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I could not find anything in the docs about how to handle different frequencies of time series. I have a Dataset A with monthly data that i want to use to predict the values from Dataset B that contains quarterly based data. So the target value e.g. quarter 1 is based on the values from month 1-3.

Dataset A (Features):

| Month | Value1 | Value2 | Value3 |
| ------------- | ------------- |

I think it will be readonable to add an option to use the original dataset when training final_estimator. This seems reasonable and has proved to be useful in some Kaggle competitions.

Reference: implementation from mlxtend
http://rasbt.github.io/mlxtend/api_subpackages/mlxtend.classifier/#stackingcvclassifier

use_features_in_secondary : bool (default: False)
If True, the meta-classifier w

Context

We would like to add torch::nn::functional::gumbel_softmax to the C++ API, so that C++ users can easily find the equivalent of Python API torch.nn.functional.gumbel_softmax.

Steps

• Add torch::nn::GumbelSoftmaxOptions to torch/csrc/api/include/torch/nn/options/activation.h (add this file if it doesn’t exist), which should include the following parameters (based on

Short description

I am trying to train Tesseract on Akkadian language. The language-specific.sh script was modified accordingly. When converting the training text to TIFF images, the text2image program crashes.

Environment

• Tesseract Version: 3.04.01
• Commit Number: the standard package in Ubuntu, package version 3.04.01-4, commit unknown
• Platform: Linux ubuntu

Line 1137 of the Caffe.Proto states "By default, SliceLayer concatenates blobs along the "channels" axis (1)."

Yet, the documentation on http://caffe.berkeleyvision.org/tutorial/layers/slice.html states, "The Slice layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices." which seems to be

In https://github.com/JuliaLang/julia/blob/606420a308d951ab810a05f6f6c50a1b805f2b47/base/reshapedarray.jl#L173-L177 it should be checked whether the requested array has offset axes.
Otherwise we can get segfaults. See #33603

In the 1D->1D case (AbstractVector) there already is such a check:
https://github.com/JuliaLang/julia/blob/606420a308d951ab810a05f6f6c50a1b805f2b47/base/reshapedarray.

I was going though the existing enhancement issues again and though it'd be nice to collect ideas for spaCy plugins and related projects. There are always people in the community who are looking for new things to build, so here's some inspiration ✨ For existing plugins and projects, check out the spaCy universe.

If you have questions about the projects I suggested,