sampling

问题 Im trying read and store samples from an audio microphone in linux using C/C++. Using PCM ioctls i setup the device to have a certain sampling rate say 10Khz using the SOUND_PCM_WRITE_RATE ioctl etc. The device gets setup correctly and im able to read back from the device after setup using the "read". int got = read(itsFd, b.getDataPtr(), b.sizeBytes()); The problem i have is that after setting the appropriate sampling rate i have a thread that continuously reads from /dev/dsp1 and stores

问题 I'm trying to create a custom layer in keras . This layer should perform a sampling over the input tensor (according to a probability distribution), and output a tensor of same size, with only values that have been sampled, the rest being zero. However no sampling functions are available in keras.backend to my knowledge. Note that this layer hasn't any trainable parameters, I just want a function that modifies the previous output. For now I'm trying to convert the input tensor from a Tensor

问题 In a Fortran program I would like to choose at random a specific variable (specifically its index) by using weights. The weights would be provided in a separate vector (element 1 would contain weight of variable 1 and so on). I have the following code who does the job without weight ( mind being an integer vector with the index of each variable in the original dataset) call rrand(xrand) j = int(nn * xrand) + 1 mvar = mind(j) 回答1: Here are two examples. The first one is integer, parameter ::

问题 I am trying to create a list of 7 groups with 100 observations. Each group can have different number of observations. All observations should be placed in one of the 7 groups. In other words, all observations should be used. The code I am using does not use all the observations. Is there a way that I can solve this? times_to_sample = 7L NN = nrow(df) sample<-replicate(times_to_sample, df[sample(NN, sample(5:15, 1L)), ], simplify = FALSE) my expected result just has to place each observation

问题 Is there a function that allow me to do negative sampling without using sampled_softmax_loss ( Tensorflow negative sampling) I am looking for negative sampling method that takes frequency of a label in training data into training account, and increases chance to negatively sample a word with high frequency. I found that negative sampling was mentioned by tensorflow official document, but I cannot find any useful implementation in the API: https://www.tensorflow.org/extras/candidate_sampling

问题 Is there a function that allow me to do negative sampling without using sampled_softmax_loss ( Tensorflow negative sampling) I am looking for negative sampling method that takes frequency of a label in training data into training account, and increases chance to negatively sample a word with high frequency. I found that negative sampling was mentioned by tensorflow official document, but I cannot find any useful implementation in the API: https://www.tensorflow.org/extras/candidate_sampling

问题 Given a standard LDA model with few 1000 topics and few millions of documents, trained with Mallet / collapsed Gibbs sampler: When inferring a new document: Why not just skip sampling and simply use the term-topic counts of the model to determine the topic assignments of the new document? I understand that applying the Gibbs sampling on the new document is taking into account the topic mixture of the new document which in turn influence how topics are composed (beta, term-freq. distributions)

问题 Given a standard LDA model with few 1000 topics and few millions of documents, trained with Mallet / collapsed Gibbs sampler: When inferring a new document: Why not just skip sampling and simply use the term-topic counts of the model to determine the topic assignments of the new document? I understand that applying the Gibbs sampling on the new document is taking into account the topic mixture of the new document which in turn influence how topics are composed (beta, term-freq. distributions)

问题 Given a standard LDA model with few 1000 topics and few millions of documents, trained with Mallet / collapsed Gibbs sampler: When inferring a new document: Why not just skip sampling and simply use the term-topic counts of the model to determine the topic assignments of the new document? I understand that applying the Gibbs sampling on the new document is taking into account the topic mixture of the new document which in turn influence how topics are composed (beta, term-freq. distributions)

问题 I am attempting to perform undersampling of the majority class using python scikit learn. Currently my codes look for the N of the minority class and then try to undersample the exact same N from the majority class. And both the test and training data have this 1:1 distribution as a result. But what I really want is to do this 1:1 distribution on the training data ONLY but test it on the original distribution in the testing data. I am not quite sure how to do the latter as there is some dict