sc = SparkContext(master)

data = sc.textFile(dataPath).map(lambda line: line.replace(character, ','))

if label == 0:

label_sum = data.map(lambda line: line.split(',')).map(lambda data: (float(data[0]), 1)).reduceByKey(add).collect()

label = data.map(lambda line: line.split(',')).map(lambda data: float(data[0])).collect()

train_data = data.map(lambda line: line.split(',')).map(lambda x: map(lambda part: float(part), x[1:len(x)]))

else:

label_sum = data.map(lambda line: line.split(',')).map(lambda data: (float(data[-1]), 1)).reduceByKey(add).collect()

label = data.map(lambda line: line.split(',')).map(lambda data: float(data[-1])).collect()

train_data = data.map(lambda line: line.split(',')).map(lambda x: map(lambda part: float(part) if part is not None else '', x[:len(x) - 1]))

model = km.train(train_data, k)

predict_data = train_data.collect()

train = len(predict_data)

acc = 0

for i in range(len(label_sum)):

ksum = np.zeros(k, dtype = int)

cur_label = label_sum[i][0]

for j in range(train):

if label[j] == cur_label:

ksum[model.predict(predict_data[j])] += 1

acc += max(ksum)

string = "KMeans Result: \n"

center = model.centers

for i in range(k):

cur = str(i) + ":" + str(center[i]) + '\n'

string += cur

string = string + "Acc: " + str((float(acc)/train) * 100) + "%"

sc.stop()

return string

#print "test:",train_data.collect()

#print model.centers