MIN_DECIMATION = 50
MAX_DECIMATION = 100
EXPERIMENT = 7
NBITS = 8
NATT = 24
oExp = Experiment()
# basemask = np.array([1, 2, 5, 9, 15, 16, 17, 21, 22, 23])
# basemask = np.array([12, 20, 22])
basemask = np.array(range(1, 25))
svmVectors = []
basemask = basemask - 1
for M in range(MIN_DECIMATION, MAX_DECIMATION + 1):
oDataSet = DataSet()
base = np.loadtxt(PATH_TO_SAVE_FEATURES + "FEATURES_M{}_CM8b_TH199.txt".format(M), usecols=basemask, delimiter=",")
classes = np.loadtxt(PATH_TO_SAVE_FEATURES + "FEATURES_M{}_CM8b_TH199.txt".format(M), dtype=object, usecols=24,
delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.normalize_data_set()
for j in range(NUMBER_OF_ROUNDS):
print(j)
oData = Data(4, 50, samples=150)
oData.random_training_test_per_class()
svm = ml.SVM_create()
svm.setKernel(ml.SVM_RBF)
oData.params = dict(kernel_type=ml.SVM_RBF, svm_type=ml.SVM_C_SVC, gamma=2.0, nu=0.0, p=0.0, coef0=0,
k_fold=10)
PATH_TO_SAVE_FEATURES = 'GLCM_FILES/EXP_04/'
NORMALIZATION_MATRIX = np.array([[9.98510800e-01, 2.25201230e-02],
[5.46968100e+03, 1.38611180e+01],
[9.99296500e-01, 5.01285000e-01],
[2.15652630e+00, 3.52501050e-03],
[9.99309000e-01, 5.40860700e-01],
[2.07592710e+02, 8.86744200e-02],
[9.99255060e-01, 1.01222746e-01],
[2.88141800e+01, 7.24622100e-02],
[9.68741850e-02, -8.40647000e-02],
[4.80887420e+04, 5.70339160e+00]])
basemask = np.array([1, 2, 5, 9, 15, 16, 17, 21, 22, 23])
basemask = basemask - 1
svm = cv2.SVM()
oDataSet = DataSet()
base = np.loadtxt(PATH_TO_SAVE_FEATURES +
"FEATURES_M{}_CM8b_TH198.txt".format(M),
usecols=basemask,
delimiter=",")
classes = np.loadtxt(PATH_TO_SAVE_FEATURES +
"FEATURES_M{}_CM8b_TH198.txt".format(M),
dtype=object,
usecols=24,
delimiter=",")
for x, y in enumerate(base):
oDataSet.add_sample_of_attribute(
np.array(list(np.float32(y)) + [classes[x]]))
oDataSet.attributes = oDataSet.attributes.astype(float)
oDataSet.attributes = (oDataSet.attributes - NORMALIZATION_MATRIX[:, 1].T) / (
NORMALIZATION_MATRIX[:, 0] - NORMALIZATION_MATRIX[:, 1])