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import sys | |
import time | |
from pylab import * | |
import scipy.stats as stats | |
""" | |
Script to visualise draws from a two parameter Indian Buffet Process. | |
usage example: |
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plot.aic <- function(fit, new=T, sd=0.1) | |
{ # code to plot AIC against order of AR model fitted | |
if (new) plot((1:length(fit$aic))-1,fit$aic,type="l",xlab="Order",ylab="AIC") else | |
lines((1:length(fit$aic))-1,fit$aic,type="l") | |
points(rnorm(1,fit$order,sd=sd),rnorm(1,sd=sd),pch=16,col="red") | |
} | |
plot.stat_ic <- function(stat, new=T, sd=0.1) | |
{ # code to plot AIC against order of AR model fitted | |
if (new) plot((1:length(stat))-1,stat,type="l",xlab="Order",ylab="AIC") else |
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from pylab import * | |
import numpy | |
def grid_sq(fr,ncol,bs,npx=2,bval=0): | |
Z = fr.shape[0] | |
z=0 | |
vbar = bval*ones((bs[0],npx)) | |
hbar = bval*ones((npx, (bs[1] + npx)*ncol + npx)) | |
grid = [hbar] | |
row = [vbar] |
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from pylab import * | |
import scipy.stats as stats | |
######################################################################################## | |
# | |
# QUICK ILLUSTRATION OF USING SCIPY.STATS FOR GAMMA AND INV-GAMMA | |
# HOW TO DO SHAPE-RATE (alpha, beta) PARAMETERISATION CORRECTLY | |
# | |
######################################################################################## |
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""" | |
Quick and dirty experimental implementation of k-nearest neighbours technique | |
""" | |
from scipy import randn | |
import scipy.stats as stats | |
import random | |
from kdata import * |
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class MultiClassDS: | |
""" | |
Generates a dataset of n points from c classes in ratio r | |
""" | |
def __init__(self, classes, length=1000, ratio=None): | |
#set params | |
self.classes, self.length, self.ratio = classes, length, ratio | |
#no ratio supplied? set it to even split | |
if not ratio: self._normalise_ratio() |
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from django.conf import settings #for RECAPTCHA_PRIVATE key | |
def verify_recaptcha(request): | |
import urllib, urllib2, re | |
values = { | |
"privatekey": settings.RECAPTCHA_PRIVATE, | |
"remoteip": request.META['REMOTE_ADDR'], | |
"challenge": request.POST['recaptcha_challenge_field'], | |
"response": request.POST['recaptcha_response_field'], | |
} |
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def vis3d(ds): | |
"""Visualise a 3d MultiClassDS""" | |
fig = plt.figure() #start pyplot | |
fig.suptitle('3d vis') #add a title | |
cols = ['g', 'b', 'r', 'y'] #colour options | |
ax = fig.add_subplot(111, projection="3d") #initialise 3d | |
ax.set_xlabel('X') #set axis labels | |
ax.set_ylabel('Y') | |
ax.set_zlabel('Z') | |
for i, clas in enumerate(ds.get_vectors()): #loop through the vectorised classes |
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import scipy as sp | |
class PDimClass: | |
""" | |
A p-dimensional class, which takes a list of Normal distribution parameters, | |
each of which defines a dimension | |
""" | |
#a list of normal distribution parameter tuples, for N(mean, sd) | |
params = [(0,1), (0,1)] |
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class TestPredictor: | |
"""Iterate a KNearest predictor and return its success rate.""" | |
def __init__(self, predictor, times=1000): | |
#set the KNearest predictor | |
self.predictor = predictor | |
#set number of iterations | |
self.times = times | |
def test(self, k=1): |
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