == "intermediate": # 中級玩家的特徵評估 moderate_traits = { 'stubborn': -0.15, # 仍然需要注意，但懲罰較輕 'independent': -0.10, 'intelligent': 0.08, # 聰明的特質可以好好發揮 'athletic': 0.06, # 運動能力可以適當訓練 'versatile': 0.07, # 多功能性可以開發 'protective': -0.08 # 保護性仍需注意 } for trait, adjustment in moderate_traits.items(): if trait in temperament_lower: temperament_adjustments += adjustment else: # advanced # 資深玩家能夠應對挑戰性特徵 advanced_traits = { 'stubborn': 0.05, # 困難特徵反而成為優勢 'independent': 0.05, 'intelligent': 0.10, 'protective': 0.05, 'strong-willed': 0.05 } for trait, bonus in advanced_traits.items(): if trait in temperament_lower: temperament_adjustments += bonus # 確保最終分數範圍更大，讓差異更明顯 final_score = max(0.05, min(1.0, score + temperament_adjustments)) return final_score def calculate_health_score(breed_name: str, user_prefs: UserPreferences) -> float: """ 計算品種健康分數，加強健康問題的影響力和與使用者敏感度的連結 1. 根據使用者的健康敏感度調整分數 2. 更嚴格的健康問題評估 3. 考慮多重健康問題的累積效應 4. 加入遺傳疾病的特別考量 """ if breed_name not in breed_health_info: return 0.5 health_notes = breed_health_info[breed_name]['health_notes'].lower() # 嚴重健康問題 - 加重扣分 severe_conditions = { 'hip dysplasia': -0.25, # 髖關節發育不良，影響生活品質 'heart disease': -0.25, # 心臟疾病，需要長期治療 'progressive retinal atrophy': -0.20, # 進行性視網膜萎縮，導致失明 'bloat': -0.22, # 胃扭轉，致命風險 'epilepsy': -0.20, # 癲癇，需要長期藥物控制 'degenerative myelopathy': -0.20, # 脊髓退化，影響行動能力 'von willebrand disease': -0.18 # 血液凝固障礙 } # 中度健康問題 - 適度扣分 moderate_conditions = { 'allergies': -0.12, # 過敏問題，需要持續關注 'eye problems': -0.15, # 眼睛問題，可能需要手術 'joint problems': -0.15, # 關節問題，影響運動能力 'hypothyroidism': -0.12, # 甲狀腺功能低下，需要藥物治療 'ear infections': -0.10, # 耳道感染，需要定期清理 'skin issues': -0.12 # 皮膚問題，需要特殊護理 } # 輕微健康問題 - 輕微扣分 minor_conditions = { 'dental issues': -0.08, # 牙齒問題，需要定期護理 'weight gain tendency': -0.08, # 易胖體質，需要控制飲食 'minor allergies': -0.06, # 輕微過敏，可控制 'seasonal allergies': -0.06 # 季節性過敏 } # 計算基礎健康分數 health_score = 1.0 # 健康問題累積效應計算 condition_counts = { 'severe': 0, 'moderate': 0, 'minor': 0 } # 計算各等級健康問題的數量和影響 for condition, penalty in severe_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['severe'] += 1 for condition, penalty in moderate_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['moderate'] += 1 for condition, penalty in minor_conditions.items(): if condition in health_notes: health_score += penalty condition_counts['minor'] += 1 # 多重問題的額外懲罰（累積效應） if condition_counts['severe'] > 1: health_score *= (0.85 ** (condition_counts['severe'] - 1)) if condition_counts['moderate'] > 2: health_score *= (0.90 ** (condition_counts['moderate'] - 2)) # 根據使用者健康敏感度調整分數 sensitivity_multipliers = { 'low': 1.1, # 較不在意健康問題 'medium': 1.0, # 標準評估 'high': 0.85 # 非常注重健康問題 } health_score *= sensitivity_multipliers.get(user_prefs.health_sensitivity, 1.0) # 壽命影響評估 try: lifespan = breed_health_info[breed_name].get('average_lifespan', '10-12') years = float(lifespan.split('-')[0]) if years < 8: health_score *= 0.85 # 短壽命顯著降低分數 elif years < 10: health_score *= 0.92 # 較短壽命輕微降低分數 elif years > 13: health_score *= 1.1 # 長壽命適度加分 except: pass # 特殊健康優勢 if 'generally healthy' in health_notes or 'hardy breed' in health_notes: health_score *= 1.15 elif 'robust health' in health_notes or 'few health issues' in health_notes: health_score *= 1.1 # 確保分數在合理範圍內，但允許更大的分數差異 return max(0.1, min(1.0, health_score)) def calculate_noise_score(breed_name: str, user_prefs: UserPreferences) -> float: """ 計算品種噪音分數，特別加強噪音程度與生活環境的關聯性評估，很多人棄養就是因為叫聲 """ if breed_name not in breed_noise_info: return 0.5 noise_info = breed_noise_info[breed_name] noise_level = noise_info['noise_level'].lower() noise_notes = noise_info['noise_notes'].lower() # 重新設計基礎噪音分數矩陣，考慮不同情境下的接受度 base_scores = { 'low': { 'low': 1.0, # 安靜的狗對低容忍完美匹配 'medium': 0.95, # 安靜的狗對一般容忍很好 'high': 0.90 # 安靜的狗對高容忍當然可以 }, 'medium': { 'low': 0.60, # 一般吠叫對低容忍較困難 'medium': 0.90, # 一般吠叫對一般容忍可接受 'high': 0.95 # 一般吠叫對高容忍很好 }, 'high': { 'low': 0.25, # 愛叫的狗對低容忍極不適合 'medium': 0.65, # 愛叫的狗對一般容忍有挑戰 'high': 0.90 # 愛叫的狗對高容忍可以接受 }, 'varies': { 'low': 0.50, # 不確定的情況對低容忍風險較大 'medium': 0.75, # 不確定的情況對一般容忍可嘗試 'high': 0.85 # 不確定的情況對高容忍問題較小 } } # 取得基礎分數 base_score = base_scores.get(noise_level, {'low': 0.6, 'medium': 0.75, 'high': 0.85})[user_prefs.noise_tolerance] # 吠叫原因評估，根據環境調整懲罰程度 barking_penalties = { 'separation anxiety': { 'apartment': -0.30, # 在公寓對鄰居影響更大 'house_small': -0.25, 'house_large': -0.20 }, 'excessive barking': { 'apartment': -0.25, 'house_small': -0.20, 'house_large': -0.15 }, 'territorial': { 'apartment': -0.20, # 在公寓更容易被觸發 'house_small': -0.15, 'house_large': -0.10 }, 'alert barking': { 'apartment': -0.15, # 公寓環境刺激較多 'house_small': -0.10, 'house_large': -0.08 }, 'attention seeking': { 'apartment': -0.15, 'house_small': -0.12, 'house_large': -0.10 } } # 計算環境相關的吠叫懲罰 living_space = user_prefs.living_space barking_penalty = 0 for trigger, penalties in barking_penalties.items(): if trigger in noise_notes: barking_penalty += penalties.get(living_space, -0.15) # 特殊情況評估 special_adjustments = 0 if user_prefs.has_children: # 孩童年齡相關調整 child_age_adjustments = { 'toddler': { 'high': -0.20, # 幼童對吵鬧更敏感 'medium': -0.15, 'low': -0.05 }, 'school_age': { 'high': -0.15, 'medium': -0.10, 'low': -0.05 }, 'teenager': { 'high': -0.10, 'medium': -0.05, 'low': -0.02 } } # 根據孩童年齡和噪音等級調整 age_adj = child_age_adjustments.get(user_prefs.children_age, child_age_adjustments['school_age']) special_adjustments += age_adj.get(noise_level, -0.10) # 訓練性補償評估 trainability_bonus = 0 if 'responds well to training' in noise_notes: trainability_bonus = 0.12 elif 'can be trained' in noise_notes: trainability_bonus = 0.08 elif 'difficult to train' in noise_notes: trainability_bonus = 0.02 # 夜間吠叫特別考量 if 'night barking' in noise_notes or 'howls' in noise_notes: if user_prefs.living_space == 'apartment': special_adjustments -= 0.15 elif user_prefs.living_space == 'house_small': special_adjustments -= 0.10 else: special_adjustments -= 0.05 # 計算最終分數，確保更大的分數範圍 final_score = base_score + barking_penalty + special_adjustments + trainability_bonus return max(0.1, min(1.0, final_score)) # 1. 計算基礎分數 print("\n=== 開始計算品種相容性分數 ===") print(f"處理品種: {breed_info.get('Breed', 'Unknown')}") print(f"品種信息: {breed_info}") print(f"使用者偏好: {vars(user_prefs)}") # 計算所有基礎分數並整合到字典中 scores = { 'space': calculate_space_score( breed_info['Size'], user_prefs.living_space, user_prefs.yard_access != 'no_yard', breed_info.get('Exercise Needs', 'Moderate') ), 'exercise': calculate_exercise_score( breed_info.get('Exercise Needs', 'Moderate'), user_prefs.exercise_time, user_prefs.exercise_type ), 'grooming': calculate_grooming_score( breed_info.get('Grooming Needs', 'Moderate'), user_prefs.grooming_commitment.lower(), breed_info['Size'] ), 'experience': calculate_experience_score( breed_info.get('Care Level', 'Moderate'), user_prefs.experience_level, breed_info.get('Temperament', '') ), 'health': calculate_health_score( breed_info.get('Breed', ''), user_prefs ), 'noise': calculate_noise_score( breed_info.get('Breed', ''), user_prefs ) } final_score = calculate_breed_compatibility_score( scores=scores, user_prefs=user_prefs, breed_info=breed_info ) # 計算環境適應性加成 adaptability_bonus = calculate_environmental_fit(breed_info, user_prefs) # 整合最終分數和加成 final_score = (final_score * 0.9) + (adaptability_bonus * 0.1) final_score = amplify_score_extreme(final_score) # 更新並返回完整的評分結果 scores.update({ 'overall': final_score, 'adaptability_bonus': adaptability_bonus }) return scores except Exception as e: print(f"\n!!!!! 發生嚴重錯誤 !!!!!") print(f"錯誤類型: {type(e).__name__}") print(f"錯誤訊息: {str(e)}") print(f"完整錯誤追蹤:") print(traceback.format_exc()) return {k: 0.6 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']} def calculate_environmental_fit(breed_info: dict, user_prefs: UserPreferences) -> float: """計算品種與環境的適應性加成""" adaptability_score = 0.0 description = breed_info.get('Description', '').lower() temperament = breed_info.get('Temperament', '').lower() # 環境適應性評估 if user_prefs.living_space == 'apartment': if 'adaptable' in temperament or 'apartment' in description: adaptability_score += 0.1 if breed_info.get('Size') == 'Small': adaptability_score += 0.05 elif user_prefs.living_space == 'house_large': if 'active' in temperament or 'energetic' in description: adaptability_score += 0.1 # 氣候適應性 if user_prefs.climate in description or user_prefs.climate in temperament: adaptability_score += 0.05 return min(0.2, adaptability_score) def calculate_breed_compatibility_score(scores: dict, user_prefs: UserPreferences, breed_info: dict) -> float: """ 1. 運動類型與時間的精確匹配 2. 進階使用者的專業需求 3. 空間利用的實際效果 4. 條件組合的嚴格評估 """ def evaluate_perfect_conditions(): """ 評估條件匹配度，特別強化： 1. 運動類型與時間的綜合評估 2. 專業技能需求評估 3. 品種特性評估 """ perfect_matches = { 'size_match': 0, 'exercise_match': 0, 'experience_match': 0, 'living_condition_match': 0, 'breed_trait_match': 0 # 新增品種特性匹配度 } # 第一部分：運動需求評估 def evaluate_exercise_compatibility(): """ 評估運動需求的匹配度，特別關注： 1. 時間與強度的合理搭配 2. 不同品種的運動特性 3. 運動類型的適配性 這個函數就像是一個體育教練，需要根據每個"運動員"(狗品種)的特點， 為他們制定合適的訓練計劃。 """ exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper() exercise_time = user_prefs.exercise_time exercise_type = user_prefs.exercise_type temperament = breed_info.get('Temperament', '').lower() description = breed_info.get('Description', '').lower() # 定義更精確的品種運動特性 breed_exercise_patterns = { 'sprint_type': { # 短跑型犬種，如 Whippet, Saluki 'identifiers': ['fast', 'speed', 'sprint', 'racing', 'coursing', 'sight hound'], 'ideal_exercise': { 'active_training': 1.0, # 完美匹配高強度訓練 'moderate_activity': 0.5, # 持續運動不是最佳選擇 'light_walks': 0.3 # 輕度運動效果很差 }, 'time_ranges': { 'ideal': (30, 60), # 最適合的運動時間範圍 'acceptable': (20, 90), # 可以接受的時間範圍 'penalty_start': 90 # 開始給予懲罰的時間點 }, 'penalty_rate': 0.8 # 超出範圍時的懲罰係數 }, 'endurance_type': { # 耐力型犬種，如 Border Collie 'identifiers': ['herding', 'working', 'tireless', 'energetic', 'stamina', 'athletic'], 'ideal_exercise': { 'active_training': 0.9, # 高強度訓練很好 'moderate_activity': 1.0, # 持續運動是最佳選擇 'light_walks': 0.4 # 輕度運動不足 }, 'time_ranges': { 'ideal': (90, 180), # 需要較長的運動時間 'acceptable': (60, 180), 'penalty_start': 60 # 運動時間過短會受罰 }, 'penalty_rate': 0.7 }, 'moderate_type': { # 一般活動型犬種，如 Labrador 'identifiers': ['friendly', 'playful', 'adaptable', 'versatile', 'companion'], 'ideal_exercise': { 'active_training': 0.8, 'moderate_activity': 1.0, 'light_walks': 0.6 }, 'time_ranges': { 'ideal': (60, 120), 'acceptable': (45, 150), 'penalty_start': 150 }, 'penalty_rate': 0.6 } } def determine_breed_type(): """改進品種運動類型的判斷，更精確識別工作犬""" # 優先檢查特殊運動類型的標識符 for breed_type, pattern in breed_exercise_patterns.items(): if any(identifier in temperament or identifier in description for identifier in pattern['identifiers']): return breed_type # 改進：根據運動需求和工作犬特徵進行更細緻的判斷 if (exercise_needs in ['VERY HIGH', 'HIGH'] or any(trait in temperament.lower() for trait in ['herding', 'working', 'intelligent', 'athletic', 'tireless'])): if user_prefs.experience_level == 'advanced': return 'endurance_type' # 優先判定為耐力型 elif exercise_needs == 'LOW': return 'moderate_type' return 'moderate_type' def calculate_time_match(pattern): """ 計算運動時間的匹配度。 這就像在判斷運動時間是否符合訓練計劃。 """ ideal_min, ideal_max = pattern['time_ranges']['ideal'] accept_min, accept_max = pattern['time_ranges']['acceptable'] penalty_start = pattern['time_ranges']['penalty_start'] # 在理想範圍內 if ideal_min <= exercise_time <= ideal_max: return 1.0 # 超出可接受範圍的嚴格懲罰 elif exercise_time < accept_min: deficit = accept_min - exercise_time return max(0.2, 1 - (deficit / accept_min) * 1.2) elif exercise_time > accept_max: excess = exercise_time - penalty_start penalty = min(0.8, (excess / penalty_start) * pattern['penalty_rate']) return max(0.2, 1 - penalty) # 在可接受範圍但不在理想範圍 else: if exercise_time < ideal_min: progress = (exercise_time - accept_min) / (ideal_min - accept_min) return 0.6 + (0.4 * progress) else: remaining = (accept_max - exercise_time) / (accept_max - ideal_max) return 0.6 + (0.4 * remaining) def apply_special_adjustments(time_score, type_score, breed_type, pattern): """ 處理特殊情況，確保運動方式真正符合品種需求。 特別加強： 1. 短跑型犬種的長時間運動懲罰 2. 耐力型犬種的獎勵機制 3. 運動類型匹配的重要性 """ # 短跑型品種的特殊處理 if breed_type == 'sprint_type': if exercise_time > pattern['time_ranges']['penalty_start']: # 加重長時間運動的懲罰 penalty_factor = min(0.8, (exercise_time - pattern['time_ranges']['penalty_start']) / 60) time_score *= max(0.3, 1 - penalty_factor) # 最低降到0.3 # 運動類型不適合時的額外懲罰 if exercise_type != 'active_training': type_score *= 0.3 # 更嚴重的懲罰 # 耐力型品種的特殊處理 elif breed_type == 'endurance_type': if exercise_time < pattern['time_ranges']['penalty_start']: time_score *= 0.5 # 維持運動不足的懲罰 elif exercise_time >= 150: # 新增：高運動量獎勵 if exercise_type in ['active_training', 'moderate_activity']: time_bonus = min(0.3, (exercise_time - 150) / 150) time_score = min(1.0, time_score * (1 + time_bonus)) type_score = min(1.0, type_score * 1.2) # 運動強度不足的懲罰 if exercise_type == 'light_walks': if exercise_time > 90: type_score *= 0.4 # 加重懲罰 else: type_score *= 0.5 return time_score, type_score # 執行評估流程 breed_type = determine_breed_type() pattern = breed_exercise_patterns[breed_type] # 計算基礎分數 time_score = calculate_time_match(pattern) type_score = pattern['ideal_exercise'].get(exercise_type, 0.5) # 應用特殊調整 time_score, type_score = apply_special_adjustments(time_score, type_score, breed_type, pattern) # 根據品種類型決定最終權重 if breed_type == 'sprint_type': if exercise_time > pattern['time_ranges']['penalty_start']: # 超時時更重視運動類型的匹配度 return (time_score * 0.3) + (type_score * 0.7) else: return (time_score * 0.5) + (type_score * 0.5) elif breed_type == 'endurance_type': if exercise_time < pattern['time_ranges']['penalty_start']: # 時間不足時更重視時間因素 return (time_score * 0.7) + (type_score * 0.3) else: return (time_score * 0.6) + (type_score * 0.4) else: return (time_score * 0.5) + (type_score * 0.5) # 第二部分：專業技能需求評估 def evaluate_expertise_requirements(): care_level = breed_info.get('Care Level', 'MODERATE').upper() temperament = breed_info.get('Temperament', '').lower() # 定義專業技能要求 expertise_requirements = { 'training_complexity': { 'HIGH': {'beginner': 0.3, 'intermediate': 0.7, 'advanced': 1.0}, 'MODERATE': {'beginner': 0.6, 'intermediate': 0.9, 'advanced': 1.0}, 'LOW': {'beginner': 0.9, 'intermediate': 0.95, 'advanced': 0.9} }, 'special_traits': { 'working': 0.2, # 工作犬需要額外技能 'herding': 0.2, # 牧羊犬需要特殊訓練 'intelligent': 0.15,# 高智商犬種需要心智刺激 'independent': 0.15,# 獨立性強的需要特殊處理 'protective': 0.1 # 護衛犬需要適當訓練 } } # 基礎分數 base_score = expertise_requirements['training_complexity'][care_level][user_prefs.experience_level] # 特殊特徵評估 trait_penalty = 0 for trait, penalty in expertise_requirements['special_traits'].items(): if trait in temperament: if user_prefs.experience_level == 'beginner': trait_penalty += penalty elif user_prefs.experience_level == 'advanced': trait_penalty -= penalty * 0.5 # 專家反而因應對特殊特徵而加分 return max(0.2, min(1.0, base_score - trait_penalty)) # 第三部分：生活環境評估 def evaluate_living_conditions(): size = breed_info['Size'] exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper() # 空間需求矩陣 space_requirements = { 'apartment': { 'Small': 1.0, 'Medium': 0.4, 'Large': 0.2, 'Giant': 0.1 }, 'house_small': { 'Small': 0.9, 'Medium': 1.0, 'Large': 0.5, 'Giant': 0.3 }, 'house_large': { 'Small': 0.8, 'Medium': 0.9, 'Large': 1.0, 'Giant': 1.0 } } # 基礎空間分數 space_score = space_requirements.get(user_prefs.living_space, space_requirements['house_small'])[size] # 活動空間需求調整 if exercise_needs in ['HIGH', 'VERY HIGH']: if user_prefs.living_space != 'house_large': space_score *= 0.8 # 院子可用性評估 yard_scores = { 'no_yard': 0.7, 'shared_yard': 0.85, 'private_yard': 1.0 } space_score *= yard_scores.get(user_prefs.yard_access, 0.8) return space_score # 第四部分：品種特性評估（新增） def evaluate_breed_traits(): temperament = breed_info.get('Temperament', '').lower() description = breed_info.get('Description', '').lower() trait_scores = [] # 評估性格特徵 if user_prefs.has_children: if 'good with children' in description: trait_scores.append(1.0) elif 'patient' in temperament or 'gentle' in temperament: trait_scores.append(0.8) else: trait_scores.append(0.5) # 評估適應性 adaptability_keywords = ['adaptable', 'versatile', 'flexible'] if any(keyword in temperament for keyword in adaptability_keywords): trait_scores.append(1.0) else: trait_scores.append(0.7) return sum(trait_scores) / len(trait_scores) if trait_scores else 0.7 # 計算各項匹配分數 perfect_matches['exercise_match'] = evaluate_exercise_compatibility() perfect_matches['experience_match'] = evaluate_expertise_requirements() perfect_matches['living_condition_match'] = evaluate_living_conditions() perfect_matches['size_match'] = evaluate_living_conditions() # 共用生活環境評估 perfect_matches['breed_trait_match'] = evaluate_breed_traits() return perfect_matches def calculate_weights(): """ 1. 條件極端度對權重的影響 2. 多重條件組合的權重調整 3. 品種特性對權重分配的影響 """ # 基礎權重設定 base_weights = { 'space': 0.20, 'exercise': 0.20, 'experience': 0.20, 'grooming': 0.15, 'noise': 0.15, 'health': 0.10 } def analyze_condition_extremity(): """評估各條件的極端程度及其影響""" extremities = {} # 運動時間極端度分析 def analyze_exercise_extremity(): if user_prefs.exercise_time <= 30: return ('extremely_low', 0.9) elif user_prefs.exercise_time <= 60: return ('low', 0.7) elif user_prefs.exercise_time >= 180: return ('extremely_high', 0.9) elif user_prefs.exercise_time >= 120: return ('high', 0.7) return ('moderate', 0.4) # 空間限制極端度分析 def analyze_space_extremity(): space_extremity = { 'apartment': ('highly_restricted', 0.9), 'house_small': ('restricted', 0.6), 'house_large': ('spacious', 0.4) } return space_extremity.get(user_prefs.living_space, ('moderate', 0.5)) # 經驗水平極端度分析 def analyze_experience_extremity(): experience_extremity = { 'beginner': ('low', 0.8), 'intermediate': ('moderate', 0.5), 'advanced': ('high', 0.7) } return experience_extremity.get(user_prefs.experience_level, ('moderate', 0.5)) # 整合各項極端度評估 extremities['exercise'] = analyze_exercise_extremity() extremities['space'] = analyze_space_extremity() extremities['experience'] = analyze_experience_extremity() return extremities def calculate_weight_adjustments(extremities): """ 1. 高運動量時對耐力型犬種的偏好 2. 專家級別對工作犬種的偏好 3. 條件組合的整體評估 """ adjustments = {} temperament = breed_info.get('Temperament', '').lower() is_working_dog = any(trait in temperament for trait in ['herding', 'working', 'intelligent', 'tireless']) # 空間權重調整邏輯保持不變 if extremities['space'][0] == 'highly_restricted': if extremities['exercise'][0] in ['high', 'extremely_high']: adjustments['space'] = 1.8 # 降低空間限制的權重 adjustments['exercise'] = 2.5 # 提高運動能力的權重 else: adjustments['space'] = 2.5 adjustments['noise'] = 2.0 elif extremities['space'][0] == 'restricted': adjustments['space'] = 1.8 adjustments['noise'] = 1.5 elif extremities['space'][0] == 'spacious': adjustments['space'] = 0.8 adjustments['exercise'] = 1.4 # 改進運動需求權重調整 if extremities['exercise'][0] in ['high', 'extremely_high']: # 提高運動量高時的基礎分數 base_exercise_adjustment = 2.2 if user_prefs.living_space == 'apartment': base_exercise_adjustment = 2.5 # 特別獎勵公寓住戶的高運動量 adjustments['exercise'] = base_exercise_adjustment if extremities['exercise'][0] in ['extremely_low', 'extremely_high']: base_adjustment = 2.5 if extremities['exercise'][0] == 'extremely_high': if is_working_dog: base_adjustment = 3.0 # 工作犬在高運動量時獲得更高權重 adjustments['exercise'] = base_adjustment elif extremities['exercise'][0] in ['low', 'high']: adjustments['exercise'] = 1.8 # 改進經驗需求權重調整 if extremities['experience'][0] == 'low': adjustments['experience'] = 2.2 if breed_info.get('Care Level') == 'HIGH': adjustments['experience'] = 2.5 elif extremities['experience'][0] == 'high': if is_working_dog: adjustments['experience'] = 2.5 # 提高專家對工作犬的權重 if extremities['exercise'][0] in ['high', 'extremely_high']: adjustments['experience'] = 2.8 # 特別強化高運動量工作犬 else: adjustments['experience'] = 1.8 # 綜合條件影響 def adjust_for_combinations(): # 保持原有的基礎邏輯 if (extremities['space'][0] == 'highly_restricted' and extremities['exercise'][0] in ['high', 'extremely_high']): adjustments['space'] = adjustments.get('space', 1.0) * 1.3 adjustments['exercise'] = adjustments.get('exercise', 1.0) * 1.3 # 新增：專家 + 大空間 + 高運動量 + 工作犬的組合 if (extremities['experience'][0] == 'high' and extremities['space'][0] == 'spacious' and extremities['exercise'][0] in ['high', 'extremely_high'] and is_working_dog): adjustments['exercise'] = adjustments.get('exercise', 1.0) * 1.4 adjustments['experience'] = adjustments.get('experience', 1.0) * 1.4 if extremities['space'][0] == 'spacious': for key in ['grooming', 'health', 'noise']: if key not in adjustments: adjustments[key] = 1.2 def ensure_minimum_score(score): if all([ extremities['exercise'][0] in ['high', 'extremely_high'], breed_matches_exercise_needs(), # 檢查品種是否適合該運動量 score < 0.85 ]): return 0.85 return score adjust_for_combinations() return adjustments # 獲取條件極端度 extremities = analyze_condition_extremity() # 計算權重調整 weight_adjustments = calculate_weight_adjustments(extremities) # 應用權重調整 final_weights = base_weights.copy() for key, adjustment in weight_adjustments.items(): if key in final_weights: final_weights[key] *= adjustment return final_weights def apply_special_case_adjustments(score): """ 1. 條件組合的協同效應 2. 品種特性的特殊要求 3. 極端情況的處理 """ severity_multiplier = 1.0 def evaluate_spatial_exercise_combination(): """ 評估空間與運動需求的組合影響 修改重點：移除對高運動需求的懲罰,只保留體型相關評估 """ multiplier = 1.0 if user_prefs.living_space == 'apartment': # 移除運動需求相關的懲罰 # 只保留體型的基本評估,但降低懲罰程度 if breed_info['Size'] in ['Large', 'Giant']: multiplier *= 0.7 # 從0.5提升到0.7,因為大型犬確實需要考慮空間限制 return multiplier def evaluate_experience_combination(): """評估經驗需求的複合影響""" multiplier = 1.0 temperament = breed_info.get('Temperament', '').lower() care_level = breed_info.get('Care Level', 'MODERATE') # 新手飼主的特殊考量 if user_prefs.experience_level == 'beginner': # 高難度品種的嚴格限制 if care_level == 'HIGH': if user_prefs.has_children: multiplier *= 0.5 else: multiplier *= 0.6 # 特殊性格特徵的影響 challenging_traits = ['independent', 'dominant', 'protective', 'strong-willed'] trait_count = sum(1 for trait in challenging_traits if trait in temperament) if trait_count > 0: multiplier *= (0.8 ** trait_count) # 進階飼主的特殊考量 elif user_prefs.experience_level == 'advanced': if care_level == 'LOW' and breed_info.get('Exercise Needs') == 'LOW': multiplier *= 0.9 # 對專家來說可能過於簡單 return multiplier def evaluate_breed_specific_requirements(): """評估品種特定的要求，加強運動需求的判斷""" multiplier = 1.0 exercise_time = user_prefs.exercise_time exercise_type = user_prefs.exercise_type # 檢查品種的基本特性 temperament = breed_info.get('Temperament', '').lower() description = breed_info.get('Description', '').lower() exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper() # 加強運動需求的匹配判斷 if exercise_needs == 'LOW': if exercise_time > 90: # 如果用戶運動時間過長 multiplier *= 0.5 # 給予更強的懲罰 elif exercise_needs == 'VERY HIGH': if exercise_time < 60: # 如果用戶運動時間過短 multiplier *= 0.5 if 'sprint' in temperament: if exercise_time > 120 and exercise_type != 'active_training': multiplier *= 0.7 if any(trait in temperament for trait in ['working', 'herding']): if exercise_time < 90 or exercise_type == 'light_walks': multiplier *= 0.7 return multiplier def evaluate_environmental_impact(): """評估環境因素的影響""" multiplier = 1.0 # 時間限制的影響 if user_prefs.time_availability == 'limited': if breed_info.get('Exercise Needs').upper() in ['VERY HIGH', 'HIGH']: multiplier *= 0.7 # 噪音敏感度的影響 if user_prefs.noise_tolerance == 'low': if breed_info.get('Breed') in breed_noise_info: if breed_noise_info[breed_info['Breed']]['noise_level'].lower() == 'high': multiplier *= 0.6 return multiplier # 整合所有特殊情況的評估 severity_multiplier *= evaluate_spatial_exercise_combination() severity_multiplier *= evaluate_experience_combination() severity_multiplier *= evaluate_breed_specific_requirements() severity_multiplier *= evaluate_environmental_impact() # 確保最終分數在合理範圍內 final_score = score * severity_multiplier return max(0.2, min(1.0, final_score)) def calculate_base_score(scores: dict, weights: dict) -> float: """ 計算基礎分數，更寬容地處理極端組合 """ # 進一步降低關鍵指標閾值，使系統更包容極端組合 critical_thresholds = { 'space': 0.45, # 進一步降低閾值 'exercise': 0.45, 'experience': 0.55, 'noise': 0.55 } critical_failures = [] for metric, threshold in critical_thresholds.items(): if scores[metric] < threshold: critical_failures.append((metric, scores[metric])) base_score = sum(scores[k] * weights[k] for k in scores.keys()) if critical_failures: space_exercise_penalty = 0 other_penalty = 0 for metric, score in critical_failures: if metric in ['space', 'exercise']: space_exercise_penalty += (critical_thresholds[metric] - score) * 0.15 # 降低懲罰 else: other_penalty += (critical_thresholds[metric] - score) * 0.3 total_penalty = (space_exercise_penalty + other_penalty) / 2 base_score *= (1 - total_penalty) if len(critical_failures) > 1: base_score *= (0.98 ** (len(critical_failures) - 1)) # 進一步降低多重失敗懲罰 return base_score def evaluate_condition_interactions(scores: dict) -> float: """ 評估不同條件間的相互影響，更寬容地處理極端組合 """ interaction_penalty = 1.0 # 只保留最基本的經驗相關評估 if user_prefs.experience_level == 'beginner': if breed_info.get('Care Level') == 'HIGH': interaction_penalty *= 0.95 # 運動時間與類型的基本互動也降低懲罰程度 exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper() if exercise_needs == 'VERY HIGH' and user_prefs.exercise_type == 'light_walks': interaction_penalty *= 0.95 return interaction_penalty def calculate_adjusted_perfect_bonus(perfect_conditions: dict) -> float: """ 計算完美匹配獎勵，但更注重條件的整體表現。 """ bonus = 1.0 # 降低單項獎勵的影響力 bonus += 0.06 * perfect_conditions['size_match'] bonus += 0.06 * perfect_conditions['exercise_match'] bonus += 0.06 * perfect_conditions['experience_match'] bonus += 0.03 * perfect_conditions['living_condition_match'] # 如果有任何條件表現不佳，降低整體獎勵 low_scores = [score for score in perfect_conditions.values() if score < 0.6] if low_scores: bonus *= (0.85 ** len(low_scores)) # 確保獎勵不會過高 return min(1.25, bonus) def apply_breed_specific_adjustments(score: float) -> float: """ 根據品種特性進行最終調整。 考慮品種的特殊性質和限制因素。 """ # 檢查是否存在極端不匹配的情況 exercise_mismatch = False size_mismatch = False experience_mismatch = False # 運動需求極端不匹配 if breed_info.get('Exercise Needs', 'MODERATE').upper() == 'VERY HIGH': if user_prefs.exercise_time < 90 or user_prefs.exercise_type == 'light_walks': exercise_mismatch = True # 體型與空間極端不匹配 if user_prefs.living_space == 'apartment' and breed_info['Size'] in ['Large', 'Giant']: size_mismatch = True # 經驗需求極端不匹配 if user_prefs.experience_level == 'beginner' and breed_info.get('Care Level') == 'HIGH': experience_mismatch = True # 根據不匹配的數量進行懲罰 mismatch_count = sum([exercise_mismatch, size_mismatch, experience_mismatch]) if mismatch_count > 0: score *= (0.8 ** mismatch_count) return score # 計算動態權重 weights = calculate_weights() # 正規化權重 total_weight = sum(weights.values()) normalized_weights = {k: v/total_weight for k, v in weights.items()} # 計算基礎分數 base_score = calculate_base_score(scores, normalized_weights) # 評估條件互動 interaction_multiplier = evaluate_condition_interactions(scores) # 計算完美匹配獎勵 perfect_conditions = evaluate_perfect_conditions() perfect_bonus = calculate_adjusted_perfect_bonus(perfect_conditions) # 計算初步分數 preliminary_score = base_score * interaction_multiplier * perfect_bonus # 應用品種特定調整 final_score = apply_breed_specific_adjustments(preliminary_score) # 確保分數在合理範圍內，並降低最高可能分數 max_possible_score = 0.96 # 降低最高可能分數 min_possible_score = 0.3 return min(max_possible_score, max(min_possible_score, final_score)) def amplify_score_extreme(score: float) -> float: """優化分數分布，提供更高的分數範圍""" def smooth_curve(x: float, steepness: float = 12) -> float: import math return 1 / (1 + math.exp(-steepness * (x - 0.5))) if score >= 0.9: position = (score - 0.9) / 0.1 return 0.96 + (position * 0.04) # 90-100的原始分映射到96-100 elif score >= 0.8: position = (score - 0.8) / 0.1 return 0.90 + (position * 0.06) # 80-90的原始分映射到90-96 elif score >= 0.7: position = (score - 0.7) / 0.1 return 0.82 + (position * 0.08) # 70-80的原始分映射到82-90 elif score >= 0.5: position = (score - 0.5) / 0.2 return 0.75 + (smooth_curve(position) * 0.07) # 50-70的原始分映射到75-82 else: position = score / 0.5 return 0.70 + (smooth_curve(position) * 0.05) # 50以下的原始分映射到70-75