得到模型(model) 0x03 求解模型 数学基础1(凸优化) 数学基础2(拉格朗日乘子法) 求解上述:仅含等式约束的优化问题 同样的求解上述:含有等式和不等式的优化问题 数学基础3(对偶求解) 求解SVM的凸优化问题 0x04 分类讨论 0x05 SVM code 讨论一种不同的线性分类和回归方法,每种学习算法都具有不同的归纳偏倚,做不...
def plot_svm(kernel, df_input, y, C, gamma, coef): svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0] Z = Z.reshape(xx.shape) fig =...
# print(np.c_[xx.flatten(), yy.flatten()].shape)#(250000, 2)--->对应了网格点坐标矩阵中的每一个坐标点 # 用训练好的分类器去预测各个坐标点中的数据的标签为[1]的值(其他为0)---全为1的位置就是决策边界z= model.predict(np.c_[xx.flatten(), yy.flatten()]) #z (250000,) # print...
def plot_svm(kernel, df_input, y, C, gamma, coef): svc_model = svm.SVC(kernel=kernel, C=C, gamma=gamma, coef0=coef, random_state=11, probability=True).fit(df_input, y) Z = svc_model.predict_proba(np.c_[xx.ravel(), yy.ravel()])[:, 0] Z = Z.reshape(xx.shape) fig =...
y=df['s_code']# y values h=0.2# stepinmeshgrid x_min,x_max=df_pca.iloc[:,0].min(),df_pca.iloc[:,0].max()y_min,y_max=df_pca.iloc[:,1].min(),df_pca.iloc[:,1].max()xx,yy=np.meshgrid(np.arange(x_min-0.5,x_max+0.5,h),#create meshgrid ...
df['s_code'] = [dict_y.get(i) for i in df['rank']] df.head() pca = PCA(n_components=2) pca_result = pca.fit_transform(array_s) df_pca = pd.DataFrame(pca_result, columns=['PCA_1','PCA_2']) df = pd.concat([df, df_pca], axis=1) ...
clf = init_svm_instance('D:/codeRepo/SVMImageClassification/bbld','2') end = time.time() print("初始化耗时:", (end - start)) result = svm_infer(clf,'D:/codeRepo/SVMImageClassification/bbld/0/ld_222283.jpg',['ld','ldk']) ...
("svc", SVC(kernel="rbf", gamma=gamma))# 只需要指定kernel="rbf"即可,然后指定gamma])# 绘制决策边界defplot_decision_boundary(model, axis): x0, x1 = np.meshgrid( np.linspace(axis[0], axis[1],int((axis[1] - axis[0]) *100)).reshape(1, -1), ...
svm_model = SVC(C=1, kernel = 'rbf') # 保存模型 with open('./log/knn.pkl',"wb") as f: pickle.dump(svm_model,f) # 读取模型 with open('./log/knn_pickle.pkl',"rb") as f: clf_1 = pickle.load(f) 1. 2. 3. 4. ...
from sklearn import svm import plotly.graph_objects as go y = df['s_code'] # y values h = 0.2 # step in meshgrid x_min, x_max = df_pca.iloc[:, 0].min(), df_pca.iloc[:, 0].max() y_min, y_max = df_pca.iloc[:, 1].min(...