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Update scoring_calculation_system.py
Browse files- scoring_calculation_system.py +169 -183
scoring_calculation_system.py
CHANGED
@@ -129,39 +129,37 @@ class UserPreferences:
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@staticmethod
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def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float:
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"""
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計算品種額外加分,考慮多個維度但不包含家庭相容性評分
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"""
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bonus = 0.0
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temperament = breed_info.get('Temperament', '').lower()
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description = breed_info.get('Description', '').lower()
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# 1. 壽命加分(最高0.
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try:
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lifespan = breed_info.get('Lifespan', '10-12 years')
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years = [int(x) for x in lifespan.split('-')[0].split()[0:1]]
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longevity_bonus = min(0.
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bonus += longevity_bonus
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except:
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pass
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# 2. 性格特徵加分(最高0.
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positive_traits = {
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'friendly': 0.
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'gentle': 0.
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'patient': 0.
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'intelligent': 0.
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'adaptable': 0.
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'affectionate': 0.
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'easy-going': 0.
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'calm': 0.
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}
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#
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experience_multiplier = {
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'beginner': 1.3,
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'intermediate':
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'advanced': 0.
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}.get(user_prefs.experience_level, 1.0)
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negative_traits = {
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@@ -174,86 +172,63 @@ def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> fl
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}
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personality_score = sum(value for trait, value in positive_traits.items() if trait in temperament)
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personality_score += sum(value for trait, value in negative_traits.items() if trait in temperament)
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bonus += max(-0.15, min(0.
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# 3. 適應性加分(最高0.
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adaptability_bonus = 0.0
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if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment":
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adaptability_bonus += 0.
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if 'adaptable' in temperament or 'versatile' in temperament:
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adaptability_bonus += 0.
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#
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if user_prefs.yard_access == "no_yard":
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if "needs space" in description or "requires yard" in description:
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adaptability_bonus -= 0.05
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elif user_prefs.yard_access == "private_yard":
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if "active" in description or "energetic" in description:
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adaptability_bonus += 0.
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-
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climate_terms = {
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'cold': ['thick coat', 'winter', 'cold climate'],
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'hot': ['short coat', 'warm climate', 'heat tolerant'],
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'moderate': ['adaptable', 'all climate']
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}
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if any(term in description for term in climate_terms[user_prefs.climate]):
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adaptability_bonus += 0.02
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bonus += min(0.1, adaptability_bonus)
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# 4. 專門技能評估(最高0.
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skill_bonus = 0.0
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exercise_level = user_prefs.exercise_time / 60.0
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special_abilities = {
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'working': 0.
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'herding': 0.
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'hunting': 0.
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'tracking': 0.
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'agility': 0.
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'sporting': 0.
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}
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for ability, value in special_abilities.items():
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if ability in temperament.lower() or ability in description:
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#
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time_multiplier = 1.2 if user_prefs.time_availability == 'flexible' else 0.8
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if user_prefs.experience_level == 'advanced':
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skill_bonus += value * 1.
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elif user_prefs.experience_level == '
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skill_bonus += value *
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else:
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skill_bonus += value
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if user_prefs.experience_level == 'beginner':
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special_needs_score -= 0.08
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elif user_prefs.experience_level == 'intermediate':
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special_needs_score -= 0.04
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# 檢查社交需求
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if 'social needs' in description or 'requires companionship' in description:
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if user_prefs.time_availability == 'limited':
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special_needs_score -= 0.06
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elif user_prefs.time_availability == 'flexible':
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special_needs_score += 0.02
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# 檢查獨立性
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if 'independent' in temperament:
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if user_prefs.time_availability == 'limited':
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special_needs_score += 0.03
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bonus += max(-0.15, min(0.1, special_needs_score))
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@staticmethod
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@@ -638,130 +613,99 @@ def calculate_compatibility_score(breed_info: dict, user_prefs: UserPreferences)
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# final_score = max(0.2, min(1.0, score + temperament_adjustments))
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# return final_score
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def calculate_experience_score(care_level: str, user_experience: str, temperament: str
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"""
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"""
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#
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base_scores = {
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"High": {
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"beginner": 0.12,
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"intermediate": 0.65,
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"advanced": 0.85
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},
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"Moderate": {
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"beginner": 0.35,
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"intermediate": 0.75,
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"advanced": 0.
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},
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"Low": {
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"beginner": 0.
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"intermediate": 0.85,
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"advanced": 0.
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}
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}
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# 取得基礎分數
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score = base_scores.get(care_level, base_scores["Moderate"])[user_experience]
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#
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breed_difficulty_penalty = 0.0
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difficult_characteristics = {
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'working': 0.08,
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'guardian': 0.10,
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'primitive': 0.12,
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'independent': 0.08,
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'strong-willed': 0.07
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}
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for trait, penalty in difficult_characteristics.items():
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if trait in temperament.lower():
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# 即使是進階使用者也會受到一定程度的懲罰
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if user_experience == "advanced":
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breed_difficulty_penalty += penalty * 0.4
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elif user_experience == "intermediate":
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breed_difficulty_penalty += penalty * 0.7
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else:
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breed_difficulty_penalty += penalty
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# 性格特徵評估
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temperament_lower = temperament.lower()
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temperament_adjustments = 0.0
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if user_experience == "
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'eager to please': 0.08,
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'patient': 0.06
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}
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for trait,
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if trait in temperament_lower:
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temperament_adjustments += penalty * 1.2
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for trait, bonus in easy_traits.items():
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if trait in temperament_lower:
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temperament_adjustments +=
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elif user_experience == "intermediate":
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moderate_traits = {
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'intelligent': 0.
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'stubborn': -0.06,
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'independent': -0.05
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}
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for trait, adjustment in moderate_traits.items():
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if trait in temperament_lower:
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temperament_adjustments += adjustment
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else: #
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}
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for trait, adjustment in
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if trait in temperament_lower:
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temperament_adjustments += adjustment
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#
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'strong-willed': 0.05
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}
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for
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if
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temperament_adjustments -= safety_penalty
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# 確保最終分數在合理範圍內,且有適當的上限
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final_score = score - breed_difficulty_penalty + temperament_adjustments
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return max(0.2, min(0.92, final_score))
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def calculate_health_score(breed_name: str) -> float:
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"""計算品種健康分數"""
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# 計算加權總分
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weighted_score = sum(score * weights[category] for category, score in scores.items())
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def amplify_score(score):
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final_score = amplify_score(weighted_score)
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#
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scores = {k: round(v, 4) for k, v in scores.items()}
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scores['overall'] = round(final_score, 4)
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except Exception as e:
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print(f"Error details: {str(e)}")
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@staticmethod
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def calculate_breed_bonus(breed_info: dict, user_prefs: 'UserPreferences') -> float:
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"""計算品種額外加分,強化進階使用者的評分"""
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bonus = 0.0
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temperament = breed_info.get('Temperament', '').lower()
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description = breed_info.get('Description', '').lower()
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# 1. 壽命加分(最高0.08)- 提高上限
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try:
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lifespan = breed_info.get('Lifespan', '10-12 years')
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years = [int(x) for x in lifespan.split('-')[0].split()[0:1]]
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longevity_bonus = min(0.08, (max(years) - 10) * 0.015) # 提高係數
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bonus += longevity_bonus
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except:
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pass
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# 2. 性格特徵加分(最高0.20)- 提高上限
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positive_traits = {
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'friendly': 0.06,
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'gentle': 0.06,
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'patient': 0.06,
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'intelligent': 0.05,
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'adaptable': 0.05,
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'affectionate': 0.05,
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'easy-going': 0.04,
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'calm': 0.04
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}
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# 根據經驗等級調整負面特徵的影響
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experience_multiplier = {
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'beginner': 1.3,
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'intermediate': 0.8,
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'advanced': 0.5 # 大幅降低進階使用者的負面特徵懲罰
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}.get(user_prefs.experience_level, 1.0)
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negative_traits = {
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}
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personality_score = sum(value for trait, value in positive_traits.items() if trait in temperament)
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if user_prefs.experience_level == 'advanced':
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personality_score *= 1.2 # 進階使用者得到更多正面特徵加分
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personality_score += sum(value for trait, value in negative_traits.items() if trait in temperament)
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bonus += max(-0.15, min(0.20, personality_score))
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# 3. 適應性加分(最高0.15)- 提高上限
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adaptability_bonus = 0.0
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if breed_info.get('Size') == "Small" and user_prefs.living_space == "apartment":
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adaptability_bonus += 0.07
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if 'adaptable' in temperament or 'versatile' in temperament:
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adaptability_bonus += 0.08
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# 考慮環境適應因素
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if user_prefs.yard_access == "no_yard":
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if "needs space" in description or "requires yard" in description:
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adaptability_bonus -= 0.05 * experience_multiplier # 根據經驗調整懲罰
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elif user_prefs.yard_access == "private_yard":
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if "active" in description or "energetic" in description:
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adaptability_bonus += 0.05
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bonus += min(0.15, adaptability_bonus)
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# 4. 專門技能評估(最高0.15)- 提高上限和進階使用者加分
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skill_bonus = 0.0
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exercise_level = user_prefs.exercise_time / 60.0
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special_abilities = {
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'working': 0.05 if exercise_level >= 1.5 else 0.02,
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'herding': 0.05 if exercise_level >= 1.5 else 0.02,
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'hunting': 0.05 if exercise_level >= 1.5 else 0.02,
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'tracking': 0.04 if exercise_level >= 1.0 else 0.02,
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'agility': 0.04 if exercise_level >= 1.0 else 0.02,
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'sporting': 0.04 if exercise_level >= 1.2 else 0.02
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}
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for ability, value in special_abilities.items():
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if ability in temperament.lower() or ability in description:
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# 進階使用者在專門技能方面得到更多加分
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if user_prefs.experience_level == 'advanced':
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skill_bonus += value * 1.5
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elif user_prefs.experience_level == 'intermediate':
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skill_bonus += value * 1.2
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else:
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skill_bonus += value
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# 根據時間可用性調整技能加分
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time_multiplier = {
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'flexible': 1.2,
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'moderate': 1.0,
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'limited': 0.8
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}.get(user_prefs.time_availability, 1.0)
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skill_bonus *= time_multiplier
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bonus += min(0.15, skill_bonus)
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# 最終分數範圍調整,提高上限
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return min(0.6, max(-0.20, bonus))
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@staticmethod
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# final_score = max(0.2, min(1.0, score + temperament_adjustments))
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# return final_score
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def calculate_experience_score(care_level: str, user_experience: str, temperament: str) -> float:
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"""
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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
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"""
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# 調整基礎分數矩陣,提高 advanced 的基本分數
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base_scores = {
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"High": {
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"beginner": 0.35,
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"intermediate": 0.75,
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"advanced": 0.92 # 提高上限
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},
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"Moderate": {
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"beginner": 0.45,
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"intermediate": 0.82,
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"advanced": 0.95
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},
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"Low": {
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"beginner": 0.60,
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"intermediate": 0.85,
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"advanced": 0.98
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636 |
}
|
637 |
}
|
638 |
|
|
|
639 |
score = base_scores.get(care_level, base_scores["Moderate"])[user_experience]
|
640 |
|
641 |
+
# 性格特徵評估
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|
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|
642 |
temperament_lower = temperament.lower()
|
643 |
temperament_adjustments = 0.0
|
644 |
|
645 |
+
if user_experience == "advanced":
|
646 |
+
# 進階使用者的特徵評估
|
647 |
+
advanced_traits = {
|
648 |
+
'intelligent': 0.08, # 提高正面特徵加分
|
649 |
+
'independent': 0.06,
|
650 |
+
'strong-willed': 0.05,
|
651 |
+
'energetic': 0.05,
|
652 |
+
'protective': 0.04,
|
653 |
+
'dominant': -0.02, # 降低負面特徵懲罰
|
654 |
+
'stubborn': -0.02,
|
655 |
+
'aggressive': -0.05
|
|
|
|
|
656 |
}
|
657 |
|
658 |
+
for trait, adjustment in advanced_traits.items():
|
|
|
|
|
|
|
|
|
659 |
if trait in temperament_lower:
|
660 |
+
temperament_adjustments += adjustment
|
661 |
|
662 |
elif user_experience == "intermediate":
|
663 |
moderate_traits = {
|
664 |
+
'intelligent': 0.05,
|
665 |
+
'adaptable': 0.04,
|
666 |
+
'easy-going': 0.04,
|
667 |
'stubborn': -0.06,
|
668 |
+
'independent': -0.05,
|
669 |
+
'dominant': -0.05,
|
670 |
+
'aggressive': -0.08
|
671 |
}
|
672 |
|
673 |
for trait, adjustment in moderate_traits.items():
|
674 |
if trait in temperament_lower:
|
675 |
temperament_adjustments += adjustment
|
676 |
+
|
677 |
+
else: # beginner
|
678 |
+
beginner_traits = {
|
679 |
+
'gentle': 0.05,
|
680 |
+
'friendly': 0.05,
|
681 |
+
'easy-going': 0.04,
|
682 |
+
'stubborn': -0.10,
|
683 |
+
'independent': -0.08,
|
684 |
+
'dominant': -0.08,
|
685 |
+
'aggressive': -0.12
|
686 |
}
|
687 |
|
688 |
+
for trait, adjustment in beginner_traits.items():
|
689 |
if trait in temperament_lower:
|
690 |
temperament_adjustments += adjustment
|
691 |
+
|
692 |
+
# 特殊能力評估 - 對進階使用者加分
|
693 |
+
if user_experience == "advanced":
|
694 |
+
special_abilities = {
|
695 |
+
'working': 0.04,
|
696 |
+
'hunting': 0.04,
|
697 |
+
'herding': 0.04,
|
698 |
+
'guard': 0.03,
|
699 |
+
'agility': 0.03
|
|
|
700 |
}
|
701 |
|
702 |
+
for ability, bonus in special_abilities.items():
|
703 |
+
if ability in temperament_lower:
|
704 |
+
temperament_adjustments += bonus
|
705 |
+
|
706 |
+
# 確保最終分數在合理範圍內
|
707 |
+
final_score = max(0.3, min(0.98, score + temperament_adjustments))
|
708 |
+
return final_score
|
|
|
|
|
|
|
|
|
|
|
|
|
709 |
|
710 |
def calculate_health_score(breed_name: str) -> float:
|
711 |
"""計算品種健康分數"""
|
|
|
883 |
# 計算加權總分
|
884 |
weighted_score = sum(score * weights[category] for category, score in scores.items())
|
885 |
|
886 |
+
# def amplify_score(score):
|
887 |
+
# """
|
888 |
+
# 優化分數放大函數,確保分數範圍合理且結果一致
|
889 |
+
# """
|
890 |
+
# # 基礎調整
|
891 |
+
# adjusted = (score - 0.35) * 1.8
|
892 |
|
893 |
+
# # 使用 3.2 次方使曲線更平滑
|
894 |
+
# amplified = pow(adjusted, 3.2) / 5.8 + score
|
895 |
|
896 |
+
# # 特別處理高分區間,確保不超過95%
|
897 |
+
# if amplified > 0.90:
|
898 |
+
# # 壓縮高分區間,確保最高到95%
|
899 |
+
# amplified = 0.90 + (amplified - 0.90) * 0.5
|
900 |
|
901 |
+
# # 確保最終分數在合理範圍內(0.55-0.95)
|
902 |
+
# final_score = max(0.55, min(0.95, amplified))
|
903 |
|
904 |
+
# # 四捨五入到小數點後第三位
|
905 |
+
# return round(final_score, 3)
|
906 |
|
907 |
+
# final_score = amplify_score(weighted_score)
|
908 |
+
|
909 |
+
# # 四捨五入所有分數
|
910 |
+
# scores = {k: round(v, 4) for k, v in scores.items()}
|
911 |
+
# scores['overall'] = round(final_score, 4)
|
912 |
|
913 |
+
# return scores
|
|
|
|
|
914 |
|
915 |
+
def amplify_score(score):
|
916 |
+
"""
|
917 |
+
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
|
918 |
+
|
919 |
+
1. 調整基礎計算係數,使分數差異更明顯
|
920 |
+
2. 重新設計高分區間的處理
|
921 |
+
3. 確保進階使用者能獲得更高分數
|
922 |
+
4. 維持適當的分數下限
|
923 |
+
"""
|
924 |
+
# 第一階段:基礎調整
|
925 |
+
# 降低基礎調整係數,使分數變化更平滑
|
926 |
+
adjusted = (score - 0.30) * 1.5
|
927 |
+
|
928 |
+
# 第二階段:非線性轉換
|
929 |
+
# 使用較小的指數,讓分數差異更明顯
|
930 |
+
amplified = pow(adjusted, 2.8) / 4.2 + score
|
931 |
+
|
932 |
+
# 第三階段:分數區間處理
|
933 |
+
if amplified > 0.85:
|
934 |
+
# 高分區間的處理更細緻
|
935 |
+
extra = (amplified - 0.85)
|
936 |
+
amplified = 0.85 + extra * 0.6
|
937 |
+
elif amplified < 0.65:
|
938 |
+
# 低分區間給予適當提升
|
939 |
+
boost = (0.65 - amplified) * 0.3
|
940 |
+
amplified += boost
|
941 |
+
|
942 |
+
# 第四階段:確保分數在合理範圍內
|
943 |
+
# 提高最低分數,降低最高分數
|
944 |
+
final_score = max(0.60, min(0.92, amplified))
|
945 |
+
|
946 |
+
# 第五階段:微調
|
947 |
+
# 讓分數更容易落在中間區間
|
948 |
+
if 0.75 <= final_score <= 0.85:
|
949 |
+
variance = (random.random() - 0.5) * 0.02
|
950 |
+
final_score += variance
|
951 |
+
|
952 |
+
# 確保最終分數在絕對範圍內
|
953 |
+
final_score = max(0.60, min(0.92, final_score))
|
954 |
+
|
955 |
+
return round(final_score, 3)
|
956 |
|
957 |
except Exception as e:
|
958 |
print(f"Error details: {str(e)}")
|