#bsub < job.bsub
import numpy as np
from sklearn.cluster import AffinityPropagation
from sklearn import cluster
import os
import threading
from job_thread_executioner import ThreadExecutioner
from job_basic import createFolders
from job_basic import getParameters
#this is for windows:
#os.chdir("C:/MiCluster.Test/")
methodName = "affinity_propagation"
dataset, thread_limit, rounds = getParameters()
executioner = ThreadExecutioner(thread_limit)
createFolders(methodName, dataset)
#for multi-threading
def worker(X, damping):
method = AffinityPropagation(damping=damping)
method.fit(X)
key = methodName + "/length_" + length + "/" + deg + "/individuals/affinity_propagation_damping_" + str(
damping)
np.savetxt(key + "_labels.csv", method.labels_, fmt="%d")
X = np.loadtxt("cs_datasets/" + dataset + ".csv", delimiter=",")
for damping in [0.5, 0.6, 0.7, 0.8, 0.9]:
#thread_limit = 10
#length_reactivity = "_21"
#deg = "wt"
#k = 3
#init = 'random'
thread_limit = 10
rounds = 100
if (len(sys.argv) > 1):
thread_limit = int(sys.argv[1].strip())
if (len(sys.argv) > 2):
rounds = int(sys.argv[2].strip())
executioner = ThreadExecutioner(thread_limit)
methodName = "kmeans"
#this is for multithreading
def worker(length_reactivity, deg, X, k, init, round):
"""multithreading worker"""
method = KMeans(k, init=init)
method.fit(X)
key = methodName + "/length" + length_reactivity + "/" + deg + "/individuals/kmeans_k_" + str(
k) + "_init_" + init + "_round_" + str(round)
np.savetxt(key + "_labels.csv", method.labels_, fmt="%d")
#create folders
import os
import numpy as np
from scipy.cluster import hierarchy
from job_thread_executioner import ThreadExecutioner
from job_basic import createFolders
from job_basic import getParameters
os.chdir("C:\\Icas.Test\\")
methodName = "hierarchical"
dataset, thread_limit, rounds = getParameters()
executioner = ThreadExecutioner(5)
createFolders(methodName, dataset)
#thread_limit = 10
#rounds = 100
#dataset = "cs_rna_distance_triangle_wt_71"
upper_triangle = np.loadtxt("cs_datasets/" + dataset + ".csv", delimiter=",")
hierarchy_result = hierarchy.linkage(upper_triangle)
for k in range(3, 15):
print "k=" + str(k)
cutree = hierarchy.cut_tree(hierarchy_result, n_clusters=[k])
alist = []
for i in range(0, len(cutree)):