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coleparkinsoncoleparkinson
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KMeans Homework
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‎submissions/Parkinson/kMeansBonus.txt‎

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+75 "C" level submission
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I chose the wine data set from UC Irvine's Machine Learning Repository. The data set is the result of a chemical
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analysis of wines grown in the same region. The numbers represent 13 different properties of the wine like abv, hue,
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proline etc..
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URL: http://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data

‎submissions/Parkinson/myKMeans.py‎

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wineData = [
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[ 1, 14.23, 1.71, 2.43, 15.6, 127., 2.8, 3.06, .28, 2.29, 5.64, 1.04, 3.92, 1065.],
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[ 1, 13.2, 1.78, 2.14, 11.2, 100., 2.65, 2.76, .26, 1.28, 4.38, 1.05, 3.4, 1050.],
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[ 1, 13.16, 2.36, 2.67, 18.6, 101., 2.8, 3.24, .3, 2.81, 5.68, 1.03, 3.17, 1185. ],
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[ 1, 14.37, 1.95, 2.5, 16.8, 113., 3.85, 3.49, .24, 2.18, 7.8, .86, 3.45, 1480. ],
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[ 1, 13.24, 2.59, 2.87, 21., 118., 2.8, 2.69, .39, 1.82, 4.32, 1.04, 2.93, 735.],
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[ 1, 14.2, 1.76, 2.45, 15.2, 112., 3.27, 3.39, .34, 1.97, 6.75, 1.05, 2.85, 1450. ],
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[ 1, 14.39, 1.87, 2.45, 14.6, 96., 2.5, 2.52, .3, 1.98, 5.25, 1.02, 3.58, 1290. ],
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[ 1, 14.06, 2.15, 2.61, 17.6, 121., 2.6, 2.51, .31, 1.25, 5.05, 1.06, 3.58, 1295. ],
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[ 1, 14.83, 1.64, 2.17, 14., 97., 2.8, 2.98, .29, 1.98, 5.2, 1.08, 2.85, 1045. ],
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[ 1, 13.86, 1.35, 2.27, 16., 98., 2.98, 3.15, .22, 1.85, 7.22, 1.01, 3.55, 1045. ],
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[ 1, 14.1, 2.16, 2.3, 18., 105., 2.95, 3.32, .22, 2.38, 5.75, 1.25, 3.17, 1510. ],
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[ 1, 14.12, 1.48, 2.32, 16.8, 95., 2.2, 2.43, .26, 1.57, 5., 1.17, 2.82, 1280. ],
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[ 1, 13.75, 1.73, 2.41, 16., 89., 2.6, 2.76, .29, 1.81, 5.6, 1.15, 2.9, 1320. ],
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[ 1, 14.75, 1.73, 2.39, 11.4, 91., 3.1, 3.69, .43, 2.81, 5.4, 1.25, 2.73, 1150. ],
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[ 1, 14.38, 1.87, 2.38, 12., 102., 3.3, 3.64, .29, 2.96, 7.5, 1.2, 3., 1547. ],
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[ 1, 13.63, 1.81, 2.7, 17.2, 112., 2.85, 2.91, .3, 1.46, 7.3, 1.28, 2.88, 1310. ],
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[ 1, 14.3, 1.92, 2.72, 20., 120., 2.8, 3.14, .33, 1.97, 6.2, 1.07, 2.65, 1280. ],
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[ 1,13.83,1.57,2.62,20.,115.,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130. ],
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[ 1,14.19,1.59,2.48,16.5,108.,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680. ],
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[ 1,13.64,3.1,2.56,15.2,116.,2.7,3.03,.17,1.66,5.1,.96,3.36,845. ],
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[ 1,14.06,1.63,2.28,16.,126.,3,3.17,.24,2.1,5.65,1.09,3.71,780. ],
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[ 1,12.93,3.8,2.65,18.6,102.,2.41,2.41,.25,1.98,4.5,1.03,3.52,770. ],
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[ 1,13.71,1.86,2.36,16.6,101.,2.61,2.88,.27,1.69,3.8,1.11,4,1035. ],
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[ 1,12.85,1.6,2.52,17.8,95.,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015. ],
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[ 1,13.5,1.81,2.61,20.,96.,2.53,2.61,.28,1.66,3.52,1.12,3.82,845. ],
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[ 1,13.05,2.05,3.22,25.,124.,2.63,2.68,.47,1.92,3.58,1.13,3.2,830. ],
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[ 1,13.39,1.77,2.62,16.1,93.,2.85,2.94,.34,1.45,4.8,.92,3.22,1195. ],
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[ 1,13.3,1.72,2.14,17.,94.,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285. ],
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[ 1,13.73,1.5,2.7,22.5,101.,3.,3.25,.29,2.38,5.7,1.19,2.71,1285. ],
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[ 1,13.58,1.66,2.36,19.1,106.,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515. ],
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[ 1,13.51,1.8,2.65,19.,110.,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095. ],
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[ 1,13.48,1.81,2.41,20.5,100.,2.7,2.98,.26,1.86,5.1,1.04,3.47,920. ],
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[ 1,13.28,1.64,2.84,15.5,110.,2.6,2.68,.34,1.36,4.6,1.09,2.78,880. ],
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[ 1,13.05,1.65,2.55,18,98.,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105. ],
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[ 1,14.22,3.99,2.51,13.2,128.,3,3.04,.2,2.08,5.1,.89,3.53,760. ],
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[ 1,13.56,1.71,2.31,16.2,117.,3.15,3.29,.34,2.34,6.13,.95,3.38,795. ],
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[ 3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415. ],
149+
[ 3,12.87,4.61,2.48,21.5,86.,1.7,.65,.47,.86,7.65,.54,1.86,625. ],
150+
[ 3,13.32,3.24,2.38,21.5,92.,1.93,.76,.45,1.25,8.42,.55,1.62,650. ],
151+
[ 3,13.08,3.9,2.36,21.5,113.,1.41,1.39,.34,1.14,9.40,.57,1.33,550. ],
152+
[ 3,13.5,3.12,2.62,24.,123.,1.4,1.57,.22,1.25,8.60,.59,1.3,500.],
153+
[ 3,12.79,2.67,2.48,22.,112.,1.48,1.36,.24,1.26,10.8,.48,1.47,480.],
154+
[3,13.11,1.9,2.75,25.5,116.,2.2,1.28,.26,1.56,7.1,.61,1.33,425.],
155+
[3,13.23,3.3,2.28,18.5,98.,1.8,.83,.61,1.87,10.52,.56,1.51,675.],
156+
[3,12.58,1.29,2.1,20.,103.,1.48,.58,.53,1.4,7.6,.58,1.55,640.],
157+
[3,13.17,5.19,2.32,22.,93.,1.74,.63,.61,1.55,7.9,.6,1.48,725.],
158+
[3,13.84,4.12,2.38,19.5,89.,1.8,.83,.48,1.56,9.01,.57,1.64,480.],
159+
[3,12.45,3.03,2.64,27.,97.,1.9,.58,.63,1.14,7.5,.67,1.73,880.],
160+
[3,14.34,1.68,2.7,25.,98.,2.8,1.31,.53,2.7,13,.57,1.96,660.],
161+
[3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620.],
162+
[3,12.36,3.83,2.38,21.,88.,2.3,.92,.5,1.04,7.65,.56,1.58,520.],
163+
[3,13.69,3.26,2.54,20.,107.,1.83,.56,.5,.8,5.88,.96,1.82,680.],
164+
[3,12.85,3.27,2.58,22.,106.,1.65,.6,.6,.96,5.58,.87,2.11,570.],
165+
[3,12.96,3.45,2.35,18.5,106.,1.39,.7,.4,.94,5.28,.68,1.75,675.],
166+
[3,13.78,2.76,2.3,22.,90.,1.35,.68,.41,1.03,9.58,.7,1.68,615.],
167+
[3,13.73,4.36,2.26,22.5,88.,1.28,.47,.52,1.15,6.62,.78,1.75,520.],
168+
[3,13.45,3.7,2.6,23.,111.,1.7,.92,.43,1.46,10.68,.85,1.56,695.],
169+
[3,12.82,3.37,2.3,19.5,88.,1.48,.66,.4,.97,10.26,.72,1.75,685.],
170+
[3,13.58,2.58,2.69,24.5,105.,1.55,.84,.39,1.54,8.66,.74,1.8,750.],
171+
[3,13.4,4.6,2.86,25.,112.,1.98,.96,.27,1.11,8.5,.67,1.92,630.],
172+
[3,12.2,3.03,2.32,19.,96.,1.25,.49,.4,.73,5.5,.66,1.83,510.],
173+
[3,12.77,2.39,2.28,19.5,86.,1.39,.51,.48,.64,9.899999,.57,1.63,470.],
174+
[3,14.16,2.51,2.48,20.,91.,1.68,.7,.44,1.24,9.7,.62,1.71,660.],
175+
[3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740.],
176+
[3,13.4,3.91,2.48,23.,102.,1.8,.75,.43,1.41,7.3,.7,1.56,750.],
177+
[3,13.27,4.28,2.26,20.,120.,1.59,.69,.43,1.35,10.2,.59,1.56,835.],
178+
[3,13.17,2.59,2.37,20.,120.,1.65,.68,.53,1.46,9.3,.6,1.62,840.],
179+
[3,14.13,4.1,2.74,24.5,96.,2.05,.76,.56,1.35,9.2,.61,1.6,560.],
180+
]
181+
182+
Examples = {
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'WineClasses': {
184+
'data': wineData,
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'k': [3, 7, 8],
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},
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}
188+

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