def main():
# set program constants:
K = 10 # max number of clusters
B = 100 # number of null realisations created by gap test
N = 100 # number of points in the input data cloud
# generate clusters:
n1, n2, n3 = 20, 20, 10
n4 = N - n1 - n2 - n3
cluster_vec = [
sgt.genCluster((0.3, 0.7), 0.1, n1),
sgt.genCluster((0.1, 0.3), 0.1, n2),
sgt.genCluster((0.7, 0.5), 0.1, n3),
sgt.genCluster((0.5, 0.1), 0.1, n4)
]
# create data set:
data_mat = np.concatenate(
(cluster_vec[0], cluster_vec[1], cluster_vec[2], cluster_vec[3]),
axis=0)
data_mat = sgt.genPoisson(N) # uniform point cloud
# convert data to network:
dist_mat = snt.getDistanceMatrix(data_mat)
den_obj = sst.DendroTest(K).fit(dist_mat)
# plot the top p levels of the dendrogram
spt.plotDendroTest(data_mat, den_obj, fname='../media/dendro3.png')
def main():
# set program constants:
K = 10 # max number of clusters
B = 100 # number of null realisations created by gap test
N = 100 # number of points in the input data cloud
# generate clusters:
n1, n2 = 30, 20
n3 = N - n1 - n2
cluster_vec = [
sgt.genCluster((0.3, 0.7), 0.1, n1),
sgt.genCluster((0.1, 0.3), 0.1, n2),
sgt.genCluster((0.7, 0.5), 0.1, n3)
]
# create data set:
data_mat = np.concatenate((cluster_vec[0], cluster_vec[1], cluster_vec[2]),
axis=0)
#data_mat = sgt.genPoisson(N) # uniform point cloud
# convert data to network:
dist_mat = snt.getDistanceMatrix(data_mat)
# perform gap test
gap_obj = sst.GapTest(K, B).fit(dist_mat)
spt.plotGapTest(data_mat, gap_obj, fname='../media/gap.png')
def main():
# generate clusters:
data_mat = np.concatenate((sgt.genCluster(
(0.2, 0.5), 0.15, 6), sgt.genCluster(
(0.6, 0.7), 0.15, 5), sgt.genCluster((0.7, 0.2), 0.15, 3)),
axis=0)
# convert data to network:
dist_mat = snt.getDistanceMatrix(data_mat)
#GX = snt.graphFromDistMatrix(DX)
# perform clustering on the network:
#clustering = AgglomerativeClustering(n_clusters=3).fit(X) # with raw data
clustering = AgglomerativeClustering(n_clusters=3,
linkage='average',
affinity='precomputed').fit(
dist_mat) # with distance matrix
# plot it:
#spt.plotData(X, annotate=True)
#spt.plotData(X, labels=clustering.labels_)
#spt.plotData(X, labels=clustering.labels_, fName='../media/clusters.pdf')
#spt.plotGraph(GX)
#spt.plotData(X, fName="../media/pointCloud.png")
#spt.plotClustersGraph(clustering.labels_, fname='../media/clusters.graph.png')
#spt.plotPointsGraph(X)
spt.plotClustersAndGraph(data_mat,
clustering.labels_,
ffolder='../media/',
flabel=sys.argv[1])
def main():
N = 200
K = 10
B = 5
# generate fake data
data_mat = np.concatenate((sgt.genAnnulusCluster(
(0.5, 0.5), 0.4, 0.3, N), sgt.genCluster((0.5, 0.5), 0.1, 100)),
axis=0)
# get distance matrix:
dist_mat = snt.getDistanceMatrix(data_mat)
# cluster it
clustering = AgglomerativeClustering(n_clusters=2,
linkage='single',
affinity='precomputed').fit(dist_mat)
#elb_obj = sst.ElbowTest(K).fit(dist_mat, linkage='single')
jump_obj = sst.JumpTest(K, B).fit(dist_mat, linkageMethod='single')
# plot it:
#spt.plotData(data_mat, labels=clustering.labels_, fname='../media/ring.singleLink1.png')
#spt.plotElbowTest(data_mat, elb_obj)
spt.plotJumpTest(data_mat, jump_obj, fname='../media/jumpRing.png')
def main():
# generate the data:
data = np.concatenate((sgt.genCluster(
(0.2, 0.2), 0.1, 20), sgt.genCluster(
(0.6, 0.7), 0.1, 20), sgt.genCluster((0.8, 0.2), 0.1, 20)),
axis=0)
#data = sgt.genPoisson(60)
#spt.plotData(data)
# compute distance matrix:
dist = snt.getDistanceMatrix(data)
# cluster the data:
clustering = AgglomerativeClustering(linkage='average',
affinity='precomputed',
n_clusters=3).fit(dist)
# for each cluster, print cluster and shape histogram:
#spt.plotData(data, labels=clustering.labels_)
mpt.plotShapeHist(data, dist, labels=clustering.labels_)
def main():
K = 10 # max number of clusers
# fix fake data parameters:
n1, n2, n3 = 30, 20, 15 # number of points in each cluster
R1, R2, R3 = 0.3, 0.1, 0.1 # max radius of each cluster
# generate clusters:
X1 = sgt.genCluster((0.3, 0.7), R1, n1)
X2 = sgt.genCluster((0.1, 0.3), R2, n2)
X3 = sgt.genCluster((0.7, 0.5), R3, n3)
# create data set:
n = n1 + n2 + n3 # number of points in fake data set
X = np.concatenate((X1, X2, X3), axis=0)
#X = sgt.genPoisson(n) # uniform point cloud
# convert data to network:
dist_mat = snt.getDistanceMatrix(X)
# perform elbow test:
elbow_obj = sst.ElbowTest(K).fit(dist_mat)
spt.plotElbowTest(X, elbow_obj, fname='../media/elbow.png')