class FeaturePreprocessorTest(unittest.TestCase):
def setUp(self):
self.path = 'D:/dane/'
self.feature_preprocessor = FeaturePreprocessor(self.path)
self.feature_preprocessor.apply()
def test_number_of_organs(self):
self.assertEqual(
len(self.feature_preprocessor.list_with_features_object), 6)
def test_get_features(self):
dic = self.feature_preprocessor.get_feature()
def test_get_classificator(self):
dic = self.feature_preprocessor.get_data_for_classificator()
class FeaturePreprocessorTest(unittest.TestCase):
def setUp(self):
self.path = 'D:/dane/'
self.feature_preprocessor = FeaturePreprocessor(self.path)
self.feature_preprocessor.apply()
def test_number_of_organs(self):
self.assertEqual(len(self.feature_preprocessor.list_with_features_object), 6)
def test_get_features(self):
dic = self.feature_preprocessor.get_feature()
def test_get_classificator(self):
dic = self.feature_preprocessor.get_data_for_classificator()
__author__ = 'Agnieszka' __author__ = 'Agnieszka' from DataClassification.FeaturePreprocessor import FeaturePreprocessor __author__ = 'Agnieszka' import gc __author__ = 'Agnieszka' import numpy as np import matplotlib.pyplot as plt from sklearn import svm, datasets, cross_validation path = 'D:/dane/' feature_preprocessor = FeaturePreprocessor(path) feature_preprocessor.apply() data, label = feature_preprocessor.get_data_for_classificator() # import some data to play with X = data[:, :-1] # avoid this ugly slicing by using a two-dim dataset y = data[:, -1] h = .02 # step size in the mesh # we create an instance of SVM and fit out data. We do not scale our # data since we want to plot the support vectors C = 1.0 # SVM regularization parameter #svc = svm.SVC(kernel='linear', C=C)
def setUp(self):
self.path = 'D:/dane/'
self.feature_preprocessor = FeaturePreprocessor(self.path)
self.feature_preprocessor.apply()
from DataClassification.FeaturePreprocessor import FeaturePreprocessor
__author__ = 'Agnieszka'
import gc
__author__ = 'Agnieszka'
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets, cross_validation
path = 'D:/dane/'
feature_preprocessor = FeaturePreprocessor(path)
feature_preprocessor.apply()
data,label=feature_preprocessor.get_data_for_classificator()
# import some data to play with
X = data[:,:-1]
# avoid this ugly slicing by using a two-dim dataset
y = data[:,-1]
X = X[y != 6]
y = y[y != 6]
h = .02 # step size in the mesh
# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
C = 1.0 # SVM regularization parameter
svc = svm.SVC(kernel='linear', C=C)
def setUp(self):
self.path = 'D:/dane/'
self.feature_preprocessor = FeaturePreprocessor(self.path)
self.feature_preprocessor.apply()