subsampling

问题 I noticed that each time I save a jpg file in PHP, it is saved with sub-sampling. How to remove that? I'm using GD library. 回答1: I believe newer versions of libgd disable chroma-subsampling if you set the quality to 90 or higher. Failing that, you could consider using PHP Imagick and disabling chroma sub-sampling with: $img->setSamplingFactors(array('1x1', '1x1', '1x1')); 来源： https://stackoverflow.com/questions/57350007/how-to-disable-sub-sampling-when-saving-jpg-image-using-php-gd-library

来源： https://stackoverflow.com/questions/30885047/how-to-non-randomly-sample-every-n-rows-in-dplyr

问题 I am working on running RandomForest. I've imported point data representing used and unused sites and created a raster stack from raster GIS layers. I've created a SpatialPointDataFrame with all of my used and unused points with their underlying raster values attached. require(sp) require(rgdal) require(raster) #my raster stack xvariables <- stack(rlist) #rlist = a list of raster layers # Reading in the spatial used and unused points. ldata <- readOGR(dsn=paste(path, "DATA", sep="/"), layer

问题 I am working on running RandomForest. I've imported point data representing used and unused sites and created a raster stack from raster GIS layers. I've created a SpatialPointDataFrame with all of my used and unused points with their underlying raster values attached. require(sp) require(rgdal) require(raster) #my raster stack xvariables <- stack(rlist) #rlist = a list of raster layers # Reading in the spatial used and unused points. ldata <- readOGR(dsn=paste(path, "DATA", sep="/"), layer

问题 I have this problem that I want to plot a data distribution where some values occur frequently while others are quite rare. The number of points in total is around 30.000. Rendering such a plot as png or (god forbid) pdf takes forever and the pdf is much too large to display. So I want to subsample the data just for the plots. What I would like to achieve is to remove a lot of points where they overlap (where the density is high), but keep the ones where the density is low with almost

问题 I want to take one random Site for every Region, create a new data frame, and repeat these processes until all Site are sampled. So, each data frame will NOT contain the same Site from the same Region. A few Regions in my real data frame have more Sites (Region C has 4 Sites) than the other Regions. I want remove those rows (perhaps I should do this before making multiple data frames). Here is an example data frame (real one has >100 Regions and >10 Sites per Region): mydf <- read.table

问题 I would like to subsample a data frame at hourly intervals from a datetime column, beginning with the time value in the first row of the data frame. My data frame runs at 10-minute intervals from the first to the last row. Example data is below: structure(list(datetime = structure(1:19, .Label = c("30/03/2011 05:09", "30/03/2011 05:19", "30/03/2011 05:29", "30/03/2011 05:39", "30/03/2011 05:49", "30/03/2011 05:59", "30/03/2011 06:09", "30/03/2011 06:19", "30/03/2011 06:29", "30/03/2011 06:39"

问题 How can a 1:1 stratified sampling be performed in python? Assume the Pandas Dataframe df to be heavily imbalanced. It contains a binary group and multiple columns of categorical sub groups. df = pd.DataFrame({'id':[1,2,3,4,5], 'group':[0,1,0,1,0], 'sub_category_1':[1,2,2,1,1], 'sub_category_2':[1,2,2,1,1], 'value':[1,2,3,1,2]}) display(df) display(df[df.group == 1]) display(df[df.group == 0]) df.group.value_counts() For each member of the main group==1 I need to find a single match of group=

问题 I'm doing some analysis in R where I need to work with some large datasets (10-20GB, stored in .csv, and using the read.csv function). As I will also need to merge and transform the large .csv files with other data frames, I don't have the computing power or memory to import the entire file. I was wondering if anyone knows of a way to import a random percentage of the csv. I have seen some examples where people have imported the entire file and then used a separate function to create another

问题 In the documentation of SciKit-Learn Random Forest classifier , it is stated that The sub-sample size is always the same as the original input sample size but the samples are drawn with replacement if bootstrap=True (default). What I dont understand is that if the sample size is always the same as the input sample size than how can we talk about a random selection. There is no selection here because we use all the (and naturally the same) samples at each training. Am I missing something here?