This is a guide on how to install Caffe for Ubuntu 16.04 and above, without GPU support (No CUDA required).
sudo apt-get install libopencv-dev python-opencv
version: '2.2' | |
services: | |
elasticsearch: | |
image: docker.elastic.co/elasticsearch/elasticsearch:6.4.1 | |
container_name: elasticsearch | |
environment: | |
- cluster.name=docker-cluster | |
- bootstrap.memory_lock=true | |
- "ES_JAVA_OPTS=-Xms512m -Xmx512m" | |
ulimits: |
%matplotlib inline | |
import numpy as np | |
import matplotlib.pyplot as plt | |
import pandas as pd | |
from sklearn.datasets.samples_generator import make_blobs | |
class K_Means: | |
def __init__(self, k=3, max_iterations = 500): | |
self.k = k |
/** | |
* Lightweight script to detect whether the browser is running in Private mode. | |
* @returns {Promise<boolean>} | |
* | |
* Live demo: | |
* @see https://output.jsbin.com/tazuwif | |
* | |
* This snippet uses Promises. If you want to run it in old browsers, polyfill it: | |
* @see https://cdn.jsdelivr.net/npm/es6-promise@4/dist/es6-promise.auto.min.js | |
* |
Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.
from flask import abort, make_response, jsonify | |
abort(make_response(jsonify(message="Message goes here"), 400)) |
# You need to install scikit-learn: | |
# sudo pip install scikit-learn | |
# | |
# Dataset: Polarity dataset v2.0 | |
# http://www.cs.cornell.edu/people/pabo/movie-review-data/ | |
# | |
# Full discussion: | |
# https://marcobonzanini.wordpress.com/2015/01/19/sentiment-analysis-with-python-and-scikit-learn | |
import threading | |
# A thread-safe implementation of Singleton pattern | |
# To be used as mixin or base class | |
class Singleton(object): | |
# use special name mangling for private class-level lock | |
# we don't want a global lock for all the classes that use Singleton | |
# each class should have its own lock to reduce locking contention | |
__lock = threading.Lock() |
""" | |
This module provides a simple WSGI profiler middleware for finding | |
bottlenecks in web application. It uses the profile or cProfile | |
module to do the profiling and writes the stats to the stream provided | |
To use, run `flask_profiler.py` instead of `app.py` | |
see: http://werkzeug.pocoo.org/docs/0.9/contrib/profiler/ | |
and: http://blog.miguelgrinberg.com/post/the-flask-mega-tutorial-part-xvi-debugging-testing-and-profiling | |
""" |