def run():
# fname = "data/cho.txt"
options = get_options()
fname = options.input
no_of_clusters = options.no_of_clusters
expr_value, cluster_no, map_with_true_values, map_of_gene_id = get_points(fname)
# print expr_value
# print len(cluster_no)
# no_of_clusters = 5 #
gene_id_array = range(1, no_of_clusters + 1)
old_centroids = []
for i in range(no_of_clusters):
old_centroids.append(expr_value.get(gene_id_array[i]))
# print old_centroids
# print len(old_centroids)
count = 0
# print len(old_centroids), len(expr_value)
while True:
count += 1
clusters = {}
cluster_and_gene_id = {}
# print sys.float_info.max
for cur_gene in range(1, len(expr_value) + 1): # since gene starts with 1
min_dist = sys.float_info.max
nearest_centroid = 0
for cur_centroid in range(len(old_centroids)):
# print len(old_centroids[cur_centroid])
# print expr_value
dist = get_euclidean_distance(old_centroids[cur_centroid], expr_value[cur_gene])
if dist < min_dist:
min_dist = dist
nearest_centroid = cur_centroid
if nearest_centroid in cluster_and_gene_id:
t = cluster_and_gene_id[nearest_centroid]
t.append(cur_gene)
else:
t = []
t.append(cur_gene)
cluster_and_gene_id[nearest_centroid] = t
if nearest_centroid in clusters:
t = clusters[nearest_centroid]
t.append(expr_value[cur_gene])
else:
t = []
t.append(expr_value[cur_gene])
clusters[nearest_centroid] = t
# print clusters
# print cluster_and_gene_id
new_centroids = get_new_centroids(clusters)
# print old_centroids
# print new_centroids
got_result = True
for i in range(len(new_centroids)):
if new_centroids[i] != old_centroids[i]:
got_result = False
break
if not got_result:
old_centroids = new_centroids
count += 1
else:
print "How many Iterations is it taking to compute the cluster:", count
for id in cluster_and_gene_id:
print id + 1, "th cluster with " , str(len(cluster_and_gene_id[id])), " Points"
# print cluster_and_gene_id
break
print "clusters are done"
output = []
for i in cluster_and_gene_id:
output.append(cluster_and_gene_id[i])
# print "cluster and result of genes"
# print cluster_and_gene_id
result_of_genes = {}
for entry in cluster_and_gene_id:
for i in cluster_and_gene_id[entry]:
result_of_genes[i] = entry + 1
f = open('result.txt','w')
str_file = ""
for i in map_of_gene_id:
if map_of_gene_id[i] == result_of_genes[i]:
str_file += str(result_of_genes[i]) + "\n"
else:
str_file += "-1\n"
f.write(str_file) # python will convert \n to os.linesep
f.close()
# Validation
# ====================================
our_truth = [[0 for row in range(len(expr_value) + 1)] for col in range(len(expr_value) + 1)]
for a in output:
for i in range(len(a)):
for j in range(len(a)):
# print i, j, a
our_truth[a[i]][a[j]] = 1
# print "our truth"
# print our_truth
ground_truth = [[0 for row in range(len(expr_value) + 1)] for col in range(len(expr_value) + 1)]
for entry in map_with_true_values:
temp = map_with_true_values[entry]
for i in range(len(temp)):
for j in range(len(temp)):
ground_truth[temp[i]][temp[j]] = 1
# print ground_truth
# construct the distance matrix
distance_matrix = [[0.0 for row in range(len(expr_value) + 1)] for col in range(len(expr_value) + 1)]
for i in range(1, len(expr_value) + 1):
for j in range(1, len(expr_value) + 1):
if i != j:
list1 = expr_value[i]
list2 = expr_value[j]
distance_matrix[i][j] = v.find_distance(list1, list2)
else:
distance_matrix[i][j] = 0.0
jac = v.get_jaccard(ground_truth, our_truth)
cor = v.get_corrlation(distance_matrix, our_truth)
ran = v.get_rand(ground_truth, our_truth)
print "Jaccard Coefficient is ", jac
print "Correlation is ", cor
print "Rand Index is ", ran
# matplotlib.mlab.PCA("result.txt")
# np.loadtxt("result.txt")
X = np.loadtxt("data/cho.txt")[:,2:]
res = a = np.loadtxt('result.txt')
mlab_pca = mlab.PCA(X)
plt.scatter(mlab_pca.Y[:,0],mlab_pca.Y[:,1], c = res)
plt.show()
