Clastering Diploma

print(__doc__)


import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import csv

from sklearn.cluster import KMeans
from sklearn import datasets

centers = [[1, 1], [-1, -1], [1, -1]]
iris = datasets.load_iris()
X = iris.data
y = iris.target

arr = []

with open('dgma5.csv') as csvfile:
    readCSV = csv.reader(csvfile, delimiter=',')
    for row in readCSV:
        row = [float(i.replace(",",".")) for i in row]
        arr.append(row)

arr = np.array(arr)
kmeans = KMeans(n_clusters = 3)
fig = plt.figure(figsize=(12, 8))
plt.clf()
ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)
plt.cla()
kmeans.fit(arr)

centroids = kmeans.cluster_centers_
labels = kmeans.labels_
clasters = kmeans.clasters_

print("Centroids:")
print(centroids)

ax.scatter(arr[:, 1], arr[:, 0], arr[:, 2], c=labels.astype(np.float))

for i in range(3):
    ax.scatter(centroids[i,1], centroids[i,0], centroids[i,2], s=200, c="black", marker="D")

ax.set_xlabel('Petal width')
ax.set_ylabel('Sepal length')
ax.set_zlabel('Petal length')
# fignum = fignum + 1

# Plot the ground truth
# fig = plt.figure(fignum, figsize=(10, 6))
# plt.clf()
# ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

# plt.cla()



# for name, label in [('Setosa', 0),
#                     ('Versicolour', 1),
#                     ('Virginica', 2)]:
#     ax.text3D(X[y == label, 3].mean(),
#               X[y == label, 0].mean() + 1.5,
#               X[y == label, 2].mean(), name,
#               horizontalalignment='center',
#               bbox=dict(alpha=.5, edgecolor='w', facecolor='w'))
# Reorder the labels to have colors matching the cluster results
# y = np.choose(y, [1, 2, 0]).astype(np.float)
# print(y)
# ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y)

# ax.set_xlabel('Petal width')
# ax.set_ylabel('Sepal length')
# ax.set_zlabel('Petal length')
plt.show()


#Обдумать разные варианты кластеризации

# Центроида - DONE
# Вывести кластеры в таблицы - CURRENT
# Дополнительные характеристики - RESEARCH
# Условия окончания кластеризации - RESEARCH

# Сколько параметров свернулось в фактор и какие параметры
# Прогнать старые данные