cgp | Cartesian Genetic Programming for Go | Machine Learning library

I am following the tutorial for Strapi on CGP App Engine (nodejs- standard env) and unable to get the app to start because the connection is being refused Error: connect ECONNREFUSED 127.0.0.1:5432 by the GCP Postgres instance (Public IP) .

1. GCP Service Principle Persmissions: @appspot.gserviceaccount.com has Cloud SQL Client for the App Engine default service account so this should apply to all App Engine Services.
2. I have other App Engine Services (python) connecting successfully to other Postgres Databases. This tells me I have the correct permissions, Cloud SQL Admin API enabled, and the correct username/password.
3. The code works locally (Docker) while linking the GCP Postgres database, but only with TCP routing, not a Unix Socket SQL proxy:

I have a system where I'm using the consensus opinion of weighted votes to predict a binary outcome.

Since elections are topical we can use it as an example. Say I do an analysis on various pollsters over the years and assign them a weighted vote based on how accurate they were. Pollster Skyler ends up with a vote weight of 3 and Pollster Oakely was twice as accurate and ends up with a vote weight of 6. In CGP Grey fashion (a youtuber who sometimes talks about election mechanics) Skyler predicts that the Tiger candidate will win the open city council seat and Oakley predicts that the SnowLepoard candidate will win it. In this example, the projected winner, based on total weighted votes, would be SnowLepoard with a majority weighted vote percent of (6/9) 0.66667. The actual outcome of any given election would of course vary, but generally, if the weights are good, we'd expect the win probability of a given candidate to increases as the majority weighted vote percent increases and, likewise, if the majority percent is 50% for the election to be a real tossup.

With that example in mind, I'm trying to do a logistic regression on a dataset where the only independent variable is the majority weighted vote percent. I've tried a couple of different methods and while both generally agreed that win probability increases as majority weighted vote percent increases, they disagree as to how much and neither really respects the idea that a 50/50 majority weighted vote percent indicates a true 50/50 probability.

The blue line is the logistic regression done through seaborn and the green dots are the logistic regression done through sklearn. Code below. I dont think that the underlying mechanics of a logistic regression change from one library to another so clearly if they're producing different outputs for the same input, my setup is wrong.

• Why are these two libraries producing different regressions?
• How can I force the regression, for either library, to treat a weighted vote majority of 0.5 as a 50% win probability? I can probably just fill in a mass of dummy data to force the conclusion but I feel like theres got to be a more elegant way.

I have a problem with my AutoML code. I want to link it to a web project. I've used the Google tutorial but it doesn't work. I receive this error in my console :

I am totaly new to the cloud in any way. I started some weeks ago with the Azure cloud and we setting up a project using many different products of Azure. At the moment we think about setting up the project on a way that we are not trapped by Microsoft and are able to switch to GCP or AWS. For most products we use I have found similar ones in the other Clouds but I wonder if there is somthing like Azure Data Factory in AWS or CGP? I could not find something in my first google research.

Best and thanks for your help

I have several files in a direcotry such as :

I'm using a C library which has a struct called dataSet which is used throughout the library. I can declare the struct in my C++ code and pass it to the methods in the C library and it works fine, but I don't know how to access items from this struct in C++ as I get read file access violation. I'm not sure if I should be using the extern command somehow.

main.cpp:

I'm using GCP and serverless framework. And wish to create a scheduled function. For AWS lambdas i can add to the .yml:

I'd like to calculate % of stocks above rolling mean, therefore, I need to group the data by 'Date' and want to keep the 'Date' column. Percentages are calculated correctly, however, instead of actual dates I'm getting 'NaN' values. The 'Date' column is not the data frame index.