def main():
features, labels = three_dimension.generate_data()
three_dimension.show_data(features, labels)
pca = PCA(features, 2)
res = np.array(pca.process())
# print(res)
plt.title('Reduced three dimensions data.')
plt.scatter(res[np.argwhere(labels == 0), 0], res[np.argwhere(labels == 0), 1], c='b')
plt.scatter(res[np.argwhere(labels == 1), 0], res[np.argwhere(labels == 1), 1], c='g')
plt.show()
plt.close()
dots, labels = two_dimension.generate_data()
two_dimension.show_data(dots, labels)
pca = PCA(dots, 1)
res = np.array(pca.process())
# print(res)
plt.title('Reduced two dimensions data.')
plt.scatter(res[np.argwhere(labels == 0), 0], np.zeros(shape=res[np.argwhere(labels == 0), 0].shape), c='b')
plt.scatter(res[np.argwhere(labels == 1), 0], np.zeros(shape=res[np.argwhere(labels == 0), 0].shape), c='g')
plt.show()
plt.close()
def main():
features, labels = three_dimension.generate_data()
three_dimension.show_data(features, labels)
dne = DNE(features, labels, 2)
res = np.array(dne.process())
# print(res)
plt.title('Reduced three dimensions data.')
plt.scatter(res[np.argwhere(labels == 0), 0], res[np.argwhere(labels == 0), 1], c='b')
plt.scatter(res[np.argwhere(labels == 1), 0], res[np.argwhere(labels == 1), 1], c='g')
plt.show()
plt.close()
def main():
marker = 'x'
features, labels = three_dimension.generate_data()
three_dimension.show_data(features, labels)
pca = PCA(copy.deepcopy(features), 2)
res = np.array(pca.process())
# print(res)
plt.title('PCA')
plt.scatter(res[np.argwhere(labels == 0), 0],
res[np.argwhere(labels == 0), 1],
c='b',
marker=marker)
plt.scatter(res[np.argwhere(labels == 1), 0],
res[np.argwhere(labels == 1), 1],
c='g',
marker=marker)
plt.show()
plt.close()
dne = DNE(copy.deepcopy(features), labels, 2)
res = np.array(dne.process())
# print(res)
plt.title('DNE')
plt.scatter(res[np.argwhere(labels == 0), 0],
res[np.argwhere(labels == 0), 1],
c='b',
marker=marker)
plt.scatter(res[np.argwhere(labels == 1), 0],
res[np.argwhere(labels == 1), 1],
c='g',
marker=marker)
plt.show()
plt.close()
lpp = LPP(copy.deepcopy(features), 2, t=200)
res = np.array(lpp.process())
# print(res)
plt.title('LPP')
plt.scatter(res[np.argwhere(labels == 0), 0],
res[np.argwhere(labels == 0), 1],
c='b',
marker=marker)
plt.scatter(res[np.argwhere(labels == 1), 0],
res[np.argwhere(labels == 1), 1],
c='g',
marker=marker)
plt.show()
plt.close()
ldne = LDNE(copy.deepcopy(features), labels, 2, t=10)
res = np.array(ldne.process())
# print(res)
plt.title('LDNE')
plt.scatter(res[np.argwhere(labels == 0), 0],
res[np.argwhere(labels == 0), 1],
c='b',
marker=marker)
plt.scatter(res[np.argwhere(labels == 1), 0],
res[np.argwhere(labels == 1), 1],
c='g',
marker=marker)
plt.show()
plt.close()