"aifh")
sys.path.append(aifh_dir)
from normalize import Normalize
k = 3
# find the Iris data set
irisFile = os.path.dirname(os.path.realpath(__file__))
irisFile = os.path.abspath(irisFile + "../../datasets/iris.csv")
# Read the Iris data set.
print('Reading CSV file: ' + irisFile)
norm = Normalize()
iris_data = norm.load_csv(irisFile)
# Prepare the iris data set.
classes = norm.col_extract(iris_data, 4)
norm.col_delete(iris_data, 4)
for i in range(0, 4):
norm.make_col_numeric(iris_data, i)
# Cluster the Iris data set.
res, idx = kmeans2(np.array(iris_data), k)
for cluster_num in range(0, k):
print("Cluster #" + str(cluster_num + 1))
for i in range(0, len(idx)):
if idx[i] == cluster_num:
print(str(iris_data[i]) + "," + classes[i])
aifh_dir = os.path.abspath(aifh_dir + os.sep + ".." + os.sep + "lib" + os.sep + "aifh")
sys.path.append(aifh_dir)
from normalize import Normalize
k = 3
# find the Iris data set
irisFile = os.path.dirname(os.path.realpath(__file__))
irisFile = os.path.abspath(irisFile + "../../datasets/iris.csv")
# Read the Iris data set.
print('Reading CSV file: ' + irisFile)
norm = Normalize()
iris_data = norm.load_csv(irisFile)
# Prepare the iris data set.
classes = norm.col_extract(iris_data, 4)
norm.col_delete(iris_data, 4)
for i in range(0, 4):
norm.make_col_numeric(iris_data, i)
# Cluster the Iris data set.
res, idx = kmeans2(np.array(iris_data), k)
for cluster_num in range(0, k):
print( "Cluster #" + str(cluster_num + 1))
for i in range(0, len(idx)):
if idx[i] == cluster_num:
print( str(iris_data[i]) + "," + classes[i])
