feat: 增加jcbf和sk_test

jcbf.py

@@ -0,0 +1,23 @@
+import requests
+from furl import furl
+import time
+import json
+
+file = open("data.json", "w+")
+
+base_url = "https://www.ttyingqiu.com/"
+f = furl(base_url)
+
+f.path = "/static/no_cache/league/zc/jsbf/ttyq2020/jsbf_2022-11-06.json"
+f.args["v"] = int(time.time()*1000)
+print(f.url)
+print(r.status_code)
+print(r.json())
+file.write(json.dumps(r.json()))
+file.close()
+
+
+url = "/static/no_cache/league/zc/jsbf/ttyq2020/jczq/jsbf_2022-11-06.json?v=1667799791084"
+url = "https://www.ttyingqiu.com/static/no_cache/league/zc/jsbf/ttyq2020/jczq/2022-11-06/oz_407_6.json?v=1667799791169"
+
+match_list = data["matchList"]

sk_test.py

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+import sklearn
+from sklearn import linear_model, tree
+
+# 普通最小二乘法
+reg = linear_model.LinearRegression()
+reg.fit([[0, 0], [1, 1], [2, 2]], [0, 1, 2])
+result = reg.predict([[3, 1]])
+print(reg.coef_)
+print(result)
+
+# 贝叶斯岭回归
+X = [[0., 0.], [1., 1.], [2., 2.], [3., 3.]]
+Y = [0., 1., 2., 3.]
+reg = linear_model.BayesianRidge()
+reg.fit(X, Y)
+result = reg.predict([[1, 0]])
+print(reg.coef_)
+print(result)
+
+# 决策树分类
+X = [[0, 0], [1, 1]]
+Y = [0, 1]
+clf = tree.DecisionTreeClassifier()
+clf = clf.fit(X, Y)
+result = clf.predict([[10., 11.]])
+print(result)
+print(clf.predict_proba([[10., 11.]]))
+# 决策树回归
+X = [[0, 0], [2, 2]]
+Y = [1.5, 6.5]
+clf = tree.DecisionTreeRegressor()
+clf = clf.fit(X, Y)
+result = clf.predict([[1, 2]])
+print(result)