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import numpy as np
import pymc
import pdb

def unconditionalProbability(Ptrans):
   """Compute the unconditional probability for the states of a
   Markov chain."""

   m = Ptrans.shape[0]

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# good
hello_world <- 1L

# bad - can I call hello(world)? It's unclear. 
hello.world <- 1L

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"""A setuptools based setup module.
See:
https://packaging.python.org/en/latest/distributing.html
https://github.com/pypa/sampleproject
"""

# Always prefer setuptools over distutils
from setuptools import setup, find_packages
# To use a consistent encoding
from codecs import open

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n <- 10000
alpha <- rnorm(n, 20, 5) # Customers spend on average $20 per transaction
plot(density(alpha))
b <- 10 # Customers who are petite spend on average $10 more per transaction
petite <- rbinom(n, size = 1, prob = 0.25) # Let's say 25% of customers are petite
eps <- rnorm(n, 0, 5) # Add some error
plot(density(eps))
y_true <- alpha + b * petite + eps
plot(density(y_true))
y_true[y_true < 0] <- 0

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# Good - I can customize the way the file is read in
df <- read.csv("foo.csv", stringsAsFactors = FALSE)
df <- read.table("foo.txt", delimiter = "\t", stringsAsFactors = FALSE)
df <- read.table("foo.txt", delimiter = "|", stringsAsFactors = FALSE)

analyze(df)

# Bad - arguments are needed to customize how foo.csv is read in
analyze("foo.csv", stringsAsFactors = FALSE)
analyze("foo.txt", delimiter = "\t", stringsAsFactors = FALSE)

paulhendricks

import numpy as np
import theano; import theano.tensor as T; import theano.tensor.nnet as nnet
X_train = np.array([[0, 0], [0, 1], [1, 0], [1, 1]]).reshape((4, 2))
y_train = np.array([0, 1, 1, 0])
x = T.dvector('x')
y = T.dscalar('y')
theta_0 = theano.shared(np.array(np.random.rand(3, 3), dtype=theano.config.floatX), name='theta_0')
theta_1 = theano.shared(np.array(np.random.rand(4, 1), dtype=theano.config.floatX), name='theta_1')
layer_1 = nnet.sigmoid(T.dot(theta_0.T, T.concatenate([np.array([1], dtype=theano.config.floatX), x])))
layer_2 = T.sum(nnet.sigmoid(T.dot(theta_1.T, T.concatenate([np.array([1], dtype=theano.config.floatX), layer_1]))))

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#!/usr/bin/env julia

using Luxor

Drawing(500, 500, "/tmp/julia-flowers.png")
background("white")

function juliaflowers(cpos::Point, radius; petals=30, outercircleratio=0.75, innercircleratio=0.65)
    # clockwise, from bottom LEFT...
    points3 = ngon(cpos, radius, 3, pi/6, vertices=true)

paulhendricks

'''Trains a simple convnet on the MNIST dataset.

Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import keras
from keras.datasets import mnist

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Latency Comparison Numbers
--------------------------
L1 cache reference/hit                       1.5 ns                      4 cycles
Floating-point add/mult/FMA operation        1.5 ns                      4 cycles
L2 cache reference/hit                       5   ns                      12 ~ 17 cycles
Branch mispredict                            6   ns                      15 ~ 20 cycles
L3 cache hit (unshared cache line)          16   ns                      42 cycles
L3 cache hit (shared line in another core)  25   ns                      65 cycles
Mutex lock/unlock                           25   ns
L3 cache hit (modified in another core)     29   ns                      75 cycles

paulhendricks

Effective Modern CMake

Getting Started

For a brief user-level introduction to CMake, watch C++ Weekly, Episode 78, Intro to CMake by Jason Turner. LLVM’s CMake Primer provides a good high-level introduction to the CMake syntax. Go read it now.

After that, watch Mathieu Ropert’s CppCon 2017 talk Using Modern CMake Patterns to Enforce a Good Modular Design (slides). It provides a thorough explanation of what modern CMake is and why it is so much better than “old school” CMake. The modular design ideas in this talk are based on the book [Large-Scale C++ Software Design](https://www.amazon.de/Large-Scale-Soft