Esempio n. 1
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def test():
    test_data, test_labels = load_data(test_data_path)
    train_data, train_labels = load_data(train_data_path)

    if args.alg == 'pca':
        saved_data = np.load(
            os.path.join(args.output, args.alg + str(args.dim) + '.npz'))
        mean_vecs = saved_data['mean_vecs']
        eig_vecs = saved_data['eig_vecs']

        reduction_test_data = pca_test(np.array(test_data), mean_vecs,
                                       eig_vecs)
        reduction_train_data = pca_test(np.array(train_data), mean_vecs,
                                        eig_vecs)
    elif args.alg == 'svd':
        saved_data = np.load(
            os.path.join(args.output, args.alg + str(args.dim) + '.npz'))
        v = saved_data['v']
        mean_vecs = saved_data['mean_vecs']

        reduction_test_data = svd_test(np.array(test_data), mean_vecs, v)
        reduction_train_data = svd_test(np.array(train_data), mean_vecs, v)
    else:
        reduction_test_data = isomap(np.array(test_data), n=args.dim)
        reduction_train_data = isomap(np.array(train_data), n=args.dim)

    acc = eval(reduction_test_data, test_labels, reduction_train_data,
               train_labels)
    print('ACC = ' + str(acc))
Esempio n. 2
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def callback(iters, current, env):
    print 'Callback: ', iters

    # get all traces
    env.updategui = False
    traces = env.evaluate_policy(current)
    env.updategui = True
    pickle.dump(traces, open('traces{0}.pck'.format(iters), 'w'),
                pickle.HIGHEST_PROTOCOL)

    # measure task performance
    avg_reward = 0
    for t in traces:
        avg_reward += sum([i[2] for i in t])
    avg_reward = avg_reward / float(len(traces))
    print 'Avg reward: ', avg_reward

    # find current embedding
    ematrix = np.zeros((512, 512))
    for (i, t) in enumerate(traces):
        for (j, s) in enumerate(traces):
            ematrix[i,
                    j] = edit_distance_vc([e[1] for e in t], [l[1] for l in s],
                                          (1.0, 1.0, 1.2))
    pickle.dump(ematrix, open('ematrix{0}.pck'.format(iters), 'w'),
                pickle.HIGHEST_PROTOCOL)
    y, s, adj = isomap(ematrix)
    if len(y) < 512:
        # fallback to mds if more than 1 connected component
        print "More than 1 CC - Falling back to MDS"
        y, s = mds(ematrix)
        adj = None

    # plot stuff later because of pylab / pygame incompat on mac
    # save embedding image - multiple formats?
    #scatter(y[:,0],y[:,1], filename='scatter{0}.pdf'.format(iters))

    # save scree plot
    #plot(s[:10], filename='scree{0}.pdf'.format(iters))

    # procrustes error
    gt = env.coords_array()
    err = procrustes(gt, y)
    print "Procrustes ", err

    pickle.dump((iters, err, gt, avg_reward, current, y, s, adj),
                open('misc{0}.pck'.format(iters), 'w'),
                pickle.HIGHEST_PROTOCOL)

    env.save('iter{0}.png'.format(iters))
Esempio n. 3
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    pickle.dump((iters, err, gt, avg_reward, current, y, s, adj),
                open('misc{0}.pck'.format(iters), 'w'),
                pickle.HIGHEST_PROTOCOL)

    env.save('iter{0}.png'.format(iters))


if __name__ == '__main__':

    workspace = "{0}/wrk/lspi/bin".format(os.environ['HOME'])

    if False:
        # fix isomap issue on bad matches
        ematrix = pickle.load(open('ematrix0.pck'))
        y, s, adj = isomap(ematrix)

    if True:

        endstates = [272]  # [16]

        ogw = RBFObserverGridworldGui(
            "/Users/stober/wrk/lspi/bin/16/20comp.npy",
            "/Users/stober/wrk/lspi/bin/16/states.npy",
            endstates=endstates,
            walls=None,
            nrbf=80)

        # ogw = RBFObserverGridworld("/Users/stober/wrk/lspi/bin/16/20comp.npy", "/Users/stober/wrk/lspi/bin/16/states.npy", endstates = endstates, walls=None, nrbf=80)

        t = pickle.load(open(workspace + "/traces/complete_trace.pck"))
Esempio n. 4
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# -*- coding: utf-8 -*-
from sklearn.manifold import Isomap
import isomap as isa
import sklearn.datasets as db



def isomap(data, d, k):
    '''
    input:data(ndarray):待降维数据
          d(int):降维后数据维度
          k(int):最近的k个样本
    output:Z(ndarray):降维后数据
    '''
    # ********* Begin *********#
    iso = Isomap(n_neighbors=k, n_components=d)
    return iso.fit_transform(data)


if __name__ == '__main__':
    ir = db.load_boston()
    X1 = isa.isomap(ir.data[:10], d=2, k=4)
    X2 = isomap(ir.data[:10], d=2, k=4)
    print(X1)
    print(X2)
Esempio n. 5
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#import lle
#print shape(iris)
#a,b,newData = lle.lle(iris,2,12)
#print shape(newData)
#print newData[w0,:]
#print "---"
#print newData[w1,:]
#print "---"
#print newData[w2,:]
#
#plot(newData[w0,0],newData[w0,1],'ok')
#plot(newData[w1,0],newData[w1,1],'^k')
#plot(newData[w2,0],newData[w2,1],'vk')
#axis('off')

import isomap
print labels
newData,newLabels = isomap.isomap(iris,2,100)
print shape(newData)
print newLabels
w0 = where(newLabels==0)
w1 = where(newLabels==1)
w2 = where(newLabels==2)
plot(newData[w0,0],newData[w0,1],'ok')
plot(newData[w1,0],newData[w1,1],'^k')
plot(newData[w2,0],newData[w2,1],'vk')
axis('off')

show()
Esempio n. 6
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print np.shape(iris)
a,b,newData = lle.lle(iris,2,12)
print np.shape(newData)
print newData[w0,:]
print "---"
print newData[w1,:]
print "---"
print newData[w2,:]

pl.plot(newData[w0,0],newData[w0,1],'ok')
pl.plot(newData[w1,0],newData[w1,1],'^k')
pl.plot(newData[w2,0],newData[w2,1],'vk')
pl.axis('off')

import isomap
print labels
newData,newLabels = isomap.isomap(iris,2,100)
print np.shape(newData)
print newLabels
w0 = np.where(newLabels==0)
w1 = np.where(newLabels==1)
w2 = np.where(newLabels==2)
pl.plot(newData[w0,0],newData[w0,1],'ok')
pl.plot(newData[w1,0],newData[w1,1],'^k')
pl.plot(newData[w2,0],newData[w2,1],'vk')
pl.axis('off')

print "Done"

pl.show()
Esempio n. 7
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        iy = 10 * j + 1
        img[ix:ix + 8, iy:iy + 8] = X[i * n_img_per_row + j].reshape((8, 8))

plt.imshow(img, cmap=plt.cm.binary)
plt.xticks([])
plt.yticks([])
plt.title('A selection from the 64-dimensional digits dataset')


# Random 2D projection using a random unitary matrix
print("Computing random projection")
rp = random_projection.SparseRandomProjection(n_components=2, random_state=42)
X_projected = rp.fit_transform(X)
plot_embedding(X_projected, "Random Projection of the digits")



# Isomap projection of the digits dataset
print("Computing Isomap embedding")
t0 = time()
embedding = isomap(input=X, n_neighbors=n_neighbors, n_components=2, n_jobs=4)
print("Done.")
plot_embedding(embedding,"Isomap projection of the digits (time %.2fs)" %(time() - t0))


embedding_three_dim = isomap(input=X, n_neighbors=n_neighbors, n_components=3, n_jobs=4)
# draw 32 plots
fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(embedding_three_dim[:, 0], embedding_three_dim[:, 1], embedding_three_dim[:, 2], c=y)