def calculateLambda(self, data1, data2, num_pois):
distance = []
data_viz = dataViz.DataViz()
for i, poi in data2.iterrows():
distance.append(self.algo.calculateDis(poi, data1))
coordonnee = self.drawPoint(distance, num_pois)
coordonnee.append(data1['product_name'])
return coordonnee
'water-hardness_100g', 'choline_100g', 'phylloquinone_100g',
'beta-glucan_100g', 'inositol_100g', 'carnitine_100g'
]
df = dataCenter.cleanData(df, colslist)
cols = df.columns.values.tolist()
df.info()
clean_brand = cleanBrand.CleanBrand()
dfBrand1 = clean_brand.getBrandsClean()
df['brand1'] = dfBrand1
df['brand1'] = df['brand1'].fillna(value='NoBrand', axis=0)
# 计算两条记录的2gram的余弦值
algo = algoDis.AlgoDis()
data_Viz = dataViz.DataViz()
# 并行！！！！
num_pois = 5
num_divide_df = 100
# 开始选取参考点
timeStartKmeans = time.localtime()
print('choose POIs: ' + str(timeStartKmeans))
list_pois, pois = dataCenter.selectPOIs(df.iloc[1:5000], num_pois)
# list_pois, pois = dataCenter.selectPOIsRandom(num_pois,df)
list_pois_labels = []
for index in list_pois:
list_pois_labels.append(index)
df_copy = df
df_copy = df_copy.drop(list_pois, axis=0)