fcostin

def to_sparse(p_dense):
    states = numpy.indices(numpy.shape(p_dense))
    p_sparse = {}
    for state in states.transpose():
        state = tuple(state)
        probability = p_dense[state]
        if probability > 0:
            p_sparse[state] = p_dense[state]
    return p_sparse

fcostin

import cmepy.solver

def do_something_fancy(y):
    """
    transforms the solution y in some interesting and useful manner
    """
    return y # TODO IMPLEMENT ME

class FancySolver(object):

fcostin

# leads to a segfault in version 1.2.1

import numpy
bad = (numpy.array([0]), numpy.array(0))
numpy.asarray(bad)

fcostin

b = ['b = ', "print('%s' % str(x[0]), end='')", '\n', 'def f(*x): eval(b[1]) if type(x[0]) is str else [g(s) for s in x]', "def g(x): print('f(b[%d])' % x if type(x) is int else 'f(%s)' % x)", "0, 'b', 4, 2, 3", '2, b[5], b[6]']
def g(x): print('f(b[%d])' % x if type(x) is int else 'f(%s)' % x)
def f(*x): eval(b[1]) if type(x[0]) is str else [g(s) for s in x]
f(b[0])
f(b)
f(b[4])
f(b[2])
f(b[3])
f(b[2])
f(0, 'b', 4, 2, 3)

fcostin

a = "for x in ('a = '+repr(a), a): print(x)"
for x in ('a = '+repr(a), a): print(x)

fcostin

"""
Plot x,y pairs from csv file.

usage:

    python plot_csv.py xy_data.csv

csv format:

    assume no header row

fcostin

CRIME_DATA_URL := http://archive.ics.uci.edu/ml/machine-learning-databases/communities/communities.data
CRIME_DATA := crime.data

test: $(CRIME_DATA)
	time python test_ridge_crime.py $^
.PHONY: test

$(CRIME_DATA):
	wget -O $@ $(CRIME_DATA_URL)

fcostin

def exchange(a, i, j):
    a[i], a[j] = a[j], a[i]

def partition(a, x):
    n = len(a)
    i = 0
    j = 0
    k = n
    while j < k:
        if a[j] < x:

fcostin

on ubuntu...

sudo apt-get install xsel

then from inside vim to copy the contents of the current file to clipboard

! xsel -b < %

fcostin

"""
python & numpy port of james hague's blog post 141

ref:
    http://prog21.dadgum.com/141.html
"""

import numpy

a = numpy.array([10, 5, 9, 6, 20, 17, 1])