import numpy as np
from matplotlib import pyplot as plt
from import ListedColormap
from import KNeighborsClassifier
from sklearn.cross_validation import KFold, cross_val_score
feature_names = [
'area',
'perimeter',
'compactness',
'length of kernel',
'width of kernel',
'asymmetry coefficien',
'length of kernel groove',
]
COLOUR_FIGURE = False
def plot_decision(features, labels, num_neighbors=3):
y_min, y_max = features[:, 2].min() * .9, features[:, 2].max() * 1.1
x_min, x_max = features[:, 0].min() * .9, features[:, 0].max() * 1.1
X, Y = ((x_min, x_max, 1000), (y_min, y_max, 1000))
model = KNeighborsClassifier(num_neighbors)
(features[:, (0,2)], labels)
C = (([(), ()]).T).reshape()
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .7, .7), (.7, 1., .7), (.7, .7, 1.)])
else:
cmap = ListedColormap([(1., 1., 1.), (.2, .2, .2), (.6, .6, .6)])
fig,ax = ()
ax.set_xlim(x_min, x_max)
ax.set_ylim(y_min, y_max)
ax.set_xlabel(feature_names[0])
ax.set_ylabel(feature_names[2])
(X, Y, C, cmap=cmap)
if COLOUR_FIGURE:
cmap = ListedColormap([(1., .0, .0), (.1, .6, .1), (.0, .0, 1.)])
(features[:, 0], features[:, 2], c=labels, cmap=cmap)
else:
for lab, ma in zip(range(3), "Do^"):
(features[labels == lab, 0],
features[labels == lab, 2],
ma,
c=(1., 1., 1.),
ms=6)
return fig, ax
def load_csv_data(filename):
data = []
labels = []
datafile = open(filename)
for line in datafile:
fields = ().split('\t')
([float(field) for field in fields[:-1]])
(fields[-1])
data = (data)
labels = (labels)
return data, labels
def accuracy(test_labels, pred_lables):
correct = (test_labels == pred_lables)
n = len(test_labels)
return float(correct) / n
if __name__ == '__main__':
opt = input("raw_inputp[1 or 2]: ")
features, labels = load_csv_data('data/')
if opt == '1':
knn = KNeighborsClassifier(n_neighbors=5)
kf = KFold(len(features), n_folds=3, shuffle=True)
result_set = [((features[train], labels[train]).predict(features[test]), test) for train, test in kf]
score = [accuracy(labels[result[1]], result[0]) for result in result_set]
print(score)
elif opt == '2':
names = sorted(set(labels))
labels = ([(ell) for ell in labels])
fig, ax = plot_decision(features, labels)
()
else:
print('input 1 or 2 !')
