def gibbs_sampling(Out, x):
vision_set = []
position_set = []
name_set = []
C_t_set = []
r_t_set = []
G_set = []
pi_set = []
Sigma_set = []
mu_set = []
data_num = []
concept_num = Stick_large_L
posdist_num = Stick_large_R
MAP_X = [0 for e in range(data_set_num)] #MAX x value of map
MAP_Y = [0 for e in range(data_set_num)] #MAX y value of map
map_x = [0 for e in range(data_set_num)] #min x value of map
map_y = [0 for e in range(data_set_num)] #min y value of map
mu_0_set = []
print("")
G_0 = nonpara_tool.stick_breaking(gamma_0, concept_num) #initialize G_0
for e, dir in enumerate(Training_list):
#=============== Get mu_0 ===============
env_para = np.genfromtxt(dir + "/Environment_parameter.txt",
dtype=None,
delimiter=" ") #read environment parameter
print(env_para)
MAP_X[e] = float(env_para[0][1])
MAP_Y[e] = float(env_para[1][1])
map_x[e] = float(env_para[2][1])
map_y[e] = float(env_para[3][1])
DATA_initial_index = int(env_para[5][1])
DATA_last_index = int(env_para[6][1])
DATA_NUM = DATA_last_index - DATA_initial_index + 1 #calcurate data number
map_center_x = ((MAP_X[e] - map_x[e]) / 2) + map_x[e]
map_center_y = ((MAP_Y[e] - map_x[e]) / 2) + map_y[e]
mu_0 = np.array([map_center_x, map_center_y, 0,
0]) #get mu_0 in every environment
mu_0_set.append(mu_0)
#=============== Read data ===============
if e < len(
Training_list) - 1: #directory where training name data exists
name_data_dir = "/name/per_100/"
test_data_list = dir + "/test_num.txt"
test_data_num = test_data_read(test_data_list, test_num)
print("Training environment")
else: #directory where test name data exists
name_data_dir = "/name/per_" + repr(x) + "/"
test_data_list = dir + "/test_num.txt"
test_data_num = test_data_read(test_data_list, test_num)
print("Test environment")
position_dir = dir + position_data_dir
vision_dir = dir + vision_data_dir #if args.vision == None:
name_dir = dir + name_data_dir
position = position_data_read(position_dir, DATA_NUM,
test_data_num) #read positon data
position_set.append(position)
vision = vision_data_read(vision_dir, vision_increment, DATA_NUM,
test_data_num) #read vision data
vision_set.append(vision)
name = Name_data_read(name_dir, name_increment, DATA_NUM,
test_data_num) #read name data
name_set.append(name)
#=============== Print infomation of read data ================
print("Read environment directory: " + dir)
print("Data number: " + repr(DATA_NUM))
print("Test data number list")
print(test_data_num)
print("Name vector sum")
print(sum(name / name_increment))
print("")
#if test environment, next processing is done
if len(test_data_num) > 1:
DATA_NUM = DATA_NUM - len(test_data_num)
Learnig_data_num = (DATA_last_index - DATA_initial_index +
1) / Slide #learning data number
data_num.append(DATA_NUM)
#=============== Initialize local parameters ===============
G = np.random.dirichlet(G_0 + gamma) #initialize G
G_set.append(G)
pi_e = []
for i in range(concept_num):
pi = nonpara_tool.stick_breaking(beta,
Stick_large_R) #initialize pi
pi_e.append(pi)
pi_set.append(pi_e)
c_t = [1000 for n in xrange(DATA_NUM)] #initialize c_t
C_t_set.append(c_t)
r_t = [1000 for n in xrange(DATA_NUM)] #initialize r_t
r_t_set.append(r_t)
mu = init_mu(posdist_num, position, DATA_NUM) #initialize mu
mu_set.append(mu)
Sigma = np.array([[[sigma_init, 0.0, 0.0, 0.0],
[0.0, sigma_init, 0.0, 0.0],
[0.0, 0.0, sigma_init, 0.0],
[0.0, 0.0, 0.0, sigma_init]]
for i in range(posdist_num)]) #initialize Sigma
Sigma_set.append(Sigma)
VISION_DIM = len(vision_set[0][0])
NAME_DIM = len(name_set[0][0])
phi_v = np.array([[float(1.0) / VISION_DIM for i in range(VISION_DIM)]
for j in range(concept_num)]) #initialize phi^v
phi_n = np.array([[float(1.0) / NAME_DIM for i in range(NAME_DIM)]
for j in range(concept_num)]) #initialize phi^n
region_choice = [dr for dr in range(posdist_num)] #[0,1,2,...,posdist - 1]
class_choice = [dc for dc in range(concept_num)] #[0,1,2,...,concept - 1]
for iter in xrange(iteration):
print("iteration " + repr(iter + 1))
posdist_count = [
[0.0 for i in range(posdist_num)] for j in range(data_set_num)
] #store the number of position distributions in all environments
concept_count_set = [
] #store the number of concepts in all environments
for e in xrange(data_set_num):
#=============== Sampling r_t ===============
concept_posdist_count = [
[0.0 for i in range(posdist_num)] for j in range(concept_num)
] #store the number of position distributions in concepts
concept_count = [0.0 for i in range(concept_num)
] #store the number of concepts in environment e
gauss_prob_set = np.zeros(
(posdist_num, data_num[e]), dtype=float
) #store log(p(x_t|mu_{r_t},Sigma_{r_t}) + p(r_t|pi_{C_t}))
if iter == 0:
C_t = np.random.randint(concept_num,
size=data_num[e]) #initialize C_t
else:
C_t = C_t_set[e]
pi_e = np.array(pi_set[e])
pi_data = np.array([pi_e[C_t[d]] for d in range(data_num[e])])
pi_data = np.log(pi_data)
for i in range(posdist_num):
gauss_prob = ss.multivariate_normal.logpdf(
position_set[e], mu_set[e][i], Sigma_set[e][i]
) + pi_data[:,
i] #caluculate log(p(x_t|mu_{r_t},Sigma_{r_t})+p(r_t|pi_{C_t}))
gauss_prob_set[i] += gauss_prob
gauss_prob_set = gauss_prob_set.T
max_posdist = np.max(gauss_prob_set, axis=1)
gauss_prob_set = gauss_prob_set - max_posdist[:, None]
gauss_prob_set = np.exp(gauss_prob_set)
sum_set = np.sum(gauss_prob_set, axis=1)
gauss_prob_set = gauss_prob_set / sum_set[:, None]
for d in xrange(0, data_num[e], Slide):
r_t_set[e][d] = np.random.choice(
region_choice, p=gauss_prob_set[d]) #sampling r_t
posdist_count[e][r_t_set[e][d]] += 1.0
r_t = r_t_set[e]
#=============== Sampling C_t ===============
multi_prob_set = np.zeros(
(concept_num, data_num[e]), dtype=float
) #store log(p(v_t|phi^v_{C_t}) + p(n_t|phi^n_{C_t}) + p(C_t|G))
phi_v_log = np.log(phi_v)
phi_n_log = np.log(phi_n)
pi_data = np.array([pi_e.T[r_t[d]] for d in range(data_num[e])])
pi_data = np.log(pi_data)
G_log = np.log(G_set[e])
for i in range(concept_num):
vision_prob = vision_set[e].dot(phi_v_log[i])
name_prob = name_set[e].dot(phi_n_log[i])
modal_prob = vision_prob + name_prob + pi_data[:, i]
modal_prob = modal_prob + G_log[i]
multi_prob_set[i] += modal_prob
multi_prob_set = multi_prob_set.T
max_concept = np.max(multi_prob_set, axis=1)
multi_prob_set = multi_prob_set - max_concept[:, None]
multi_prob_set = np.exp(multi_prob_set)
sum_concept_set = np.sum(multi_prob_set, axis=1)
multi_prob_set = multi_prob_set / sum_concept_set[:, None]
for d in xrange(0, data_num[e], Slide):
C_t_set[e][d] = np.random.choice(
class_choice, p=multi_prob_set[d]) #sampling C_t
concept_count[C_t_set[e][d]] += 1.0
concept_posdist_count[C_t_set[e][d]][r_t[d]] += 1.0
#=============== Sampling mu and Sigma ==========
for r in xrange(posdist_num):
pos_r = [] #store position data in position distribution r
#===== calculate average =====
for d in xrange(data_num[e]):
if r_t_set[e][d] == r:
pos_r.append(position_set[e][d])
sum_pose = np.zeros(4) #([0.0,0.0,0.0,0.0])
for i in xrange(len(pos_r)):
for j in xrange(4):
sum_pose[j] += pos_r[i][j]
bar_pose = np.zeros(4) #([0.0,0.0,0.0,0.0])
for i in xrange(4):
if sum_pose[i] != 0:
bar_pose[i] = sum_pose[i] / len(pos_r)
#===== calculate Mu =====
Mu = (kappa_0 * mu_0_set[e] + len(pos_r) * bar_pose) / (
kappa_0 + len(pos_r)) #Mu updated
#===== calculate Matrix_R =====
bar_pose_matrix = np.matrix(bar_pose)
Matrix_R = np.zeros([4, 4])
for i in xrange(len(pos_r)):
pos_r_matrix = np.matrix(pos_r[i])
Matrix_R += ((pos_r_matrix - bar_pose_matrix).T *
(pos_r_matrix - bar_pose_matrix))
#===== calculate Psi =====
ans = ((bar_pose_matrix - mu_0_set[e]).T *
(bar_pose_matrix - mu_0_set[e])) * (
(kappa_0 * len(pos_r)) / (kappa_0 + len(pos_r)))
Psi = psi_0 + Matrix_R + ans #Psi updated
#===== update hyper parameter (Kappa,Nu) =====
Kappa = kappa_0 + len(pos_r) #Kappa updated
Nu = nu_0 + len(pos_r) #Nu updated
#===== sampling mu and Sigma from wishrt distribution =====
Sigma_set[e][r] = Prob_Cal.sampling_invwishartrand(
Nu, Psi) #sampling Sigma
Sigma_temp = Sigma_set[e][r] / Kappa
mu_set[e][r] = np.random.multivariate_normal(
Mu, Sigma_temp) #sampling Mu
if len(pos_r) == 0: #if no asigned data
p = np.array([
random.uniform(map_x[e], MAP_X[e]),
random.uniform(map_y[e], MAP_Y[e]),
random.uniform(-1.0, 1.0),
random.uniform(-1.0, 1.0)
])
mu_set[e][r] = p
Sigma_set[e][r] = np.array([[sigma_init, 0.0, 0.0, 0.0],
[0.0, sigma_init, 0.0, 0.0],
[0.0, 0.0, sigma_init, 0.0],
[0.0, 0.0, 0.0, sigma_init]])
#=============== Sampling pi and G ===============
for c in range(concept_num):
pi_set[e][c] = np.random.dirichlet(concept_posdist_count[c] +
beta) #sampling pi
G_set[e] = np.random.dirichlet(concept_count + (gamma * G_0) +
50) + 1e-100 #sampling G
concept_count_set.append(concept_count)
#=============== Sampling phi^v and phi^n and G_0 ===============
total_vision_set = [] #store the number of vision (visual feature)
total_name_set = [] #store the number of name
concept_count = [
0.0 for i in range(concept_num)
] #array to store the number of concepts in all environments
for c in xrange(concept_num):
vision_c = []
name_c = []
for e in xrange(data_set_num):
for d in xrange(data_num[e]):
if C_t_set[e][d] == c:
vision_c.append(vision_set[e][d])
name_c.append(name_set[e][d])
concept_count[c] += 1.0
total_vision = BoF.bag_of_feature(vision_c, VISION_DIM)
total_vision_set.append(total_vision)
total_vision = total_vision + alpha_v
phi_v[c] = np.random.dirichlet(
total_vision) + 1e-100 #sampling phi_v
total_name = BoF.bag_of_feature(name_c, NAME_DIM)
total_name_set.append(total_name)
total_name = total_name + alpha_n
phi_n[c] = np.random.dirichlet(
total_name) + 1e-100 #sampling phi_n
concept_count[c] += gamma_0
if len(vision_c) == 0:
phi_v[c] = np.array(
[float(1.0) / VISION_DIM for i in range(VISION_DIM)])
phi_n[c] = np.array(
[float(1.0) / NAME_DIM for i in range(NAME_DIM)])
G_0 = np.random.dirichlet(concept_count) #sampling G_0
if ((iter + 1) % iteration) == 0:
print("")
exist_concept_num = 0
for i in range(concept_num):
if concept_count[i] > gamma_0:
exist_concept_num += 1
print("Concept number existing:" + repr(exist_concept_num))
exist_posdist_num = [0 for e in range(data_set_num)]
for e in range(data_set_num):
for r in range(posdist_num):
if posdist_count[e][r] > 0:
exist_posdist_num[e] += 1
print("Position distribution number existing:" +
repr(exist_posdist_num) + "\n")
print(posdist_count)
#========== Saving ==========
print("--------------------------------------------")
Out_put_dir = "gibbs_result/similar_result/env_num_" + Out + "/per_" + repr(
x) + "_iter_" + repr(iter + 1)
print(Out_put_dir)
try:
os.mkdir(Out_put_dir)
except OSError:
shutil.rmtree(Out_put_dir)
os.mkdir(Out_put_dir)
#saving finish time
finish_time = time.time() - start_time
f = open(Out_put_dir + "/time.txt", "w")
f.write("time:" + repr(finish_time) + " seconds.")
f.close()
f = open(Out_put_dir + "/training_dataset", 'w')
for i, d in enumerate(Training_list):
w = repr(i) + ": " + d + "\n"
f.write(w)
f.close()
#=============== saving environment parameter ===============
for i in range(data_set_num):
os.mkdir(Out_put_dir + "/dataset" + repr(i))
os.mkdir(Out_put_dir + "/dataset" + repr(i) + "/mu")
os.mkdir(Out_put_dir + "/dataset" + repr(i) + "/sigma")
np.savetxt(
Out_put_dir + "/dataset" + repr(i) + "/data_concept.txt",
C_t_set[i])
np.savetxt(
Out_put_dir + "/dataset" + repr(i) + "/data_posdist.txt",
r_t_set[i])
np.savetxt(
Out_put_dir + "/dataset" + repr(i) + "/posdist_count.txt",
posdist_count[i])
np.savetxt(Out_put_dir + "/dataset" + repr(i) + "/pi.csv",
pi_set[i])
np.savetxt(Out_put_dir + "/dataset" + repr(i) + "/G.txt",
G_set[i])
np.savetxt(
Out_put_dir + "/dataset" + repr(i) + "/concept_count.txt",
concept_count_set[i])
f = open(Out_put_dir + "/dataset" + repr(i) + "/Parameter.txt",
"w")
f.write("max_x_value_of_map: " + repr(MAP_X[i]) +
"\nMax_y_value_of_map: " + repr(MAP_Y[i]) +
"\nMin_x_value_of_map: " + repr(map_x[i]) +
"\nMin_y_value_of_map: " + repr(map_y[i]) +
"\nConcept_num: " + repr(exist_concept_num) +
"\nPosition_distribution_num: " +
repr(exist_posdist_num[i]) + "\nData_num: " +
repr(data_num[i]) + "\nDataset: " + Training_list[i] +
"\nSliding_data_parameter: " + repr(Slide) +
"\nName_dim: " + repr(NAME_DIM) + "\nVision_dim: " +
repr(VISION_DIM) + "\nVision_diric: " +
repr(args.vision) + "\nStick_breaking_process_max: " +
repr(Stick_large_L) + "\nname_not: " +
repr(args.name_not) + "\ntest_num: " + repr(test_num))
f.close()
f = open(
Out_put_dir + "/dataset" + repr(i) +
"/data_concept_posdist.txt", "w")
for d in range(data_num[i]):
if w > 0:
f.write("data:" + repr(d) + " C_t:" +
repr(C_t_set[i][d]) + " r_t:" +
repr(r_t_set[i][d]) + "\n")
f.close()
#=============== Saving Gaussian distrbution ===============
for j in xrange(posdist_num):
np.savetxt(
Out_put_dir + "/dataset" + repr(i) + "/mu/gauss_mu" +
repr(j) + ".csv", mu_set[i][j])
np.savetxt(
Out_put_dir + "/dataset" + repr(i) +
"/sigma/gauss_sigma" + repr(j) + ".csv",
Sigma_set[i][j])
#=============== saving concept parameter ===============
np.savetxt(Out_put_dir + "/phi_v.csv", phi_v)
np.savetxt(Out_put_dir + "/phi_n.csv", phi_n)
np.savetxt(Out_put_dir + "/bag_of_vision.txt", total_vision_set)
np.savetxt(Out_put_dir + "/bag_of_name.txt", total_name_set)
f = open(Out_put_dir + "/hyper_parameter.txt", "w")
f.write("alpha_v: " + repr(alpha_v) + ("\nalpha_n: ") +
repr(alpha_n) + ("\ngamma_0: ") + repr(gamma_0) +
("\nkappa_0: ") + repr(kappa_0) + ("\nnu_0: ") +
repr(nu_0) + "\ngamma: " + repr(gamma) + "\nbeta: " +
repr(beta) + "\ninitial sigma: " + repr(sigma_init) +
"\nsitck break limit: " + repr(Stick_large_L) +
"\nvision_increment: " + repr(vision_increment) +
"\nname_increment: " + repr(name_increment) + "\npsi: [" +
repr(psi_0[0][0]) + "\n" + repr(psi_0[1][0]) + "\n" +
repr(psi_0[2][0]) + "\n" + repr(psi_0[3][0]) + "]")
f.close()
print name_data_dir
def gibbs(data_pose, data_feature, data_word, word_data_ind):
if args.Nonpara:
pi = nonpara_tool.stick_breaking(gamma, Stick_large_L)
clas_num = len(pi)
print "Stick breakin process doneļ¼"
else:
global clas_num
print clas_num
#Initializing the mean of Gussian distribution
Myu_Ct = np.array([[
random.uniform(map_x, MAP_X),
random.uniform(map_y, MAP_Y),
random.uniform(-1, 1),
random.uniform(-1, 1)
] for i in xrange(clas_num)])
#########======initialize Gaussian parameter:mu===========###########
for j in range(clas_num):
index = random.uniform(0, clas_num)
p = data_pose[int(index)]
Myu_Ct[0] = p
#########==================#############
initial_mu = []
for i, p in enumerate(Myu_Ct):
initial_mu.append(p)
initial_mu = np.array(initial_mu)
#Initializing covariance matrix of positional Gaussian dis
Sigma_Ct = [
np.matrix([[sigma_init, 0.0, 0.0, 0.0], [0.0, sigma_init, 0.0, 0.0],
[0.0, 0.0, sigma_init, 0.0], [0.0, 0.0, 0.0, sigma_init]])
for i in range(clas_num)
]
#Initializing the mean of Multinomial distribution for Image features
fi_Ct = np.array([[float(1.0) / FEATURE_DIM for i in range(FEATURE_DIM)]
for j in range(clas_num)])
if args.Word:
#Initializing the mean of Multinomial distribution for Words
ramda_Ct = np.array(
[[float(1.0) / word_class for i in range(word_class)]
for j in range(clas_num)])
C_t = np.array([n for n in xrange(clas_num)])
#Initializing class index of data.
data_c = np.array([1000 for n in xrange(DATA_NUM)])
for iter in xrange(iteration):
print 'Iteration.' + repr(iter + 1) + '\n'
#<<<<<Sampling class index C_t<<<<
print 'Sampling calss index...\n'
for d in xrange(0, DATA_NUM, Slide):
prob_C_t = np.array([0.0 for i in xrange(clas_num)])
for i in range(clas_num):
prob_C_t[i] += Prob_Cal.multi_gaussian_log(
data_pose[d], Myu_Ct[i], Sigma_Ct[i])
prob_C_t[i] += math.log(pi[i])
prob_C_t[i] += Prob_Cal.multi_nomial_log(
data_feature[d], fi_Ct[i])
if args.Word:
if sum(data_word[d]) != 0:
data_word[d] = np.array(data_word[d])
prob_C_t[i] += Prob_Cal.multi_nomial_log(
data_word[d], ramda_Ct[i])
max_class = np.argmax(prob_C_t)
prob_C_t -= prob_C_t[max_class]
prob_C_t = np.exp(prob_C_t)
prob_C_t = Prob_Cal.normalize(prob_C_t) #Normalize weight.
data_c[d] = np.random.choice(C_t, p=prob_C_t)
print 'Iteration:', iter + 1, 'Data:', d + DATA_initial_index, 'max_prob', max_class, ":", prob_C_t[
max_class], 'Class index', data_c[d]
#<<<<<Sampling Gaussian parameter Myu_Ct , Sigma_Ct.
print 'Started sampling parameters of Position Gaussian dist...\n'
for c in xrange(clas_num):
pose_c = []
#========Calculating average====
for d in xrange(len(data_c)):
if data_c[d] == c:
pose_c.append(data_pose[d])
sum_pose = np.array([0.0, 0.0, 0.0, 0.0])
for i in xrange(len(pose_c)):
for j in xrange(4):
sum_pose[j] += pose_c[i][j]
bar_pose = np.array([0.0, 0.0, 0.0, 0.0])
for i in xrange(4):
if sum_pose[i] != 0:
bar_pose[i] = sum_pose[i] / len(pose_c)
#=========Calculating Mu=============
Mu = (kappa_0 * mu_0 + len(pose_c) * bar_pose) / (kappa_0 +
len(pose_c))
#=========Calculating Matrix_C===================
bar_pose_matrix = np.matrix(bar_pose)
Matrix_C = np.zeros([4, 4])
for i in xrange(len(pose_c)):
pose_c_matrix = np.matrix(pose_c[i])
Matrix_C += ((pose_c_matrix - bar_pose_matrix).T *
(pose_c_matrix - bar_pose_matrix))
#=======Calculating Psai===============
ans = ((bar_pose_matrix - mu_0).T *
(bar_pose_matrix - mu_0)) * ((kappa_0 * len(pose_c)) /
(kappa_0 + len(pose_c)))
Psai = psai_0 + Matrix_C + ans
#=======Updating hyper parameter:Kappa,Nu===============================
Kappa = kappa_0 + len(pose_c)
Nu = nu_0 + len(pose_c)
#============Sampling fron wishrt dist====================
Sigma_Ct[c] = Prob_Cal.sampling_invwishartrand(Nu, Psai)
Sigma_temp = Sigma_Ct[c] / Kappa
Myu_Ct[c] = np.random.multivariate_normal(Mu, Sigma_temp)
#No asigned data
if len(pose_c) == 0:
index = random.uniform(0, clas_num)
p = data_pose[int(index)]
Myu_Ct[c] = p
#Myu_Ct[c]=[random.uniform(map_x,MAP_X ),random.uniform(map_y,MAP_Y),random.uniform(-1,1),random.uniform(-1,1)]
Sigma_Ct[c] = np.matrix([[sigma_init, 0.0, 0.0, 0.0],
[0.0, sigma_init, 0.0, 0.0],
[0.0, 0.0, sigma_init, 0.0],
[0.0, 0.0, 0.0, sigma_init]])
#print Myu_Ct[c]
#print Sigma_Ct[c]
print 'Finished sampling parameters of Position Gaussian dist...\n'
#<<<<<<Sampling Parameter of Multinomial fi_Ct>>>>>>>>>>>>>>>>>>
print 'Started sampling parameters of Image features Multinomial dist...\n'
for c in xrange(clas_num):
feat_c = []
for d in xrange(len(data_c)):
if data_c[d] == c:
feat_c.append(data_feature[d])
#print repr(j+1)+' '+repr(feat_c)+'\n'
total_feat = BoF.bag_of_feature(feat_c, FEATURE_DIM)
total_feat = total_feat + alfa
fi_Ct[c] = np.random.dirichlet(total_feat)
if len(feat_c) == 0:
fi_Ct[c] = [
float(1.0) / FEATURE_DIM for i in range(FEATURE_DIM)
]
print 'Finished sampling parameters of Image features Multinomial dist...\n'
#If you estimate space name,you should set Word as True
#<<<<Sampling word dist parametrer:ramda_ct>>>>>>>>>>>>>>>>>>>>>
if args.Word:
print 'Started sampling parameters of word Multinomial dist...\n'
word_distribusion = []
for c in xrange(clas_num):
word_c = []
for d in xrange(len(data_c)):
if data_c[d] == c:
word_c.append(data_word[d])
total_word = BoF.bag_of_words(word_c, word_class)
word_distribusion.append(total_word)
total_word = total_word + beta
ramda_Ct[c] = np.random.dirichlet(total_word)
#Not data in class
if len(word_c) == 0:
ramda_Ct[c] = [
float(1.0) / word_class for i in range(word_class)
]
print 'Finished sampling parameters of Word Multinomial dist...\n'
# If you use Nonparametric Bayse model,you should set Nonpara as True.
if args.Nonpara:
print 'Started sampling parameters of index Multinomial dist...\n'
#<<<<<Sampling paremeter(pi) of class multinomial dist>>>>>>>>>>>
class_count = [0 for i in range(clas_num)]
for c in xrange(clas_num):
for d in xrange(len(data_c)):
if data_c[d] == c:
class_count[c] += 1.0
class_count[c] += gamma
pi = np.random.dirichlet(class_count)
print 'Finished sampling parameters of index Multinomial dist...\n'
print 'Iteration ', iter + 1, ' Done..\n'
C_num = clas_num
if args.Nonpara:
C_num = 0
for i in range(len(class_count)):
if class_count[i] > gamma:
C_num += 1
print "Class num:" + repr(C_num) + "\n"
#=====Saving===========================================
os.mkdir(Out_put_dir)
os.mkdir(Out_put_dir + "/mu")
os.mkdir(Out_put_dir + "/sigma")
os.mkdir(Out_put_dir + "/image_multi")
os.mkdir(Out_put_dir + "/class")
os.mkdir(Out_put_dir + "/word")
for i in xrange(clas_num):
#Writing parameter of positional Gaussian dist
np.savetxt(Out_put_dir + "/mu/gauss_mu" + repr(i) + ".csv", Myu_Ct[i])
np.savetxt(Out_put_dir + "/sigma/gauss_sgima" + repr(i) + ".csv",
Sigma_Ct[i])
#Writing parameter of image features multinomial dist
np.savetxt(Out_put_dir + "/image_multi/fi_" + repr(i) + ".csv", fi_Ct)
#Writing class indexes
all = []
#k=0
for r in xrange(len(data_c)):
if i == data_c[r]:
r = r + DATA_initial_index
all.append(r)
np.savetxt(Out_put_dir + "/class/class" + repr(i) + ".txt",
all,
fmt="%d")
if args.Word:
#Writing parameters of word multinomial dist
f = open(
Out_put_dir + "/word/word_distribution" + repr(i) + '.txt',
'w')
for w in xrange(word_class):
f.write(repr(ramda_Ct[i][w]) + "\n")
f.close()
#Writing all index
np.savetxt(Out_put_dir + "/all_class.csv", data_c, fmt="%d")
#saving finish time
finish_time = time.time() - start_time
f = open(Out_put_dir + "/time.txt", "w")
f.write("time:" + repr(finish_time) + " seconds.")
f.close()
#====Writing Parameter===========
f = open(Out_put_dir + "/Parameter.txt", "w")
f.write("max_x_value_of_map: " + repr(MAP_X) + "\nMax_y_value_of_map: " +
repr(MAP_Y) + "\nMin_x_value_of_map: " + repr(map_x) +
"\nMin_y_value_of_map: " + repr(map_y) + "\nNumber_of_place: " +
repr(clas_num) + "\nData_num: " + repr(Learnig_data_num) +
"\nSliding_data_parameter: " + repr(Slide) + "\nWord_class: " +
repr(word_class) + "\nDataset: " + data_diric +
"\nEstimated_place_num: " + repr(C_num) +
"\nNonparametric_Bayse: " + repr(args.Nonpara) +
"\nImage_feature_dim: " + repr(FEATURE_DIM) +
"\nUsing_word_data: " + repr(args.Word) +
"\nStick_breaking_process_max: " + repr(Stick_large_L))
f.close()
f = open(Out_put_dir + "/hyper parameter.txt", "w")
f.write("alfa: " + repr(alfa) + "\n beta: " + repr(beta) +
("\nkappa_0: ") + repr(kappa_0) + ("\nnu_0: ") + repr(nu_0) +
"\nmu_0: " + repr(mu_0) + "\npsai_0: " + repr(psai_0) +
"\ngamma: " + repr(gamma) + "\ninitial sigma: " +
repr(sigma_init) + "\nsitck break limit: " + repr(Stick_large_L))
if args.Word:
f.write("\nspace_name:")
for i in range(len(space_name)):
f.write(space_name[i] + ",")
f.write("\nIteration:" + repr(iteration))
f.close()
print "%1.3f second" % (finish_time)
if args.Nonpara:
np.savetxt(Out_put_dir + "/pi.csv", pi)
np.savetxt(Out_put_dir + "/initial_mu.csv", initial_mu)
np.savetxt(Out_put_dir + "/last_mu.csv", Myu_Ct)
if args.Word:
f = open(Out_put_dir + "/word_data_class.txt", "w")
f.write("data space_name class\n")
for j in word_data_ind:
vec = data_word[j]
for i in range(len(space_name)):
if vec[i] != 0:
f.write(
repr(j + DATA_initial_index) + " " + space_name[i] +
" " + repr(data_c[j]) + "\n")