Compare SVM mode yoga movement classification accuracy with Linear kernel, Polynomial kernel, RBF (Radial Basis Function) kernel, LSTM with accuracy up to 98%. In addition, it also supports adjusting the practitioner's movements according to standard movements. machine-learning computer-vision deep-le...
svc= svm.SVC(C=1, kernel='linear') svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:]) 为了获得一个更好的预测精确度度量,我们可以把我们使用的数据折叠交错地分成训练集和测试集: importnumpy as np X_folds= np.array_split(X_digits, 3) y_folds= n...
svc = svm.SVC(C=1, kernel='linear') svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:]) 1. 2. 3. 4. 5. 6. 为了获得一个更好的预测精确度度量,我们可以把我们使用的数据折叠交错地分成训练集和测试集: import numpy as np X_folds = np.array_sp...
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kernel_function=chi2_kernelifnot(self.model_kernel=='linear'orself.model_kernel=='rbf')elseself.model_kernel self.model=SVC(C=1,kernel=kernel_function,gamma=1,probability=True)elifmodel_name=='lr': self.model=LR(C=1,penalty='l1',tol=1e-6)else:ifmodel_name=='xgb': ...
def select_feature_from_model(X, y, max_features): from sklearn.feature_selection import SelectFromModel X_scaled = pd.DataFrame(preprocessing.scale(X), columns=X.keys()) classifier = SVC(kernel='linear', class_weight='balanced', C=0.025) sfm = SelectFromModel(classif...
[1] elif algorithm == 'svm_rfe': from sklearn.svm import SVC from sklearn.feature_selection import RFE estimator = SVC(random_state=R_SEED, kernel='linear') selector = RFE(estimator, 5, step=0.1) selector.fit(X, y) for x in sorted( zip(map(lambda x: round(x, 4), sel...
kernels = self.component_config["kernels"]# dirty str fix because sklearn is expecting# str not instance of basestr...tuned_parameters = [{"C": C,"kernel": [str(k)forkinkernels]}]# aim for 5 examples in each foldcv_splits = self._num_cv_splits(y)returnGridSearchCV(SVC(C=1,...
[SVC(kernel='rbf'),SVC(kernel='poly'),RandomForestClassifier(n_estimators=100),LogisticRegressionCV()]:print(clf.__class__.__name__,''ifnothasattr(clf,'kernel')elseclf.kernel)clf.fit(X_train,y_train)clean_dataset_score=clf.score(X_test,y_test)forindexinrange(X.shape[1]):X_train...
{ 'kernel': ('linear', 'rbf'), 'C': [1, 10], 'gamma': np.linspace(0, 0.01, num=11) } grid = GridSearchCV(svm.SVC(), param_grid=parameters, cv=kfolds, scoring='accuracy') grid.fit(D_scaled, train_labels) end = time.time() colorprint(Color.BLUE, ...