#s1.sample(1e3, temperature=1)
#print("Gaussian Sampler error: %f" % c.approx_error_data(s1.db, norm_test, labels))
#gavg = c.calc_gavg(s1.db, grid, 50).reshape(-1,n)
#myplot(p.subplot(2,3,2),gavg)
# MPM Model
n,gext,grid = get_grid_data(np.vstack(( data0, data1 )), positive=True)
dist0 = MPMDist(data0,kmax=1)
dist1 = MPMDist(data1,kmax=1)
mpm = MPMCls(dist0, dist1)
#s2 = samc.SAMCRun(mpm, burn=0, stepscale=1000, refden=1, thin=10,
#lim_iters=100, low_margin=0.2, high_margin=-0.5)
#s2.sample(2e5, temperature=2)
mh = mh.MHRun(mpm, burn=100, thin=20)
mh.sample(5e3,verbose=False)
print("MPM Sampler error: %f" % mpm.approx_error_data(mh.db, test, labels))
numlam = 200
gavg = mpm.calc_gavg(mh.db, grid, numlam=numlam).reshape(-1,n)
#g = mpm.calc_curr_g(grid).reshape(-1,n)
ga1 = mpm.dist0.calc_db_g(mh.db, mh.db.root.object.dist0, grid, numlam=numlam).reshape(-1,n)
ga2 = mpm.dist1.calc_db_g(mh.db, mh.db.root.object.dist1, grid, numlam=numlam).reshape(-1,n)
myplot(p.subplot(2,3,2),gavg,data0,data1)
myplot(p.subplot(2,3,3),ga1,data0,data1)
myplot(p.subplot(2,3,4),ga2,data0,data1)
myplot(p.subplot(2,3,5),gavg,test[:500,:],test[500:,:])
p.subplot(2,3,6)
p.plot(test[500:,0], test[500:,1],'m.',alpha=0.5)
labels = np.hstack((np.zeros(N / 2), np.ones(N / 2)))
bayes0 = GaussianBayes(np.zeros(D), 1, 8, np.eye(D) * 3, norm_trn_data0)
bayes1 = GaussianBayes(np.zeros(D), 1, 8, np.eye(D) * 3, norm_trn_data1)
# Gaussian Analytic
gc = GaussianCls(bayes0, bayes1)
errors['gauss'] = gc.approx_error_data(norm_tst_data, labels)
print("Gaussian Analytic error: %f" % errors['gauss'])
# MPM Model
numlam = 100
dist0 = MPMDist(trn_data0, kmax=1, priorkappa=80, lammove=0.02, mumove=0.1)
dist1 = MPMDist(trn_data1, kmax=1, priorkappa=80, lammove=0.02, mumove=0.1)
mpm = MPMCls(dist0, dist1)
mh = mh.MHRun(mpm, burn=100, thin=20)
mh.sample(2e3, verbose=False)
errors['mpm'] = mpm.approx_error_data(mh.db, tst_data, labels, numlam=numlam)
print("MPM Sampler error: %f" % errors['mpm'])
output['acceptance'] = float(mh.accept_loc) / mh.total_loc
output['seed'] = seed
p.figure()
def myplot(ax, g, data0, data1, gext):
ax.plot(data0[:, 0], data0[:, 1], 'g.', label='0', alpha=0.3)
ax.plot(data1[:, 0], data1[:, 1], 'r.', label='1', alpha=0.3)
ax.legend(fontsize=8, loc='best')
im = ax.imshow(g, extent=gext, aspect='equal', origin='lower')
import pandas as pa
import samcnet.mh as mh
from samcnet.mixturepoisson import *
trn_data0 = pa.read_csv('tests/ex_data_0.csv', header=None)
trn_data1 = pa.read_csv('tests/ex_data_1.csv', header=None)
predict_samples = pa.read_csv('tests/ex_data_predict.csv', header=None)
dist0 = MPMDist(trn_data0)
dist1 = MPMDist(trn_data1)
mpm = MPMCls(dist0, dist1)
mh = mh.MHRun(mpm, burn=1000, thin=50, verbose=True)
mh.sample(1e4)
print(mpm.predict(mh.db, predict_samples))
mh.db.close()
bayes0 = GaussianBayes(np.zeros(D), 1, 8, np.eye(D) * 3, norm_trn_data0)
bayes1 = GaussianBayes(np.zeros(D), 1, 8, np.eye(D) * 3, norm_trn_data1)
gc = GaussianCls(bayes0, bayes1)
output['gausserr'] = gc.approx_error_data(norm_tst_data, tst_labels)
print("Gaussian Analytic error: %f" % output['gausserr'])
# MPM Model
dist0 = MPMDist(trn_data0, kmax=1, priorkappa=120, mumove=0.1, lammove=0.05)
dist1 = MPMDist(trn_data1, kmax=1, priorkappa=120, mumove=0.1, lammove=0.05)
mpm = MPMCls(dist0, dist1)
mh = mh.MHRun(mpm, burn=100, thin=20)
t1 = time()
iters = 2e3
numlam = 50
mh.sample(iters, verbose=False)
output['mpmerr'] = mpm.approx_error_data(mh.db,
tst_data,
tst_labels,
numlam=numlam)
print("MPM Sampler error: %f" % output['mpmerr'])
print "Whole run with %d iters and %d numlam took %f seconds" % (iters, numlam,
time() - t1)
p.figure()
def myplot(ax, g, data0, data1, gext):
ax.plot(data0[:, 0], data0[:, 1], 'g.', label='0', alpha=0.3)
ax.plot(data1[:, 0], data1[:, 1], 'r.', label='1', alpha=0.3)
ax.legend(fontsize=8, loc='best')
print("skSVM error: %f" % output['svmerr'])
# Gaussian Analytic
bayes0 = GaussianBayes(np.zeros(D), 1, 8, np.eye(D)*3, norm_trn_data0)
bayes1 = GaussianBayes(np.zeros(D), 1, 8, np.eye(D)*3, norm_trn_data1)
gc = GaussianCls(bayes0, bayes1)
output['gausserr'] = gc.approx_error_data(norm_tst_data, tst_labels)
print("Gaussian Analytic error: %f" % output['gausserr'])
# MPM Model
dist0 = MPMDist(trn_data0,kmax=1)
dist1 = MPMDist(trn_data1,kmax=1)
mpm = MPMCls(dist0, dist1)
mh = mh.MHRun(mpm, burn=1, thin=2)
mh.sample(40,verbose=False)
output['mpmerr'] = mpm.approx_error_data(mh.db, tst_data, tst_labels,numlam=200)
print("MPM Sampler error: %f" % output['mpmerr'])
if 'WORKHASH' in os.environ:
import zmq,time,zlib
import simplejson as js
ctx = zmq.Context()
socket = ctx.socket(zmq.REQ)
socket.connect('tcp://'+server+':7000')
#data = mh.read_db()
data = zlib.compress(js.dumps(output))
socket.send(os.environ['WORKHASH'], zmq.SNDMORE)
socket.send(data)
socket.recv()
bayes0 = GaussianBayes(np.zeros(D), 1, 8, np.eye(D)*3, norm_trn_data0)
bayes1 = GaussianBayes(np.zeros(D), 1, 8, np.eye(D)*3, norm_trn_data1)
gc = GaussianCls(bayes0, bayes1)
output['gausserr'] = gc.approx_error_data(norm_tst_data, tst_labels)
print("Gaussian Analytic error: %f" % output['gausserr'])
# MPM Model
dist0 = MPMDist(trn_data0,kmax=1,priorkappa=120,mumove=0.1,lammove=0.05)
dist1 = MPMDist(trn_data1,kmax=1,priorkappa=120,mumove=0.1,lammove=0.05)
mpm = MPMCls(dist0, dist1)
mh = mh.MHRun(mpm, burn=100, thin=20)
t1=time()
iters = 2e3
numlam = 50
mh.sample(iters,verbose=False)
output['mpmerr'] = mpm.approx_error_data(mh.db, tst_data, tst_labels,numlam=numlam)
print("MPM Sampler error: %f" % output['mpmerr'])
print "Whole run with %d iters and %d numlam took %f seconds" % (iters, numlam, time()-t1)
p.figure()
def myplot(ax,g,data0,data1,gext):
ax.plot(data0[:,0], data0[:,1], 'g.',label='0', alpha=0.3)
ax.plot(data1[:,0], data1[:,1], 'r.',label='1', alpha=0.3)
ax.legend(fontsize=8, loc='best')
im = ax.imshow(g, extent=gext, aspect='equal', origin='lower')
p.colorbar(im,ax=ax)
ax.contour(g, [0.0], extent=gext, aspect=1, origin='lower', cmap = p.cm.gray)
def jit(x):
return x+np.random.randn(*x.shape)/4.0
import pandas as pa
import samcnet.mh as mh
from samcnet.mixturepoisson import *
trn_data0 = pa.read_csv('tests/ex_data_0.csv', header=None)
trn_data1 = pa.read_csv('tests/ex_data_1.csv', header=None)
predict_samples = pa.read_csv('tests/ex_data_predict.csv', header=None)
dist0 = MPMDist(trn_data0)
dist1 = MPMDist(trn_data1)
mpm = MPMCls(dist0, dist1)
mh = mh.MHRun(mpm, burn=1000, thin=50, verbose=True)
mh.sample(1e4)
print(mpm.predict(mh.db, predict_samples))
mh.db.close()
