scatter plot
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig=plt.figure()
ax=fig.add_subplot(111, projection='3d')
x=[0.5,2.2,3.9,2.1,0.5,0.8,2.7,2.5,2.8,0.1]
y=[4.5,1.5,3.5,1.9,3.2,4.3,1.1,3.5,3.9,4.1]
z=[2.5,0.1,1.1,4.9,1.2,2.6,3.1,2.8,1.5,2.9]

import sklearn.preprocessing as p
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import scipy.spatial.distance as d

def initialize_clusters(points, k):
    """Initializes clusters as k randomly selected points from points."""
    return points[np.random.randint(points.shape[0], size=k)]

# Function for calculating the distance between centroids
def get_distances(centroid, points):
    """Returns the distance the centroid is from each data point in points."""
    return d.cityblock(points , centroid)

k = 3
maxiter = 50

X=np.array( [[0.5, 4.5, 2.5],
        [2.2, 1.5, 0.1],
        [3.9, 3.5, 1.1],
        [2.1, 1.9, 4.9],
        [0.5, 3.2, 1.2],
        [0.8, 4.3, 2.6],
        [2.7, 1.1, 3.1],
        [2.5, 3.5, 2.8],
        [2.8, 3.9, 1.5],
        [0.1, 4.1, 2.9]])
# scaler = p.MinMaxScaler()
# X = scaler.fit_transform(X)

# Initialize our centroids by picking random data points
centroids = initialize_clusters(X, k)

# Initialize the vectors in which we will store the
# assigned classes of each data point and the
# calculated distances from each centroid
classes = np.zeros(X.shape[0], dtype=np.float64)
distances = np.zeros([X.shape[0], k], dtype=np.float64)

# Loop for the maximum number of iterations
for i in range(maxiter):
    # Assign all points to the nearest centroid
    for i, c in enumerate(centroids):
        distances[:, i] = get_distances(c, X)
    # Determine class membership of each point
    # by picking the closest centroid
    classes = np.argmin(distances, axis=1)
    # Update centroid location using the newly
    # assigned data point classes
    for c in range(k):
        centroids[c] = np.mean(X[classes == c], 0)
group_colors = ['skyblue', 'coral', 'lightgreen']
colors = [group_colors[j] for j in classes]

fig= plt.figure()
ax=fig.add_subplot(111, projection='3d')
ax.scatter(X[:,0],X[:,1],X[:,2], alpha=0.5)
ax.scatter(centroids[:,0], centroids[:,1], color=['blue', 'darkred', 'green'], marker='o', lw=2)
print(centroids)
plt.show()