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class OnlineLearner(object):
    def __init__(self, **kwargs):
        self.last_misses = 0.
        self.iratio = 0.
        self.it = 1.
        self.l = kwargs["l"]
        self.max_ratio = -np.inf
        self.threshold = 500.

    def hinge_loss(self, vector, cls, weight):

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# Requires pstoedit (http://www.pstoedit.net/) and gdal ( http://www.gdal.org/ ) 

# Convert PDF to DXF format, assign each color to a different layer, and only write the layer that corresponds to 
# hex color #B8B9BA
pstoedit -f 'dxf: -ctl -dumplayernames -layers CB8-B9-BA' map_housing_stock_north.pdf parcels.dxf

# Convert DXF to KML 
ogr2ogr -f "KML" parcels.kml parcels.dxf

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def recordDistances(candidates, data_d, data_model):

  # The record array has two elements, the first element is an array
  # of floats that has length equal the number of fields. The second
  # argument is a array of length 2 which stores the id of the
  # considered elements in the pair.

    fields = data_model['fields']

    field_dtype = [('names', 'a20', len(fields)), ('values', 'f4',

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# In local_settings.py

def topic_classifier(title) :
    if 'Damage to vehicle claim' in title :
        return ['Routine', 'Damage to vehicle claim']

    if 'Damage to property claim' in title :
        return ['Routine', 'Damage to property claim']

    if 'Excessive water rate claim' in title :

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# Read in the data
days <- read.csv('all_years.csv')

# Plot all the data
plot(total_count ~ temp_max, days, col=rgb(0,0,0, .3))

# Fit a cubic polynomial to the data
m1 <- lm(total_count ~ temp_max + I(temp_max^2) + I(temp_max^3), days)

# Extract the coefficents

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from numpy import *
import pymc
from scipy import stats
from scipy.stats import distributions as d

#parameters about the da
dimensions = 5
observations = 30
shape = (dimensions, observations)

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from numpy import *
import pymc
from scipy import stats
from scipy.stats import distributions as d

#parameters about the da
dimensions = 5
observations = 30
shape = (dimensions, observations)

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""" 
Author: Oliver Mitevski

References:
A Generalized Linear Model for Principal Component Analysis of Binary Data, 
Andrew I. Schein; Lawrence K. Saul; Lyle H. Ungar

The code was translated and adapted from Jakob Verbeek's 
"Hidden Markov models and mixtures for Binary PCA" implementation in MATLAB

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import three
chi = three.city('chicago')


yesterday_graffiti = chi.requests(service_code='4fd3b167e750846744000005',
                                  extensions='true',
                                  page_size=500,
                                  start='10-25-2012')

print len(yesterday_graffiti)

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