pipeline

问题 I have isolated the problem to the below code snippet: Notice below that null string gets assigned to LATEST_FILE_NAME='' when the script is run using ksh ; but the script assigns the value to variable $LATEST_FILE_NAME correctly when run using sh . This in turn affects the value of $FILE_LIST_COUNT . But as the script is in KornShell (ksh), I am not sure what might be causing the issue. When I comment out the tee command in the below line, the ksh script works fine and correctly assigns the

问题 So I need to access in bulk many different hashes (in StackExchange.Redis, I have different RedisKey's). What is the best (fastest) way to do it? For example, for these two possible implementations, is either correct? Which one works better? List<Task<HashEntry[]>> list = new List<Task<HashEntry[]>>(); List<RedisKey> keys; //Previously initialized list of keys foreach (var key in keys) { var task = db.HashGetAllAsync(key); list.Add(task); } await Task.WhenAll(list); 2. List<Task<HashEntry[]>>

问题 Consider the following script: function g { [CmdletBinding()] param ( [parameter(ValueFromPipelineByPropertyName = $true)]$x, [parameter(ValueFromPipelineByPropertyName = $true)]$y, [parameter(ValueFromPipelineByPropertyName = $true)]$z ) process { $retval = @{psbp=@{};mibp=@{};x=$x;y=$y;z=$z} $PSBoundParameters.Keys | % { $retval.psbp.$_ = $PSBoundParameters.$_ } $PSCmdlet.MyInvocation.BoundParameters.Keys | % { $retval.mibp.$_ = $PSCmdlet.MyInvocation.BoundParameters.$_} return New-Object

问题 I'm trying to figure out how to built a workflow for sklearn.neighbors.KNeighborsRegressor that includes: normalize features feature selection (best subset of 20 numeric features, no specific total) cross-validates hyperparameter K in range 1 to 20 cross-validates model uses RMSE as error metric There's so many different options in scikit-learn that I'm a bit overwhelmed trying to decide which classes I need. Besides sklearn.neighbors.KNeighborsRegressor , I think I need: sklearn.pipeline

问题 This is my first machine learning project and the first time that I use ColumnTransformer. My aim is to perform two steps of data preprocessing, and use ColumnTransformer for each of them. In the first step, I want to replace the missing values in my dataframe with the string 'missing_value' for some features, and the most frequent value for the remaining features. Therefore, I combine these two operations using ColumnTransformer and passing to it the corresponding columns of my dataframe. In

问题 After reading this: When an interrupt occurs, what happens to instructions in the pipeline? There is not much information on what happens to software interrupts but we do learn the following: Conversely, exceptions, things like page faults, mark the instruction affected. When that instruction is about to commit, at that point all later instructions after the exception are flushed, and instruction fetch is redirected. I was wondering what would happen to software interrupts (INT 0xX) in the

问题 I'm trying export one pipeline created in datafactory v2 or migrate to another, but not found the option, Could you help me please 回答1: As I know, you could learn about Continuous Integration in Azure Data Factory. You could find below statement in the Continuous integration and deployment in Azure Data Factory. For Azure Data Factory, continuous integration & deployment means moving Data Factory pipelines from one environment (development, test, production) to another. To do continuous

问题 I am trying to create a custom transformer for a Python sklearn pipeline based on guidance from this tutorial: http://danielhnyk.cz/creating-your-own-estimator-scikit-learn/ Right now my custom class/transformer looks like this: class SelectBestPercFeats(BaseEstimator, TransformerMixin): def __init__(self, model=RandomForestRegressor(), percent=0.8, random_state=52): self.model = model self.percent = percent self.random_state = random_state def fit(self, X, y, **fit_params): """ Find features

问题 I want to build a Pipeline in sklearn and test different models using GridSearchCV. Just an example (please do not pay attention on what particular models are chosen): reg = LogisticRegression() proj1 = PCA(n_components=2) proj2 = MDS() proj3 = TSNE() pipe = [('proj', proj1), ('reg' , reg)] pipe = Pipeline(pipe) param_grid = { 'reg__c': [0.01, 0.1, 1], } clf = GridSearchCV(pipe, param_grid = param_grid) Here if I want to try different models for dimensionality reduction, I need to code

问题 Can anyone provide me the shader that are similar to the Fixed function Pipeline? I need the Fragment shader default the most, because I found a similar vertex shader online. But if you have a pair that should be fine! I want to use fixed pipeline, but have the flexability of shaders, so I need similar shaders so I'll be able to mimic the functionality of the fixed pipeline. Thank you very much! I'm new here so if you need more information tell me:D This is what I would like to replicate: