def create_character_classification_count_matrix():
'''
This function do the following:
1. Creates a character classifier with 90 training examples
2. Runs a test with 10 test examples for every character. The results are put in a
matrix M so element M[i][j] contains the number of test examples for the character
with index i that are classified to be the character with index j.
'''
extracor = SimpleImageFeatureExtractor(nr_of_divisions=11,
size_classification_factor=3.4)
training_examples, test_examples = extracor.extract_training_and_test_examples(File(File(File(".."),".."),"character_examples").getCanonicalPath(),
nr_of_training_examples=90,
nr_of_test_examples=10)
classifier = CharacterClassifier(training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=SpecializedHMM.InitMethod.count_based)
alphabet = get_example_alphabet()
classification_count_matrix = zeros(len(alphabet),len(alphabet))
for label, examples in test_examples:
character = label.lower()
character_index = alphabet.index(character)
for example in examples:
classified_character = classifier.classify_character_string(example).lower()
classified_character_index = alphabet.index(classified_character)
count = classification_count_matrix[character_index][classified_character_index]
classification_count_matrix[character_index][classified_character_index] = count + 1
return classification_count_matrix
def create_character_classification_count_matrix():
'''
This function do the following:
1. Creates a character classifier with 90 training examples
2. Runs a test with 10 test examples for every character. The results are put in a
matrix M so element M[i][j] contains the number of test examples for the character
with index i that are classified to be the character with index j.
'''
extracor = SimpleImageFeatureExtractor(nr_of_divisions=11,
size_classification_factor=3.4)
training_examples, test_examples = extracor.extract_training_and_test_examples(
File(File(File(".."), ".."), "character_examples").getCanonicalPath(),
nr_of_training_examples=90,
nr_of_test_examples=10)
classifier = CharacterClassifier(
training_examples,
nr_of_hmms_to_try=1,
fraction_of_examples_for_test=0,
train_with_examples=False,
initialisation_method=SpecializedHMM.InitMethod.count_based)
alphabet = get_example_alphabet()
classification_count_matrix = zeros(len(alphabet), len(alphabet))
for label, examples in test_examples:
character = label.lower()
character_index = alphabet.index(character)
for example in examples:
classified_character = classifier.classify_character_string(
example).lower()
classified_character_index = alphabet.index(classified_character)
count = classification_count_matrix[character_index][
classified_character_index]
classification_count_matrix[character_index][
classified_character_index] = count + 1
return classification_count_matrix
def add_matricis(m1,m2):
'''
Returns r = m1 + m2, where m1 and m2 are two matrices of the same size.
The following will property will hold: r(i,j) = m1(i,j) + m2(i,j)
'''
r = zeros(len(m1), len(m1[0]))
for i in range(len(m1)):
for j in range(len(m1[0])):
r[i][j] = m1[i][j] + m2[i][j]
return r
def add_matricis(m1, m2):
'''
Returns r = m1 + m2, where m1 and m2 are two matrices of the same size.
The following will property will hold: r(i,j) = m1(i,j) + m2(i,j)
'''
r = zeros(len(m1), len(m1[0]))
for i in range(len(m1)):
for j in range(len(m1[0])):
r[i][j] = m1[i][j] + m2[i][j]
return r
def create_character_classification_count_matrix_from_several_tests(nr_of_tests=10):
'''
This function runs the function create_character_classification_count_matrix
nr_of_tests times. The sum of all resulting matrices is returned. The test examples
are selected randomly so create_character_classification_count_matrix does not return
the same result every time.
'''
alphabet_size = len(get_example_alphabet())
result_sum = zeros(alphabet_size, alphabet_size)
for i in range(nr_of_tests):
result = create_character_classification_count_matrix()
result_sum = add_matricis(result_sum, result)
return result_sum
def create_character_classification_count_matrix_from_several_tests(
nr_of_tests=10):
'''
This function runs the function create_character_classification_count_matrix
nr_of_tests times. The sum of all resulting matrices is returned. The test examples
are selected randomly so create_character_classification_count_matrix does not return
the same result every time.
'''
alphabet_size = len(get_example_alphabet())
result_sum = zeros(alphabet_size, alphabet_size)
for i in range(nr_of_tests):
result = create_character_classification_count_matrix()
result_sum = add_matricis(result_sum, result)
return result_sum
