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@drewda
Created October 19, 2011 18:23
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Jenks natural breaks classification
# code from http://danieljlewis.org/files/2010/06/Jenks.pdf
# described at http://danieljlewis.org/2010/06/07/jenks-natural-breaks-algorithm-in-python/
def getJenksBreaks( dataList, numClass ):
dataList.sort()
mat1 = []
for i in range(0,len(dataList)+1):
temp = []
for j in range(0,numClass+1):
temp.append(0)
mat1.append(temp)
mat2 = []
for i in range(0,len(dataList)+1):
temp = []
for j in range(0,numClass+1):
temp.append(0)
mat2.append(temp)
for i in range(1,numClass+1):
mat1[1][i] = 1
mat2[1][i] = 0
for j in range(2,len(dataList)+1):
mat2[j][i] = float('inf')
v = 0.0
for l in range(2,len(dataList)+1):
s1 = 0.0
s2 = 0.0
w = 0.0
for m in range(1,l+1):
i3 = l - m + 1
val = float(dataList[i3-1])
s2 += val * val
s1 += val
w += 1
v = s2 - (s1 * s1) / w
i4 = i3 - 1
if i4 != 0:
for j in range(2,numClass+1):
if mat2[l][j] >= (v + mat2[i4][j - 1]):
mat1[l][j] = i3
mat2[l][j] = v + mat2[i4][j - 1]
mat1[l][1] = 1
mat2[l][1] = v
k = len(dataList)
kclass = []
for i in range(0,numClass+1):
kclass.append(0)
kclass[numClass] = float(dataList[len(dataList) - 1])
countNum = numClass
while countNum >= 2:#print "rank = " + str(mat1[k][countNum])
id = int((mat1[k][countNum]) - 2)
#print "val = " + str(dataList[id])
kclass[countNum - 1] = dataList[id]
k = int((mat1[k][countNum] - 1))
countNum -= 1
return kclass
def getGVF( dataList, numClass ):
"""
The Goodness of Variance Fit (GVF) is found by taking the
difference between the squared deviations
from the array mean (SDAM) and the squared deviations from the
class means (SDCM), and dividing by the SDAM
"""
breaks = getJenksBreaks(dataList, numClass)
dataList.sort()
listMean = sum(dataList)/len(dataList)
print listMean
SDAM = 0.0
for i in range(0,len(dataList)):
sqDev = (dataList[i] - listMean)**2
SDAM += sqDev
SDCM = 0.0
for i in range(0,numClass):
if breaks[i] == 0:
classStart = 0
else:
classStart = dataList.index(breaks[i])
classStart += 1
classEnd = dataList.index(breaks[i+1])
classList = dataList[classStart:classEnd+1]
classMean = sum(classList)/len(classList)
print classMean
preSDCM = 0.0
for j in range(0,len(classList)):
sqDev2 = (classList[j] - classMean)**2
preSDCM += sqDev2
SDCM += preSDCM
return (SDAM - SDCM)/SDAM
# written by Drew
# used after running getJenksBreaks()
def classify(value, breaks):
for i in range(1, len(breaks)):
if value < breaks[i]:
return i
return len(breaks) - 1
@Tofunmi1
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How do I use this on Google cloud

@cvargas-xbrein
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How do I use this on Google cloud

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