bhtucker

"""
Simple worker showing different worker ids sharing/incrementing the same memory region
uwsgi --http :9090 --wsgi-file foobar.py --master --processes 4 --threads 2 --stats 127.0.0.1:9191 --sharedarea 2
Then just keep refreshing localhost:9090
"""

import uwsgi

INT_ORDS = {48, 49, 50, 51, 52, 53, 54, 55, 56, 57}

bhtucker

from flask import Flask, jsonify
from flask_sqlalchemy import SQLAlchemy

app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql://test@localhost/test'
db = SQLAlchemy(app)
db.init_app(app)


class User(db.Model):

bhtucker

# -*- coding: utf-8 -*-
"""
    markov_generator_pipeline
    ~~~~~~~~~~~~~~~~~~~~~~~~~

    An example markov generator that reads corpus one line at a time
    and uses numpy for storing / drawing word likelihoods

    Example source text:
    http://www.gutenberg.org/cache/epub/28339/pg28339.txt

bhtucker

import random

GEORGI_TEXT = """
    GEORGI IS HERE!!!!
    Maybe you should [C]all for help.
"""


class Scene(object):

bhtucker

bhtucker

create materialized view ladder as (
with
	match_points as (
		select 
			case ... end as aff_points,
			case ... end as neg_points,
			aff_id,
			neg_id
		),

bhtucker

bhtucker

"""
Demo of using Hogwild algorthim for parallel learning with shared memory
Uses sklearn's LogisticRegression for accuracy comparison

Output
('initial accuracy:', 0.45333333333333331)
worker 25974 score 0.93
worker 25975 score 0.92
worker 25976 score 0.88
worker 25974 score 0.94

bhtucker

"""
Implementation of the example from https://inst.eecs.berkeley.edu/~cs61c/fa14/projs/02/
"""
import operator

class Puzzle(object):
    """General game representation"""
    def solution(self):
        return Position(0)

bhtucker