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首選——高斯核函數

import numpy as np import pandas as pd import matplotlib as mpl import matplotlib.pyplot as plt from sklearn import svm from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score# 'sepal length', 'sepal width', 'petal length', 'petal width' iris_feature = u'花萼長度', u'花萼寬度', u'花瓣長度', u'花瓣寬度'if __name__ == "__main__":path = '/Users/zhucan/Desktop/iris.csv' # 數據文件路徑data = pd.read_csv(path, header=None)x, y = data[range(4)], data[4]#字符串映射為0，1，2三個類別y = pd.Categorical(y).codesx = x[[0, 1]]x_train, x_test, y_train, y_test = train_test_split(x, y, random_state=1, train_size=0.6)# 分類器 SVC classification ovr用若干個二分類得到一個三分類clf = svm.SVC(C=0.1, kernel='linear', decision_function_shape='ovr')# clf = svm.SVC(C=0.8, kernel='rbf', gamma=20, decision_function_shape='ovr')clf.fit(x_train, y_train.ravel())# 準確率print(clf.score(x_train, y_train)) # 精度print('訓練集準確率：', accuracy_score(y_train, clf.predict(x_train)))print(clf.score(x_test, y_test))print('測試集準確率：', accuracy_score(y_test, clf.predict(x_test)))# decision_functionprint('decision_function:\n', clf.decision_function(x_train))print('\npredict:\n', clf.predict(x_train)) 0.8777777777777778 訓練集準確率： 0.8777777777777778 0.7377049180327869 測試集準確率： 0.7377049180327869到三個分類器的距離 decision_function:[[ 0.77012025 2.22752333 3.25141611 -0.24994564][ 0.72449894 2.2375207 3.27995319 -0.24915267][ 3.25675197 2.19909745 0.78766834 -0.25165556][ 3.2365702 2.22433941 0.80405863 -0.25157315][ 0.82102496 2.23135422 3.21875372 -0.25038056][ 3.24340533 2.14809174 0.89650978 -0.25114765][ 0.82696908 3.23547716 2.20967506 -0.25046731][ 0.77046828 2.18930222 3.26499186 -0.24959236][ 1.11964505 3.23028535 2.06731009 -0.25098136][ 0.78502606 2.23589281 3.23544913 -0.25020768]]predict:[2 2 0 0 2 0 1 2 1 2 2 2 0 1 1 2 1 1 1 0 0 0 1 0 1 2 1 0 0 1 0 2 1 2 2 2 22 1 0 1 0 1 2 0 2 0 0 2 2 1 0 0 1 0 2 0 2 2 0 1 0 1 0 1 1 3 0 2 0 1 1 0 11 1 0 2 0 0 1 1 2 2 1 2 2 1 2 0]

?高斯核——γ高斯分布的精度，γ越大，高斯分布精度越大

import numpy as np from sklearn import svm from sklearn.model_selection import GridSearchCV # 0.17 grid_search import matplotlib.pyplot as pltif __name__ == "__main__":N = 50np.random.seed(0)x = np.sort(np.random.uniform(0, 6, N), axis=0)y = 2*np.sin(x) + 0.1*np.random.randn(N)x = x.reshape(-1, 1)print('x =\n', x)print('y =\n', y)model = svm.SVR(kernel='rbf')c_can = np.logspace(-2, 2, 10)gamma_can = np.logspace(-2, 2, 10)svr = GridSearchCV(model, param_grid={'C': c_can, 'gamma': gamma_can}, cv=5)svr.fit(x, y)print('驗證參數：\n', svr.best_params_) x =[[0.1127388][0.12131038][0.36135283][0.42621635][0.5227758 ][0.70964656][0.77355779][0.86011972][1.26229537][1.58733367][1.89257011][2.1570474 ][2.18226463]]y =[ 0.05437325 0.43710367 0.65611482 0.78304981 0.87329469 1.380880421.23598022 1.49456731 1.81603293 2.03841677 1.84627139 1.547977961.63479377 1.53337832 1.22278185 1.15897721 0.92928812 0.950656380.72022281 0.69233817 -0.06030957 -0.23617129 -0.23697659 -0.34160192-0.69007014 -0.48527812 -1.00538468 -1.00756566 -0.98948253 -1.05661601-1.17133143 -1.46283398 -1.47415531 -1.61280243 -1.7131299 -1.78692494-1.85631003 -1.98989791 -2.11462751 -1.90906396 -1.95199287 -2.14681169-1.77143442 -1.55815674 -1.48840245 -1.35114367 -1.27027958 -1.04875251-1.00128962 -0.67767925] 驗證參數：{'C': 35.93813663804626, 'gamma': 0.5994842503189409}

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