resampling

问题 I have a very unbalanced dataset ,, thousands of healthy participants and 21 patients (16 male and 5 female) ,, I want to use bootstrapping to define a new sampler but with control of age and gender. this is the method i'm using parametric_bootstrap_boot <- function(x) { # Perform bootstrap using boot package # Estimate mean mu <- boot(x, samplemean, R=1000)$t0 #Estimate sd sd <- boot(x, samplesd, R=1000)$t0 # Sample 21 observations set.seed(1) samples <- rnorm(21,mu,sd) return(samples) } how

问题 The below code works fine, however, I am interested to run it in parallel. I have tried different plans within future and future.apply but couldn't managed. Any help appreciated. I am running on windows OS, 8 cores. library(mlr3verse) library(future.apply) #> Warning: package 'future.apply' was built under R version 3.6.3 #> Loading required package: future #> Warning: package 'future' was built under R version 3.6.3 library(future) future::plan(multicore) tsk_clf = tsk("sonar") tsk_clf$col

问题 I'm trying to use ffmpeg/libswresample to resample streaming audio in my c++ application. Changing the sample width works well and the result sounds as one would expect; however, when changing the sample rate the result is somewhat crackly. I am unsure if it is due to incorrect usage of the libswresample library, or if I'm misunderstanding the resampling theory. Here is my resampling process, simplified for demonstration's sake: //Externally supplied data const uint8_t* in_samples //contains

问题 I'm trying to resample the WasapiLoopbackCapture's output from my soundcards 44100Hz, 16bit, 2 channel waveformat to a 16000Hz, 16bit, 1 channel format for later use in a System.Net.Sockets.NetworkStream (I want to write the converted bytes to the network stream) But I have no idea how to start even! I'm really new to signal processing and I've tried searching for tutorials but I just can't wrap my head around how to do this. Here's what I got so far: void StartRecording() { capture = new

问题 I am new to Python and apologize in advance, if it is too simple. Cannot find anything and this question did not help. My code is # Split data y = starbucks_smote.iloc[:, -1] X = starbucks_smote.drop('label', axis = 1) # Count labels by type counter = Counter(y) print(counter) Counter({0: 9634, 1: 2895}) # Transform the dataset oversample = SMOTE() X, y = oversample.fit_resample(X, y) # Print the oversampled dataset counter = Counter(y) print(counter) Counter({0: 9634, 1: 9634}) How to save

问题 I'm trying to resample a decoded audio frame from 48KHz to 44.1KHz using the libswresample API. The code I have is the following: // 'frame' is the original decoded audio frame AVFrame *output_frame = av_frame_alloc(); // Without this, there is no sound at all at the output (PTS stuff I guess) av_frame_copy_props(output_frame, frame); output_frame->channel_layout = audioStream->codec->channel_layout; output_frame->sample_rate = audioStream->codec->sample_rate; output_frame->format =

问题 I have some JS that makes some manipulations with images. I want to have pixelart-like graphics, so I had to enlarge original images in graphics editor. But I think it'd be good idea to make all the manipulations with the small image and then enlarge it with html5 functionality. This will save bunch of processing time (because now my demo ( warning : domain-name may cause some issues at work etc) loads extremely long in Firefox, for example). But when I try to resize the image, it gets

来源： https://stackoverflow.com/questions/36222928/pandas-ohlc-aggregation-on-ohlc-data

来源： https://stackoverflow.com/questions/36222928/pandas-ohlc-aggregation-on-ohlc-data

来源： https://stackoverflow.com/questions/36222928/pandas-ohlc-aggregation-on-ohlc-data