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16 code.py

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+import pandas as pd
+import numpy as np
+import statsmodels.api as sm
+
+rawstat = pd.read_table('/Users/wangtianyi/Documents/python_work/poisson regression.csv')
+offensive = rawstat.iloc[:,[0,1,2,3,4,5]]
+goal = rawstat.iloc[:,6]
+
+offensive_add = sm.add_constant(offensive)
+poisson_result = sm.GLM(goal, offensive_add, family=sm.families.Poisson()).fit()
+print(poisson_result.summary())
+
+offensive_reduced = offensive.iloc[:,[1,2,5]]
+offensive_add = sm.add_constant(offensive_reduced)
+poisson_result = sm.GLM(goal, offensive_add, family=sm.families.Poisson()).fit()
+print(poisson_result.summary())

17 code.py

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+import pandas as pd
+import numpy as np
+import scipy as sp
+import matplotlib.pyplot as plt
+from sklearn import svm
+
+ratio = []
+
+rawstat = pd.read_table('/Users/wangtianyi/Documents/python_work/linear inseparable.csv')
+category = rawstat.iloc[:,0]
+pass_ratio = rawstat.iloc[:,1] / rawstat.iloc[:,2]
+shot_ratio = rawstat.iloc[:,3] / rawstat.iloc[:,4]
+
+ratio.append(pass_ratio)
+ratio.append(shot_ratio)
+ratio = np.array(ratio)
+ratio = ratio.astype('float')
+
+linear_svm = svm.SVC(kernel='linear', C=1e10).fit(ratio.T, category)
+
+x_min, x_max = ratio[0].min() - 0.05, ratio[0].max() + 0.05
+y_min, y_max = ratio[1].min() - 0.05, ratio[1].max() + 0.05
+xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))
+xy = np.vstack([xx.ravel(), yy.ravel()]).T
+Z = linear_svm.decision_function(xy).reshape(xx.shape)
+
+plt.scatter(pass_ratio[category == 0], shot_ratio[category == 0], c='r', marker = 'o')
+plt.scatter(pass_ratio[category == 1], shot_ratio[category == 1], c='b', marker = '^')
+plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])
+plt.title("playing style discrimination with SVM")
+plt.xlabel('pass ratio')
+plt.ylabel('shot ratio')
+plt.show()

18 code.py

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+import pandas as pd
+import numpy as np
+import scipy as sp
+import matplotlib.pyplot as plt
+from sklearn import svm
+
+ratio = []
+
+rawstat = pd.read_table('/Users/wangtianyi/Documents/python_work/linear inseparable.csv')
+category = rawstat.iloc[:,0]
+pass_ratio = rawstat.iloc[:,1] / rawstat.iloc[:,2]
+shot_ratio = rawstat.iloc[:,3] / rawstat.iloc[:,4]
+
+ratio.append(pass_ratio)
+ratio.append(shot_ratio)
+ratio = np.array(ratio)
+ratio = ratio.astype('float')
+
+linear_svm = svm.SVC(kernel='rbf', gamma=5, C=1e6).fit(ratio.T, category)
+
+x_min, x_max = ratio[0].min() - 0.05, ratio[0].max() + 0.05
+y_min, y_max = ratio[1].min() - 0.05, ratio[1].max() + 0.05
+xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.01), np.arange(y_min, y_max, 0.01))
+xy = np.vstack([xx.ravel(), yy.ravel()]).T
+Z = linear_svm.decision_function(xy).reshape(xx.shape)
+
+plt.scatter(pass_ratio[category == 0], shot_ratio[category == 0], c='r', marker = 'o')
+plt.scatter(pass_ratio[category == 1], shot_ratio[category == 1], c='b', marker = '^')
+plt.contour(xx, yy, Z, colors='k', levels=[-1, 0, 1], alpha=0.5, linestyles=['--', '-', '--'])
+plt.title("playing style discrimination with kernel SVM")
+plt.xlabel('pass ratio')
+plt.ylabel('shot ratio')
+plt.show()

linear inseparable.csv

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+category	long pass	short pass	header shot	kick shot
+0.00 	52.00 	767.00 	2.00 	12.00 
+0.00 	61.00 	557.00 	2.00 	17.00 
+0.00 	48.00 	624.00 	2.00 	17.00 
+0.00 	41.00 	735.00 	2.00 	11.00 
+0.00 	41.00 	643.00 	4.00 	24.00 
+0.00 	34.00 	743.00 	5.00 	20.00 
+0.00 	49.00 	647.00 	5.00 	13.00 
+0.00 	42.00 	887.00 	3.00 	18.00 
+0.00 	43.00 	804.00 	1.00 	14.00 
+0.00 	56.00 	902.00 	1.00 	14.00 
+0.00 	45.00 	614.00 	1.00 	8.00 
+0.00 	45.00 	774.00 	0.00 	12.00 
+0.00 	36.00 	734.00 	2.00 	13.00 
+0.00 	31.00 	803.00 	3.00 	23.00 
+0.00 	31.00 	774.00 	0.00 	24.00 
+0.00 	48.00 	591.00 	0.00 	14.00 
+0.00 	33.00 	842.00 	2.00 	21.00 
+0.00 	57.00 	433.00 	2.00 	18.00 
+0.00 	49.00 	895.00 	3.00 	11.00 
+0.00 	37.00 	824.00 	2.00 	20.00 
+0.00 	39.00 	632.00 	1.00 	14.00 
+0.00 	41.00 	857.00 	2.00 	16.00 
+0.00 	51.00 	660.00 	1.00 	10.00 
+0.00 	44.00 	814.00 	2.00 	19.00 
+0.00 	51.00 	685.00 	0.00 	19.00 
+0.00 	70.00 	663.00 	2.00 	18.00 
+0.00 	43.00 	706.00 	2.00 	17.00 
+0.00 	51.00 	635.00 	0.00 	9.00 
+0.00 	46.00 	970.00 	0.00 	13.00 
+0.00 	60.00 	818.00 	0.00 	17.00 
+0.00 	51.00 	955.00 	1.00 	17.00 
+0.00 	47.00 	595.00 	3.00 	17.00 
+0.00 	77.00 	474.00 	0.00 	17.00 
+0.00 	51.00 	995.00 	3.00 	16.00 
+0.00 	43.00 	864.00 	0.00 	19.00 
+0.00 	59.00 	743.00 	3.00 	12.00 
+0.00 	31.00 	861.00 	1.00 	18.00 
+0.00 	41.00 	599.00 	1.00 	12.00 
+1.00 	76.00 	213.00 	4.00 	12.00 
+1.00 	73.00 	198.00 	3.00 	5.00 
+1.00 	58.00 	312.00 	5.00 	5.00 
+1.00 	68.00 	457.00 	1.00 	11.00 
+1.00 	65.00 	257.00 	3.00 	3.00 
+1.00 	67.00 	236.00 	2.00 	5.00 
+1.00 	72.00 	253.00 	2.00 	7.00 
+1.00 	75.00 	347.00 	0.00 	5.00 
+1.00 	70.00 	271.00 	1.00 	6.00 
+1.00 	69.00 	210.00 	3.00 	4.00 
+1.00 	71.00 	399.00 	2.00 	7.00 
+1.00 	59.00 	359.00 	2.00 	5.00 
+1.00 	74.00 	220.00 	0.00 	5.00 
+1.00 	69.00 	374.00 	2.00 	6.00 
+1.00 	79.00 	373.00 	6.00 	14.00 
+1.00 	74.00 	373.00 	2.00 	5.00 
+1.00 	75.00 	253.00 	3.00 	3.00 
+1.00 	51.00 	432.00 	1.00 	11.00 
+1.00 	67.00 	464.00 	4.00 	13.00 
+1.00 	71.00 	374.00 	8.00 	9.00 
+1.00 	71.00 	297.00 	4.00 	10.00 
+1.00 	68.00 	381.00 	0.00 	10.00 
+1.00 	86.00 	310.00 	4.00 	11.00 
+1.00 	80.00 	326.00 	4.00 	11.00 
+1.00 	60.00 	210.00 	0.00 	3.00 
+1.00 	80.00 	288.00 	2.00 	6.00 
+1.00 	65.00 	401.00 	3.00 	5.00 
+1.00 	83.00 	372.00 	2.00 	8.00 
+1.00 	74.00 	331.00 	3.00 	6.00 
+1.00 	69.00 	347.00 	0.00 	6.00 
+1.00 	83.00 	308.00 	3.00 	8.00 
+1.00 	92.00 	367.00 	3.00 	5.00 
+1.00 	62.00 	473.00 	1.00 	14.00 
+1.00 	68.00 	274.00 	4.00 	6.00 
+1.00 	69.00 	334.00 	3.00 	10.00 
+1.00 	76.00 	309.00 	1.00 	8.00 
+1.00 	70.00 	165.00 	3.00 	6.00 
+1.00 	60.00 	356.00 	1.00 	6.00