After some time I respond to my self,
the solution was maked in Python with Pandas.
This link is very ussefull:
Iterating through groups on: http://pandas.pydata.org/pandas-docs/stable/groupby.html
Also the book "Python for Data Analysis, West McKinney" pag 255
This video show how to make calculation:
ANOVA 2: Calculating SSW and SSB (total sum of squares within and between) | Khan Academy
https://www.youtube.com/watch?v=j9ZPMlVHJVs
[code]```
def getDFrameFixed2D():
y = np.array([3,2,1,5,3,4,5,6,7])
k = np.array([1,1,1,2,2,2,3,3,3])
clusters = pd.DataFrame([[a,b] for a,b in zip(y,k)],columns=['Var1','K2'])
# print (clusters.head()) print("shape(0):",clusters.shape[0])
return clusters
X2D=getDFrameFixed2D()
MainMean = X2D['Var1'].mean(0)
print("Main mean:",MainMean)
grouped = X2D['Var1'].groupby(X2D['K2'])
print("-----Iterating Over Groups-------------")
Wss=0
Bss=0
for name, group in grouped:
#print(type(name))
#print(type(group))
print("Group key:",name)
groupmean = group.mean(0)
groupss = sum((group-groupmean)**2)
print(" groupmean:",groupmean)
print(" groupss:",groupss)
Wss+= groupss
Bss+= ((groupmean - MainMean)**2)*len(group)
print("----------------------------------")
print("Wss:",Wss)
print("Bss:",Bss)
print("T=B+W:",Bss+Wss)
Tss = np.sum((X-X.mean(0))**2)
print("Tss:",Tss)
print("----------------------------------")
//your code here