hamilton

import sys

class Rhuthmos(object):
	def __init__(self):
		"""
		wts = word to structure.  A hash.
		stw = structure to word.  A tree.
		"""
		self.wts = {}
		self.stw = {}

hamilton

import sys

class Rhuthmos(object):
	def __init__(self):
		"""
		wts = word to structure.  A hash.
		stw = structure to word.  A tree.
		"""
		self.wts = {}
		self.stw = {}

hamilton

# stochastically generated poem from Darwin's _On the Origin of Species_.

He often begins his selection,
Done nothing, and all the species of shells in the same genus have been checked.
Mainly distinctive of each species, has been victorious in a reversed direction.
But I believe the above effect.

Competition by having inhabited a protected station,
Its contingencies of extinction will tend to intersect.
Connect and except secondly by a rotation.

hamilton

>>> from rhymeless import Rhymeless
>>> poem_generator = Rhymeless()
>>> book = open("books/on_the_origin_of_species.txt", "r")
>>> lines = []
>>> for line in book:
...     lines.append(line)
... 
>>> book = "".join(lines)
>>>
>>> # the book variable is just a huge string.  I could also train

hamilton

import numpy as np
import som
from grid import hexagonal_grid # comes with som repository
from distance import euclidean # also comes with the som repository

##### Create a funky, curved data set in 3 dimensions. #####
data = np.transpose(np.matrix([
	np.random.normal(0,1,20000),
	np.random.normal(0,1,20000),
	np.random.normal(0,1,20000)

hamilton

import lsh

lsh_machine = lsh.LSH(assignment_name="example")

# data is a dictionary of the form:
#{user_id: set([item_id1, item_id2, ...]), ...}

# Depending on the input size, training can take a while.
# But it will use all your cores to do so, and will
# automatically cache the data for assignment_name.  This

hamilton

import urllib2
import os
import re
from sgmllib import SGMLParser


print '-' * 80
print '           History of Electronic / Electroacoustic Music, 1937-2000         '
print '-' * 80
print 

hamilton

# Here's a small experiment in hacking together a formula notation for 
# Python, in the style of R. This is without reference to any matrices, but 
# it's pretty straightforward to see how one might plug such a system into a 
# matrix library.

# I bet this could easily extend to other R formula features.

class Predictor(object):
    def __init__(self,label):
        self.label = label

hamilton

date,nbrhd,count
1997-01-01,Inner Richmond,121
1997-01-01,Japantown,7
1997-01-01,Glen Park,16
1997-01-01,Western Addition,45
1997-01-01,Outer Richmond,144
1997-01-01,Tenderloin,56
1997-01-01,Financial District/South Beach,6
1997-01-01,Excelsior,56
1997-01-01,Presidio Heights,36

hamilton

/*
Cloning dplyr in Javascript, sort of
====================================

Works on arrays of objects.

usage:

var transformation = DS.data(arrayOfObjects)
    .standardize()