__author__ = 'mike-bowles'

import urllib2

import numpy

from sklearn import datasets, linear_model

from math import sqrt

import matplotlib.pyplot as plot

#read data into iterable

target_url = "http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"

data = urllib2.urlopen(target_url)

xList = []

labels = []

names = []

firstLine = True

for line in data:

if firstLine:

names = line.strip().split(";")

firstLine = False

else:

#split on semi-colon

row = line.strip().split(";")

#put labels in separate array

labels.append(float(row[-1]))

#remove label from row

row.pop()

#convert row to floats

floatRow = [float(num) for num in row]

xList.append(floatRow)

#Normalize columns in x and labels

nrows = len(xList)

ncols = len(xList[0])

#calculate means and variances

xMeans = []

xSD = []

for i in range(ncols):

col = [xList[j][i] for j in range(nrows)]

mean = sum(col)/nrows

xMeans.append(mean)

colDiff = [(xList[j][i] - mean) for j in range(nrows)]

sumSq = sum([colDiff[i] * colDiff[i] for i in range(nrows)])

stdDev = sqrt(sumSq/nrows)

xSD.append(stdDev)

#use calculate mean and standard deviation to normalize xList

xNormalized = []

for i in range(nrows):

rowNormalized = [(xList[i][j] - xMeans[j])/xSD[j] for j in range(ncols)]

xNormalized.append(rowNormalized)

#Normalize labels

meanLabel = sum(labels)/nrows

sdLabel = sqrt(sum([(labels[i] - meanLabel) * (labels[i] - meanLabel) for i in range(nrows)])/nrows)

labelNormalized = [(labels[i] - meanLabel)/sdLabel for i in range(nrows)]

#initialize a vector of coefficients beta

beta = [0.0] * ncols

#initialize matrix of betas at each step

betaMat = []

betaMat.append(list(beta))

#number of steps to take

nSteps = 350

stepSize = 0.004

nzList = []

for i in range(nSteps):

#calculate residuals

residuals = [0.0] * nrows

for j in range(nrows):

labelsHat = sum([xNormalized[j][k] * beta[k] for k in range(ncols)])

residuals[j] = labelNormalized[j] - labelsHat

#calculate correlation between attribute columns from normalized wine and residual

corr = [0.0] * ncols

for j in range(ncols):

corr[j] = sum([xNormalized[k][j] * residuals[k] for k in range(nrows)]) / nrows

iStar = 0

corrStar = corr[0]

for j in range(1, (ncols)):

if abs(corrStar) < abs(corr[j]):

iStar = j; corrStar = corr[j]

beta[iStar] += stepSize * corrStar / abs(corrStar)

betaMat.append(list(beta))

nzBeta = [index for index in range(ncols) if beta[index] != 0.0]

for q in nzBeta:

if (q in nzList) == False:

nzList.append(q)

nameList = [names[nzList[i]] for i in range(len(nzList))]

print(nameList)

for i in range(ncols):

#plot range of beta values for each attribute

coefCurve = [betaMat[k][i] for k in range(nSteps)]

xaxis = range(nSteps)

plot.plot(xaxis, coefCurve)

plot.xlabel("Steps Taken")

plot.ylabel(("Coefficient Values"))

plot.show()