# coding: utf-8
import numpy as np
from runner import Runner
from mlcin.prototypes.rps import RandomPrototypeSelection
from mlcin.utils.keel import load_dataset
from mlcin.utils.graphics import plot_and_save
if __name__ == '__main__':
# creating prototype generation object
rps = RandomPrototypeSelection()
datasets = ['banana', 'normal', 'normal_multimodal']
for dataset in datasets:
X_orig, y_orig = load_dataset('datasets/artificial/' + dataset + '.data')
y_orig = np.asarray(y_orig, dtype=int)
rps.fit(X_orig, y_orig)
X, y = rps.reduce_data()
print dataset + '\treduction: %.2f' % (1.0 - float(y.shape[0])/len(y_orig))
plot_and_save(X_orig, y_orig, title='ORIGINAL', filename='images/ORIG_' + dataset + '.png')
plot_and_save(X, y, title='RSP', filename='images/RSP_' + dataset + '.png')
def get_prototypes(self, X, y):
rps = RandomPrototypeSelection()
rps.fit(X, y).reduce_data()
return rps.get_prototypes()
def get_prototypes(self, X, y):
rps = RandomPrototypeSelection()
rps.fit(X, y).reduce_data()
print rps.get_prototypes()
return rps.get_prototypes()
# coding: utf-8
import numpy as np
from runner import Runner
from mlcin.prototypes.rps import RandomPrototypeSelection
from mlcin.utils.keel import load_dataset
from mlcin.utils.graphics import plot_and_save
if __name__ == '__main__':
# creating prototype generation object
rps = RandomPrototypeSelection()
datasets = ['banana', 'normal', 'normal_multimodal']
for dataset in datasets:
X_orig, y_orig = load_dataset('datasets/artificial/' + dataset +
'.data')
y_orig = np.asarray(y_orig, dtype=int)
rps.fit(X_orig, y_orig)
X, y = rps.reduce_data()
print dataset + '\treduction: %.2f' % (1.0 -
float(y.shape[0]) / len(y_orig))
plot_and_save(X_orig,
y_orig,
title='ORIGINAL',
filename='images/ORIG_' + dataset + '.png')
plot_and_save(X,
y,
title='RSP',
filename='images/RSP_' + dataset + '.png')