def load_wine_data_set(self):
# Load a non linearly separable dataset
# There are 13 different features in the Wine dataset, describing the chemical properties of the 178 wine
# samples, and each sample belongs to one of three different classes, 1, 2, and 3, which refer to the three
# different types of grape grown in the same region in Italy but derived from different wine cultivars, as
# described in the dataset summary (https://archive. ics.uci.edu/ml/machine-learning-databases/wine/wine.names).
# load wine data set
df = pd.read_csv(
FilesystemUtils.get_resources_data_file_name('wine/wine.data'),
header=None)
# create headers
df.columns = [
'Class label', 'Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash',
'Magnesium', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols',
'Proanthocyanins', 'Color intensity', 'Hue',
'OD280/OD315 of diluted wines', 'Proline'
]
# print sample info:
# values of class labels
print('Class labels', np.unique(df['Class label']))
# 10% of the shuffled data set samples with respective class labels (shuffling is necessary as rows are sorted
# according to class label values in increasing order
print(df.sample(frac=0.1).to_string())
# split training and test set
# separate class labels from features
self.x, self.y = df.iloc[:, 1:].values, df.iloc[:, 0].values
# save column names for later usage
self.df_columns = df.columns
def setUp(self):
# load subset of Iris data
iris_data_reader = IrisDataReader(
FilesystemUtils.get_resources_data_file_name('iris/iris.data'))
self.x, self.y = iris_data_reader.get_data()
# plotter data and save it to file
Plotter.plot_iris_data_set(
self.x,
FilesystemUtils.get_test_resources_plot_file_name(
'adaline/Adaline-Training-Set.png'))