from qiskit import QuantumCircuit, execute, Aer, transpile
from qiskit.circuit.library import ZZFeatureMap
from qiskit_machine_learning.kernels import QuantumKernel
from qiskit.aqua.components.optimizers import COBYLA
from dwave.system import EmbeddingComposite, DWaveSampler
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.svm import SVC
from scipy.stats import poisson
import nltk
from nltk.sentiment import SentimentIntensityAnalyzer
from sklearn.preprocessing import MinMaxScaler
from functools import lru_cache, wraps
import logging
from concurrent.futures import ThreadPoolExecutor
import time
import warnings
import psutil
import pickle
import sys
import math
# 配置日志
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger('QuantumFootballPrediction')
warnings.filterwarnings('ignore', category=UserWarning)
# 初始化NLP工具
try:
nltk.data.find('sentiment/vader_lexicon.zip')
except LookupError:
nltk.download('vader_lexicon', quiet=True)
sia = SentimentIntensityAnalyzer()
# 添加性能监控
def performance_monitor(func):
call_counts = {}
@wraps(func)
def wrapper(*args, **kwargs):
start_time = time.perf_counter()
start_mem = psutil.Process().memory_info().rss / 1024 / 1024 # MB
result = func(*args, **kwargs)
elapsed = time.perf_counter() - start_time
end_mem = psutil.Process().memory_info().rss / 1024 / 1024
# 获取类实例
instance = args[0] if args else None
logger_name = getattr(instance, 'logger', logger).name if instance else logger.name
# 记录执行时间与内存
logging.getLogger(logger_name).info(
f"{func.__name__}: "
f"Time={elapsed:.4f}s, "
f"Mem={end_mem - start_mem:.2f}MB, "
f"Called={call_counts.get(func.__name__, 0) + 1} times"
)
# 更新调用计数
call_counts[func.__name__] = call_counts.get(func.__name__, 0) + 1
# 量子电路特定指标
if 'quantum' in func.__name__ and isinstance(result, dict) and 'circuit' in result:
circuit = result['circuit']
logging.getLogger(logger_name).debug(
f"Quantum circuit depth: {circuit.depth()}, gates: {len(circuit)}"
)
return result
return wrapper
class QuantumMatchPredictor:
"""量子比赛预测器 - 核心量子预测组件"""
def __init__(self, n_qubits=4):
self.n_qubits = n_qubits
self.optimizer = COBYLA(maxiter=100)
self.backend = Aer.get_backend('qasm_simulator')
self.feature_map = ZZFeatureMap(n_qubits, reps=2)
def build_circuit(self, features: np.ndarray) -> QuantumCircuit:
"""构建参数化预测线路 - 优化为O(n)复杂度"""
qc = QuantumCircuit(self.n_qubits)
# 特征编码层 - 使用特征值直接映射到旋转门
for i, val in enumerate(features[:self.n_qubits]):
qc.ry(val * np.pi, i) # 特征值映射到[0, π]范围
# 添加特征映射层
qc.compose(self.feature_map, inplace=True)
return qc
@performance_monitor
def predict(self, features: np.ndarray, shots=1024) -> float:
"""量子预测核心 - 返回主胜概率"""
qc = self.build_circuit(features)
# 添加测量
qc.measure_all()
# 执行并返回概率
try:
tqc = transpile(qc, self.backend, optimization_level=1)
result = execute(tqc, self.backend, shots=shots).result()
counts = result.get_counts(tqc)
# 返回全1状态的概率作为主胜概率
return counts.get('1'*self.n_qubits, 0) / shots
except Exception as e:
logger.error(f"量子预测执行失败: {e}")
# 返回随机值作为后备
return np.random.uniform(0.4, 0.6)
class QuantumFootballPredictionModel:
# 资源管理常量
MAX_QUBITS = 8
MAX_BATCH_SIZE = 8
_quantum_shots = 5000
def __init__(self, biomechanics_model_path=None):
# 联赛特异性权重模板
self.league_templates = {
'英超': {'体能系数': 1.3, '顺力度': 0.9, '特殊规则': '补时>5分钟时大球概率+15%'},
'意甲': {'防守强度': 1.5, '往绩克制': 1.2, '特殊规则': '平局概率基准值+8%'},
'德甲': {'主场优势': 1.4, '前锋缺阵影响': 2, '特殊规则': '半场领先最终胜率92%'},
'西甲': {'技术流修正': 1.2, '裁判影响': 0.7, '特殊规则': '强队让1.25盘口下盘率61%'}
}
# 量子计算资源
self.quantum_resources = self.detect_quantum_resources()
logger.info(f"检测到量子资源: {self.quantum_resources}")
# 量子计算后端
self.quantum_simulator = Aer.get_backend('qasm_simulator')
self.dwave_sampler = None
if self.quantum_resources.get('dwave', False):
try:
self.dwave_sampler = EmbeddingComposite(DWaveSampler())
logger.info("成功连接D-Wave量子处理器")
except Exception as e:
logger.warning(f"无法连接D-Wave量子处理器: {e}")
# 预加载模型
self.biomechanics_model = None
if biomechanics_model_path:
try:
self.biomechanics_model = tf.keras.models.load_model(biomechanics_model_path)
logger.info("成功加载生物力学模型")
except Exception as e:
logger.error(f"加载生物力学模型失败: {e}")
# 初始化量子匹配预测器
self.quantum_predictor = QuantumMatchPredictor(n_qubits=4)
self.lstm_model = self.build_lstm_model()
self._energy_cache = {}
self.energy_matrix = np.array([[0.5, -0.3], [-0.3, 0.5]]) # 预计算矩阵
@property
def quantum_shots(self):
if self.quantum_resources.get('dwave', False):
return 2000 # D-Wave系统采样更可靠，可减少次数
elif self.quantum_resources.get('ibmq', False):
return 8192 # IBMQ硬件需要更多采样
else:
return self._quantum_shots # 默认模拟器采样次数
def detect_quantum_resources(self):
"""检测可用量子计算资源"""
resources = {
"local_simulator": True,
"ibmq": False,
"dwave": False
}
try:
from qiskit import IBMQ
IBMQ.load_account()
providers = IBMQ.providers()
resources["ibmq"] = len(providers) > 0
except Exception as e:
logger.info(f"IBM Quantum访问失败: {e}")
try:
from dwave.cloud import Client
client = Client.from_config()
if client.get_solvers():
resources["dwave"] = True
client.close()
except Exception as e:
logger.info(f"D-Wave访问失败: {e}")
return resources
def build_lstm_model(self):
"""优化后的LSTM变盘预测模型"""
model = tf.keras.Sequential([
tf.keras.layers.LSTM(128, return_sequences=True, input_shape=(60, 3)),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.LSTM(64),
tf.keras.layers.Dense(32, activation='relu'),
tf.keras.layers.Dense(3, activation='softmax')
])
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
return model
# ========================
# 输入层：数据标准化
# ========================
@performance_monitor
def standardize_input(self, raw_data):
"""数据标准化处理"""
standardized = {}
# 基础信息
league_weights = {'英超': 1.2, '西甲': 1.15, '意甲': 1.1, '德甲': 1.15, '法甲': 1.05}
weather_weights = {'晴天': 0, '多云': -0.1, '小雨': -0.2, '大雨': -0.3, '雪': -0.4}
standardized['league'] = league_weights.get(raw_data.get('league', '其他'), 1.0)
standardized['weather'] = weather_weights.get(raw_data.get('weather', '晴天'), 0)
standardized['pitch'] = raw_data.get('pitch_condition', 0.85)
# 赛季周数（用于动态权重计算）
match_week = raw_data.get('match_week', 1)
standardized['match_week'] = min(max(match_week, 1), 38) # 限制在1-38周范围内
# 球队实力
home_rank = raw_data.get('home_rank', 10)
away_rank = raw_data.get('away_rank', 10)
home_win_rate = raw_data.get('home_win_rate', 0.5)
away_win_rate = raw_data.get('away_win_rate', 0.5)
home_rank_weight = 1 / max(home_rank, 1) # 防止除零
away_rank_weight = 1 / max(away_rank, 1)
# 向量化计算
home_factors = np.array([home_win_rate, home_rank_weight])
away_factors = np.array([away_win_rate, away_rank_weight])
weights = np.array([0.3, 0.7])
standardized['home_strength'] = np.dot(home_factors, weights)
standardized['away_strength'] = np.dot(away_factors, weights)
# 保存原始胜率用于量子预测
standardized['home_win_rate'] = home_win_rate
standardized['away_win_rate'] = away_win_rate
# 交锋历史
historical_win_rate = raw_data.get('historical_win_rate', 0.5)
historical_win_odds_rate = raw_data.get('historical_win_odds_rate', 0.5)
standardized['history_advantage'] = (
0.6 * historical_win_rate +
0.4 * historical_win_odds_rate
)
# 伤停影响
injury_impact = 0
injuries = raw_data.get('injuries', {})
for position, count in injuries.items():
if position == 'forward':
injury_impact -= 0.2 * count
elif position == 'goalkeeper':
injury_impact -= 0.3 * count
elif position == 'defender':
injury_impact -= 0.15 * count
elif position == 'midfielder':
injury_impact -= 0.1 * count
standardized['injury_impact'] = min(max(injury_impact, -1.0), 0) # 限制范围
# 赛程疲劳
matches_last_7_days = raw_data.get('matches_last_7_days', 0)
travel_distance = raw_data.get('travel_distance', 0)
standardized['fatigue'] = min(
0.3 * min(matches_last_7_days, 4) + # 限制最多4场
0.7 * min(travel_distance / 1000, 5.0), # 限制最多5000公里
10.0 # 最大值限制
)
# 赔率数据
standardized['william_odds'] = raw_data.get('william_odds', [2.0, 3.0, 3.5])
standardized['asian_odds'] = raw_data.get('asian_odds', [0.85, 0.95, 1.0, 0.92])
# 市场热度
betfair_volume_percent = raw_data.get('betfair_volume_percent', 0.5)
theoretical_probability = raw_data.get('theoretical_probability', 0.5)
if theoretical_probability > 0:
standardized['market_heat'] = min(max(
(betfair_volume_percent - theoretical_probability) / theoretical_probability,
-1.0), 1.0) # 限制在[-1,1]范围
else:
standardized['market_heat'] = 0.0
# 3D姿态数据
if 'player_movement' in raw_data and self.biomechanics_model is not None:
try:
standardized['injury_risk'] = self.injury_risk_prediction(raw_data['player_movement'])
except Exception as e:
logger.error(f"伤病风险预测失败: {e}")
standardized['injury_risk'] = 0.0
else:
standardized['injury_risk'] = 0.0
return standardized
# ========================
# 特征引擎层
# ========================
def injury_risk_prediction(self, player_movement):
"""3D姿态伤病预测"""
asymmetry = self.calc_joint_asymmetry(player_movement)
load_dist = self.weight_distribution_imbalance(player_movement)
# 确保输入为二维数组
input_data = np.array([[asymmetry, load_dist]])
return self.biomechanics_model.predict(input_data, verbose=0)[0][0]
def calc_joint_asymmetry(self, movement_data):
"""计算关节不对称性"""
# 假设movement_data包含左右数据
left_side = movement_data.get('left_side', np.zeros(10))
right_side = movement_data.get('right_side', np.zeros(10))
return np.mean(np.abs(left_side - right_side))
def weight_distribution_imbalance(self, movement_data):
"""计算重量分布不平衡"""
front_load = movement_data.get('front_load', np.zeros(10))
back_load = movement_data.get('back_load', np.zeros(10))
return np.abs(np.mean(front_load - back_load))
@performance_monitor
def quantum_clustering(self, odds_matrix):
"""量子聚类广实区间判定"""
try:
# 限制特征维度
feature_dimension = min(odds_matrix.shape[1], 4)
feature_map = ZZFeatureMap(feature_dimension=feature_dimension, reps=2)
q_kernel = QuantumKernel(feature_map=feature_map,
quantum_instance=self.quantum_simulator)
return SVC(kernel=q_kernel.evaluate).fit_predict(odds_matrix)
except Exception as e:
logger.error(f"量子聚类失败: {e}")
# 返回随机结果作为后备
return np.random.randint(0, 2, size=odds_matrix.shape[0])
def momentum_calculation(self, recent_form, history_advantage, form_continuity, odds_match, market_sentiment):
"""顺力度量化计算"""
return min(max(
0.4 * recent_form +
0.3 * history_advantage +
0.15 * form_continuity +
0.1 * odds_match +
0.05 * market_sentiment,
0.0), 10.0) # 限制在[0,10]范围
@lru_cache(maxsize=100)
def energy_balance_model(self, home_energy, away_energy, match_type):
"""优化后的动态能量守恒模型"""
# 添加对match_type的处理
if match_type not in ['regular', 'derby', 'cup']:
match_type = 'regular'
# 根据比赛类型设置参数
gamma = 0.8 if match_type == 'derby' else 0.5 if match_type == 'cup' else 0.3
t = self.standardized_data.get('fatigue', 0)
team_energy = np.array([home_energy, away_energy])
fatigue_vector = np.array([-gamma * t, gamma * t])
dE = np.dot(self.energy_matrix, team_energy) + fatigue_vector
# 限制能量变化范围
dE[0] = min(max(dE[0], -1.0), 1.0)
dE[1] = min(max(dE[1], -1.0), 1.0)
return dE[0], dE[1]
# ========================
# 量子优化层
# ========================
@performance_monitor
def quantum_annealing_optimizer(self, prob_dist, market_data):
"""量子退火赔率平衡器"""
# 参数校验
if not all(0 <= p <= 1 for p in prob_dist):
logger.warning("概率分布值超出范围，进行归一化")
total = sum(prob_dist)
if total > 0:
prob_dist = [p/total for p in prob_dist]
else:
prob_dist = [1/3, 1/3, 1/3]
if self.dwave_sampler is None:
logger.warning("D-Wave采样器不可用，使用经典优化")
# 经典模拟退火作为后备
from scipy.optimize import minimize
def objective(x):
return -np.dot(prob_dist, x)
# 约束条件：概率和为1
constraints = ({'type': 'eq', 'fun': lambda x: sum(x) - 1})
bounds = [(0, 1) for _ in range(3)]
initial_guess = prob_dist
result = minimize(objective, initial_guess, method='SLSQP',
bounds=bounds, constraints=constraints)
return result.x if result.success else [1/3, 1/3, 1/3]
h = {0: -prob_dist[0], 1: -prob_dist[1], 2: -prob_dist[2]}
J = {(0,1): market_data.get('arbitrage', 0),
(0,2): -market_data.get('deviation', 0),
(1,2): market_data.get('trend', 0)}
try:
response = self.dwave_sampler.sample_ising(h, J, num_reads=1000)
sample = response.first.sample
# 将样本转换为概率调整
adjusted_probs = [prob_dist[i] * (1 + 0.1 * sample[i]) for i in range(3)]
# 归一化
total = sum(adjusted_probs)
return [p/total for p in adjusted_probs] if total > 0 else [1/3, 1/3, 1/3]
except Exception as e:
logger.error(f"量子退火优化失败: {e}")
return prob_dist
def lstm_odds_change(self, historical_odds):
"""LSTM赔率变盘预测"""
if historical_odds is None or len(historical_odds) < 60:
logger.warning("历史赔率数据不足，使用简单平均")
return np.argmax(np.mean(historical_odds, axis=0)) if historical_odds else 0
# 数据预处理
scaler = MinMaxScaler()
scaled_odds = scaler.fit_transform(historical_odds)
X = scaled_odds.reshape(1, scaled_odds.shape[0], scaled_odds.shape[1])
# 预测变盘方向
prediction = self.lstm_model.predict(X, verbose=0)
return np.argmax(prediction)
@performance_monitor
def quantum_score_simulation(self, home_energy, away_energy, hist_avg):
"""优化后的量子蒙特卡洛比分预测"""
# 使用更高效的量子比特分配方案
qc = QuantumCircuit(4) # 减少到4个量子比特以提高效率
# 主客队能量编码
theta_home = home_energy * np.pi
theta_away = away_energy * np.pi
# 应用旋转门
qc.ry(theta_home, 0)
qc.ry(theta_away, 1)
# 历史交锋影响
hist_factor = min(1.0, abs(hist_avg[0] - hist_avg[1]) * 0.5)
if hist_avg[0] > hist_avg[1]:
qc.crx(hist_factor * np.pi, 0, 2)
else:
qc.crx(hist_factor * np.pi, 1, 3)
# 纠缠量子比特以增加相关性 - O(n)复杂度
for i in range(self.n_qubits - 1):
qc.cx(i, i+1)
# 测量
qc.measure_all()
# 量子采样
backend = self.quantum_simulator
try:
tqc = transpile(qc, backend, optimization_level=3)
job = execute(tqc, backend, shots=self.quantum_shots)
result = job.result()
counts = result.get_counts(tqc)
except Exception as e:
logger.error(f"量子比分模拟失败: {e}")
return f"{round(hist_avg[0])}-{round(hist_avg[1])}"
# 改进的比分解码逻辑
goal_counts = {}
for state, count in counts.items():
# 前2位表示主队进球数，后2位表示客队进球数
home_goals = int(state[:2], 2) % 5 # 限制最大进球数为4
away_goals = int(state[2:], 2) % 5
# 应用泊松分布修正
home_goals = min(home_goals, 4)
away_goals = min(away_goals, 4)
score = f"{home_goals}-{away_goals}"
goal_counts[score] = goal_counts.get(score, 0) + count
# 返回出现频率最高的比分
return max(goal_counts, key=goal_counts.get)
# ========================
# 动态修正层
# ========================
def apply_league_template(self, league_type, features):
"""应用联赛特异性模板"""
template = self.league_templates.get(league_type, {})
for feature, factor in template.items():
if feature in features and isinstance(features[feature], (int, float)):
features[feature] *= factor
return features
def enhanced_cold_warning(self, team_energy, market_data):
"""增强型冷门预警"""
risk_score = 0
# 能量赔率偏离
fair_odds = market_data.get('fair_odds', 0.5)
if abs(team_energy - fair_odds) > 0.25:
risk_score += 35
# 社交媒体情绪
if market_data.get('social_sentiment', 0) > 0.65:
risk_score += 20
# 主力轮换检测
training_intensity = market_data.get('training_intensity', 50)
coach_tone = market_data.get('coach_tone', 'normal')
if training_intensity < 45 and coach_tone == 'conservative':
risk_score += 25
return min(risk_score, 100) # 上限100
def inplay_calibration(self, pre_match_pred, match_status):
"""修复后的滚球实时校准"""
win_prob = pre_match_pred['home_win_prob']
# 领先方被压制
if 'xG_diff' in match_status and 'possession' in match_status:
if match_status['xG_diff'] < -0.8 and match_status.get('score_lead', 0) == 1:
win_prob -= 0.15 * (1 - match_status['possession']/100)
# 红牌影响
if match_status.get('red_card', False):
card_team = match_status.get('card_team', 0) # 0=主队, 1=客队
minute = match_status.get('minute', 0)
time_factor = max(0, (90 - minute) / 90)
if card_team == 0: # 主队红牌
win_prob -= 0.4 * time_factor
else: # 客队红牌
win_prob += 0.25 * time_factor
# 确保概率在[0,1]范围内
win_prob = max(0.0, min(1.0, win_prob))
# 更新预测结果
updated_pred = pre_match_pred.copy()
updated_pred['home_win_prob'] = win_prob
return updated_pred
# ========================
# 决策输出层
# ========================
def win_probability(self, home_rating, away_rating, william_odds, return_rate=0.95, fatigue_factor=0):
"""修复后的胜平负概率计算"""
rating_diff = (home_rating - away_rating) / 5
base_prob = 1 / (1 + np.exp(-rating_diff))
european_prob = return_rate / william_odds[0] if william_odds[0] > 0 else base_prob
p_home_win = base_prob * 0.7 + european_prob * 0.3
if fatigue_factor > 2.0:
away_rating_adj = away_rating * (1 - min(fatigue_factor/10, 0.3))
rating_diff_adj = (home_rating - away_rating_adj) / 5
base_prob_adj = 1 / (1 + np.exp(-rating_diff_adj))
p_home_win = base_prob_adj * 0.6 + european_prob * 0.4
# 改进的平局概率计算
draw_prob = (1 - abs(home_rating - away_rating)/10) * 0.3
# 考虑赔率中的平局概率
if len(william_odds) > 1 and william_odds[1] > 0:
draw_prob = (draw_prob + return_rate/william_odds[1]) / 2
# 确保概率在合理范围内
p_home_win = min(max(p_home_win, 0.1), 0.8)
draw_prob = min(max(draw_prob, 0.1), 0.5)
p_away_win = 1 - p_home_win - draw_prob
# 归一化处理
total = p_home_win + draw_prob + p_away_win
p_home_win /= total
draw_prob /= total
p_away_win /= total
return p_home_win, draw_prob, p_away_win
# ========================
# 动态权重计算
# ========================
def calculate_dynamic_weights(self, match_week):
"""
计算量子模型和传统模型的动态权重
使用公式: α(t) = 1 / (1 + exp(-k(t - t0)))
其中 t0 = 20 (赛季中段), k = 0.1
量子权重: w_q = α(t) × 0.7 + 0.3
传统权重: w_c = 1 - w_q
"""
t = match_week
t0 = 20 # 赛季中段
k = 0.1 # 调整参数
# 计算α(t)
alpha = 1 / (1 + math.exp(-k * (t - t0)))
# 量子权重在0.3-1.0之间变化
w_quantum = alpha * 0.7 + 0.3
w_classic = 1 - w_quantum
logger.info(f"动态权重计算: 赛季周数={t}, α={alpha:.3f}, 量子权重={w_quantum:.3f}, 传统权重={w_classic:.3f}")
return w_quantum, w_classic
# ========================
# 量子特征预测
# ========================
def quantum_feature_prediction(self, features: dict) -> float:
"""
使用量子模型预测比赛特征
特征顺序: home_strength, away_strength, history_advantage, fatigue
"""
try:
# 准备量子预测的特征数组
quantum_features = np.array([
features['home_strength'],
features['away_strength'],
features['history_advantage'],
features['fatigue']
])
# 进行量子预测
return self.quantum_predictor.predict(quantum_features)
except Exception as e:
logger.error(f"量子特征预测失败: {e}")
# 返回随机值作为后备
return np.random.uniform(0.4, 0.6)
# ========================
# 整合预测流程
# ========================
@performance_monitor
def predict(self, raw_data):
"""整合预测流程 - 包含量子与传统模型动态融合"""
try:
# 1. 数据标准化
self.standardized_data = self.standardize_input(raw_data)
league = raw_data.get('league', '英超')
# 2. 应用联赛模板
self.apply_league_template(league, self.standardized_data)
# 3. 计算球队能量
home_energy = self.standardized_data['home_strength'] * (1 + self.standardized_data['history_advantage'])
away_energy = self.standardized_data['away_strength'] * (1 - self.standardized_data['injury_impact'])
# 4. 量子比分模拟
hist_avg = raw_data.get('historical_avg_score', [1.5, 1.2])
predicted_score = self.quantum_score_simulation(home_energy, away_energy, hist_avg)
# 5. 传统胜平负概率计算
william_odds = self.standardized_data['william_odds']
home_win, draw, away_win = self.win_probability(
home_energy, away_energy, william_odds,
fatigue_factor=self.standardized_data['fatigue']
)
# 6. 量子特征预测
quantum_prediction = self.quantum_feature_prediction({
'home_strength': self.standardized_data['home_strength'],
'away_strength': self.standardized_data['away_strength'],
'history_advantage': self.standardized_data['history_advantage'],
'fatigue': self.standardized_data['fatigue']
})
# 7. 动态融合量子与传统预测
match_week = self.standardized_data.get('match_week', 1)
w_quantum, w_classic = self.calculate_dynamic_weights(match_week)
# 融合主胜概率
fused_home_win = w_quantum * quantum_prediction + w_classic * home_win
# 保持相对比例不变
scale = (1 - fused_home_win) / (1 - home_win) if home_win < 1.0 else 1.0
fused_draw = draw * scale
fused_away_win = away_win * scale
# 归一化处理
total = fused_home_win + fused_draw + fused_away_win
fused_home_win /= total
fused_draw /= total
fused_away_win /= total
fused_probs = [fused_home_win, fused_draw, fused_away_win]
# 8. 量子退火优化赔率
market_data = {
'arbitrage': raw_data.get('arbitrage_opportunity', 0.05),
'deviation': raw_data.get('odds_deviation', 0.1),
'trend': raw_data.get('market_trend', 0.0)
}
optimized_probs = self.quantum_annealing_optimizer(fused_probs, market_data)
# 9. 冷门预警
cold_risk = self.enhanced_cold_warning(home_energy, {
'fair_odds': 1/(william_odds[0] * 1.05),
'social_sentiment': raw_data.get('social_sentiment', 0.5),
'training_intensity': raw_data.get('training_intensity', 60),
'coach_tone': raw_data.get('coach_tone', 'normal')
})
# 10. 构建最终结果
result = {
'predicted_score': predicted_score,
'home_win_prob': optimized_probs[0],
'draw_prob': optimized_probs[1],
'away_win_prob': optimized_probs[2],
'cold_risk': f"{cold_risk}%",
'key_factors': {
'home_energy': home_energy,
'away_energy': away_energy,
'injury_impact': self.standardized_data['injury_impact'],
'fatigue_factor': self.standardized_data['fatigue'],
'weather_impact': self.standardized_data['weather'],
'quantum_prediction': quantum_prediction,
'w_quantum': w_quantum,
'w_classic': w_classic
},
'quantum_optimized': True if self.dwave_sampler else False
}
# 11. 如果有滚球数据，进行实时校准
if 'inplay_data' in raw_data:
result = self.inplay_calibration(result, raw_data['inplay_data'])
return result
except Exception as e:
logger.exception("预测过程中发生错误")
return {
'error': str(e),
'fallback_prediction': self.fallback_prediction(raw_data)
}
def fallback_prediction(self, raw_data):
"""经典方法作为后备预测"""
# 简单基于排名和赔率的预测
home_rank = raw_data.get('home_rank', 10)
away_rank = raw_data.get('away_rank', 10)
home_adv = (away_rank - home_rank) * 0.05
# 基本胜率计算
home_win = 0.4 + home_adv
away_win = 0.3 - home_adv
draw = 0.3
# 考虑赔率影响
if 'william_odds' in raw_data:
odds = raw_data['william_odds']
implied_home = 1/odds[0]
home_win = (home_win + implied_home) / 2
return {
'home_win_prob': home_win,
'draw_prob': draw,
'away_win_prob': away_win,
'predicted_score': "1-1",
'cold_risk': "30%",
'note': "经典预测方法"
}
# 示例使用
if __name__ == "__main__":
# 创建示例数据
sample_data = {
'league': '英超',
'weather': '小雨',
'pitch_condition': 0.8,
'home_rank': 3,
'away_rank': 8,
'home_win_rate': 0.7,
'away_win_rate': 0.4,
'historical_win_rate': 0.6,
'historical_win_odds_rate': 0.55,
'injuries': {'forward': 1, 'defender': 2},
'matches_last_7_days': 2,
'travel_distance': 800, # 公里
'william_odds': [1.8, 3.4, 4.2],
'asian_odds': [0.85, 0.95, 1.0, 0.92],
'betfair_volume_percent': 0.65,
'theoretical_probability': 0.55,
'historical_avg_score': [1.8, 1.0],
'arbitrage_opportunity': 0.03,
'odds_deviation': 0.15,
'market_trend': -0.1,
'social_sentiment': 0.7,
'training_intensity': 40,
'coach_tone': 'conservative',
'match_type': 'derby', # 比赛类型
'match_week': 5 # 赛季第5周
}
# 添加3D姿态数据（简化示例）
sample_data['player_movement'] = {
'left_side': np.array([0.8, 0.7, 0.9, 0.85, 0.75]),
'right_side': np.array([0.6, 0.65, 0.7, 0.8, 0.75]),
'front_load': np.array([0.5, 0.55, 0.6]),
'back_load': np.array([0.4, 0.45, 0.5])
}
# 创建模型实例并预测
model = QuantumFootballPredictionModel()
prediction = model.predict(sample_data)
print("\n足球比赛预测结果:")
print(f"预测比分: {prediction.get('predicted_score', 'N/A')}")
print(f"主胜概率: {prediction.get('home_win_prob', 0):.2%}")
print(f"平局概率: {prediction.get('draw_prob', 0):.2%}")
print(f"客胜概率: {prediction.get('away_win_prob', 0):.2%}")
print(f"量子预测值: {prediction.get('key_factors', {}).get('quantum_prediction', 0):.2%}")
print(f"量子权重: {prediction.get('key_factors', {}).get('w_quantum', 0):.2f}")
print(f"传统权重: {prediction.get('key_factors', {}).get('w_classic', 0):.2f}")
print(f"冷门风险: {prediction.get('cold_risk', 'N/A')}")
print(f"是否量子优化: {prediction.get('quantum_optimized', False)}")
# 添加滚球数据示例
sample_data['inplay_data'] = {
'minute': 60,
'score': '1-0',
'possession': 45, # 主队控球率%
'xG_diff': -0.7, # 主队预期进球差
'red_card': True,
'card_team': 0 # 0=主队
}
# 滚球校准
inplay_prediction = model.predict(sample_data)
print("\n滚球校准后主胜概率:", f"{inplay_prediction.get('home_win_prob', 0):.2%}")
最新发布
本文介绍了概率校准在预测模型中的重要性，特别是在监督学习的分类问题中。通过案例展示模型输出概率与真实概率之间的差距，并通过Brier分数评估校准效果。常见模型如Logistic回归、朴素贝叶斯、SVM和随机森林在校准前后的表现对比，强调了校准对提高预测准确性的关键作用。文章还提到了不同校准方法，如sigmoid校准和非参数的等渗校准，并通过实例解释了这两种方法的适用场景。
最低0.47元/天 解锁文章
扫一扫
1万+
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
