pipeline

来源： https://stackoverflow.com/questions/47234312/how-to-properly-chain-multiple-jq-statements-together-when-processing-json-in-th

来源： https://stackoverflow.com/questions/47234312/how-to-properly-chain-multiple-jq-statements-together-when-processing-json-in-th

来源： https://stackoverflow.com/questions/58590528/azure-devops-how-to-prevent-multiple-pipelines-run-at-same-time

问题 I'm trying to single out a component/transformer from a fitted pipeline to inspect it's behavior. However, when I retrieved the component, the component is showed as unfitted, but using the pipeline as a whole works without problem. This suggest the pipeline is fitted and the components are fitted as well. Can someone explain why, and also suggest how to inspect a component in a fitted pipeline? Here's a reproducible example: import pandas as pd import numpy as np from sklearn.compose import

问题 I'm trying to single out a component/transformer from a fitted pipeline to inspect it's behavior. However, when I retrieved the component, the component is showed as unfitted, but using the pipeline as a whole works without problem. This suggest the pipeline is fitted and the components are fitted as well. Can someone explain why, and also suggest how to inspect a component in a fitted pipeline? Here's a reproducible example: import pandas as pd import numpy as np from sklearn.compose import

问题 I'm trying to single out a component/transformer from a fitted pipeline to inspect it's behavior. However, when I retrieved the component, the component is showed as unfitted, but using the pipeline as a whole works without problem. This suggest the pipeline is fitted and the components are fitted as well. Can someone explain why, and also suggest how to inspect a component in a fitted pipeline? Here's a reproducible example: import pandas as pd import numpy as np from sklearn.compose import

问题 I want to build an sklearn VotingClassifier ensemble out of multiple different models (Decision Tree, SVC, and a Keras Network). All of them need a different kind of data preprocessing, which is why I made a pipeline for each of them. # Define pipelines # DTC pipeline featuriser = Featuriser() dtc = DecisionTreeClassifier() dtc_pipe = Pipeline([('featuriser',featuriser),('dtc',dtc)]) # SVC pipeline scaler = TimeSeriesScalerMeanVariance(kind='constant') flattener = Flattener() svc = SVC(C =

问题 I want to build an sklearn VotingClassifier ensemble out of multiple different models (Decision Tree, SVC, and a Keras Network). All of them need a different kind of data preprocessing, which is why I made a pipeline for each of them. # Define pipelines # DTC pipeline featuriser = Featuriser() dtc = DecisionTreeClassifier() dtc_pipe = Pipeline([('featuriser',featuriser),('dtc',dtc)]) # SVC pipeline scaler = TimeSeriesScalerMeanVariance(kind='constant') flattener = Flattener() svc = SVC(C =

问题 It works like a charm when the RGB module of the camera has a resolution of 1280x720, and FPS as 15 frames/sec. The depth mode and IMU work fine in all the settings. But if the resolution is increased above 1280x720 - 15 frames/sec, I face a Runtime error: backend-v412.cpp:988 - Frames didn't arrive within 5 seconds. Other forms of this error: 10:41:49 [Warn] .../backend-v4l2.cpp:988 - Frames didn't arrived within 5 seconds It seems like the pipeline is not able to handle the framebuffers,

问题 I have a project where I have 4 environments (dev, test, staging and prod) and we have branches for each (develop, test, staging master respectively). We use npm version to bump version in package.json but also add a git tag. After that we run the build and on success of that, we push the commit and tag created by the npm version command. So in my pipeline job, I have this (simplified): dev build: stage: build only: - develop@username/reponame script: - npm version patch -m "[ci skip] %s" -