def _meanshift(dataset, *bandwidths):
if not bandwidths:
bandwidths = [0.1, 0.3, 0.5, 0.7, 0.9]
instances = []
for bw in bandwidths:
instances.append(MeanShift(estimate_bandwidth(dataset, bw)))
return instances
def _meanshift(dataset, *bandwidths):
if not bandwidths:
bandwidths = [0.1, 0.3, 0.5, 0.7, 0.9]
instances = []
for bw in bandwidths:
instances.append(MeanShift(estimate_bandwidth(dataset, bw)))
return instances
"""-------Select pcar1 as the principal component------- """
x_pc=pcar1
########################################################################
"""-------Implement KMeans------- """
(res1,idx1,plot_id1)=calc_kmeans.getplotid(z,2)
(res2,idx2,plot_id2)=calc_kmeans.getplotid(z,3)
(res3,idx3,plot_id3)=calc_kmeans.getplotid(z,4)
########################################################################
"""------- Implement Mean Shift Clustering Algorithm: ------- """
bw=estimate_bandwidth(z,quantile=0.3)
xms=MeanShift(bandwidth=bw)
xms.fit(z)
labs=xms.labels_
centr=xms.cluster_centers_# Find the Cluster centers
labs_uniq=np.unique(labs) # Number of unique labels
nclusts=len(labs_uniq) # Number of Clusters
print 'Implementing Mean-Shift Clustering'
########################################################################
plotdat.Plotdata(z,z1,1) # --- Plots the given data
plotall=analysis_plt.PlotAll(2) #-- Plots all analysis
plotall.plt_dat(x,y,z,xg,xp,con,rcon,nclusts,labs,centr,idx1,idx2,idx3,res1,res2,res3,plot_id1,plot_id2,plot_id3)
sltn.giv_ans(3,x,y,z,z_h,plot_id2,res2,nclusts,labs,centr)# plots final results
np.random.seed(0)
n_points_per_cluster = 250
n_clusters = 3
n_points = n_points_per_cluster*n_clusters
means = np.array([[1,1],[-1,-1],[1,-1]])
std = .6
clustMed = []
X = np.empty((0, 2))
for i in range(n_clusters):
X = np.r_[X, means[i] + std * np.random.randn(n_points_per_cluster, 2)]
################################################################################
# Compute clustering with MeanShift
bandwidth = estimate_bandwidth(X, quantile=0.3)
ms = MeanShift(bandwidth=bandwidth)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
print "number of estimated clusters : %d" % n_clusters_
################################################################################
# Plot result
import pylab as pl
from itertools import cycle
np.random.seed(0)
n_points_per_cluster = 250
n_clusters = 3
n_points = n_points_per_cluster * n_clusters
means = np.array([[1, 1], [-1, -1], [1, -1]])
std = .6
clustMed = []
X = np.empty((0, 2))
for i in range(n_clusters):
X = np.r_[X, means[i] + std * np.random.randn(n_points_per_cluster, 2)]
################################################################################
# Compute clustering with MeanShift
bandwidth = estimate_bandwidth(X, quantile=0.3)
ms = MeanShift(bandwidth=bandwidth)
ms.fit(X)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
print "number of estimated clusters : %d" % n_clusters_
################################################################################
# Plot result
import pylab as pl
from itertools import cycle
