def get_FTC_mimicry():
'''
Returns a numpy.array of size (number of samples, number of
features) with feature values of all mimicry attack results in
the FTC scenario.
'''
pdfs = utility.get_pdfs(config.get('results', 'FTC_mimicry'))
if not pdfs:
# Generate the attack files
attack_mimicry('FTC')
pdfs = utility.get_pdfs(config.get('results', 'FTC_mimicry'))
print 'Loading feature vectors from mimicry attack results...'
results = numpy.zeros((len(pdfs), FeatureDescriptor.get_feature_count()))
for i in range(len(pdfs)):
results[i,] = FeatureEdit(pdfs[i]).retrieve_feature_vector_numpy()
return results, [1.0 for i in range(len(pdfs))]
def get_FTC_mimicry():
'''
Returns a numpy.array of size (number of samples, number of
features) with feature values of all mimicry attack results in
the FTC scenario.
'''
pdfs = utility.get_pdfs(config.get('results', 'FTC_mimicry'))
if not pdfs:
# Generate the attack files
attack_mimicry('FTC')
pdfs = utility.get_pdfs(config.get('results', 'FTC_mimicry'))
print 'Loading feature vectors from mimicry attack results...'
results = numpy.zeros((len(pdfs), FeatureDescriptor.get_feature_count()))
for i in range(len(pdfs)):
results[i, ] = FeatureEdit(pdfs[i]).retrieve_feature_vector_numpy()
return results, [1.0 for i in range(len(pdfs))]
def csv2numpy(csv_in):
'''
Parses a CSV input file and returns a tuple (X, y) with
training vectors (numpy.array) and labels (numpy.array), respectfully.
csv_in - name of a CSV file with training data points;
the first column in the file is supposed to be named
'class' and should contain the class label for the data
points; the second column of this file will be ignored
(put data point ID here).
'''
# Parse CSV file
csv_rows = list(csv.reader(open(csv_in, 'rb')))
classes = {'FALSE': 0, 'TRUE': 1}
rownum = 0
# Count exact number of data points
TOTAL_ROWS = 0
for row in csv_rows:
if row[0] in classes:
# Count line if it begins with a class label (boolean)
TOTAL_ROWS += 1
# X = vector of data points, y = label vector
X = numpy.array(numpy.zeros(
(TOTAL_ROWS, FeatureDescriptor.get_feature_count())),
dtype=numpy.float64,
order='C')
y = numpy.array(numpy.zeros(TOTAL_ROWS), dtype=numpy.float64, order='C')
file_names = []
for row in csv_rows:
# Skip line if it doesn't begin with a class label (boolean)
if row[0] not in classes:
continue
# Read class label from first row
y[rownum] = classes[row[0]]
featnum = 0
file_names.append(row[1])
for featval in row[2:]:
if featval in classes:
# Convert booleans to integers
featval = classes[featval]
X[rownum, featnum] = float(featval)
featnum += 1
rownum += 1
return X, y, file_names