theta | Generic , modular and configurable formal verification

I found the problem. Problem was that scipy was missing in my anaconda virtual environment. I thought scipy was installed when I saw;

AttributeError: module 'scipy.ndimage' has no attribute 'interpolation'

Thanks for the tip @simpleApp. And I'm sorry to bother you with the mistake of absent-mindedness... Solution is the installing scipy.

The problem is - the code is rendering the complete path - start-to-end each time and start is same for all animations.

The idea for loaders is - start point has to change after each animation - something like -

1. Start at angle 0
2. Go up to angle 45 /// or 60 whatever you want this to be
3. Change start angle to next logical step - say 30
4. Then render up to 75 /// or 90 depending on what you chose previously

In this arrangement, you have to keep drawing a certain portion of the shape by changing start point continuously.

In practice, achieving a smooth transition between different start point values may prove out to be difficult than it seems. You can find an example here - https://github.com/SVProgressHUD/SVProgressHUD/blob/master/SVProgressHUD/SVIndefiniteAnimatedView.m#L48-L102

UPDATE

The link I shared above has all the hints in it. This library uses an image as a mask and then rotate that continuously. Mask shape can be anything you like - you already have code for that.

You just need to create an image that's appropriate for your animation. See what their asset looks like

and AFTER masking what their animation looks like -

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:

Your problem is one of mathematics. You said "As observed, the side length is 10px". It very definitely is not 10px. The distance from (10,5) to (5,0) is sqrt(5*5 + 5*5), which is 7.07. That's exactly what we expect for a square that is inscribed in a circle of radius 5: 5 x sqrt(2).

And that's what the other squares are as well.

FOLLOWUP

As an added bonus, here is a function that returns the radius of the circle that circumscribes a regular polygon with N sides of length L:

Thank you ..I have solved the problem .When anything changes in the table, I need to remove the existing curve containing all the circles and build/add a new one, or replace the data

I believe this will work:

You can convert cartesian coordinates (x, y) into polar ones (R, fi), add theta to fi and then convert back to cartesian:

You can replace your 0 values with NAs, for example with, dplyr::na_if(0) on your dataframe:

You have several options

You could construct the model string on the fly. [The model argument to run.jags can contain a character string instead of a file name, so there's no need to write to a file and then read it in again.]

You can add an element to your data list (x in your code) that contains the number of observations,