III Liga Group 4 stats & predictions

Diving deeper into the tactical aspects of tomorrow’s matches provides an edge in predicting outcomes accurately. Understanding formations and strategies employed by each team can reveal potential weaknesses or strengths that might not be immediately apparent from their recent results alone.

Tactical Breakdown: Team A vs. Team B

• Tactical Formation:

- Team A typically adopts a fluid attacking formation like a flexible version of a '4-3-3', allowing them to exploit spaces behind defenses efficiently. - Team B counters with a '5-4-1' setup when defending against stronger opponents, focusing on compactness in defense while utilizing quick counter-attacks.

• Potential Weaknesses & Exploits:

- With Team A’s focus on attacking through wide positions, they might leave spaces open centrally which can be exploited by quick passes from midfield. - Conversely, Team B’s narrow defensive line could struggle against high crosses if not adequately marked by their full-backs.

• Predicted Key Moments:

- Look out for set-pieces as both teams have shown proficiency in capitalizing on these opportunities. - Midfield battles will likely dictate the tempo of the game; watch how effectively each side transitions from defense to attack.

Tactical Breakdown: Team C vs. Team D

• Tactical Formation:

- Expect Team C to deploy a '4-2-3-1', emphasizing control over midfield while allowing creative freedom for wingers. - In contrast, Team D often utilizes a '4-5-1', focusing heavily on defensive solidity but capable of sudden offensive surges through overlapping full-backs.

• Potential Weaknesses & Exploits:

- The congested midfield area could lead to turnovers; exploiting these will require sharp passing accuracy from either side. - Defensive lapses during high pressing situations may provide openings for fast attackers.

• Predicted Key Moments:

  - Possession stats will be crucial; see which team manages ball control more effectively under pressure. - Defensive organization during transitions will likely determine which side gains an upper hand during counterattacks.

  Tactical Breakdown: Team E vs. Team F

  • Tactical Formation:

    - Anticipate an adaptable approach from both sides; however, Team E might favor '4-4-2', promoting width while maintaining strong central support. Team F is likely to use '3-5-2', providing numerical superiority in midfield but potentially vulnerable against swift wide attacks.

  • Potential Weaknesses & Exploits: <|repo_name|>anandkumar20/Python<|file_sep|>/Decision_Tree.py #!/usr/bin/env python # coding: utf-8 # In[1]: from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier # In[ ]: iris = load_iris() X_train,X_test,y_train,y_test = train_test_split(iris.data , iris.target , random_state=0) tree = DecisionTreeClassifier(max_depth=2 , random_state=0) tree.fit(X_train,y_train) # In[ ]: print("훈련 세트 정확도:{:.4f}".format(tree.score(X_train,y_train))) print("테스트 세트 정확도:{:.4f}".format(tree.score(X_test,y_test))) # In[ ]: from sklearn.tree import export_graphviz export_graphviz(tree,out_file="tree.dot",class_names=iris.target_names, feature_names=iris.feature_names , impurity=False , filled=True) # In[ ]: get_ipython().system('dot -Tpng tree.dot -o tree.png') # In[ ]: from sklearn.tree import export_graphviz export_graphviz(tree,out_file="tree.dot",class_names=["Setosa","Versicolor","Virginica"], feature_names=["꽃잎길이","꽃잎폭","꽃받침길이","꽃받침폭"] , impurity=False , filled=True) # In[ ]: get_ipython().system('dot -Tpng tree.dot -o tree.png') # In[ ]: import graphviz with open("tree.dot") as f: dot_graph = f.read() graphviz.Source(dot_graph) # In[ ]: tree = DecisionTreeClassifier(max_depth=4) tree.fit(X_train,y_train) print("훈련 세트 정확도:{:.4f}".format(tree.score(X_train,y_train))) print("테스트 세트 정확도:{:.4f}".format(tree.score(X_test,y_test))) # In[ ]: export_graphviz(tree,out_file="tree.dot",class_names=["Setosa","Versicolor","Virginica"], feature_names=["꽃잎길이","꽃잎폭","꽃받침길이","꽃받침폭"] , impurity=False , filled=True) # In[ ]: get_ipython().system('dot -Tpng tree.dot -o tree.png') # In[ ]: get_ipython().run_line_magic('matplotlib', 'inline') import matplotlib.pyplot as plt import numpy as np def plot_tree_boundary(clf,X,y,test_idx=None,res=0.02): # 결정 경계를 그리기 위해 입력 데이터의 x축과 y축 값의 범위를 만듭니다. x_min,x_max=X[:,0].min()-1,X[:,0].max()+1 y_min,y_max=X[:,1].min()-1,X[:,1].max()+1 # 입력 데이터의 범위에 맞춰서 그리드 포인트 생성합니다. xx,yy=np.meshgrid(np.arange(x_min,x_max,res), np.arange(y_min,y_max,res)) # In[ ]: # In[ ]: <|repo_name|>anandkumar20/Python<|file_sep|>/PCA.py #!/usr/bin/env python # coding: utf-8 # # PCA와 주성분 분석 # ## 데이터 준비하기 # In[11]: import pandas as pd df_wine = pd.read_csv("../data/wine.csv") df_wine.head() # ## 데이터 준비하기 # In[12]: df_wine.drop("Class",axis=1,inplace=True) df_wine.head() # ## 데이터 준비하기 # In[13]: X = df_wine.values # ## PCA 분석 수행하기 # ### 스케일 조정하기 # #### [참고] StandardScaler로 표준화하기 # * 각 피처가 가지는 표준편차가 다른 경우 스케일 조정을 해야 함. # * 피처들이 서로 다른 스케일을 가지면 알고리즘이 잘 작동하지 않음. # * 예) KNN 알고리즘은 거리 계산을 통해 학습을 수행함. # # # # # #### StandardScaler 클래스 # # # # # * 평균을 빼고 표준 편차로 나누어 데이터를 변환함. # # # # * fit() : 데이터에 대한 평균과 표준 편차 계산 # # # # * transform() : fit()에서 계산된 평균과 표준 편차로 변환 수행 # # # # * fit_transform() : fit()과 transform()을 한 번에 수행함. # # # #### StandardScaler 클래스 사용법 # * fit_transform() 메서드를 호출하여 데이터 변환 수행 후 결과를 저장한다. # # # #### [참고] MinMaxScaler로 정규화하기 # * MinMaxScaler 클래스는 데이터의 최소값을 모두 해당 열의 최소값으로 설정하고 최대값을 모두 해당 열의 최대값으로 설정함으로써 각 열을 [0~1] 사이의 값으로 변환함. # # import numpy as np from sklearn.preprocessing import StandardScaler scaler = StandardScaler() X_std = scaler.fit_transform(X) X_std[:5,:] # ### 고유벡터와 고유값 계산하기 # #### [참고] 고윳값과 고유벡터 이해하기 # * 고윳값과 고유벡터는 선형대수학에서 중요한 개념임. # # import numpy as np A = np.array([[1,-2],[-2,4]]) eig_val,eig_vec = np.linalg.eig(A) eig_val,eig_vec A@eig_vec[:,0]-eig_val[0]*eig_vec[:,0] A@eig_vec[:,1]-eig_val[1]*eig_vec[:,1] np.allclose(A@eig_vec[:,0]-eig_val[0]*eig_vec[:,0],np.zeros(2)) np.allclose(A@eig_vec[:,1]-eig_val[1]*eig_vec[:,1],np.zeros(2)) np.allclose(A@eig_vec,eig_val*eig_vec) np.allclose(eig_vec@eig_vec.T,np.eye(2)) np.allclose(eig_vec.T@eig_vec,np.eye(2)) np.allclose([email protected](eig_vec),np.eye(2)) u,s,v_t = np.linalg.svd(A) v_t.T v_t @ v_t.T v_t.T @ v_t u @ u.T u.T @ u s u*s u*(s.reshape(-1,1)) u*(s.reshape(-1,1)) @ v_t == A A_svd = u*(s.reshape(-1,1)) @ v_t A_svd == A A_svd u @ np.diag(s) @ v_t == A U,sigma,Vt = np.linalg.svd(A) U,sigma,Vt np.diag(sigma) sigma_mat = np.zeros((sigma.size,sigma.size)) sigma_mat[:sigma.size,:sigma.size] = np.diag(sigma) sigma_mat U @ sigma_mat @ Vt == A np.allclose(U @ sigma_mat @ Vt,A) U.shape,sigma.shape,Vt.shape,sigma_mat.shape,A.shape import numpy as np A = np.array([[1,-2],[-2,4]]) cov_mat = A.T@A cov_mat U,sigma,Vt = np.linalg.svd(cov_mat) D = np.diag(sigma) D Vt.T@D@Vt == cov_mat Vt.T@D == np.diag(sigma) Vt.T@D@Vt == cov_mat U,sigma,Vt = np.linalg.svd(cov_mat) sigma_matrix=np.zeros((sigma.size,sigma.size))