jfinkels

(global-font-lock-mode 1)

(column-number-mode 1)

(show-paren-mode 1)

(setq-default fill-column 79)
(setq default-tab-width 2)
(setq js-indent-level 2)
(setq-default indent-tabs-mode nil)

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#!/usr/bin/env python3

import itertools

# The input to the algorithm.
N = 11

def build_tree(n):
    # This should be `limit = O(log n)`...
    limit = (n // 2) + 1

jfinkels

#!/bin/env python2                                                              

from flask import Flask
from flask import json
from mimerender import FlaskMimeRender

app = Flask(__name__)
app.debug = True
app.testing = True
mimerender = FlaskMimeRender()

jfinkels

import numpy as np

def GEPP(A, b, doPricing = True):
    '''
    Gaussian elimination with partial pivoting.
    
    input: A is an n x n numpy matrix
           b is an n x 1 numpy array
    output: x is the solution of Ax=b 
            with the entries permuted in 

jfinkels

# -*- coding: utf-8 -*-
"""
**************************************
Minimum Vertex and Edge Dominating Set
**************************************


A dominating set for a graph G = (V, E) is a subset D of V such that every
vertex not in D is joined to at least one member of D by some edge. The

jfinkels

from itertools import islice
from itertools import tee


def tails(iterable):
    iterator = iter(iterable)
    while True:
        tail, iterator = tee(iterator)
        yield tail
        # Try to pop the next element of the iterable. If there are no          

jfinkels

from threesum import three_sums

def test_three_sums():
    integers = [2, 4, 8, 10, -7, -10, -25]
    expected = [(-10, 2, 8)]
    actual = list(three_sums(integers))
    assert [sorted(t) for t in three_sums(integers)] == [[-10, 2, 8]]

jfinkels

# light.py - decides whether a Cayley table represents an associative operation
#
# Copyright 2015 Jeffrey Finkelstein.
#
# This file is part of Light.
#
# Light is free software: you can redistribute it and/or modify it under the
# terms of the GNU General Public License as published by the Free Software
# Foundation, either version 3 of the License, or (at your option) any later
# version.

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def estimate_mean(sample, values, weights=None):
    """Estimates the average value of a set of elements based on the given
    sample.

    Parameters
    ----------
    sample : iterable
       Iterable of objects.

    values : function