truth | Fluent assertions for Java and Android | Testing library

Here's a working example to compare one image to another. You can expand it to compare multiple at once. Two test input images with slight differences:

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There's ways to do such things in pandas:

Should be pretty simple. Just change get_loc(XXX, ...) to get_indexer([XXX], ...)[0]:

As you said, this is really a common problem, you will find the answers from Truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all()

But to address your particular case, this is not a very efficient way of doing it as the dataset is huge. You should use Numpy. That will shorten the runtime drastically.

I see two issues with your snippet

1. There is a Typo. You used "BUY" and then used "Buy". Python is case-sensitive.
2. The col Action is getting tested for "BUY" entirely. The fix is to use row (Not a pythonic way, but a small fix)

I'm about 95% sure this is fine for older versions of Log4j. Three reasons:

1. I'm on version 1.2. I found the Log4j JAR file on my system, unzipped it, and looked for anything mentioning JNDI:

If the hyperspectral dataset is given to you as a large image with many channels, I suppose that the classification of each pixel should depend on the pixels around it (otherwise I would not format the data as an image, i.e. without grid structure). Given this assumption, breaking up the input picture into 1x1 parts is not a good idea as you are loosing the grid structure.

I further suppose that the order of the channels is arbitrary, which implies that convolution over the channels is probably not meaningful (which you however did not plan to do anyways).

Instead of reformatting the data the way you did, you may want to create a model that takes an image as input and also outputs an "image" containing the classifications for each pixel. I.e. if you have 10 classes and take a (145, 145, 200) image as input, your model would output a (145, 145, 10) image. In that architecture you would not have any fully-connected layers. Your output layer would also be a convolutional layer.

That however means that you will not be able to keep your current architecture. That is because the tasks for MNIST/CIFAR10 and your hyperspectral dataset are not the same. For MNIST/CIFAR10 you want to classify an image in it's entirety, while for the other dataset you want to assign a class to each pixel (while most likely also using the pixels around each pixel).

Some further ideas:

• If you want to turn the pixel classification task on the hyperspectral dataset into a classification task for an entire image, maybe you can reformulate that task as "classifying a hyperspectral image as the class of it's center (or top-left, or bottom-right, or (21th, 104th), or whatever) pixel". To obtain the data from your single hyperspectral image, for each pixel, I would shift the image such that the target pixel is at the desired location (e.g. the center). All pixels that "fall off" the border could be inserted at the other side of the image.
• If you want to stick with a pixel classification task but need more data, maybe split up the single hyperspectral image you have into many smaller images (e.g. 10x10x200). You may even want to use images of many different sizes. If you model only has convolution and pooling layers and you make sure to maintain the sizes of the image, that should work out.

I would recommend keeping the linter stuff only in the config of pre-commit.

pre-commit doesn't necessarily run as a pre-commit hook. You can run the checks every time by pre-commit run --all-files or if you want to run it only on given files with pre-commit run --files path/to/file.

You can even say which which check should run, e.g. pre-commit run black --all-files

No.

Flexbox is for laying boxes out in a row or column.

Float is for making text wrap around boxes.

You need float for this.

You can try with
df["BTCgtRES"] = df["date"].apply(lambda x: 1 if x in breakout_candles else -1).
Please be aware that searching in the list at each iteration might be computationally expensive, so you might want to use dict instead as a simple enough alternative.