def test():
''' test against matlab routine. double check! because there ain't no CVX for this'''
import scipy.io as spio
import matplotlib.pyplot as plt
import designerConv
import convFourier
# load data from a fakel1 data set created by 'testL1.m'
D = spio.loadmat('fakeL1.mat')
m = D['m'].astype('int64').flatten()
p = D['p'].astype('int64').flatten()
q = D['q'].astype('int64').flatten()
wTrue = D['wTrue']
y = D['sig'].flatten()
print 'size of m,p,q ' + repr(m) + ' ' + repr(p) + ' ' + repr(q)
print 'and thes is mp ' + repr(m*p)
rho = 1
lmb = 0.2
W = lasso(m,(p+1)*m,rho,lmb)
# A = designerConv.convOperator(m,p,q)
A = convFourier.convFFT(m,p,q)
A.changeWeights(wTrue)
print wTrue.shape
print y.shape
z = W.solveL1(y, A)
zTrue = D['zTrue'].flatten()
zM = D['zd'].flatten()
print np.linalg.norm(zM-z)
plt.figure(83)
plt.plot(range(z.size), np.abs(z), range(zTrue.size), np.abs(zTrue),
range(zM.size), np.abs(zM))
plt.show()
def test():
''' test against matlab routine. double check! because there ain't no CVX for this'''
import scipy.io as spio
import matplotlib.pyplot as plt
import designerConv
import convFourier
# load data from a fakel1 data set created by 'testL1.m'
D = spio.loadmat('fakeL1.mat')
m = D['m'].astype('int64').flatten()
p = D['p'].astype('int64').flatten()
q = D['q'].astype('int64').flatten()
wTrue = D['wTrue']
y = D['sig'].flatten()
print 'size of m,p,q ' + repr(m) + ' ' + repr(p) + ' ' + repr(q)
print 'and thes is mp ' + repr(m * p)
rho = 1
lmb = 0.2
W = lasso(m, (p + 1) * m, rho, lmb)
# A = designerConv.convOperator(m,p,q)
A = convFourier.convFFT(m, p, q)
A.changeWeights(wTrue)
print wTrue.shape
print y.shape
z = W.solveL1(y, A)
zTrue = D['zTrue'].flatten()
zM = D['zd'].flatten()
print np.linalg.norm(zM - z)
plt.figure(83)
plt.plot(range(z.size), np.abs(z), range(zTrue.size), np.abs(zTrue),
range(zM.size), np.abs(zM))
plt.show()
def test():
import convFourier
import time
import matplotlib.pyplot as plt
p = 5
q = 10
m = 1000
''' create the operator, initialize '''
A = convFourier.convFFT(m, p, q)
w = [
np.random.randn(q, p) / np.sqrt(q),
np.random.randn(q, p) / np.sqrt(q)
]
A.changeWeights(w[0])
C = convFourier.convFFT(m, p, q)
C.changeWeights(w[1])
'''create random x'''
x = [
np.random.randn(m * (p + 1)).astype('complex128'),
np.random.randn(m * (p + 1)).astype('complex128')
]
B = [dtm(m, p, q, fourier=True) for ix in xrange(2)]
for Q, wl in zip(B, w):
Q.changeWeights(wl)
''' apply functional operator and its transpose '''
tm = time.time()
yf = [A.mtx(x[0]), C.mtx(x[1])]
print 'single itme ' + repr(time.time() - tm)
tm = time.time()
yp = [Q.mtx(xl) for Q, xl in zip(B, x)]
print 'new time ' + repr(time.time() - tm)
print 'yf s ' + repr(yf[0].shape)
print 'yp s ' + repr(yp[0].shape)
for a, b in zip(yf, yp):
print 'diff ' + repr(np.linalg.norm(a - b))
tm = time.time()
g = [A.mtxT(yf[0]), C.mtxT(yf[1])]
print 'making list time ' + repr(time.time() - tm)
tm = time.time()
h = [Q.mtxT(yl) for Q, yl in zip(B, yp)]
print 'new time ' + repr(time.time() - tm)
for a, b in zip(g, h):
print 'diff ' + repr(np.linalg.norm(a - b))
plt.figure(1)
plt.subplot(2, 1, 1)
plt.plot(range(yf[0].size), yf[0].real, np.arange(yp[0].size), yp[0].real)
plt.subplot(2, 1, 2)
plt.plot(range(yf[1].size), yf[1].real, np.arange(yp[1].size), yp[1].real)
plt.figure(2)
plt.subplot(2, 1, 1)
plt.plot(range(g[0].size), g[0].real, range(h[0].size), h[0].real)
plt.subplot(2, 1, 2)
plt.plot(range(g[1].size), g[1].real, range(h[1].size), h[1].real)
plt.show()
return yp
def test():
''' a testing routine to make sure that these objects behave as I expect '''
import convFourier
import time
import matplotlib.pyplot as plt
p = 5
q = 10
m = 1000
''' create the operator, initialize '''
A = convFourier.convFFT(m,p,q)
w = [np.random.randn(q,p)/np.sqrt(q), np.random.randn(q,p)/np.sqrt(q)]
A.changeWeights(w[0])
C = convFourier.convFFT(m,p,q)
C.changeWeights(w[1])
'''create random x'''
x = [np.random.randn(m*(p+1)).astype('complex128'), np.random.randn(m*(p+1)).astype('complex128')]
B = [dtm(m,p,q,fourier=True) for ix in xrange(2)]
for Q,wl in zip(B,w):
Q.changeWeights(wl)
''' apply functional operator and its transpose '''
tm = time.time()
yf = [A.mtx(x[0]), C.mtx(x[1])]
print 'single itme ' + repr(time.time()-tm)
tm = time.time()
yp = [Q.mtx(xl) for Q,xl in zip(B,x)]
print 'new time ' + repr(time.time()-tm)
print 'yf s ' + repr(yf[0].shape)
print 'yp s ' + repr(yp[0].shape)
for a,b in zip(yf,yp):
print 'diff ' + repr(np.linalg.norm(a-b))
tm = time.time()
g = [A.mtxT(yf[0]), C.mtxT(yf[1])]
print 'making list time ' + repr(time.time()-tm)
tm = time.time()
h = [Q.mtxT(yl) for Q,yl in zip(B,yp)]
print 'new time ' + repr(time.time()-tm)
for a,b in zip(g,h):
print 'diff ' + repr(np.linalg.norm(a-b))
plt.figure(1)
plt.subplot(2,1,1)
plt.plot(range(yf[0].size), yf[0].real, np.arange(yp[0].size), yp[0].real)
plt.subplot(2,1,2)
plt.plot(range(yf[1].size), yf[1].real, np.arange(yp[1].size), yp[1].real)
plt.figure(2)
plt.subplot(2,1,1)
plt.plot(range(g[0].size), g[0].real, range(h[0].size), h[0].real)
plt.subplot(2,1,2)
plt.plot(range(g[1].size), g[1].real, range(h[1].size), h[1].real)
plt.show()
return yp
def main():
''' main routine '''
comm = MPI.COMM_WORLD
rk = comm.Get_rank()
nProc = comm.Get_size()
plain = True
if len(sys.argv) >= 2:
if sys.argv[1] == 'plain':
plain = False
if len(sys.argv) >= 3:
dts = sys.argv[2]
else:
dts = 'plmr'
m = 50000 # size of data
p = 25 # number of filters
q = 300 # length of filters
if dts == 'plmr':
rho = 1
lmb = 0.5
xi = 0.2
elif dts == 'mpk':
rho = 0.1
lmb = 1e-6
xi = 0.2
fac = 1.0
# np.sqrt((m/q)/2.0)
''' initialize MPI routine '''
''' load data, given rank '''
y = getData(m, dts, rank=rk)
''' initialize weights '''
# D = spio.loadmat('fakew.mat')
# wt = D['wini']
# wt = np.random.randn(q,p)/np.sqrt(q) #D['w']
wt = weightInit(p, q, comm)
if plain:
A = convFFT(m, p, q, fct=fac)
optl1 = lasso(m, m * (p + 1), rho, lmb)
else:
print 'Doing plain!'
A = convOperator(m, p, q)
optl1 = lasso(m, m * (p), rho, lmb)
newWW = weightsUpdate.weightsUpdate(m, p, q, xi, fct=fac)
newWW.wp = wt
rrz = list()
gap = list()
''' begin loop '''
for itz in range(1000):
ws = newWW.wp
A.changeWeights(newWW.wp)
tm = time()
z = optl1.solveL1(y, A)
rrz = optl1.rrz # .append(optl1.rrz.pop())
gap = optl1.gap #.append(optl1.gap.pop())
print 'rank ' + repr(rk) + ' of ' + repr(
nProc) + ' solved l1 itr: ' + repr(itz) + ' time: ' + repr(time() -
tm)
tm = time()
if plain:
wmx = newWW.updateFourier(y, wt, z)
else:
wmx = newWW.updatePlain(y, wt, z)
print 'rank ' + repr(rk) + ' of ' + repr(
nProc) + ' solved w update itr: ' + repr(itz) + ' time: ' + repr(
time() - tm)
tm = time()
wt = weightAgg(wmx, p, q, comm)
wp = newWW.wp
print 'rank ' + repr(rk) + ' of ' + repr(
nProc) + ' have new weights itr: ' + repr(itz) + ' time: ' + repr(
time() - tm)
outd = {
'y': y,
'z': z,
'wt': wt,
'wp': wp,
'm': m,
'p': p,
'q': q,
'rho': rho,
'lmb': lmb,
'xi': xi,
'rrz': rrz,
'gap': gap,
'ws': ws
}
if plain & (dts == 'plmr'):
spio.savemat(
'miniOut_' + repr(p) + '_' + repr(nProc) + '_' + repr(rk),
outd)
elif plain & (dts == 'mpk'):
spio.savemat('miniOutMPK_' + repr(nProc) + '_' + repr(rk), outd)
else:
spio.savemat('plainOut_' + repr(nProc) + '_' + repr(rk), outd)
return y, z, optl1, A, wt
''' for n iterations '''
''' calculate local weights '''
''' all reduce '''
def main():
''' main routine '''
comm = MPI.COMM_WORLD
rk = comm.Get_rank()
nProc = comm.Get_size()
plain = True
if len(sys.argv) >= 2:
if sys.argv[1] == 'plain':
plain = False
if len(sys.argv) >= 3:
dts = sys.argv[2]
else:
dts = 'plmr'
m = 50000 # size of data
p = 25 # number of filters
q = 300 # length of filters
if dts == 'plmr':
rho = 1
lmb = 0.5
xi = 0.2
elif dts == 'mpk':
rho = 0.1
lmb = 1e-6
xi = 0.2
fac = 1.0; # np.sqrt((m/q)/2.0)
''' initialize MPI routine '''
''' load data, given rank '''
y = getData(m,dts,rank=rk)
''' initialize weights '''
# D = spio.loadmat('fakew.mat')
# wt = D['wini']
# wt = np.random.randn(q,p)/np.sqrt(q) #D['w']
wt = weightInit(p,q,comm)
if plain:
A = convFFT(m,p,q,fct=fac)
optl1 = lasso(m,m*(p+1),rho,lmb)
else:
print 'Doing plain!'
A = convOperator(m,p,q)
optl1 = lasso(m,m*(p),rho,lmb)
newWW = weightsUpdate.weightsUpdate(m,p,q,xi,fct=fac)
newWW.wp = wt
rrz = list()
gap = list()
''' begin loop '''
for itz in range(1000):
ws = newWW.wp
A.changeWeights(newWW.wp)
tm = time()
z = optl1.solveL1(y, A)
rrz = optl1.rrz # .append(optl1.rrz.pop())
gap = optl1.gap #.append(optl1.gap.pop())
print 'rank ' + repr(rk) + ' of ' + repr(nProc) + ' solved l1 itr: ' + repr(itz) + ' time: ' + repr(time()-tm)
tm = time()
if plain:
wmx = newWW.updateFourier(y, wt, z)
else:
wmx = newWW.updatePlain(y, wt, z)
print 'rank ' + repr(rk) + ' of ' + repr(nProc) + ' solved w update itr: ' + repr(itz) + ' time: ' + repr(time()-tm)
tm = time()
wt = weightAgg(wmx,p,q,comm)
wp = newWW.wp
print 'rank ' + repr(rk) + ' of ' + repr(nProc) + ' have new weights itr: ' + repr(itz) + ' time: ' + repr(time()-tm)
outd = {'y':y, 'z':z, 'wt':wt,'wp':wp,'m':m,'p':p,'q':q, 'rho':rho,'lmb':lmb, 'xi':xi, 'rrz':rrz,'gap':gap, 'ws':ws }
if plain & (dts == 'plmr'):
spio.savemat('miniOut_' + repr(p) + '_' + repr(nProc) + '_' + repr(rk), outd)
elif plain & (dts == 'mpk'):
spio.savemat('miniOutMPK_' + repr(nProc) + '_' + repr(rk), outd)
else:
spio.savemat('plainOut_' + repr(nProc) + '_' + repr(rk), outd)
return y,z,optl1,A,wt
''' for n iterations '''
''' calculate local weights '''
''' all reduce '''