def row_errorsq(row, cluster_means):
"""
Calculate the feature distance between each node and its cluster mean,
to be used in an .apply to pandas dataframe
Args:
row : row of a pandas dataframe containing columns of features and
one of assigned cluster number designated 'cnum'
cluster_means : pandas dataframe of the mean of each feature in 'row'
indexed by cluster number.
Returns:
feature distance, float
"""
rowf = row.drop(['cnum'])
return (fdist(rowf, cluster_means.ix[int(row.cnum)]))
def row_errorsq(row, cluster_means):
rowf = row.drop(['cnum'])
return (fdist(rowf, cluster_means.ix[int(row.cnum)]))**2
