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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)

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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

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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)

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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

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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..."

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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