def hessian(dim, posterior, alpha):
h_diagonal = []
h_constant = []
h_a = []
for index, a_vector in enumerate(alpha):
p_sum = SUM(posterior[:, index])
a_tri_gamma = POLYGAMMA(1, a_vector)
a_tri_gamma_sum = POLYGAMMA(1, SUM(a_vector))
h_diagonal.append(DIAGONAL(1 / (-1 * p_sum * a_tri_gamma)))
h_constant.append(((a_tri_gamma_sum * SUM(1 / a_tri_gamma)) - 1) *
a_tri_gamma_sum * p_sum)
h_a.append(((-1 / p_sum) * (1 / a_tri_gamma)).reshape(1, dim + 1))
return ASARRAY(h_diagonal), h_constant, ASARRAY(h_a)
def g_estimation(data_set, size, alpha, posterior, dim, K):
G = []
data_set = CONCAT((data_set, FULL((size, 1), 1)), axis=1)
pixel_log = ASARRAY([LOG(data / SUM(data)) for data in data_set])
for index, aV in enumerate(alpha):
G.append(g_matrix_generator(aV, posterior[:, [index]], pixel_log, dim))
return ASARRAY(G)
def clusterDropTest(mix, alpha, dropingCriteria, K, DIM, pixelSize):
mixInfo = []
alphaInfo = []
for j in range(K):
if SUM(mix[:, j: j + 1]) > dropingCriteria:
mixInfo.append(mix[:, j: j + 1])
alphaInfo.append(alpha[j])
else:
print("Cluster having alpha :", alpha[j], " & Mix :", j, " is removed!")
return (ASARRAY(mixInfo).T).reshape(pixelSize, len(alphaInfo)), ASARRAY(alphaInfo).reshape(len(alphaInfo),
DIM), len(mixInfo)
def pdf(p_v, a_v):
try:
return EXP(GAMMALN(SUM(a_v)) - SUM(GAMMALN(a_v)) +
SUM(SUBS(a_v[:-1], 1) * LOG(p_v)) -
SUM(a_v) * LOG(1 + SUM(p_v)))
except RuntimeWarning:
print("pVector :>", p_v)
print("aVector :>", a_v)
print("GAMMALN(SUM(a_v)) :>", GAMMALN(SUM(a_v)))
print("GAMMALN(a_v) :>", GAMMALN(a_v))
print("LOG(p_v) :>", LOG(p_v))
print("SUM(a_v) :>", SUM(a_v))
print("LOG(1 + SUM(p_v) :>", LOG(1 + SUM(p_v)))
exit(0)
def g_estimation(self, data_set, alpha, beta, posterior, dim, K):
q_alpha = []
q_beta = []
q_alpha_square = []
q_beta_square = []
q_alpha_beta_square = []
for index, (a_vector, b_vector) in enumerate(zip(alpha, beta)):
a_d_gamma = POLYGAMMA(0, a_vector).reshape(1, dim)
b_d_gamma = POLYGAMMA(0, b_vector).reshape(1, dim)
ab_d_gamma = POLYGAMMA(0, a_vector + b_vector).reshape(1, dim)
a_t_gamma = POLYGAMMA(1, a_vector).reshape(1, dim)
b_t_gamma = POLYGAMMA(1, b_vector).reshape(1, dim)
ab_t_gamma = POLYGAMMA(1, a_vector + b_vector).reshape(1, dim)
a_data = ASARRAY([LOG(data / (1 + data)) for data in data_set]).reshape(len(data_set), dim)
b_data = ASARRAY([LOG(1 / (1 + data)) for data in data_set]).reshape(len(data_set), dim)
q_alpha.append(SUM(posterior[:, [index]] * (ab_d_gamma - a_d_gamma + a_data), axis=0).reshape(1, dim))
q_beta.append(SUM(posterior[:, [index]] * (ab_d_gamma - b_d_gamma + b_data), axis=0).reshape(1, dim))
q_alpha_square.append(SUM(posterior[:, [index]] * (ab_t_gamma - a_t_gamma), axis=0).reshape(1, dim))
q_beta_square.append(SUM(posterior[:, [index]] * (ab_t_gamma - b_t_gamma), axis=0).reshape(1, dim))
q_alpha_beta_square.append(SUM(posterior[:, [index]] * ab_t_gamma, axis=0).reshape(1, dim))
return ASARRAY(q_alpha).reshape(K, dim), ASARRAY(q_beta).reshape(K, dim), ASARRAY(q_alpha_square).reshape(K, dim), ASARRAY(
q_beta_square).reshape(K, dim), ASARRAY(q_alpha_beta_square).reshape(K, dim)
def split_stamp(input_file):
choice = parameters["-l"].lower()
if choice == "all":
choice = range(7)
else:
choices = {
'kingdom': 0,
'family': 4,
'class': 2,
'order': 3,
'strain': 7,
'phylum': 1,
'genus': 5,
'species': 6,
"all": 7
}
choice = [choices[choice]]
for p in choice:
h = {}
f = open(input_file)
line = f.readline()
head = line.split()[p]
info = line.split()[8:]
c = 0
for line in f:
line = line.split("\t")
temp_head = line[p]
temp_info = line[8:]
if temp_head not in h:
h[temp_head] = []
h[temp_head] += [[float(x) for x in temp_info]]
f.close()
o = open(head + "__" + input_file.replace(".spf", ".xls"), "w+")
o.write(head + "\t" + "\t".join(info) + "\n")
for i in h:
o.write(i + "\t" +
"\t".join([str(x)
for x in list(SUM(h[i], axis=0))]) + "\n")
o.close()
print "It's done with the STAMP file splitting :)"
def HELLINGER_loss(a, y):
return (1 / sqrt(2)) * SUM((sqrt(a) - sqrt(y))**2)
def KULLBACKLEIBLER_loss(a, y):
return SUM(where(a != 0, y * nan_to_num(log(y / a)), 0)) - SUM(y) + SUM(a)
def KULLBACK_loss(a, y):
return SUM(where(a != 0, y * nan_to_num(log(y / a)), 0))
def CROSS_ENTROPY_loss(a, y):
return SUM(nan_to_num(-y * log(a) - (1 - y) * log(1 - a)))
def normalise(result):
return result/(SUM(result)*1.)
def mix_updater(posterior, size, cluster):
return (SUM(posterior, axis=0) / size).reshape(1, cluster)
def posterior_estimator(pdf, mix):
return ASARRAY([(mix * pV) / SUM(mix * pV) for pV in pdf]).reshape(len(pdf), mix.size)
def geo_transformation(pixels, DIM, pixelSize):
return ASARRAY(
[pixels[:, d:d + 1] / (1 + SUM(pixels[:, 0:d], axis=1).reshape(pixelSize, 1)) for d in
range(DIM)]).T.reshape(
pixelSize, DIM)
* @param {Integer} a.
* @return {String} which contains the Sum of Array.
*/
"""
if __name__ == '__main__':
K = CONST['K']
print("K :>", K)
cluster_drop_val = CONST['cluster_drop_val']
alpha, data, dim, size, mix = initial_algorithm(K)
counter = 0
obj = {'alpha': []}
while True:
pdf, posterior = estimation_step(K, mix, alpha, data)
mix, alpha = maximization_step(K, alpha, data, dim, posterior, size)
obj['alpha'].append(alpha)
# mix, alpha, K = cluster_drop_test(mix, alpha, cluster_drop_val, K, dim)
converge = convergence_test(obj['alpha'], CONST["algConverge"])
labels = predict_labels(posterior)
counter = counter + 1
print("Converge :>", converge)
if converge:
print("predictLabels :>", labels)
print("################### Final Parameters ###################")
print("K : ", K)
print("Mix : ", mix, SUM(mix))
print("Alpha : ", alpha)
print("Counter : ", counter)
exit()
def g_matrix_generator(alpha, posterior, log_pixels, dim):
return SUM(
posterior *
(POLYGAMMA(0, SUM(alpha)) - POLYGAMMA(0, alpha).reshape(1, dim + 1) +
log_pixels.reshape(len(log_pixels), dim + 1)),
axis=0).reshape(dim + 1, 1)
