# Copyright (c) 2015, Qiurui He # Department of Engineering, University of Cambridge import numpy as np import pods from gpcdata import GPCData # 2D, default axis naming data = pods.datasets.crescent_data(seed=498) X = data['X'] Y = data['Y'] Y[Y.flatten() == -1] = 0 d = GPCData(X, Y) print d print d.getDataShape() # print d.getClass(0) # print d.getClass(1) # 4D - Iris dataset data = pods.datasets.iris() X = data['X'] Y = data['Y'] versi_ind = np.where(Y == 'Iris-versicolor') virgi_ind = np.where(Y == 'Iris-virginica') X = np.hstack((X[versi_ind,:], X[virgi_ind,:])).squeeze() Ynum = np.zeros(Y.size) Ynum[virgi_ind] = 1 Ynum = np.hstack((Ynum[versi_ind], Ynum[virgi_ind])).reshape(X.shape[0], 1) d = GPCData(X, Ynum, XLabel=('Sepal length', 'Sepal width', 'Petal length', 'Petal width'), YLabel=['Versicolor', 'Virginica']) print d # xx, yy, xt, yt = d.kFoldSplits()