calibration

问题 I'm trying to connect to INCA through the .NET API in order to navigate the folder structure of an ASAP2 project. Specifically, I want to get an object that represents the "Target" folder that I've highlighted below. This is proving pretty tricky--The API provides a ton of classes that have the name "Folder" in them: Folder , Asap2ProjectFolder , and IncaFolder . So what do I need to know in order to retrieve the "Target" folder? 回答1: Your first impulse might be to think of the Target folder

问题 I’ve been having some trouble with the plotCalibration() function, I have managed to get it to work before, but recently whilst working with another dataset (here is a link to the .Rda data file), I have been unable to shake off an error message which keeps cropping up: > plotCalibration(data = data, cOutcome = 2, predRisk = data$sortmort) Error in plotCalibration(data = data, cOutcome = 2, predRisk = data$sortmort) : The specified outcome is not a binary variable.` When I’ve tried to set the

来源： https://stackoverflow.com/questions/36478560/opencv-c-stereo-calib-example

来源： https://stackoverflow.com/questions/36478560/opencv-c-stereo-calib-example

来源： https://stackoverflow.com/questions/58918538/how-kitti-calibration-matrix-was-calculated

来源： https://stackoverflow.com/questions/58918538/how-kitti-calibration-matrix-was-calculated

问题 I'm working on a multiclass classification problem with different classifiers, working with Python and scikit-learn. I want to use the predicted probabilities, basically to compare the predicted probabilities of the different classifiers for a specific case. I started reading about 'calibration' (here and here for example) and I became confused. For what I understood: a well-calibrated probability means that that a probability also reflects the fraction of a certain class. 1) Does this imply

问题 I'm working on a multiclass classification problem with different classifiers, working with Python and scikit-learn. I want to use the predicted probabilities, basically to compare the predicted probabilities of the different classifiers for a specific case. I started reading about 'calibration' (here and here for example) and I became confused. For what I understood: a well-calibrated probability means that that a probability also reflects the fraction of a certain class. 1) Does this imply

问题 I have fit a LOESS local regression to some data and I want to be able to find the X value associated with a given Y value. plot(cars, main = "Stopping Distance versus Speed") car_loess <- loess(cars$dist~cars$speed,span=.5) lines(1:50, predict(car_loess,data.frame(speed=1:50))) I was hoping that I could use teh inverse.predict function from the chemCal package, but that does not work for LOESS objects. Does anyone have any idea how I might be able to do this calibrationa in a better way than

问题 I need to find velocity of a device(Samsung Galaxy s3). I've now read quite a lot of stackoverflow questions concerning it, but still confused as for what I should use. My observations so far: 1) Somebody did this to find velocity from accelerometer sensor's data. But in my case the device's movement will not have constant slope or straight trajectory. 2) Integration will give much drift. 3) Kalman filtering can be used but it's hard to implement and CPU consuming 4) Complementary filter can