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

from sklearn.cluster import KMeans
from sklearn import datasets

from kHLL.kestimate import HyperKEstimator
from kHLL.hash.image import md5_for_vec

np.random.seed(5)

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

kestimator = HyperKEstimator(1, 5, md5_for_vec, 20)
kestimator.train(X)

estimators = {'k_means_iris_3': KMeans(n_clusters=3),
              'k_means_iris_hyper': KMeans(n_clusters=kestimator.getK())}


fignum = 1
for name, est in estimators.items():
    fig = plt.figure(fignum, figsize=(9, 5))
    plt.clf()
    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

    plt.cla()
    est.fit(X)
    labels = est.labels_

    ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float))

    ax.w_xaxis.set_ticklabels([])
    ax.w_yaxis.set_ticklabels([])
    ax.w_zaxis.set_ticklabels([])
    ax.set_xlabel('Petal width')
    ax.set_ylabel('Sepal length')
    ax.set_zlabel('Petal length')
    fignum = fignum + 1


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

from sklearn.cluster import KMeans
from sklearn import datasets

from kHLL.kestimate import HyperKEstimator
from kHLL.hash.image import md5_for_vec

np.random.seed(5)

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

kestimator = HyperKEstimator(1, 5, md5_for_vec, 20)
kestimator.train(X)

estimators = {'k_means_iris_3': KMeans(n_clusters=3),
              'k_means_iris_hyper': KMeans(n_clusters=kestimator.getK())}


fignum = 1
for name, est in estimators.items():
    fig = plt.figure(fignum, figsize=(9, 5))
    plt.clf()
    ax = Axes3D(fig, rect=[0, 0, .95, 1], elev=48, azim=134)

    plt.cla()
    est.fit(X)
    labels = est.labels_

    ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=labels.astype(np.float))

    ax.w_xaxis.set_ticklabels([])
    ax.w_yaxis.set_ticklabels([])
    ax.w_zaxis.set_ticklabels([])
    ax.set_xlabel('Petal width')
    ax.set_ylabel('Sepal length')
    ax.set_zlabel('Petal length')
    fignum = fignum + 1


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

from sklearn.cluster import KMeans
from sklearn import datasets

np.random.seed(5)

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

# Plot the ground truth
fig = plt.figure(fignum, figsize=(9, 5))
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)
ax.scatter(X[:, 3], X[:, 0], X[:, 2], c=y)

ax.w_xaxis.set_ticklabels([])
ax.w_yaxis.set_ticklabels([])
ax.w_zaxis.set_ticklabels([])
ax.set_xlabel('Petal width')
ax.set_ylabel('Sepal length')
ax.set_zlabel('Petal length')
plt.show()

import time
import numpy as np
import matplotlib.pyplot as plt

from sklearn.cluster import KMeans
from sklearn.mixture import DPGMM
from sklearn import datasets

from kHLL.kestimate import HyperKEstimator
from kHLL.hash.image import md5_for_vec


np.random.seed(5)

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



estimators = {'dpgmm': DPGMM(),
              'k_means_iris_hyper': KMeans(n_clusters=kestimator.getK())}

## DPGMM
try_count = 5
dpgmm_elapsed_times = []
hyper_kmeans_elapsed_times = []
for i in xrange(1, try_count):
    start = time.time()
    for j in xrange(0, i):
        dpgmm_model = DPGMM()
        dpgmm_model.fit(X)
    dpgmm_elapsed_times.append(time.time() - start)
    start = time.time()
    for j in xrange(0, i):
        kestimator = HyperKEstimator(1, 5, md5_for_vec, 20)
        kestimator.train(X)
        kmeans_model= KMeans(n_clusters=3)
        kmeans_model.fit(X)
    hyper_kmeans_elapsed_times.append(time.time() - start)

x = range(1, try_count)


fig = plt.figure()
axes = fig.add_axes([0.1, 0.1, 0.8, 0.8])
axes.plot(x, dpgmm_elapsed_times, 'r')
axes.plot(x, hyper_kmeans_elapsed_times, 'g')
axes.set_xlabel('x')
axes.set_ylabel('y')
axes.set_title('Comparison between DPGMM and HyperKMeans');