alapini

"""
Python code for fractional differencing of pandas time series
illustrating the concepts of the article "Preserving Memory in Stationary Time Series" 
by Simon Kuttruf

While this code is dedicated to the public domain for use without permission, the author disclaims any liability in connection with the use of this code.
"""

import numpy as np 
import pandas as pd

alapini

# install mkl
RUN apt update && apt install -y --force-yes apt-transport-https && \
  wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB && \
  apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS-2019.PUB && \
  sh -c 'echo deb https://apt.repos.intel.com/mkl all main > /etc/apt/sources.list.d/intel-mkl.list' && \
  apt-get update && DEBIAN_FRONTEND=noninteractive apt-get -y install cpio intel-mkl-64bit-2018.3-051 && \
  (find /opt/intel -name "ia32*" -exec rm -rf {} \; || echo "removing ia32 binaries") ; \
  (find /opt/intel -name "examples" -type d -exec rm -rf {} \; || echo "removing examples") ; \
  (find /opt/intel -name "benchmarks" -exec rm -rf {} \; || echo "removing benchmarks") ; \
  (find /opt/intel -name "documentation*" -exec rm -rf {} \; || echo "removing documentation") ; \

alapini

extern mod sync;

// str op trait
use std::str::StrSlice;
// for tcp listen
use std::io::{TcpListener, TcpStream};
use std::io::net::ip::SocketAddr;
// for trait
use std::io::{Listener, Writer, Acceptor, Buffer};
// for spawn

alapini

"""
Hierarchial Clustering.
The goal of gist is to show to use scikit-learn to perform agglomerative clustering when:
1. There is a need for a custom distance metric (like levenshtein distance)
2. Use the distance in sklearn's API.

Adapted from: sklearn's FAQ.
http://scikit-learn.org/stable/faq.html
"""

alapini

import time
import numba
from numba import jit
import numpy as np

#input matrices
matrix1 = np.random.rand(30,30)
matrix2 = np.random.rand(30,30)
rmatrix = np.zeros(shape=(30,30))

alapini

from numba import cuda

gpus = cuda.gpus.lst

for gpu in gpus:
    with gpu:
        meminfo = cuda.current_context().get_memory_info()
        print("%s, free: %s bytes, total, %s bytes" % (gpu, meminfo[0], meminfo[1]))

alapini

:: Launch PyCharm from within conda environment

:: This will ensure that all necessary environment variables are set.
:: Modify the paths and environment name for your installation, if necessary.
:: This configuration is for PyCharm x64 installed with JetBrains Toolbox and 
:: will launch the latest installed version.

@set condaroot="%userprofile%\Anaconda3"
@set pycharmroot="%localappdata%\JetBrains\Toolbox\apps\PyCharm-P\ch-0"
@set env=base

alapini

alapini

// Source : https://gist.github.com/Azerothian/dc84f009a94579b5bb43#gistcomment-2002977
function isObject(avar) {
    // cosntructor.name tested for `function Animal(){}; var a = new Animal(); isObject(a);` will return true otherwise as it is [Object object]
    return Object.prototype.toString.call(avar) === '[object Object]' && avar.constructor.name === 'Object';
}
function createGqlQuery(obj) {
    let shape = [];
  
    for (let [key, val] of Object.entries(obj))
      shape.push(isObject(val) ? `${key} { ${createGqlQuery(val)} }` : key);

alapini

https://github.com/Microsoft/vscode/blob/c2d56115afa1647f204ec9afec3a078fc1dac67a/src/vs/workbench/api/electron-browser/mainThreadSaveParticipant.ts#L333