Categories
Development

Add a composite constraint and view it, MySQL/MariaDB

Add

Adding constraint NetAppToken composing of 3 columns: network, application, token.
Note: you supposed to have chosen a right db:
use <database_name>;

ALTER TABLE crypto   
    ADD CONSTRAINT NetAppToken UNIQUE (network, application, token);

View

SELECT 
   table_schema,  
   table_name,    
   constraint_name
FROM information_schema.table_constraints
WHERE table_name = 'crypto';

Result

+--------------+------------+-----------------+
| table_schema | table_name | constraint_name |
+--------------+------------+-----------------+
| admin_crypto | crypto     | PRIMARY         |
| admin_crypto | crypto     | NetAppToken     |
+--------------+------------+-----------------+
2 rows in set (0.06 sec)
Categories
Development

CURL request into Curl PHP code

Recently I needed to transform the CURL request into the PHP Curl code, binary data and compressed option having been involved. See the query itself:

curl 'https://terraswap-graph.terra.dev/graphql' 
-H 'Accept-Encoding: gzip, deflate, br' 
-H 'Content-Type: application/json' 
-H 'Accept: application/json' 
-H 'Connection: keep-alive' 
-H 'DNT: 1' 
-H 'Origin: https://terraswap-graph.terra.dev' 
--data-binary '{"query":"{\n  pairs {\n    pairAddress\n    latestLiquidityUST\n    token0 {\n      tokenAddress\n      symbol\n    }\n    token1 {\n      tokenAddress\n      symbol\n    }\n    commissionAPR\n    volume24h {\n      volumeUST\n    }\n  }\n}\n"}' 
--compressed
Categories
Development

Auth proxy with JAVA

In the post we’ll show how to leverage auth ptoxy (with login/pass) for JAVA application.

Categories
Development

Vesta CP install SSL certificate for a subdomain

In this post I’ll share how I’ve added a LetsEncrypt SSL certificate to a subdomain at VPS with Centos 7 using Vesta CP.

Categories
Development

Subdomain at Centos 7 with Laravel project

This post is devoted to the steps of how to create subdomain (Centos 7 and Vesta CP) and map a [Laravel] project folder to it.

Categories
Challenge Featured Review SaaS

Data collectors to scrape tough websites

Recently we encountered a new powerful scraping service called Data Collector [of Bright Data]. The life-test and thorough drill-in are coming soon. Yet now we want to highlight it main features that has badly (in positive sense, strongly) impressed us.

Categories
Development

How to add Git Personal Access Token (PAT) into git console

  1. Remove previous git origin
git remote remove origin
  1. Add new origin with PAT (<Token>) :
git remote add origin https://<TOKEN>@github.com/<USERNAME>/<REPO>.git
  1. Push once with –set-upstream
git push --set-upstream origin main

Now you might commit changes to the remote repo without adding PAT into a push command every time.

If you need to create PAT, use the following tut.

Categories
Data Mining

Random Forest vs Gradient boosting

The objective of the task is to build a model so that we can, as optimally as this data allows, relate molecular information, to an actual biological response.

We have shared the data in the comma separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing an actual biological response; the molecule was seen to elicit this response (1), or not (0). The remaining columns represent molecular descriptors (D1 through D1776), these are calculated properties that can capture some of the characteristics of the molecule – for example size, shape, or elemental constitution. The descriptor matrix has been normalized.

Categories
Data Mining

Bagging and Random Forest

In this post we do several tasks performing the Bagging and the Random Forest Classificators.

We gradually develop classifier for the Bagging on randomized trees that in its final stage matches the Random Forest algorithm.

We’ll also build the RandomForestClassifier of sklearn.ensemble and learn of its quality depending on (1) number of trees, (2) max features used for each tree node, and (3) max tree depth.

Bagging is an ensemble algorithm that fits multiple models on different subsets of a training dataset, then combines the predictions from all models.

Random forest is an extension of bagging that also randomly selects subsets of features used in each data sample.

Categories
Data Mining

Sklearn, Random Forest

The objective of the task is to build a model so that we can, as optimally as this data allows, relate molecular information, to an actual biological response.

We have shared the data in the comma separated values (CSV) format. Each row in this data set represents a molecule. The first column contains experimental data describing an actual biological response; the molecule was seen to elicit this response (1), or not (0). The remaining columns represent molecular descriptors (D1 through D1776), these are calculated properties that can capture some of the characteristics of the molecule – for example size, shape, or elemental constitution. The descriptor matrix has been normalized.

Source.