4. Liga East stats & predictions

Unlock the Thrill of Football 4. Liga East Slovakia

Welcome to the ultimate destination for all your Football 4. Liga East Slovakia needs! Here, you'll find the latest match updates, expert betting predictions, and in-depth analysis to keep you ahead of the game. Whether you're a die-hard fan or a casual observer, our platform provides you with all the insights and information you need to enjoy every match. With daily updates, you'll never miss a beat in this exciting league.

Why Follow Football 4. Liga East Slovakia?

The Football 4. Liga East Slovakia is not just another league; it's a battleground where passion meets talent. Each match is a showcase of emerging talent and strategic brilliance, making it a must-watch for football enthusiasts. The league's dynamic nature ensures that no two matches are alike, offering endless excitement and unpredictability.

Daily Match Updates

Stay informed with our comprehensive daily match updates. We provide detailed reports on every game, including scores, key moments, and standout performances. Our team of expert analysts covers every aspect of the match, ensuring you have all the information at your fingertips.

• Real-time score updates
• Match highlights and key moments
• In-depth analysis of team strategies
• Player performance reviews

Expert Betting Predictions

Betting on football can be both thrilling and challenging. Our expert analysts offer precise betting predictions to help you make informed decisions. With years of experience and a deep understanding of the league, our predictions are based on thorough research and statistical analysis.

• Accurate match predictions
• Insights into team form and player fitness
• Analysis of historical data and trends
• Strategic betting tips and advice

In-Depth Team Analysis

Understanding team dynamics is crucial for predicting match outcomes. Our platform offers in-depth analysis of each team in the league, covering their strengths, weaknesses, and tactical approaches. Get to know your favorite teams better with our detailed reports.

• Team formation and tactics
• Key players and their impact
• Recent performance trends
• Upcoming fixtures and potential challenges

Player Profiles and Insights

Discover the stars of Football 4. Liga East Slovakia with our detailed player profiles. Learn about their backgrounds, career highlights, and what makes them stand out on the field. Our insights help you appreciate the talent that shapes each match.

• Detailed player biographies
• Statistical performance data
• Interviews and personal stories
• Impact on team dynamics and strategies

Interactive Features for Fans

Engage with other fans through our interactive features designed to enhance your viewing experience. Participate in discussions, share your opinions, and connect with a community that shares your passion for football.

• Fan forums and discussion boards
• Social media integration for real-time updates
• Polls and quizzes to test your knowledge
• User-generated content and fan stories

The History of Football 4. Liga East Slovakia

The Football 4. Liga East Slovakia has a rich history that adds depth to its current competitions. Understanding its origins and evolution provides context to the matches we watch today.

• The founding of the league and its early years
• Significant milestones and achievements
• Influential figures in the league's history
• The league's impact on Slovakian football culture

The Role of Youth Academies

Youth academies play a pivotal role in nurturing future stars for the Football 4. Liga East Slovakia. These institutions are breeding grounds for talent, providing young athletes with the skills and opportunities needed to succeed at higher levels.

• Overview of top youth academies in Slovakia
<|repo_name|>kikozhuo/WeiboDataAnalyse<|file_sep|>/README.md # WeiboDataAnalyse 分析数据来源于：[Sina Weibo Sentiment Dataset](http://www.cs.cornell.edu/people/pabo/movie-review-data/) ## 简介 - 这是一个微博数据分析的项目，主要包括数据预处理、词云展示、情感分析和聚类分析。 - 在这个项目中，我使用了python的各种库，包括numpy、scipy、sklearn、matplotlib、jieba、wordcloud、snownlp等。 ## 数据预处理 - 数据预处理包括数据清洗和特征提取两部分。 - 数据清洗包括去除无用信息，如用户信息、微博的链接等；去除停用词；去除非中文字符；去除特殊符号；分词。 - 特征提取包括对文本进行TF-IDF计算和LDA主题模型计算。TF-IDF计算之后会得到一个文档与词语的矩阵，其中每一行表示一个文档，每一列表示一个词语，矩阵中的值表示该词语在该文档中的权重。LDA主题模型计算之后会得到一个文档与主题的矩阵，其中每一行表示一个文档，每一列表示一个主题，矩阵中的值表示该主题在该文档中的权重。 ## 情感分析 - 使用SVM（支持向量机）分类器对微博进行情感分析。使用了网上开源的SVM分类器训练好的模型。模型下载地址：[SVM微博情感分类器](https://github.com/bojone/sentiment-classification)。 ## 聚类分析 - 使用K-means聚类算法对微博进行聚类分析。根据TF-IDF矩阵和LDA主题模型矩阵进行聚类。选择不同的特征进行聚类，然后比较两种特征下聚类效果的差异。 <|file_sep|># -*- coding: utf-8 -*- import numpy as np import pandas as pd import jieba from sklearn import svm from sklearn.externals import joblib import os def clean_stopwords(line): """去除停用词""" stopwords = [line.strip() for line in open('stopwords.txt', 'r', encoding='utf-8').readlines()] result = [] for word in line: if word not in stopwords: result.append(word) return result def clean_content(content): """数据清洗""" content = content.replace("转发理由：", "") content = content.replace("转发微博", "") content = content.replace("原始链接", "") content = content.replace("http://t.cn", "") content = content.replace("http://weibo.com", "") content = content.replace("http://m.weibo.cn", "") content = content.replace("http://weibo.com/p/100808b0c7e5f3ed8c74c1e6d1d8a6a11e34b8?from=page_100808b0c7e5f3ed_profile&wvr=6&mod=weibotime&type=comment#_rnd1515822264139", "") #content = re.sub("[^u4e00-u9fa5]", "", content) words = jieba.cut(content) words = clean_stopwords(words) return " ".join(words) def train(): """训练模型""" data_path = './data/' svm_model_path = './model/' test_file = data_path + 'test_set.csv' train_file_pos = data_path + 'train_set_pos.csv' train_file_neg = data_path + 'train_set_neg.csv' train_file_neu = data_path + 'train_set_neu.csv' test_data_pos = pd.read_csv(test_file)[['content', 'sentiment']] test_data_pos['content'] = test_data_pos['content'].apply(clean_content) test_data_pos['sentiment'] = test_data_pos['sentiment'].apply(lambda x:1 if x=='positive' else -1) test_data_neg = pd.read_csv(test_file)[['content', 'sentiment']] test_data_neg['content'] = test_data_neg['content'].apply(clean_content) test_data_neg['sentiment'] = test_data_neg['sentiment'].apply(lambda x:1 if x=='positive' else -1) test_data_neu = pd.read_csv(test_file)[['content', 'sentiment']] test_data_neu['content'] = test_data_neu['content'].apply(clean_content) test_data_neu['sentiment'] = test_data_neu['sentiment'].apply(lambda x:1 if x=='positive' else -1) pos_train_x_list = [] pos_train_y_list = [] for index, row in test_data_pos.iterrows(): pos_train_x_list.append(row['content']) pos_train_y_list.append(row['sentiment']) neg_train_x_list = [] neg_train_y_list = [] for index, row in test_data_neg.iterrows(): neg_train_x_list.append(row['content']) neg_train_y_list.append(row['sentiment']) neu_train_x_list = [] neu_train_y_list = [] for index, row in test_data_neu.iterrows(): neu_train_x_list.append(row['content']) neu_train_y_list.append(row['sentiment']) print('pos shape:', len(pos_train_x_list)) print('neg shape:', len(neg_train_x_list)) print('neu shape:', len(neu_train_x_list)) print('Loading training data...') pos_train_x_all_list=[] for i in range(10): pos_train_x_all_list += pd.read_csv(train_file_pos).iloc[i::10]['content'].tolist() neg_train_x_all_list=[] for i in range(10): neg_train_x_all_list += pd.read_csv(train_file_neg).iloc[i::10]['content'].tolist() print('pos shape:', len(pos_train_x_all_list)) print('neg shape:', len(neg_train_x_all_list)) pos_train_y_all_list=[1]*len(pos_train_x_all_list) neg_train_y_all_list=[-1]*len(neg_train_x_all_list) print('Loading training data done!') print('Training model...') svm_model_file=svm_model_path+'svm.pkl' if not os.path.exists(svm_model_file): train_x=pos_train_x_all_list+neg_train_x_all_list train_y=pos_train_y_all_list+neg_train_y_all_list model=svm.SVC(kernel='linear') model.fit(train_x, train_y) joblib.dump(model,svm_model_file) else: print('loading existing model...') model=joblib.load(svm_model_file) print('Training model done!') return model def predict(model): """测试""" data_path='./data/' model_path='./model/' svm_model_file=model_path+'svm.pkl' model=joblib.load(svm_model_file) test_file=data_path+'test_set.csv' df_test=pd.read_csv(test_file)[['content']] df_test=df_test.dropna() df_test=pd.DataFrame({'text':df_test.content.apply(clean_content)}) df_test=df_test.dropna() y_pred=model.predict(df_test.text.tolist()) df_test.loc[:,'label']=y_pred print(df_test.label.value_counts()) df_result=df_test[df_test.label==1] result=[] for index,row in df_result.iterrows(): result.append([row.text,row.label]) return result if __name__ == '__main__': model=train() result=predict(model) print(result)<|file_sep|># -*- coding: utf-8 -*- from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.decomposition import LatentDirichletAllocation from sklearn.cluster import KMeans from sklearn.metrics.pairwise import euclidean_distances from scipy.spatial.distance import cdist import matplotlib.pyplot as plt import numpy as np import pandas as pd def get_topic_words(model): topics=[] for topic_idx, topic in enumerate(model.components_): topics.append([model.feature_names_[i] for i in topic.argsort()[:-20 -1:-1]]) return topics def get_topic_distribution(model): topic_distribution=[] for topic_idx, topic in enumerate(model.components_): topic_distribution.append(topic) return topic_distribution def get_document_topics(tfidf_matrix,model): document_topics=[] for document_index in range(len(tfidf_matrix)): document_topics.append(model.transform(tfidf_matrix[document_index])) return document_topics def clustering(X,n_clusters,kmeans=True,distance_metric='euclidean'): kmeans_model=KMeans(n_clusters=n_clusters).fit(X) if kmeans: y=kmeans_model.labels_ else: y=cdist(X,kmeans_model.cluster_centers_,distance_metric).argmin(axis=1) score=euclidean_distances(X,kmeans_model.cluster_centers_) distribution=np.mean(score,axis=0)/np.sum(score,axis=0) return y,distribution,kmeans_model.cluster_centers_ def silhouette_score(X,y): d=np.zeros((len(X),len(X))) for i,x_i in enumerate(X): d[i]=np.linalg.norm(x_i-X,axis=1) mi=np.mean(d[y==y[:,None]],axis=1)-np.mean(d[y!=y[:,None]],axis=1)/(len(y)-1) si=(np.max(d[y==y[:,None]],axis=1)-mi)/np.max(np.vstack([mi,np.max(d[y!=y[:,None]],axis=1)]),axis=0) score=np.mean(si) return score if __name__ == '__main__': data_path='./data/' tfidf_matrix=pd.read_pickle(data_path+'tfidf_matrix.pkl') tfidf_matrix=tfidf_matrix.toarray() document_topics=pd.read_pickle(data_path+'document_topics.pkl') document_topics=document_topics.toarray() document_length=pd.read_pickle(data_path+'document_length.pkl') X=tfidf_matrix y,distribution,kmeans_cluster_centers=clustering(X,n_clusters=10,kmeans=True,distance_metric='euclidean') score=silhouette_score(X,y) X=document_topics y,distribution,kmeans_cluster_centers=clustering(X,n_clusters=10,kmeans=True,distance_metric='euclidean') score=silhouette_score(X,y) X=document_length.reshape(-1,1) y,distribution,kmeans_cluster_centers=clustering(X,n_clusters=10,kmeans=False,distance_metric='euclidean') score=silhouette_score(X,y)<|file_sep|># -*- coding: utf-8 -*- from sklearn.datasets import fetch_20newsgroups from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.decomposition import LatentDirichletAllocation import matplotlib.pyplot as plt import pandas as pd def get_topic_words(model): topics=[] for topic_idx, topic in enumerate(model.components_): topics.append([model.feature_names_[i] for i in topic.argsort()[:-20 -1:-1]]) return topics def get_topic_distribution(model): topic_distribution=[] for topic_idx, topic in enumerate(model.components_): topic_distribution.append(topic) return topic_distribution if __name__ == '__main__': data_path='./data/' tfidf_matrix=pd.read_pickle(data_path+'tfidf_matrix.pkl') tfidf_matrix=tfidf_matrix.toarray() vectorizer=TfidfVectorizer() vocab_dict=dict(zip(vectorizer.get_feature_names(),list(range(len(vectorizer.get_feature_names()))))) llda=LDA(n_components=n_components,n_jobs=-1,max_iter=max_iter) document_topics=llda.fit_transform(tfidf_matrix) topic_words=get_topic_words(llda) topic_distribution=get_topic_distribution(llda) pd.DataFrame(topic_words).to_csv(data_path+'topic_words.csv',index=False) pd.DataFrame(topic_distribution).to_csv(data_path+'topic_distribution.csv',index=False) pd.DataFrame(document_topics).to_pickle(data_path+'document_topics.pkl')<|file_sep|># -*- coding: utf-8 -*- import pandas as pd import numpy as np import jieba.posseg as pseg from wordcloud import WordCloud def get_word_count(df_text_column,n=None,pos=None): word_count_dict={} text_column=df_text_column.values.tolist() if n==None: text_column=[x.split() for x in text_column] else: text_column=[x.split()[:n] for x in text_column] if pos==None: for line in text_column: for word,_pos_ in line: word_count_dict[word]=word_count_dict.get(word,0)+1 else: for line in text_column: for word,_pos_ in line: if _pos_.startswith(pos): word_count_dict[word]=word_count_dict.get(word,0)+1 word_count_sorted=[(k,v) for k,v in sorted(word_count_dict.items(),key=lambda item:item[1],reverse=True)] return word_count_sorted if __name__ == '__main__': data_path='./data/' topic_words=pd.read_csv(data_path+'topic_words.csv').values.tolist() wordcloud_dict={} for i,line in enumerate(topic_words): line_str=' '.join(line) wordcloud_dict[i]=line_str font_size=max([len(line) for line in wordcloud_dict