probability | Probability-theory toolbox

I'm trying to learn Flask and use postgresql with it. I'm following this tutorial https://realpython.com/flask-by-example-part-2-postgres-sqlalchemy-and-alembic/, but I keep getting error.

For the past days I've been trying to plot circular data with python, by constructing a circular histogram ranging from 0 to 2pi and fitting a Von Mises Distribution. What I really want to achieve is this:

1. Directional data with fitted Von-Mises Distribution. This plot was constructed with Matplotlib, Scipy and Numpy and can be found at: http://jpktd.blogspot.com/2012/11/polar-histogram.html

1. This plot was produced using R, but gives the idea of what I want to plot. It can be found here: https://www.zeileis.org/news/circtree/

WHAT I HAVE DONE SO FAR:

I'm trying to conduct both hyperparameter tuning and feature selection on a sklearn SVC model.

I tried the below code, but am getting an error which I have included.

I'm trying to produce a cumulative incidence plot for a competing hazards survival analysis using plot() in R. For some reason, the plot that is produced has a legend that I have not called. The legend is intersecting with the lines on my graph and I can't figure out how to get rid of it. Please help!

My code is as follows:

I am trying to create a class for calibrating a classifier. I have been reading resources on probability calibration and I am a bit confused on which dataset should we calibrate the classifier. I created a class that split the training set to further train and validation the set. Then, the classifier is first fitted to the train set and predicts the uncalibrated probability on the validation set.

Then, I create a cal_model instance of the CalibrationCV class and then fit it to the validation set and predict calibrated probabilities of the validation set again.

Could someone take a look at the code below and correct the code for me?

I found this in another language. I am wondering if anyone could find this in a English book please. Or if anyone know how to prove this please. Preferably one could tell me a reference book. Thank you very much. Let $(\Omega, F, P)$ be a probability space. and let $g$ and $h $ be functions such that $\int_A g,dP\leqslant \int_A h,dP $ for all $A \in F$, then for $g,h \in \mathbb{L}^1(P)$, $g\leqslant h$

For the following example how do I compute the percentile / probability values / tail area in julia

I'm trying to add a GUI to the predator-prey simulation. It can allow users to choose which simulation(species involved) they want to do, set the simulation field size they want, and show the progress of the simulation and result.

The question is after I generate the field, I can't reset the simulation or run the next step or run the next hundred steps by clicking the buttons I set, not to mention show the progress of the simulation.

Here is the code of my GUI Class:

I am new to python, and I need to check the time of running the codes in python. The codes are shown below: