Kmeans_gcmh.py

import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.utils import check_random_state

random_state = np.random.RandomState(0)
simData = pd.read_csv('./summary.additive_select.csv', ',')

CLUSTER=11
CLUSTER = CLUSTER + 1
BIAS = 'GCMH'
OUTPUT = './kmeans_1d_gcmh_full/'


ks = simData['KS']
bbgp = simData['BBGP']
gcmh = simData['GCMH']
name = simData['2L_POS']

feature = pd.concat([gcmh], axis=1)

# K-MEANS Algorithm

init_C = range(2,CLUSTER)
inertias = []


inerPATH = OUTPUT + 'KMEANS_' + BIAS + '_' + str(CLUSTER) + '.csv'
inertiaW= open(inerPATH, 'w')

x = feature[BIAS]
for n in range(2, CLUSTER):
    model = KMeans(n_clusters=n,random_state=random_state, n_jobs=4, algorithm='full', max_iter=300)
    feature['label'] = model.fit_predict(feature)
    inertiaW.write(str(model.inertia_)+'\n')
    result = pd.concat([name, feature], axis=1)
    result.to_csv(OUTPUT + 'Kmeans' + str(n) + '.csv')

    fig = plt.figure()
    sns.set_style('white')
    plt.scatter(x=x, y=feature['label'], c=model.labels_.astype(float))
    plt.savefig(OUTPUT + 'KMEANS_' + str(n) + '.png')
    plt.close()

inertiaW.close()