start new:
tmux
start new with session name:
tmux new -s myname
var parser = document.createElement('a'); | |
parser.href = "http://example.com:3000/pathname/?search=test#hash"; | |
parser.protocol; // => "http:" | |
parser.hostname; // => "example.com" | |
parser.port; // => "3000" | |
parser.pathname; // => "/pathname/" | |
parser.search; // => "?search=test" | |
parser.hash; // => "#hash" | |
parser.host; // => "example.com:3000" |
#-*- coding:utf-8 -*- | |
"""Utilities to evaluate pairwise distances or metrics between 2 | |
sets of points. | |
Distance metrics are a function d(a, b) such that d(a, b) < d(a, c) if objects | |
a and b are considered "more similar" to objects a and c. Two objects exactly | |
alike would have a distance of zero. | |
One of the most popular examples is Euclidean distance. | |
To be a 'true' metric, it must obey the following four conditions:: |
# This is a skeleton for testing models including examples of validations, callbacks, | |
# scopes, instance & class methods, associations, and more. | |
# Pick and choose what you want, as all models don't NEED to be tested at this depth. | |
# | |
# I'm always eager to hear new tips & suggestions as I'm still new to testing, | |
# so if you have any, please share! | |
# | |
# @kyletcarlson | |
# | |
# This skeleton also assumes you're using the following gems: |
# Importing the libraries | |
from scipy.stats.stats import pearsonr | |
import pandas as pd | |
import numpy as np | |
import json | |
data = pd.read_csv("../../Downloads/data_books.csv", sep = ",", header=None, | |
names=['Reviewer', 'Book', 'Rating']) | |
# Picking 2 books |
While this gist has been shared and followed for years, I regret not giving more background. It was originally a gist for the engineering org I was in, not a "general suggestion" for any React app.
Typically I avoid folders altogether. Heck, I even avoid new files. If I can build an app with one 2000 line file I will. New files and folders are a pain.
class ClusteringQuality: | |
""" | |
Instances of this class implement the two measures of clustering quality discussed in the article, namely the davies | |
bouldin index and the silhouette index. It also implements a number of useful helper methods. | |
:param solution: the clustering solution of type Clustering | |
:param minimum: the minimum distance allowable | |
""" | |
def __init__(self, solution, minimum): | |
""" |
#!/bin/sh | |
sudo apt-get purge -y maven | |
if ! [ -e .semaphore-cache/apache-maven-3.3.3-bin.tar.gz ]; then (cd .semaphore-cache; curl -OL http://mirror.olnevhost.net/pub/apache/maven/maven-3/3.3.3/binaries/apache-maven-3.3.3-bin.tar.gz); fi | |
sudo tar -zxf .semaphore-cache/apache-maven-3.3.3-bin.tar.gz -C /usr/local/ | |
sudo ln -s /usr/local/apache-maven-3.3.3/bin/mvn /usr/bin/mvn | |
echo "Maven is on version `mvn -v`" |
from collections import defaultdict, deque | |
class Graph(object): | |
def __init__(self): | |
self.nodes = set() | |
self.edges = defaultdict(list) | |
self.distances = {} | |
def add_node(self, value): |
You are Manus, an AI agent created by the Manus team. | |
You excel at the following tasks: | |
1. Information gathering, fact-checking, and documentation | |
2. Data processing, analysis, and visualization | |
3. Writing multi-chapter articles and in-depth research reports | |
4. Creating websites, applications, and tools | |
5. Using programming to solve various problems beyond development | |
6. Various tasks that can be accomplished using computers and the internet |