reshape | R package to flexible rearrange

This is one way to do it:

Seems to be a prime case for fancy indexing (advanced indexing).

Something like this should do the trick, though I didn't verify it on an actual image. I've added a section to reconstruct the image in the beginning, because it is easier to work with the array as a whole than broken into parts. Likely, you can refactor this and avoid splitting it to begin with.

Normally, I would do something with scipy.ndimage.morphology.distance_transform_edt but this gives the assume that isotropic sizes of the voxel:

It does no such thing! You are looking for the sampling= parameter. From the latest version of the docs:

Spacing of elements along each dimension. If a sequence, must be of length equal to the input rank; if a single number, this is used for all axes. If not specified, a grid spacing of unity is implied.

The wording "sampling" or "spacing" is probably a bit mysterious if you think of pixels as little squares/cubes, and that is probably why you missed it. In most situations, it is better to think of pixels as point samples on a grid, with fixed spacing between samples. I recommend Alvy Ray's a pixel is not a little square for a better understanding of this terminology.

You can use sklearn to calculate f1_score

It seems your X is pandas.DataFrame and in DataFrame code X[0] searchs column with name 0 but X doesn't have it.

If you want to get row with number 0 then you may need X.iloc[0]

BTW:

When I run

This can be done using slicing:

You can use numpy.column_stack:

This is not a bug but because of the internals of PowerTransformer. Have a look at these lines of your error stack trace:

You need to set self.theta to be an array, not a scalar (at least in this specific problem).

In your case, (intercepted-augmented) X is a '3 by n' array, so try self.theta = [0, 0, 0] for example. This will correct the specific error 'bool' object has no attribute 'mean'. Still, this will just produce preds as a zero vector; you haven't fit the model yet.

To let you know how I approached the error, I first went to the exact line the error message was pointing to, and put print(preds == y) before the line, and it printed out False. I guess what you expected was a vector of True and Falses. Your y seemed okay; it was a vector (a list to be specific). So I tried print(pred), which showed me a '3 by n' array, which is weird. Now going up from that line, I found out that pred comes from predict_prob(), especially np.dot(X, self.theta). Here, when X is a '3 by n' array and self.theta is a scalar, numpy seems to multiply the scalar to each item in the array and return the array (having the same dimension as the original array), instead of doing matrix multiplication! So you need to explicitly provide self.theta as an array (conforming to the dimension of X).

Hope the answer and the reasoning behind it helped.

As for the red line you mentioned in the comment, I guess it is also because you are not fitting the model. (To see the problem, put print(probs) before plt.countour(...). You'll see an array with 0.5 only.)

So try putting model.fit(X, y) before preds = model.predict(X). (You'll also need to put self.verbose = verbose in the __init__().)

After that, I get the following: