from dataclasses import dataclass from breed_health_info import breed_health_info from breed_noise_info import breed_noise_info @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 def __post_init__(self): """在初始化後運行，用於設置派生值""" 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 # } # # 年齡評估這樣能更細緻 # 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) # return min(0.5, max(-0.25, bonus)) @staticmethod def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float: """計算品種額外加分，強化進階使用者的評分""" bonus = 0.0 temperament = breed_info.get('Temperament', '').lower() description = breed_info.get('Description', '').lower() # 1. 壽命加分（最高0.08）- 提高上限 try: lifespan = breed_info.get('Lifespan', '10-12 years') years = [int(x) for x in lifespan.split('-')[0].split()[0:1]] longevity_bonus = min(0.08, (max(years) - 10) * 0.015) # 提高係數 bonus += longevity_bonus except: pass # 2. 性格特徵加分（最高0.20）- 提高上限 positive_traits = { 'friendly': 0.06, 'gentle': 0.06, 'patient': 0.06, 'intelligent': 0.05, 'adaptable': 0.05, 'affectionate': 0.05, 'easy-going': 0.04, 'calm': 0.04 } # 根據經驗等級調整負面特徵的影響 experience_multiplier = { 'beginner': 1.3, 'intermediate': 0.8, 'advanced': 0.5 # 大幅降低進階使用者的負面特徵懲罰 }.get(user_prefs.experience_level, 1.0) negative_traits = { 'aggressive': -0.08 * experience_multiplier, 'stubborn': -0.06 * experience_multiplier, 'dominant': -0.06 * experience_multiplier, 'aloof': -0.04 * experience_multiplier, 'nervous': -0.05 * experience_multiplier, 'protective': -0.04 * experience_multiplier } personality_score = sum(value for trait, value in positive_traits.items() if trait in temperament) if user_prefs.experience_level == 'advanced': personality_score *= 1.2 # 進階使用者得到更多正面特徵加分 personality_score += sum(value for trait, value in negative_traits.items() if trait in temperament) bonus += max(-0.15, min(0.20, personality_score)) # 3. 適應性加分（最高0.15）- 提高上限 adaptability_bonus = 0.0 if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment": adaptability_bonus += 0.07 if 'adaptable' in temperament or 'versatile' in temperament: adaptability_bonus += 0.08 # 考慮環境適應因素 if user_prefs.yard_access == "no_yard": if "needs space" in description or "requires yard" in description: adaptability_bonus -= 0.05 * experience_multiplier # 根據經驗調整懲罰 elif user_prefs.yard_access == "private_yard": if "active" in description or "energetic" in description: adaptability_bonus += 0.05 bonus += min(0.15, adaptability_bonus) # 4. 專門技能評估（最高0.15）- 提高上限和進階使用者加分 skill_bonus = 0.0 exercise_level = user_prefs.exercise_time / 60.0 special_abilities = { 'working': 0.05 if exercise_level >= 1.5 else 0.02, 'herding': 0.05 if exercise_level >= 1.5 else 0.02, 'hunting': 0.05 if exercise_level >= 1.5 else 0.02, 'tracking': 0.04 if exercise_level >= 1.0 else 0.02, 'agility': 0.04 if exercise_level >= 1.0 else 0.02, 'sporting': 0.04 if exercise_level >= 1.2 else 0.02 } for ability, value in special_abilities.items(): if ability in temperament.lower() or ability in description: # 進階使用者在專門技能方面得到更多加分 if user_prefs.experience_level == 'advanced': skill_bonus += value * 1.5 elif user_prefs.experience_level == 'intermediate': skill_bonus += value * 1.2 else: skill_bonus += value # 根據時間可用性調整技能加分 time_multiplier = { 'flexible': 1.2, 'moderate': 1.0, 'limited': 0.8 }.get(user_prefs.time_availability, 1.0) skill_bonus *= time_multiplier bonus += min(0.15, skill_bonus) # 最終分數範圍調整，提高上限 return min(0.6, max(-0.20, bonus)) @staticmethod def calculate_additional_factors(breed_info: dict, user_prefs: 'UserPreferences') -> dict: """計算額外的評估因素""" 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 } temperament = breed_info.get('Temperament', '').lower() size = breed_info.get('Size', 'Medium') # 1. 多功能性評估 versatile_traits = ['intelligent', 'adaptable', 'trainable', 'athletic'] working_roles = ['working', 'herding', 'hunting', 'sporting', 'companion'] trait_score = sum(0.2 for trait in versatile_traits if trait in temperament) role_score = sum(0.2 for role in working_roles if role in breed_info.get('Description', '').lower()) factors['versatility'] = min(1.0, trait_score + role_score) # 2. 可訓練度評估 trainable_traits = { 'intelligent': 0.3, 'eager to please': 0.3, 'trainable': 0.2, 'quick learner': 0.2 } factors['trainability'] = min(1.0, sum(value for trait, value in trainable_traits.items() if trait in temperament)) # 3. 能量水平評估 exercise_needs = breed_info.get('Exercise Needs', 'MODERATE').upper() energy_levels = { 'VERY HIGH': 1.0, 'HIGH': 0.8, 'MODERATE': 0.6, 'LOW': 0.4, 'VARIES': 0.6 } factors['energy_level'] = energy_levels.get(exercise_needs, 0.6) # 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 } coat_penalty = 0.2 if any(term in breed_info.get('Description', '').lower() for term in ['long coat', 'double coat']) else 0 factors['grooming_needs'] = min(1.0, grooming_levels.get(grooming_needs, 0.6) + coat_penalty) # 5. 社交需求評估 social_traits = ['friendly', 'social', 'affectionate', 'people-oriented'] antisocial_traits = ['independent', 'aloof', 'reserved'] social_score = sum(0.25 for trait in social_traits if trait in temperament) antisocial_score = sum(-0.2 for trait in antisocial_traits if trait in temperament) factors['social_needs'] = min(1.0, max(0.0, social_score + antisocial_score)) # 6. 氣候適應性評估 climate_terms = { 'cold': ['thick coat', 'winter', 'cold climate'], 'hot': ['short coat', 'warm climate', 'heat tolerant'], 'moderate': ['adaptable', 'all climate'] } climate_matches = sum(1 for term in climate_terms[user_prefs.climate] if term in breed_info.get('Description', '').lower()) factors['weather_adaptability'] = min(1.0, climate_matches * 0.3 + 0.4) # 基礎分0.4 return factors @staticmethod def calculate_family_safety_score(breed_info: dict, children_age: str) -> float: """ 計算品種與家庭/兒童的安全相容性分數，作為calculate_compatibility_score的一部分 參數: breed_info (dict): 品種資訊 children_age (str): 兒童年齡組別 ('toddler', 'school_age', 'teenager') 返回: float: 0.2-0.95之間的安全分數 """ temperament = breed_info.get('Temperament', '').lower() size = breed_info.get('Size', 'Medium') # 基礎安全分數（根據體型） base_safety_scores = { "Small": 0.85, "Medium": 0.75, "Large": 0.65, "Giant": 0.55 } safety_score = base_safety_scores.get(size, 0.70) # 年齡相關的調整 age_factors = { 'toddler': { 'base_modifier': -0.15, 'size_penalty': { "Small": -0.05, "Medium": -0.10, "Large": -0.20, "Giant": -0.25 } }, 'school_age': { 'base_modifier': -0.08, 'size_penalty': { "Small": -0.02, "Medium": -0.05, "Large": -0.10, "Giant": -0.15 } }, 'teenager': { 'base_modifier': -0.05, 'size_penalty': { "Small": 0, "Medium": -0.02, "Large": -0.05, "Giant": -0.08 } } } # 應用年齡相關調整 age_adjustment = age_factors.get(children_age, age_factors['school_age']) safety_score += age_adjustment['base_modifier'] safety_score += age_adjustment['size_penalty'].get(size, -0.10) # 正面特徵評估 positive_traits = { 'gentle': 0.10, 'patient': 0.10, 'good with children': 0.15, 'friendly': 0.08, 'calm': 0.08, 'tolerant': 0.08, 'playful': 0.05 } # 危險特徵評估（更嚴格的懲罰） dangerous_traits = { 'aggressive': -0.25, 'territorial': -0.20, 'protective': -0.15, 'nervous': -0.15, 'dominant': -0.15, 'strong-willed': -0.12, 'independent': -0.10, 'energetic': -0.08 } # 計算特徵分數 for trait, bonus in positive_traits.items(): if trait in temperament: safety_score += bonus * 0.8 # 降低正面特徵的影響力 for trait, penalty in dangerous_traits.items(): if trait in temperament: # 對幼童加重懲罰 if children_age == 'toddler': safety_score += penalty * 1.3 # 對青少年略微減輕懲罰 elif children_age == 'teenager': safety_score += penalty * 0.8 else: safety_score += penalty # 特殊風險評估 description = breed_info.get('Description', '').lower() if 'history of' in description: safety_score -= 0.15 if 'requires experienced' in description: safety_score -= 0.10 # 將分數限制在合理範圍內 return max(0.2, min(0.95, safety_score)) 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的優化版本""" 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: """空間分數計算""" # 基礎空間需求矩陣 base_scores = { "Small": {"apartment": 0.95, "house_small": 1.0, "house_large": 0.90}, "Medium": {"apartment": 0.60, "house_small": 0.90, "house_large": 1.0}, "Large": {"apartment": 0.30, "house_small": 0.75, "house_large": 1.0}, "Giant": {"apartment": 0.15, "house_small": 0.55, "house_large": 1.0} } # 取得基礎分數 base_score = base_scores.get(size, base_scores["Medium"])[living_space] # 運動需求調整 exercise_adjustments = { "Very High": -0.15 if living_space == "apartment" else 0, "High": -0.10 if living_space == "apartment" else 0, "Moderate": 0, "Low": 0.05 if living_space == "apartment" else 0 } adjustments = exercise_adjustments.get(exercise_needs.strip(), 0) # 院子獎勵 if has_yard and size in ["Large", "Giant"]: adjustments += 0.10 elif has_yard: adjustments += 0.05 return min(1.0, max(0.1, base_score + adjustments)) def calculate_exercise_score(breed_needs: str, user_time: int) -> float: """運動需求計算""" exercise_needs = { 'VERY HIGH': {'min': 120, 'ideal': 150, 'max': 180}, 'HIGH': {'min': 90, 'ideal': 120, 'max': 150}, 'MODERATE': {'min': 45, 'ideal': 60, 'max': 90}, 'LOW': {'min': 20, 'ideal': 30, 'max': 45}, 'VARIES': {'min': 30, 'ideal': 60, 'max': 90} } breed_need = exercise_needs.get(breed_needs.strip().upper(), exercise_needs['MODERATE']) # 計算匹配度 if user_time >= breed_need['ideal']: if user_time > breed_need['max']: return 0.9 # 稍微降分，因為可能過度運動 return 1.0 elif user_time >= breed_need['min']: return 0.8 + (user_time - breed_need['min']) / (breed_need['ideal'] - breed_need['min']) * 0.2 else: return max(0.3, 0.8 * (user_time / breed_need['min'])) def calculate_grooming_score(breed_needs: str, user_commitment: str, breed_size: str) -> float: """美容需求計算""" # 基礎分數矩陣 base_scores = { "High": {"low": 0.3, "medium": 0.7, "high": 1.0}, "Moderate": {"low": 0.5, "medium": 0.9, "high": 1.0}, "Low": {"low": 1.0, "medium": 0.95, "high": 0.8} } # 取得基礎分數 base_score = base_scores.get(breed_needs, base_scores["Moderate"])[user_commitment] # 體型影響調整 size_adjustments = { "Large": {"low": -0.2, "medium": -0.1, "high": 0}, "Giant": {"low": -0.3, "medium": -0.15, "high": 0}, } if breed_size in size_adjustments: adjustment = size_adjustments[breed_size].get(user_commitment, 0) base_score = max(0.2, base_score + adjustment) return base_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 # 參數說明: # care_level: 品種的照顧難度 ("High", "Moderate", "Low") # user_experience: 使用者經驗等級 ("beginner", "intermediate", "advanced") # temperament: 品種的性格特徵描述 # 返回: # float: 0.2-1.0 之間的匹配分數 # """ # # 基礎分數矩陣 - 更大的分數差異來反映經驗重要性 # base_scores = { # "High": { # "beginner": 0.12, # 降低起始分，反映高難度品種對新手的挑戰 # "intermediate": 0.65, # 中級玩家可以應付，但仍有改善空間 # "advanced": 1.0 # 資深者能完全勝任 # }, # "Moderate": { # "beginner": 0.35, # 適中難度對新手來說仍具挑戰 # "intermediate": 0.82, # 中級玩家有很好的勝任能力 # "advanced": 1.0 # 資深者完全勝任 # }, # "Low": { # "beginner": 0.72, # 低難度品種適合新手 # "intermediate": 0.92, # 中級玩家幾乎完全勝任 # "advanced": 1.0 # 資深者完全勝任 # } # } # # 取得基礎分數 # score = base_scores.get(care_level, base_scores["Moderate"])[user_experience] # # 性格特徵評估 - 根據經驗等級調整權重 # temperament_lower = temperament.lower() # temperament_adjustments = 0.0 # if user_experience == "beginner": # # 新手不適合的特徵 - 更嚴格的懲罰 # difficult_traits = { # 'stubborn': -0.15, # 加重固執的懲罰 # 'independent': -0.12, # 加重獨立性的懲罰 # 'dominant': -0.12, # 加重支配性的懲罰 # 'strong-willed': -0.10, # 加重強勢的懲罰 # 'protective': -0.08, # 加重保護性的懲罰 # 'aloof': -0.08, # 加重冷漠的懲罰 # 'energetic': -0.06 # 輕微懲罰高能量 # } # # 新手友善的特徵 - 提供更多獎勵 # easy_traits = { # 'gentle': 0.08, # 增加溫和的獎勵 # 'friendly': 0.08, # 增加友善的獎勵 # 'eager to please': 0.08, # 增加順從的獎勵 # 'patient': 0.06, # 獎勵耐心 # 'adaptable': 0.06, # 獎勵適應性 # 'calm': 0.05 # 獎勵冷靜 # } # # 計算特徵調整 # for trait, penalty in difficult_traits.items(): # if trait in temperament_lower: # temperament_adjustments += penalty * 1.2 # 加重新手的懲罰 # for trait, bonus in easy_traits.items(): # if trait in temperament_lower: # temperament_adjustments += bonus # # 品種特殊調整 # if any(term in temperament_lower for term in ['terrier', 'working', 'guard']): # temperament_adjustments -= 0.12 # 加重對特定類型品種的懲罰 # elif user_experience == "intermediate": # # 中級玩家的調整更加平衡 # moderate_traits = { # 'intelligent': 0.05, # 獎勵聰明 # 'athletic': 0.04, # 獎勵運動能力 # 'versatile': 0.04, # 獎勵多功能性 # 'stubborn': -0.06, # 輕微懲罰固執 # 'independent': -0.05, # 輕微懲罰獨立性 # 'protective': -0.04 # 輕微懲罰保護性 # } # 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': 0.04, # 反轉為優勢 # 'independent': 0.04, # 反轉為優勢 # 'intelligent': 0.05, # 獎勵聰明 # 'protective': 0.04, # 獎勵保護性 # 'strong-willed': 0.03 # 獎勵強勢 # } # for trait, bonus in advanced_traits.items(): # if trait in temperament_lower: # temperament_adjustments += bonus # # 確保最終分數在合理範圍內 # final_score = max(0.2, min(1.0, score + temperament_adjustments)) # return 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，強化進階使用者的評分 """ # 調整基礎分數矩陣，提高 advanced 的基本分數 base_scores = { "High": { "beginner": 0.35, "intermediate": 0.75, "advanced": 0.92 # 提高上限 }, "Moderate": { "beginner": 0.45, "intermediate": 0.82, "advanced": 0.95 }, "Low": { "beginner": 0.60, "intermediate": 0.85, "advanced": 0.98 } } score = base_scores.get(care_level, base_scores["Moderate"])[user_experience] # 性格特徵評估 temperament_lower = temperament.lower() temperament_adjustments = 0.0 if user_experience == "advanced": # 進階使用者的特徵評估 advanced_traits = { 'intelligent': 0.08, # 提高正面特徵加分 'independent': 0.06, 'strong-willed': 0.05, 'energetic': 0.05, 'protective': 0.04, 'dominant': -0.02, # 降低負面特徵懲罰 'stubborn': -0.02, 'aggressive': -0.05 } for trait, adjustment in advanced_traits.items(): if trait in temperament_lower: temperament_adjustments += adjustment elif user_experience == "intermediate": moderate_traits = { 'intelligent': 0.05, 'adaptable': 0.04, 'easy-going': 0.04, 'stubborn': -0.06, 'independent': -0.05, 'dominant': -0.05, 'aggressive': -0.08 } for trait, adjustment in moderate_traits.items(): if trait in temperament_lower: temperament_adjustments += adjustment else: # beginner beginner_traits = { 'gentle': 0.05, 'friendly': 0.05, 'easy-going': 0.04, 'stubborn': -0.10, 'independent': -0.08, 'dominant': -0.08, 'aggressive': -0.12 } for trait, adjustment in beginner_traits.items(): if trait in temperament_lower: temperament_adjustments += adjustment # 特殊能力評估 - 對進階使用者加分 if user_experience == "advanced": special_abilities = { 'working': 0.04, 'hunting': 0.04, 'herding': 0.04, 'guard': 0.03, 'agility': 0.03 } for ability, bonus in special_abilities.items(): if ability in temperament_lower: temperament_adjustments += bonus # 確保最終分數在合理範圍內 final_score = max(0.3, min(0.98, score + temperament_adjustments)) return final_score def calculate_health_score(breed_name: str) -> float: """計算品種健康分數""" 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（降低0.15分） severe_conditions = [ 'hip dysplasia', 'heart disease', 'progressive retinal atrophy', 'bloat', 'epilepsy', 'degenerative myelopathy', 'von willebrand disease' ] # 中度健康問題（降低0.1分） moderate_conditions = [ 'allergies', 'eye problems', 'joint problems', 'hypothyroidism', 'ear infections', 'skin issues' ] # 輕微健康問題（降低0.05分） minor_conditions = [ 'dental issues', 'weight gain tendency', 'minor allergies', 'seasonal allergies' ] # 計算基礎健康分數 health_score = 1.0 # 根據問題嚴重程度扣分 severe_count = sum(1 for condition in severe_conditions if condition in health_notes) moderate_count = sum(1 for condition in moderate_conditions if condition in health_notes) minor_count = sum(1 for condition in minor_conditions if condition in health_notes) health_score -= (severe_count * 0.15) health_score -= (moderate_count * 0.1) health_score -= (minor_count * 0.05) # 壽命影響 try: lifespan = breed_health_info[breed_name].get('average_lifespan', '10-12') years = float(lifespan.split('-')[0]) if years < 8: health_score *= 0.9 elif years > 13: health_score *= 1.1 except: pass # 特殊健康優勢 if 'generally healthy' in health_notes or 'hardy breed' in health_notes: health_score *= 1.1 return max(0.2, min(1.0, health_score)) def calculate_noise_score(breed_name: str, user_noise_tolerance: str) -> float: """計算品種噪音分數""" 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() # 基礎噪音分數矩陣 base_scores = { 'low': {'low': 1.0, 'medium': 0.9, 'high': 0.8}, 'medium': {'low': 0.7, 'medium': 1.0, 'high': 0.9}, 'high': {'low': 0.4, 'medium': 0.7, 'high': 1.0}, 'varies': {'low': 0.6, 'medium': 0.8, 'high': 0.9} } # 獲取基礎分數 base_score = base_scores.get(noise_level, {'low': 0.7, 'medium': 0.8, 'high': 0.6})[user_noise_tolerance] # 吠叫原因評估 barking_reasons_penalty = 0 problematic_triggers = [ ('separation anxiety', -0.15), ('excessive barking', -0.12), ('territorial', -0.08), ('alert barking', -0.05), ('attention seeking', -0.05) ] for trigger, penalty in problematic_triggers: if trigger in noise_notes: barking_reasons_penalty += penalty # 可訓練性補償 trainability_bonus = 0 if 'responds well to training' in noise_notes: trainability_bonus = 0.1 elif 'can be trained' in noise_notes: trainability_bonus = 0.05 # 特殊情況 special_adjustments = 0 if 'rarely barks' in noise_notes: special_adjustments += 0.1 if 'howls' in noise_notes and user_noise_tolerance == 'low': special_adjustments -= 0.1 final_score = base_score + barking_reasons_penalty + trainability_bonus + special_adjustments return max(0.2, min(1.0, final_score)) # 計算所有基礎分數 scores = { 'space': calculate_space_score( breed_info['Size'], user_prefs.living_space, user_prefs.space_for_play, breed_info.get('Exercise Needs', 'Moderate') ), 'exercise': calculate_exercise_score( breed_info.get('Exercise Needs', 'Moderate'), user_prefs.exercise_time ), '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', '')), 'noise': calculate_noise_score(breed_info.get('Breed', ''), user_prefs.noise_tolerance) } breed_bonus = calculate_breed_bonus(breed_info, user_prefs) scores['breed_bonus'] = breed_bonus # 如果有小孩，加入 family_safety 分數 if user_prefs.has_children: scores['family_safety'] = calculate_family_safety_score(breed_info, user_prefs.children_age) # 優化權重配置 weights = { 'space': 0.28, 'exercise': 0.18, 'grooming': 0.12, 'experience': 0.22, 'health': 0.12, 'noise': 0.08 } if user_prefs.has_children: weights = { 'space': 0.20, 'exercise': 0.13, 'grooming': 0.08, 'experience': 0.15, 'health': 0.10, 'noise': 0.06, 'breed_bonus': 0.08, 'family_safety': 0.20 # 給予家庭安全較高權重 } # 計算加權總分 weighted_score = sum(score * weights[category] for category, score in scores.items()) # def amplify_score(score): # """ # 優化分數放大函數，確保分數範圍合理且結果一致 # """ # # 基礎調整 # adjusted = (score - 0.35) * 1.8 # # 使用 3.2 次方使曲線更平滑 # amplified = pow(adjusted, 3.2) / 5.8 + score # # 特別處理高分區間，確保不超過95% # if amplified > 0.90: # # 壓縮高分區間，確保最高到95% # amplified = 0.90 + (amplified - 0.90) * 0.5 # # 確保最終分數在合理範圍內（0.55-0.95） # final_score = max(0.55, min(0.95, amplified)) # # 四捨五入到小數點後第三位 # return round(final_score, 3) # final_score = amplify_score(weighted_score) # # 四捨五入所有分數 # scores = {k: round(v, 4) for k, v in scores.items()} # scores['overall'] = round(final_score, 4) # return scores def amplify_score(score): """ 優化分數放大函數，確保更合理的分數分布和品種間的差異 1. 調整基礎計算係數，使分數差異更明顯 2. 重新設計高分區間的處理 3. 確保進階使用者能獲得更高分數 4. 維持適當的分數下限 """ # 第一階段：基礎調整 # 降低基礎調整係數，使分數變化更平滑 adjusted = (score - 0.30) * 1.5 # 第二階段：非線性轉換 # 使用較小的指數，讓分數差異更明顯 amplified = pow(adjusted, 2.8) / 4.2 + score # 第三階段：分數區間處理 if amplified > 0.85: # 高分區間的處理更細緻 extra = (amplified - 0.85) amplified = 0.85 + extra * 0.6 elif amplified < 0.65: # 低分區間給予適當提升 boost = (0.65 - amplified) * 0.3 amplified += boost # 第四階段：確保分數在合理範圍內 # 提高最低分數，降低最高分數 final_score = max(0.60, min(0.92, amplified)) # 第五階段：微調 # 讓分數更容易落在中間區間 if 0.75 <= final_score <= 0.85: variance = (random.random() - 0.5) * 0.02 final_score += variance # 確保最終分數在絕對範圍內 final_score = max(0.60, min(0.92, final_score)) return round(final_score, 3) except Exception as e: print(f"Error details: {str(e)}") print(f"breed_info: {breed_info}") # print(f"Error in calculate_compatibility_score: {str(e)}") return {k: 0.5 for k in ['space', 'exercise', 'grooming', 'experience', 'health', 'noise', 'overall']}