def set_up_class(self, debug_1=False, debug_2=False):
self.ASC = alt_spectral_clust(self.data)
self.ASC.set_values('ISM_exit_count', self.ISM_exit_count)
self.ASC.set_values('run_debug_1', debug_1)
self.ASC.set_values('run_debug_2', debug_2)
self.ASC.set_values('Experiment_name', self.experiment_name)
self.ASC.set_values('run_hash', str(uuid.uuid4()))
self.ASC.set_values('q', self.q)
for i in range(Img_3d_array.shape[0]):
for j in range(Img_3d_array.shape[1]):
data_dic[str(Img_3d_array[i, j])] = Img_3d_array[i, j]
for i, j in data_dic.items():
before_preprocess_data = np.vstack((before_preprocess_data, j))
data = np.vstack((data, j))
data = preprocessing.scale(data)
d_matrix = sklearn.metrics.pairwise.pairwise_distances(data,
Y=None,
metric='euclidean')
sigma = 0.1 * np.median(d_matrix)
ASC = alt_spectral_clust(data)
db = ASC.db
if True: # Calculating the original clustering
ASC.set_values('q', 2)
ASC.set_values('C_num', 2)
ASC.set_values('sigma', sigma)
ASC.set_values('kernel_type', 'Gaussian Kernel')
ASC.run()
a = db['allocation']
for m in range(len(a)):
data_alloc[str(before_preprocess_data[m, :])] = a[m]
if True: # Plot the clustered image
out_img = np.zeros(Img_3d_array.shape[0:2])
#!/usr/bin/python
import sys
sys.path.append('./lib')
from alt_spectral_clust import *
import numpy as np
#from io import StringIO # StringIO behaves like a file object
from numpy import genfromtxt
import numpy.matlib
import pickle
#Initialize
X = genfromtxt('data_sets/data_1.csv', delimiter=',')
ASC = alt_spectral_clust(X)
ASC.run()
db = ASC.db
print 'U matrix\n'
print db['U_matrix'], '\n'
print 'allocation\n'
print db['allocation'], '\n'
print 'Y_matrix\n'
print db['Y_matrix'], '\n'
print 'W_matrix\n'
print db['W_matrix'], '\n'
import sys
sys.path.append("./lib")
from alt_spectral_clust import *
import numpy as np
# from io import StringIO # StringIO behaves like a file object
from numpy import genfromtxt
import numpy.matlib
import pickle
# np.set_printoptions(suppress=True)
data = genfromtxt("data_sets/data_3.csv", delimiter=",")
ASC = alt_spectral_clust(data)
omg = objective_magnitude
db = ASC.db
ASC.set_values("q", 2)
ASC.set_values("C_num", 2)
ASC.set_values("kernel_type", "Polynomial Kernel")
ASC.run()
print db["Y_matrix"]
ASC.run()
print db["Y_matrix"]
# import pdb; pdb.set_trace()
def set_up_class(self, debug_1=True, debug_2=True):
self.ASC = alt_spectral_clust(self.data)
self.ASC.set_values('run_debug_1', debug_1)
self.ASC.set_values('run_debug_2', debug_2)
self.ASC.set_values('Experiment_name', 'data_4')
