#Code taken from https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html
from time import time
import numpy as np
import matplotlib.pyplot as plt

from sklearn import metrics
from sklearn.cluster import KMeans
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import scale

np.random.seed(42)

X_digits, y_digits = load_digits(return_X_y=True)
data = scale(X_digits)

n_samples, n_features = data.shape
n_digits = len(np.unique(y_digits))
labels = y_digits

sample_size = 300

print("n_digits: %d, \t n_samples %d, \t n_features %d"
      % (n_digits, n_samples, n_features))


print(82 * '_')
print('init\t\ttime\tinertia\thomo\tcompl\tv-meas\tARI\tAMI\tsilhouette')


def bench_k_means(estimator, name, data):
    t0 = time()
    estimator.fit(data)
    print('%-9s\t%.2fs\t%i\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f\t%.3f'
          % (name, (time() - t0), estimator.inertia_,
             metrics.homogeneity_score(labels, estimator.labels_),
             metrics.completeness_score(labels, estimator.labels_),
             metrics.v_measure_score(labels, estimator.labels_),
             metrics.adjusted_rand_score(labels, estimator.labels_),
             metrics.adjusted_mutual_info_score(labels,  estimator.labels_),
             metrics.silhouette_score(data, estimator.labels_,
                                      metric='euclidean',
                                      sample_size=sample_size)))

bench_k_means(KMeans(init='k-means++', n_clusters=n_digits, n_init=10),
              name="k-means++", data=data)

bench_k_means(KMeans(init='random', n_clusters=n_digits, n_init=10),
              name="random", data=data)

# in this case the seeding of the centers is deterministic, hence we run the
# kmeans algorithm only once with n_init=1
pca = PCA(n_components=n_digits).fit(data)
bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),
              name="PCA-based",
              data=data)
print(82 * '_')

# #############################################################################
# Visualize the results on PCA-reduced data

reduced_data = PCA(n_components=2).fit_transform(data)
kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
kmeans.fit(reduced_data)

# Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02     # point in the mesh [x_min, x_max]x[y_min, y_max].

# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))

# Obtaihttp://localhost:8888/notebooks/Harpo/TeachingNotes/MachineLearning/RandomCode/Lecture_6_cluster.ipynb#n labels for each point in mesh. Use last trained model.
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1)
plt.clf()
plt.imshow(Z, interpolation='nearest',
           extent=(xx.min(), xx.max(), yy.min(), yy.max()),
           cmap=plt.cm.Paired,
           aspect='auto', origin='lower')

plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)
# Plot the centroids as a white X
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:, 0], centroids[:, 1],
            marker='x', s=169, linewidths=3,
            color='w', zorder=10)
plt.title('K-means clustering on the digits dataset (PCA-reduced data)\n'
          'Centroids are marked with white cross')
plt.xlim(x_min, x_max)
plt.ylim(y_min, y_max)
plt.xticks(())
plt.yticks(())
plt.savefig('fig_cluster.pdf',format='pdf')
plt.show()

n_digits: 10, 	 n_samples 1797, 	 n_features 64
__________________________________________________________________________________
init		time	inertia	homo	compl	v-meas	ARI	AMI	silhouette
k-means++	0.39s	69510	0.610	0.657	0.633	0.481	0.629	0.129
random   	0.19s	69907	0.633	0.674	0.653	0.518	0.649	0.131
PCA-based	0.05s	70768	0.668	0.695	0.681	0.558	0.678	0.142
__________________________________________________________________________________

import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.datasets import make_moons
from pandas import DataFrame
import numpy as np
#make_blobs, generate random gaussian cluster
n_samples = 1000
centres = [(0,0),(6,6)]
cluster_std = [1.0,1.0]
#X , y = make_blobs(n_samples=n_samples, centers=centres,n_features=2,cluster_std=cluster_std)
X , y = make_moons(n_samples=n_samples,noise=0.1)
# X is a list of 2 dimensional points, y is the classification for each point.
# scatter plotting requires x,y coords in seperate arrays
#Use panda data frames to put things in a nice table.
df = DataFrame(dict(x=X[:,0],y=X[:,1],label=y))
colors = {0:'red',1:'blue'}
fig, ax = plt.subplots()
#ax.set(ylim=(0.0,8.0))
#ax.set(xlim=(0.0,8.0))
#groupby seperaete out the two classes.
grouped = df.groupby('label')
for key,group in grouped:
    group.plot(ax=ax,kind='scatter',x='x',y='y',label=key,color=colors[key])
plt.savefig('fig_moons_ds.pdf',format='pdf')
plt.show()

from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
colors = ('red', 'blue', 'lightgreen', 'gray', 'cyan')
cmap = ListedColormap(colors[:len(np.unique(y))])
n_samples = 500
centres = [(0,0),(3,3),(6,6)]
cluster_std = [1.0,1.0,2.0]
X , y = make_blobs(n_samples=n_samples, centers=centres,n_features=3,cluster_std=cluster_std)
plt.scatter(X[:,0],X[:,1],cmap=cmap,c=y)
plt.savefig('fig_what_is_k.pdf',format='pdf')
plt.show()