'''
Created on Feb 1, 2017
@author: Alexandre Day
Purpose:
Perform density clustering on gaussian mixture
'''
from fdc import FDC
from sklearn.datasets import make_blobs
from fdc import plotting
import pickle
import numpy as np
n_true_center = 15
np.random.seed(0)
print("------> Example with %i true cluster centers <-------"%n_true_center)
X, y = make_blobs(10000, 2, n_true_center) # Generating random gaussian mixture
model = FDC(noise_threshold=0.05, nh_size=40) # specifying density clustering parameters
model.fit(X) # performing the clustering
plotting.set_nice_font() # nicer plotting font !
plotting.summary_model(model, ytrue=y, show=True, savefile="result.png")
noise_threshold = 1.0
datasets = [noisy_circles, noisy_moons, varied, aniso, blobs, no_structure]
for i_dataset, dataset in enumerate(datasets):
X, y = dataset
# normalize dataset for easier parameter selection
X = StandardScaler().fit_transform(X)
# create clustering estimators
model = FDC(noise_threshold=noise_threshold)
s=time.time()
model.fit(X)
dt=time.time()-s
n_center=len(model.idx_centers)
plt.subplot(3,2,plot_num)
plt.scatter(X[:, 0], X[:, 1], color=colors[model.cluster_label].tolist(), s=10,zorder=1)
plt.text(.99, .07, ('%.2fs' % (dt)).lstrip('0'),
transform=plt.gca().transAxes, size=15,
horizontalalignment='right',zorder=2,
bbox=dict(facecolor='white', edgecolor='black', boxstyle='round,pad=0.1')
)
plot_num+=1