Corey J. Nolet cjnolet

Basic Usage

Example of training an HDBSCAN model using the hdbscan Python package in Scikit-learn contrib:

from sklearn import datasets
from hdbscan import HDBSCAN

X = datasets.make_moons(n_samples=50, noise=0.05)

model = HDBSCAN(min_samples=5)

Brief Example of Building a KNN Graph from cuML's NearestNeighbors Output

Import cupy and cuML's NearestNeighbors estimator

import cupy
from cuml.neighbors import NearestNeighbors

Set up some variables and create some random data on GPU

Comparing cuML K-Means API Against Scikit-learn & Dask-ML

First, a quick code example of K-Means in Scikit-learn

from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans

n_centers = 5

X, _ = make_blobs(n_samples=10000, n_centers=n_centers)

Run Cisco Webex on 64-bit Fedora 28/CentOS 7

With Audio and Screen Sharing Enabled

IMPORTANT NOTE :

1. The Screen Sharing works when you use Xorg instead of Wayland.
2. In my test, I disabled SELinux but maybe it works even if SElinux is permissive.
3. This was tested and worked on the DELL VOSTRO 3560 but does not work on DELL PRECISION 7510

	echo "Downloading gcc source files..."
	curl https://ftp.gnu.org/gnu/gcc/gcc-5.4.0/gcc-5.4.0.tar.bz2 -O

	echo "extracting files..."
	tar xvfj gcc-5.4.0.tar.bz2

	echo "Installing dependencies..."
	yum install gmp-devel mpfr-devel libmpc-devel -y

	echo "Configure and install..."

	import numpy as np
	import pylab as pl
	from numpy import fft

	def fourierExtrapolation(x, n_predict):
	n = x.size
	n_harm = 10 # number of harmonics in model
	t = np.arange(0, n)
	p = np.polyfit(t, x, 1) # find linear trend in x
	x_notrend = x - p[0] * t # detrended x