def main(dataset_fn, output_fn, clusters_no): geo_locs = [] # read location data from csv file and store each location as a Point(latit,longit) object df = pd.read_csv(dataset_fn) for index, row in df.iterrows(): loc_ = Point(float(row['LAT']), float(row['LON'])) #tuples for location geo_locs.append(loc_) # run k_means clustering model = KMeans(geo_locs, clusters_no) flag = model.fit(True) if flag == -1: print("No of points are less than cluster number!") else: # save clustering results is a list of lists where each list represents one cluster model.save(output_fn)
def main(dataset_fn, output_fn, clusters_no, w): geo_locs = [] # read location data from csv file and store each location as a Point(latit,longit) object df = pd.read_csv(dataset_fn) for index, row in df.iterrows(): loc_ = Node( [float(row['X']), float(row['Y']), float(row['PreChange'])], row['ID']) geo_locs.append(loc_) # run k_means clustering w = np.array(w) model = KMeans(geo_locs, clusters_no, w) flag = model.fit(True) if flag == -1: print("No of points are less than cluster number!") else: # save clustering results is a list of lists where each list represents one cluster model.save(output_fn) model.showresult(True)