def test_paramters(test_datas,
vec_path='para/p_vecs.txt',
ps_path='para/ps.txt"',
vocab_path='para/vocab_list.txt'):
'''
测试,分类预测
:return:
'''
p_vecs = file.load_parameters(vec_path)
ps = file.load_parameters(ps_path)
vocab_list = file.read_file_list(vocab_path)
sum_nums = len(test_datas)
error_num = 0.0
for i in range(sum_nums):
data = segment.segment(str(test_datas[i]))
this_doc = np.array(set_of_words_to_vec(vocab_list, data))
result, result_p = classify_plus(this_doc, p_vecs, ps)
#result, result_p = classify_mult(this_doc, p_vecs, ps)
if (int(test_datas[i].split("\t")[0]) != result):
print result_p
print(result),
print test_datas[i]
error_num += 1
print 'error nums:', error_num, 'sum nums:', sum_nums
print 'error rate:', round(error_num / sum_nums, 3)
def word_segment(train_datas):
'''
分词,过滤掉英文字符数字标点符号单个词
:param train_datas:
:return:
'''
train_list = []
for train in train_datas:
train_list.append(segment.segment(train))
return train_list
def pool_func(path_tuple):
filename, orig_folder, dest_folder = path_tuple
im = imread(os.path.join(orig_folder, filename), 0)
ciriris, cirpupil, imwithnoise = segment(im, eyelashes_thresh, False)
# Perform normalization
polar_iris, polar_mask = normalize(imwithnoise, ciriris[1], ciriris[0],
ciriris[2], cirpupil[1], cirpupil[0],
cirpupil[2], radial_res, angular_res)
imwrite(os.path.join(dest_folder, filename.replace(".jpg", ".png")),
polar_iris * 255)
imwrite(
os.path.join(dest_folder,
filename.replace(".jpg", "") + "_mask.png"),
polar_mask * 255)
def test_paramters(test_datas):
'''
测试,分类预测
:return:
'''
p0V = file.load_parameters("p0.txt")
p1V = file.load_parameters("p1V.txt")
p1 = file.load_parameters("pAb.txt")
vocab_list = file.read_file_list("vocabList.txt")
sum_nums = len(test_datas)
error_num = 0.0
for i in range(sum_nums):
data = segment.segment(str(test_datas[i]))
this_doc = array(set_of_words_to_vec(vocab_list, data))
result = classify(this_doc, p0V, p1V, p1)
if (int(test_datas[i].split("\t")[0]) != result):
print (result),
print test_datas[i]
error_num += 1
print 'error nums:', error_num, 'sum nums:', sum_nums
print 'error rate:', round(error_num / sum_nums, 3)