废话不多说直接上代码:
import numpy as np from sklearn import datasets X,y = datasets.make_classification(n_samples=100,n_features=2,n_redundant=0,n_classes=2,random_state=7816) print(X.shape,y.shape) X = X.astype(np.float32) y = y * 2 - 1 '''分离数据''' from sklearn import model_selection as ms X_train, X_test, y_train, y_test = ms.train_test_split( X, y, test_size=0.2, random_state=42 ) import cv2 svm = cv2.ml.SVM_create() svm.setKernel(cv2.ml.SVM_LINEAR) '''开始训练''' y_train = y_train.reshape(-1, 1) # print(y_train) svm.train(X_train, cv2.ml.ROW_SAMPLE, y_train) svm.save("svmtest.mat") print ("Done\n") svm2 = cv2.ml.SVM_load("svmtest.mat") # svm2.load("svmtest.mat") # print(svm2) '''开始预测''' _, y_pred = svm2.predict(X_test) '''用scikit-learn的metrics模块计算准确率''' from sklearn import metrics print(metrics.accuracy_score(y_test, y_pred))
关键代码如下:
创建:
import cv2 svm = cv2.ml.SVM_create() svm.setKernel(cv2.ml.SVM_LINEAR)
其它的写法都是以前较老的版本,基本上都不行
加载:
svm2 = cv2.ml.SVM_load("svmtest.mat")