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【android测试】adb shell svc命令

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svc命令这个脚本在/system/bin目录下，这个命令可以用来控制电源管理，wifi开关，数据开关(就是上网流量) svc power stayon [true|false|usb|ac] //设置屏幕的常亮，true保持常亮，false不保持，usb当插入usb时常亮，ac当插入电源时常亮这个是控制usb插入的时候屏幕是否常亮，这个有地方设置，就不多说了 svc data disable 这个命令可以关闭数据连接，就是上网流量，大家都知道控制上网的开关很多，但是大都是通过在apn上的接入点加后缀来实现的，但是这个命令不会更改apn的任何设置，直接在底层关闭数据连接。应该是最彻底的，而且又不影响apn的设置。这个跟apndroid有什么区别，apndroid是在关闭上网数据的时候，已经在下载的连接可能不会被强制关闭(这个在apndroid自己的说明中也有提到)。比如你在下载一个10M的电影，下载了1M，不响下载了。用apndroid关闭连接，可能这个下载还会继续，不会马上停掉。但是用这个命令，绝对毫不留情的咔嚓掉。 svc data enable 这个是打开上网数据连接，跟上一个命令是相反的。 svc data prefer 这个命令是控制3g数据连接比wifi优先。我们都知道，正常情况下，有wifi的时候，不会用到3g连接。但是这个命令是相反

How to Configuring WCF services to work with both HTTP and HTTPS - multiple bindings not working

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问题 Iam fairly new to Silver-light and WCF, so please bear with me. I have silver-light application that calls .svc service. The service is being called successfully over https but i would also like to make it work with calls over plain http. What modifications do i need to make to my web.config and ServiceReferences.ClientConfig files below. My complete system.serviceModel section in my Web.config file is this. <system.serviceModel> <bindings> <customBinding> <binding name="MyApp.Web.GetData

how to enable probability estimates when using scikitlearn's LinearSVC classifier

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问题 How can I get the probability estimates of predictions from a sklearn.svm.LinearSVC model in similar fashion to sklearn.svm.SVC 's probability=True option that allows predict_proba() I need to avoid the quadratic fit penalty of the underlying libsvm of SVC as my training set is large. 回答1: sklearn.svm.LinearSVC does not have predict_proba method as you noticed correctly. However, you may try the following trick to circumvent this shortcoming: from sklearn.svm import LinearSVC from sklearn

Skip forbidden parameter combinations when using GridSearchCV

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问题 I want to greedily search the entire parameter space of my support vector classifier using GridSearchCV. However, some combinations of parameters are forbidden by LinearSVC and throw an exception. In particular, there are mutually exclusive combinations of the dual , penalty , and loss parameters: For example, this code: from sklearn import svm, datasets from sklearn.model_selection import GridSearchCV iris = datasets.load_iris() parameters = {'dual':[True, False], 'penalty' : ['l1', 'l2'], \

Python : How to find Accuracy Result in SVM Text Classifier Algorithm for Multilabel Class

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问题 I have used following set of code: And I need to check accuracy of X_train and X_test The following code works for me in my classification problem over multi-labeled class import numpy as np from sklearn.pipeline import Pipeline from sklearn.feature_extraction.text import CountVectorizer from sklearn.svm import LinearSVC from sklearn.feature_extraction.text import TfidfTransformer from sklearn.multiclass import OneVsRestClassifier X_train = np.array(["new york is a hell of a town", "new york

plot_decision_regions with error “Filler values must be provided when X has more than 2 training features.”

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问题 I am plotting 2D plot for SVC Bernoulli output. converted to vectors from Avg word2vec and standerdised data split data to train and test. Through grid search found the best C and gamma(rbf) clf = SVC(C=100,gamma=0.0001) clf.fit(X_train1,y_train) from mlxtend.plotting import plot_decision_regions plot_decision_regions(X_train, y_train, clf=clf, legend=2) plt.xlabel(X.columns[0], size=14) plt.ylabel(X.columns[1], size=14) plt.title('SVM Decision Region Boundary', size=16) Receive error :-

WCF .svc file served as plain text in IIS7.5 .NET 4.0 - Not under Default Web Site

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问题 FINAL UPDATE : It turns out this was a red-herring due to a compression issue with a DotNetNuke module. I had to add to the PageBlaster DNN module, so this is no longer an issue. I am deploying a WCF svc file to my live website for the first time and it is rendering/served up to the browser as plain text. It works fine on my dev environment on localhost. You can see the problem here: http://www.pokerdiy.com/test.svc The website is running on IIS 7.5.7600.16385 on Windows Server 2008 R2

Cannot understand plotting of decision boundary in SVM and LR

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问题 For example we have f(x) = x. How to plot it? We take some x then calculate y and doing this operation again, then plot chart by dots. Simple and clear. But I cannot understand so clearly plotting decision boundary - when we haven't y to plot, only x. Python code for SVM: h = .02 # step size in the mesh Y = y # we create an instance of SVM and fit out data. We do not scale our # data since we want to plot the support vectors C = 1.0 # SVM regularization parameter svc = svm.SVC(kernel='linear'

Is there a way to add WCF svc-less activation with multiple service Activations?

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问题 Given the following configuration: <serviceHostingEnvironment multipleSiteBindingsEnabled="true" > <serviceActivations> <add relativeAddress="./UserService.svc" service="Mercato.KnowledgeKube.WebSvc.Service.KnowledgeuserManagerService" factory="Mercato.KnowledgeKube.WebSvc.WCF.KnowledgeServiceHostFactory" /> <add relativeAddress="./ProductService.svc" service="Mercato.KnowledgeKube.WebSvc.Service.ProductManagerWebServices" factory="Mercato.KnowledgeKube.WebSvc.WCF.KnowledgeServiceHostFactory"

ValueError: Array contains NaN or infinity in _assert_all_finite during LinearSVC

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问题 I was trying to classify the wine data set here -http://archive.ics.uci.edu/ml/datasets/Wine+Quality using logistic regression (with method ='bfgs' and l1 norm) and caught a singular value matrix error(raise LinAlgError('Singular matrix'), in-spite of full rank [which I tested using np.linalg.matrix_rank(data[train_cols].values) ] . This is how I came to the conclusion that some features might be linear combinations of others . Towards this, I experimented of using Grid search/LinearSVC - and