在Photoshop中处理webP文件，并将图片保存为webP格式 WebP介绍 WebP是一种新的图片文件格式，由Google于2010年推出，目标是减小文件大小，但达到和JPEG格式相同的图片质量。传统基于JPEG或PNG图片格式的网页，其图片资源加载往往都占据了页面耗时的主要部分，在保证图片质量的前提下缩小图片体积，是WebP格式相较于其他图片格式的主要优势。WebP格式相较于PNG格式的优点WebP具有更优的图像数据压缩算法，在拥有肉眼无法识别差异的图像质量前提下，带来更小的图片体积；WebP支持Alpha透明和24位颜色数，不存在PNG8色彩不够丰富和毛边问题；WebP比同等PNG格式缩小文件体积80%-90%，以此提高了页面载入速度，降低了存储需求，帮助加速网站图形效果。 WebP缺点WebP格式图像的编码时间“比JPEG格式图像长8倍”。因此在相同条件下，采用WEBP格式生成栅格瓦片耗时相较其他格式长。 新版本Photoshop对WebP的原生支持在Photoshop23.2 中，Photoshop全面支持 WebP 文件格式！WebP文件现在可以在Photoshop中打开、创建、编辑和保存，无需插件或首选项设置。要打开WebP文件，只需选择并打开该文件，就像打开任何其他支持的文件或文档一样。除了开放功能，您现在还可以创建、编辑和保存WebP文件。完成文档编辑后，打开保存副本并从文件格式下拉菜单中提供的选项中选择WebP以保存WebP文件。当没有文件覆盖或数据丢失的风险时，WebP也可以在“另存为”下使用。 老版本Photoshop需插件支持（下载适用于Photoshop 23.1及以下版本的增效工具） 要直接从Photoshop 23.1及以下版本打开和保存WebP图片，只需下载并安装 WebPShop插件即可。您可以直接从拥有并维护WebP格式的Google下载WebPShop 插件（具有通用/ARM 支持）。此外，在Apache 2.0许可下，WebPShop在GitHub上可用。 下载WebPShop插件 从 谷歌 获取从 GitHub 获取直接下载链接：Windows (x64):https://github.com/webmproject/WebPShop/releases/download/v0.3.3/WebPShop_0_3_3_Win_x64.8bimacOS（解压缩 ZIP）：https://github.com/webmproject/WebPShop/releases/download/v0.3.3/WebPShop_0_3_3_Mac_Universal.zip 安装WebPShop插件下载的插件可以安装到以下位置：共享Creative Cloud位置：将为所有已安装的 Photoshop 版本加载插件，而无需重新安装插件。Windows：\Program Files\Common Files\Adobe\Plug-Ins\CCmacOS：/资源库/Application Support/Adobe/Plug-Ins/CC运行 Photoshop。隔离插件（仅限 macOS） 另外，在macOS 10.15及以上系统上，通过隔离插件可以绕过提示“无法打开WebPShop.plugin，因为无法验证开发者”。发生这种情况是因为插件未签名。最简单的隔离方法是将WebPShop插件从安装位置拖到终端窗口中，以获得正确的版本和路径。要隔离，请在终端窗口（Finder > Applications > Utilities）中运行以下代码： sudo xattr -r -d com.apple.quarantine /Library/Application\ Support/Adobe/Plug-Ins/CC/WebPShop.plugin 参考：https://helpx.adobe.com/hk_en/photoshop/kb/support-webp-image-format.html

15行代码实现获取本机所有wifi密码 import subprocess获取wifi列表output = subprocess.run(['netsh', 'wlan', 'show', 'profiles'], capture_output=True).stdout.decode('gbk').split('\n')wifis = line.split(':')[1 for line in output if "所有用户配置文件" in line]查看每个wifi对应的密码for wifi in wifis:results = subprocess.run(['netsh', 'wlan', 'show', 'profile', wifi, 'key=clear'], capture_output=True).stdout.decode('gbk', errors='ignore').split('\n') results = [line.split(':')[1][1:-1] for line in results if "关键内容" in line] try: print(f'wifi名：{wifi}，密码:{results[0]}') except IndexError: print(f'wifi名：{wifi}，密码:无法提取')input('按enter确认并退出')

Python模拟预测2022卡塔尔世界杯结果 一、前言在逛github的时候看到一个大佬发布了模拟2022世界杯的程序，在我试验了以后发现最终模拟的结果是巴西会夺冠。这应该符合很多人的想法猜测吧，哈哈哈哈。声明：本贴仅供学习交流使用，如存在侵权行为请告知我立即删帖。github原作者【QuarterTime】 --反正我是在他那里找到的，至于他是不是原作者我也就不清楚了。二、程序代码# -*- coding:utf-8 -*- # Wan Jiongming @Copyright 2022 import cv2 import numpy as np from PIL import Image, ImageDraw, ImageFont with open("country_name.txt", "r", encoding="utf-8") as f: info = f.readlines() info = list(map(lambda x:x.strip(), info)) English_name = info[:32] Chinese_name = info[32:] country_name = {} for each in zip(English_name, Chinese_name): country_name[each[0]] = each[1] def cv2AddChineseText(img, text, position, textColor=(0, 255, 0), textSize=30): if (isinstance(img, np.ndarray)): # 判断是否OpenCV图片类型 img = Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) # 创建一个可以在给定图像上绘图的对象 draw = ImageDraw.Draw(img) # 字体的格式 fontStyle = ImageFont.truetype( "simsun.ttc", textSize, encoding="utf-8") # 绘制文本 draw.text(position, text, textColor, font=fontStyle) # 转换回OpenCV格式 return cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR) def save_res(a, b, winner, prob, save_name): img = np.zeros((520, 850, 3), np.uint8) a_img_path = "./world_cup/" + country_name[a] + ".png" a_img = cv2.imdecode(np.fromfile(a_img_path, dtype = np.uint8), -1) a_img_h, a_img_w, _ = a_img.shape b_img_path = "./world_cup/" + country_name[b] + ".png" b_img = cv2.imdecode(np.fromfile(b_img_path, dtype = np.uint8), -1) b_img_h, b_img_w, _ = b_img.shape winner_img_path = "./world_cup/" + country_name[winner] + ".png" winner_img = cv2.imdecode(np.fromfile(winner_img_path, dtype = np.uint8), -1) winner_img_h, winner_img_w, _ = winner_img.shape img[10: 10 + a_img_h, 10: 10 + a_img_w] = a_img[:, :, :3] img[110 + a_img_h: 110 + a_img_h + b_img_h, 10: 10 + b_img_w] = b_img[:,:,:3] img = cv2AddChineseText(img, country_name[a] + "(" + a + ")", (10, 20 + a_img_h),(255, 255, 255), 30) img = cv2AddChineseText(img, country_name[b] + "(" + b + ")", (10, 20 + 100 + a_img_h + b_img_h), (255, 255, 255), 30) point1 = (10 + a_img_w, 10 + (a_img_h) // 2) point2 = (10 + a_img_w + 100, 10 + (a_img_h) // 2) cv2.line(img, point1, point2, (255, 255, 255), 10) point3 = (10 + a_img_w, 10 + 100 + a_img_h + (b_img_h) // 2) point4 = (10 + b_img_w + 100, 10 + 100 + a_img_h + (b_img_h) // 2) cv2.line(img, point3, point4, (255, 255, 255), 10) cv2.line(img, point2, point4, (255, 255, 255), 10) point5 = (10 + a_img_w + 100, 10 + 50 + a_img_h) point6 = (10 + a_img_w + 300, 10 + 50 + a_img_h) cv2.line(img, point5, point6, (255, 255, 255), 10) img = cv2AddChineseText(img, "胜率：{}".format(prob), (10 + a_img_w + 100 + 20, 10 + 50 + a_img_h - 40),(255, 255, 255), 30) img[10 + 50 + a_img_h - winner_img_h // 2: 10 + 50 + a_img_h + winner_img_h // 2, 10 + a_img_w + 300 : 10 + a_img_w + 300 + winner_img_w] = winner_img[:,:,:3] img = cv2AddChineseText(img, country_name[winner] + "(" + winner + ")", (10 + a_img_w + 300, 10 + 50 + a_img_h + winner_img_h // 2 + 10), (255, 255, 255), 30) cv2.imwrite(save_name, img) def save_res_draw(a, b, winner, prob, save_name): img = np.zeros((520, 850, 3), np.uint8) a_img_path = "./world_cup/" + country_name[a] + ".png" a_img = cv2.imdecode(np.fromfile(a_img_path, dtype = np.uint8), -1) a_img_h, a_img_w, _ = a_img.shape b_img_path = "./world_cup/" + country_name[b] + ".png" b_img = cv2.imdecode(np.fromfile(b_img_path, dtype = np.uint8), -1) b_img_h, b_img_w, _ = b_img.shape winner_img_path = "./world_cup/" + country_name[winner] + ".png" winner_img = cv2.imdecode(np.fromfile(winner_img_path, dtype = np.uint8), -1) winner_img_h, winner_img_w, _ = winner_img.shape img[10: 10 + a_img_h, 10: 10 + a_img_w] = a_img[:, :, :3] img[110 + a_img_h: 110 + a_img_h + b_img_h, 10: 10 + b_img_w] = b_img[:,:,:3] img = cv2AddChineseText(img, country_name[a] + "(" + a + ")", (10, 20 + a_img_h),(255, 255, 255), 30) img = cv2AddChineseText(img, country_name[b] + "(" + b + ")", (10, 20 + 100 + a_img_h + b_img_h), (255, 255, 255), 30) point1 = (10 + a_img_w, 10 + (a_img_h) // 2) point2 = (10 + a_img_w + 100, 10 + (a_img_h) // 2) cv2.line(img, point1, point2, (255, 255, 255), 10) point3 = (10 + a_img_w, 10 + 100 + a_img_h + (b_img_h) // 2) point4 = (10 + b_img_w + 100, 10 + 100 + a_img_h + (b_img_h) // 2) cv2.line(img, point3, point4, (255, 255, 255), 10) cv2.line(img, point2, point4, (255, 255, 255), 10) point5 = (10 + a_img_w + 100, 10 + 50 + a_img_h) point6 = (10 + a_img_w + 300, 10 + 50 + a_img_h) cv2.line(img, point5, point6, (255, 255, 255), 10) img = cv2AddChineseText(img, "胜率：{}".format(prob), (10 + a_img_w + 100 + 20, 10 + 50 + a_img_h - 40),(255, 255, 255), 30) # img[10 + 50 + a_img_h - winner_img_h // 2: 10 + 50 + a_img_h + winner_img_h // 2, 10 + a_img_w + 300 : 10 + a_img_w + 300 + winner_img_w] = winner_img[:,:,:3] # img = cv2AddChineseText(img, country_name[winner] + "(" + winner + ")", (10 + a_img_w + 300, 10 + 50 + a_img_h + winner_img_h // 2 + 10), (255, 255, 255), 30) cv2.imwrite(save_name, img) if __name__ == "__main__": save_res_draw("Switzerland", "Cameroon", "Switzerland", 0.62, "tmp.png")这里是第二部分代码，程序运行的主代码# -*- coding:utf-8 -*- # Wan Jiongming @Copyright 2022 import numpy as np import pandas as pd from operator import itemgetter from save_res import save_res, save_res_draw import time df = pd.read_csv("./kaggle/results.csv") df["date"] = pd.to_datetime(df["date"]) df.dropna(inplace=True) df = df[(df["date"] >= "2018-8-1")].reset_index(drop=True) rank = pd.read_csv("./kaggle/fifa_ranking-2022-10-06.csv") rank["rank_date"] = pd.to_datetime(rank["rank_date"]) rank = rank[(rank["rank_date"] >= "2018-8-1")].reset_index(drop=True) rank["country_full"] = rank["country_full"].str.replace("IR Iran", "Iran").str.replace("Korea Republic", "South Korea").str.replace("USA", "United States") rank = rank.set_index(['rank_date']).groupby(['country_full'], group_keys=False).resample('D').first().fillna(method='ffill').reset_index() df_wc_ranked = df.merge(rank[["country_full", "total_points", "previous_points", "rank", "rank_change", "rank_date"]], left_on=["date", "home_team"], right_on=["rank_date", "country_full"]).drop(["rank_date", "country_full"], axis=1) df_wc_ranked = df_wc_ranked.merge(rank[["country_full", "total_points", "previous_points", "rank", "rank_change", "rank_date"]], left_on=["date", "away_team"], right_on=["rank_date", "country_full"], suffixes=("_home", "_away")).drop(["rank_date", "country_full"], axis=1) df = df_wc_ranked # print(df[(df_wc_ranked.home_team == "Brazil") | (df.away_team == "Brazil")].tail(10)) def result_finder(home, away): if home > away: return pd.Series([0, 3, 0]) if home < away: return pd.Series([1, 0, 3]) else: return pd.Series([2, 1, 1]) results = df.apply(lambda x: result_finder(x["home_score"], x["away_score"]), axis=1) df[["result", "home_team_points", "away_team_points"]] = results # print(df[(df_wc_ranked.home_team == "Brazil") | (df.away_team == "Brazil")].tail(10)) import seaborn as sns import matplotlib.pyplot as plt # plt.figure(figsize=(15, 10)) # sns.heatmap(df[["total_points_home", "rank_home", "total_points_away", "rank_away"]].corr()) # plt.show() df["rank_dif"] = df["rank_home"] - df["rank_away"] df["sg"] = df["home_score"] - df["away_score"] df["points_home_by_rank"] = df["home_team_points"]/df["rank_away"] df["points_away_by_rank"] = df["away_team_points"]/df["rank_home"] home_team = df[["date", "home_team", "home_score", "away_score", "rank_home", "rank_away","rank_change_home", "total_points_home", "result", "rank_dif", "points_home_by_rank", "home_team_points"]] away_team = df[["date", "away_team", "away_score", "home_score", "rank_away", "rank_home","rank_change_away", "total_points_away", "result", "rank_dif", "points_away_by_rank", "away_team_points"]] home_team.columns = [h.replace("home_", "").replace("_home", "").replace("away_", "suf_").replace("_away", "_suf") for h in home_team.columns] away_team.columns = [a.replace("away_", "").replace("_away", "").replace("home_", "suf_").replace("_home", "_suf") for a in away_team.columns] team_stats = home_team.append(away_team)#.sort_values("date") team_stats_raw = team_stats.copy() stats_val = [] for index, row in team_stats.iterrows(): team = row["team"] date = row["date"] past_games = team_stats.loc[(team_stats["team"] == team) & (team_stats["date"] < date)].sort_values(by=['date'], ascending=False) last5 = past_games.head(5) goals = past_games["score"].mean() goals_l5 = last5["score"].mean() goals_suf = past_games["suf_score"].mean() goals_suf_l5 = last5["suf_score"].mean() rank = past_games["rank_suf"].mean() rank_l5 = last5["rank_suf"].mean() if len(last5) > 0: points = past_games["total_points"].values[0] - past_games["total_points"].values[-1]#qtd de pontos ganhos points_l5 = last5["total_points"].values[0] - last5["total_points"].values[-1] else: points = 0 points_l5 = 0 gp = past_games["team_points"].mean() gp_l5 = last5["team_points"].mean() gp_rank = past_games["points_by_rank"].mean() gp_rank_l5 = last5["points_by_rank"].mean() stats_val.append([goals, goals_l5, goals_suf, goals_suf_l5, rank, rank_l5, points, points_l5, gp, gp_l5, gp_rank, gp_rank_l5]) stats_cols = ["goals_mean", "goals_mean_l5", "goals_suf_mean", "goals_suf_mean_l5", "rank_mean", "rank_mean_l5", "points_mean", "points_mean_l5", "game_points_mean", "game_points_mean_l5", "game_points_rank_mean", "game_points_rank_mean_l5"] stats_df = pd.DataFrame(stats_val, columns=stats_cols) full_df = pd.concat([team_stats.reset_index(drop=True), stats_df], axis=1, ignore_index=False) home_team_stats = full_df.iloc[:int(full_df.shape[0]/2),:] away_team_stats = full_df.iloc[int(full_df.shape[0]/2):,:] home_team_stats = home_team_stats[home_team_stats.columns[-12:]] away_team_stats = away_team_stats[away_team_stats.columns[-12:]] home_team_stats.columns = ['home_'+str(col) for col in home_team_stats.columns] away_team_stats.columns = ['away_'+str(col) for col in away_team_stats.columns] match_stats = pd.concat([home_team_stats, away_team_stats.reset_index(drop=True)], axis=1, ignore_index=False) full_df = pd.concat([df, match_stats.reset_index(drop=True)], axis=1, ignore_index=False) def find_friendly(x): if x == "Friendly": return 1 else: return 0 full_df["is_friendly"] = full_df["tournament"].apply(lambda x: find_friendly(x)) full_df = pd.get_dummies(full_df, columns=["is_friendly"]) base_df = full_df[["date", "home_team", "away_team", "rank_home", "rank_away","home_score", "away_score","result", "rank_dif", "rank_change_home", "rank_change_away", 'home_goals_mean', 'home_goals_mean_l5', 'home_goals_suf_mean', 'home_goals_suf_mean_l5', 'home_rank_mean', 'home_rank_mean_l5', 'home_points_mean', 'home_points_mean_l5', 'away_goals_mean', 'away_goals_mean_l5', 'away_goals_suf_mean', 'away_goals_suf_mean_l5', 'away_rank_mean', 'away_rank_mean_l5', 'away_points_mean', 'away_points_mean_l5','home_game_points_mean', 'home_game_points_mean_l5', 'home_game_points_rank_mean', 'home_game_points_rank_mean_l5','away_game_points_mean', 'away_game_points_mean_l5', 'away_game_points_rank_mean', 'away_game_points_rank_mean_l5', 'is_friendly_0', 'is_friendly_1']] base_df_no_fg = base_df.dropna() df = base_df_no_fg def no_draw(x): if x == 2: return 1 else: return x df["target"] = df["result"].apply(lambda x: no_draw(x)) def create_db(df): columns = ["home_team", "away_team", "target", "rank_dif", "home_goals_mean", "home_rank_mean", "away_goals_mean", "away_rank_mean", "home_rank_mean_l5", "away_rank_mean_l5", "home_goals_suf_mean", "away_goals_suf_mean", "home_goals_mean_l5", "away_goals_mean_l5", "home_goals_suf_mean_l5", "away_goals_suf_mean_l5", "home_game_points_rank_mean", "home_game_points_rank_mean_l5", "away_game_points_rank_mean", "away_game_points_rank_mean_l5","is_friendly_0", "is_friendly_1"] base = df.loc[:, columns] base.loc[:, "goals_dif"] = base["home_goals_mean"] - base["away_goals_mean"] base.loc[:, "goals_dif_l5"] = base["home_goals_mean_l5"] - base["away_goals_mean_l5"] base.loc[:, "goals_suf_dif"] = base["home_goals_suf_mean"] - base["away_goals_suf_mean"] base.loc[:, "goals_suf_dif_l5"] = base["home_goals_suf_mean_l5"] - base["away_goals_suf_mean_l5"] base.loc[:, "goals_per_ranking_dif"] = (base["home_goals_mean"] / base["home_rank_mean"]) - (base["away_goals_mean"] / base["away_rank_mean"]) base.loc[:, "dif_rank_agst"] = base["home_rank_mean"] - base["away_rank_mean"] base.loc[:, "dif_rank_agst_l5"] = base["home_rank_mean_l5"] - base["away_rank_mean_l5"] base.loc[:, "dif_points_rank"] = base["home_game_points_rank_mean"] - base["away_game_points_rank_mean"] base.loc[:, "dif_points_rank_l5"] = base["home_game_points_rank_mean_l5"] - base["away_game_points_rank_mean_l5"] model_df = base[["home_team", "away_team", "target", "rank_dif", "goals_dif", "goals_dif_l5", "goals_suf_dif", "goals_suf_dif_l5", "goals_per_ranking_dif", "dif_rank_agst", "dif_rank_agst_l5", "dif_points_rank", "dif_points_rank_l5", "is_friendly_0", "is_friendly_1"]] return model_df model_db = create_db(df) X = model_db.iloc[:, 3:] y = model_db[["target"]] from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier from sklearn.model_selection import train_test_split, GridSearchCV X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state=1) gb = GradientBoostingClassifier(random_state=5) params = {"learning_rate": [0.01, 0.1, 0.5], "min_samples_split": [5, 10], "min_samples_leaf": [3, 5], "max_depth":[3,5,10], "max_features":["sqrt"], "n_estimators":[100, 200] } gb_cv = GridSearchCV(gb, params, cv = 3, n_jobs = -1, verbose = False) gb_cv.fit(X_train.values, np.ravel(y_train)) gb = gb_cv.best_estimator_ params_rf = {"max_depth": [20], "min_samples_split": [10], "max_leaf_nodes": [175], "min_samples_leaf": [5], "n_estimators": [250], "max_features": ["sqrt"], } rf = RandomForestClassifier(random_state=1) rf_cv = GridSearchCV(rf, params_rf, cv = 3, n_jobs = -1, verbose = False) rf_cv.fit(X_train.values, np.ravel(y_train)) rf = rf_cv.best_estimator_ # from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score # def analyze(model): # fpr, tpr, _ = roc_curve(y_test, model.predict_proba(X_test.values)[:,1]) #test AUC # plt.figure(figsize=(15,10)) # plt.plot([0, 1], [0, 1], 'k--') # plt.plot(fpr, tpr, label="test") # fpr_train, tpr_train, _ = roc_curve(y_train, model.predict_proba(X_train.values)[:,1]) #train AUC # plt.plot(fpr_train, tpr_train, label="train") # auc_test = roc_auc_score(y_test, model.predict_proba(X_test.values)[:,1]) # auc_train = roc_auc_score(y_train, model.predict_proba(X_train.values)[:,1]) # plt.legend() # plt.title('AUC score is %.2f on test and %.2f on training'%(auc_test, auc_train)) # plt.show() # plt.figure(figsize=(15, 10)) # cm = confusion_matrix(y_test, model.predict(X_test.values)) # sns.heatmap(cm, annot=True, fmt="d") # analyze(gb) with open("country_name.txt", "r", encoding="utf-8") as f: info = f.readlines() info = list(map(lambda x:x.strip(), info)) English_name = info[:32] Chinese_name = info[32:] country_name = {} for each in zip(English_name, Chinese_name): country_name[each[0]] = each[1] table = {'A': [['Qatar', 0, []], ['Ecuador', 0, []], ['Senegal', 0, []], ['Netherlands', 0, []]], 'B': [['England', 0, []], ['Iran', 0, []], ['United States', 0, []], ['Wales', 0, []]], 'C': [['Argentina', 0, []], ['Saudi Arabia', 0, []], ['Mexico', 0, []], ['Poland', 0, []]], 'D': [['France', 0, []], ['Australia', 0, []], ['Denmark', 0, []], ['Tunisia', 0, []]], 'E': [['Spain', 0, []], ['Costa Rica', 0, []], ['Germany', 0, []], ['Japan', 0, []]], 'F': [['Belgium', 0, []], ['Canada', 0, []], ['Morocco', 0, []], ['Croatia', 0, []]], 'G': [['Brazil', 0, []], ['Serbia', 0, []], ['Switzerland', 0, []], ['Cameroon', 0, []]], 'H': [['Portugal', 0, []], ['Ghana', 0, []], ['Uruguay', 0, []], ['South Korea', 0, []]]} groups = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'] group_count = 7 matches = [('A', 'Qatar', 'Ecuador'), ('A', 'Senegal', 'Netherlands'), ('A', 'Qatar', 'Senegal'), ('A', 'Netherlands', 'Ecuador'), ('A', 'Ecuador', 'Senegal'), ('A', 'Netherlands', 'Qatar'), ('B', 'England', 'Iran'), ('B', 'United States', 'Wales'), ('B', 'Wales', 'Iran'), ('B', 'England', 'United States'), ('B', 'Wales', 'England'), ('B', 'Iran', 'United States'), ('C', 'Argentina', 'Saudi Arabia'), ('C', 'Mexico', 'Poland'), ('C', 'Poland', 'Saudi Arabia'), ('C', 'Argentina', 'Mexico'), ('C', 'Poland', 'Argentina'), ('C', 'Saudi Arabia', 'Mexico'), ('D', 'Denmark', 'Tunisia'), ('D', 'France', 'Australia'), ('D', 'Tunisia', 'Australia'), ('D', 'France', 'Denmark'), ('D', 'Australia', 'Denmark'), ('D', 'Tunisia', 'France'), ('E', 'Germany', 'Japan'), ('E', 'Spain', 'Costa Rica'), ('E', 'Japan', 'Costa Rica'), ('E', 'Spain', 'Germany'), ('E', 'Japan', 'Spain'), ('E', 'Costa Rica', 'Germany'), ('F', 'Morocco', 'Croatia'), ('F', 'Belgium', 'Canada'), ('F', 'Belgium', 'Morocco'), ('F', 'Croatia', 'Canada'), ('F', 'Croatia', 'Belgium'), ('F', 'Canada', 'Morocco'), ('G', 'Switzerland', 'Cameroon'), ('G', 'Brazil', 'Serbia'), ('G', 'Cameroon', 'Serbia'), ('G', 'Brazil', 'Switzerland'), ('G', 'Serbia', 'Switzerland'), ('G', 'Cameroon', 'Brazil'), ('H', 'Uruguay', 'South Korea'), ('H', 'Portugal', 'Ghana'), ('H', 'South Korea', 'Ghana'), ('H', 'Portugal', 'Uruguay'), ('H', 'Ghana', 'Uruguay'), ('H', 'South Korea', 'Portugal')] def find_stats(team_1): #team_1 = "Qatar" past_games = team_stats_raw[(team_stats_raw["team"] == team_1)].sort_values("date") last5 = team_stats_raw[(team_stats_raw["team"] == team_1)].sort_values("date").tail(5) team_1_rank = past_games["rank"].values[-1] team_1_goals = past_games.score.mean() team_1_goals_l5 = last5.score.mean() team_1_goals_suf = past_games.suf_score.mean() team_1_goals_suf_l5 = last5.suf_score.mean() team_1_rank_suf = past_games.rank_suf.mean() team_1_rank_suf_l5 = last5.rank_suf.mean() team_1_gp_rank = past_games.points_by_rank.mean() team_1_gp_rank_l5 = last5.points_by_rank.mean() return [team_1_rank, team_1_goals, team_1_goals_l5, team_1_goals_suf, team_1_goals_suf_l5, team_1_rank_suf, team_1_rank_suf_l5, team_1_gp_rank, team_1_gp_rank_l5] def find_features(team_1, team_2): rank_dif = team_1[0] - team_2[0] goals_dif = team_1[1] - team_2[1] goals_dif_l5 = team_1[2] - team_2[2] goals_suf_dif = team_1[3] - team_2[3] goals_suf_dif_l5 = team_1[4] - team_2[4] goals_per_ranking_dif = (team_1[1]/team_1[5]) - (team_2[1]/team_2[5]) dif_rank_agst = team_1[5] - team_2[5] dif_rank_agst_l5 = team_1[6] - team_2[6] dif_gp_rank = team_1[7] - team_2[7] dif_gp_rank_l5 = team_1[8] - team_2[8] return [rank_dif, goals_dif, goals_dif_l5, goals_suf_dif, goals_suf_dif_l5, goals_per_ranking_dif, dif_rank_agst, dif_rank_agst_l5, dif_gp_rank, dif_gp_rank_l5, 1, 0] advanced_group = [] last_group = "" for k in table.keys(): for t in table[k]: t[1] = 0 t[2] = [] for idx, teams in enumerate(matches): draw = False team_1 = find_stats(teams[1]) team_2 = find_stats(teams[2]) features_g1 = find_features(team_1, team_2) features_g2 = find_features(team_2, team_1) probs_g1 = gb.predict_proba([features_g1]) probs_g2 = gb.predict_proba([features_g2]) team_1_prob_g1 = probs_g1[0][0] team_1_prob_g2 = probs_g2[0][1] team_2_prob_g1 = probs_g1[0][1] team_2_prob_g2 = probs_g2[0][0] team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2 team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2 if ((team_1_prob_g1 > team_2_prob_g1) & (team_2_prob_g2 > team_1_prob_g2)) | ((team_1_prob_g1 < team_2_prob_g1) & (team_2_prob_g2 < team_1_prob_g2)): draw=True for i in table[teams[0]]: if i[0] == teams[1] or i[0] == teams[2]: i[1] += 1 elif team_1_prob > team_2_prob: winner = teams[1] winner_proba = team_1_prob for i in table[teams[0]]: if i[0] == teams[1]: i[1] += 3 elif team_2_prob > team_1_prob: winner = teams[2] winner_proba = team_2_prob for i in table[teams[0]]: if i[0] == teams[2]: i[1] += 3 for i in table[teams[0]]: #adding criterio de desempate (probs por jogo) if i[0] == teams[1]: i[2].append(team_1_prob) if i[0] == teams[2]: i[2].append(team_2_prob) if last_group != teams[0]: if last_group != "": print("\n") print("小组 %s 排名: "%(last_group)) for i in table[last_group]: #adding crieterio de desempate i[2] = np.mean(i[2]) final_points = table[last_group] final_table = sorted(final_points, key=itemgetter(1, 2), reverse = True) advanced_group.append([final_table[0][0], final_table[1][0]]) for i in final_table: print("%s -------- %d"%(country_name[i[0]] + "(" + i[0] + ")", i[1])) print("\n") print("-"*10+" 开始分析小组 %s "%(teams[0])+"-"*10) if draw == False: print("小组 %s - %s vs. %s: %s 获胜，胜率： %.2f"%(teams[0], country_name[teams[1]] + "(" + teams[1] + ")", country_name[teams[2]] + "(" + teams[2] + ")", country_name[winner] + "(" + winner + ")", winner_proba)) save_res(teams[1], teams[2], winner, winner_proba, "{}-{}.png".format(teams[0], idx)) else: print("小组 %s - %s vs. %s: Draw"%(teams[0], teams[1], teams[2])) save_res_draw(teams[1], teams[2], teams[2], 0.5, "{}-{}.png".format(teams[0], idx)) last_group = teams[0] print("\n") print("小组 %s 排名: "%(last_group)) for i in table[last_group]: #adding crieterio de desempate i[2] = np.mean(i[2]) final_points = table[last_group] final_table = sorted(final_points, key=itemgetter(1, 2), reverse = True) advanced_group.append([final_table[0][0], final_table[1][0]]) for i in final_table: print("%s -------- %d"%(country_name[i[0]] + "(" + i[0] + ")", i[1])) advanced = advanced_group playoffs = {"16 强": [], "四分之一决赛": [], "半决赛": [], "决赛": []} for p in playoffs.keys(): playoffs[p] = [] actual_round = "" next_rounds = [] for p in playoffs.keys(): if p == "16 强": control = [] for a in range(0, len(advanced*2), 1): if a < len(advanced): if a % 2 == 0: control.append((advanced*2)[a][0]) else: control.append((advanced*2)[a][1]) else: if a % 2 == 0: control.append((advanced*2)[a][1]) else: control.append((advanced*2)[a][0]) playoffs[p] = [[control[c], control[c+1]] for c in range(0, len(control)-1, 1) if c%2 == 0] for i in range(0, len(playoffs[p]), 1): game = playoffs[p][i] home = game[0] away = game[1] team_1 = find_stats(home) team_2 = find_stats(away) features_g1 = find_features(team_1, team_2) features_g2 = find_features(team_2, team_1) probs_g1 = gb.predict_proba([features_g1]) probs_g2 = gb.predict_proba([features_g2]) team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2 team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2 if actual_round != p: print("-"*10) print("开始模拟 %s"%(p)) print("-"*10) print("\n") if team_1_prob < team_2_prob: print("%s vs. %s: %s 晋级，概率： %.2f"%(country_name[home] + "(" + home + ")", country_name[away] + "(" + away + ")", country_name[away] + "(" + away + ")", team_2_prob)) save_res(home, away, away, team_2_prob, "%s.png" % str(time.time()).replace(".", "_")) next_rounds.append(away) else: print("%s vs. %s: %s 晋级，概率： %.2f"%(country_name[home] + "(" + home + ")", country_name[away] + "(" + away + ")", country_name[home] + "(" + home + ")", team_1_prob)) save_res(home, away, home, team_1_prob, "%s.png" % str(time.time()).replace(".", "_")) next_rounds.append(home) game.append([team_1_prob, team_2_prob]) playoffs[p][i] = game actual_round = p else: playoffs[p] = [[next_rounds[c], next_rounds[c+1]] for c in range(0, len(next_rounds)-1, 1) if c%2 == 0] next_rounds = [] for i in range(0, len(playoffs[p])): game = playoffs[p][i] home = game[0] away = game[1] team_1 = find_stats(home) team_2 = find_stats(away) features_g1 = find_features(team_1, team_2) features_g2 = find_features(team_2, team_1) probs_g1 = gb.predict_proba([features_g1]) probs_g2 = gb.predict_proba([features_g2]) team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2 team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2 if actual_round != p: print("-"*10) print("开始模拟 %s"%(p)) print("-"*10) print("\n") if team_1_prob < team_2_prob: print("%s vs. %s: %s 晋级，概率： %.2f"%(country_name[home] + "(" + home + ")", country_name[away] + "(" + away + ")", country_name[away] + "(" + away + ")", team_2_prob)) save_res(home, away, away, team_2_prob, "%s.png" % str(time.time()).replace(".", "_")) next_rounds.append(away) else: print("%s vs. %s: %s 晋级，概率： %.2f"%(country_name[home] + "(" + home + ")", country_name[away] + "(" + away + ")", country_name[home] + "(" + home + ")", team_1_prob)) save_res(home, away, home, team_1_prob, "%s.png" % str(time.time()).replace(".", "_")) next_rounds.append(home) game.append([team_1_prob, team_2_prob]) playoffs[p][i] = game actual_round = p

逐渐走向离谱画风 暂无简介

仅凭半句就封神的诗句 战战兢兢，如临深渊，如履薄冰 。——《诗经·小雅·小旻》树深时见鹿 ，溪午不闻钟。——李白《访戴天山道士不遇》十有九人堪白眼，百无一用是书生 。——黄景仁《杂感》福王少小风流惯，不爱江山爱美人 。——陈于王《题桃花扇传奇》酒债寻常行处有，人生七十古来稀 。——杜甫《曲江二首》人生若只如初见，何事秋风悲画扇 。——纳兰性德《木兰词·拟古决绝词柬友》众里寻他千百度。蓦然回首，那人却在，灯火阑珊处 。——辛弃疾《 青玉案·元夕 》