###########################################
############## SEEDS DATASET ##############
###########################################

from main.ch02.load import load_dataset

feature_names = [
    'area',
    'perimeter',
    'compactness',
    'length of kernel',
    'width of kernel',
    'asymmetry coefficien',
    'length of kernel groove',
]
features, labels = load_dataset('seeds')

from sklearn.neighbors import KNeighborsClassifier

classifier = KNeighborsClassifier(n_neighbors=1)
from sklearn.cross_validation import KFold

kf = KFold(len(features), n_folds=5, shuffle=True)
means = []
for training, testing in kf:
    # We learn a model for this fold with `fit` and then apply it to the
    # testing data with `predict`:
    classifier.fit(features[training], labels[training])
    prediction = classifier.predict(features[testing])

    # np.mean on an array of booleans returns fraction
 def __init__(self):
     global features, labels
     features, labels = load.load_dataset("seeds")
     load.test_load(features, labels)
     logging.info("data loaded successfully")