Chris Swierczewski cswiercz

My Problems (There Are Many)

The C++ standard library containers are nice. Like, really nice. As the kind of person who worries about performance to the point that they were shy of anything other than a raw data array I've come to appreciate the usability (and speed!) of ye olde std::vector. Does that many me sound like a wannabe oldie?

Recently I came across a situation where I had, say, a vector [a, b, c, d] and wanted to create the vectors [a, b, c], [a, b, d], [a, c, d], and [b, c, d]. I didn't want to actually allocate O(N(N-1)) additional memory but learned that std::vector didn't support holding references. That's when I came across std::reference_wrapper.

Reference Wrappers

From the Cppreference page:

Introduction

WIP - There is still much to learn.

Creating a New MXNet Operator

################################################################################ Let's begin by creating a simple, near-minimal working example. I'll start with the code and then walk through each component. We will create an operator, add_one() which will take a vector as input, add one to each element, and return the result.

Sequence Diagrams in GitHub

js-sequence-diagrams turns text into UML sequence diagrams. However, jsseq and sequence code blocks do not automatically render in GitHub. A Google Chrome plugin Github sequence diagrams allows rendering of sequence diagrams within GitHub!

	Performance profile on the following Python script:

	from sage.all_cmdline import * # import sage library
	_sage_const_3 = Integer(3); _sage_const_2 = Integer(2); _sage_const_7 = Integer(7)

	from abelfunctions import *
	R = QQ['x, y']; (x, y,) = R._first_ngens(2)
	X = RiemannSurface(y*_sage_const_3 + _sage_const_2 x*_sage_const_3 y - x**_sage_const_7 )

	X.riemann_matrix()

	/// A forest contains a collection of trees and a shared storage for
	/// points in the tree. However, each of the trees need to mutably access
	/// the point store. For convenience, the trees are initialized using a
	/// pointer to the shared point store.
	///
	/// This code shows one way to perform this two-way mutable share where the
	/// forest owns all of the constituent data structures.
	///
	/// * Rc: allows multiple (reference counted) pointers to a type
	/// * RefCell: wraps the type in a mechanism that allows mutation through

	import pandas as pd
	import matplotlib.pyplot as plt

	# obtain example dataset
	iris = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')

	# get the matplotlib default color cycle
	cycle = iter(plt.rcParams['axes.prop_cycle'].by_key()['color'])

	# use pandas to plot by label

	# A custom prompt that looks like this:
	#
	# <NEWLINE>
	# (conda-environment) (current-datetime) [current-full-directory]
	# username@host $
	#
	# First clear the PS1 variable. This allows conda setup to populate the PS1 with
	# the current anaconda environment before we create a custom prompt. Necessary
	# because I want a newline before each prompt. (Maybe better way?)
	#


	# coding: utf-8

	# In[1]:

	R = QQbar['x,y']
	x,y = R.gens()

	g1 = x^4 + x^2y^2 + (-2)x^2y - xy^2 + y^2
	g2 = -x^7 + 2x^3y + y^3