def run_search(self):
print "begin PES:"
try:
del (self.pesobj)
except:
pass
self.pesobj = PES.PES_inplane(
self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
self.para['axis'],
self.para['value'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'],
noS=True)
self.train_costest()
[Qmin, ymin,
ierror] = self.pesobj.search_acq(self.costest,
self.para['sfn'],
volper=self.para['volper'])
return [Qmin, 0., [sp.NaN]]
def run_search(self):
print "begin PESIS:"
try:
#print "aaa"
del (self.pesobj)
#print "bbb"
except:
print "ccc"
self.pesobj = PES.PES(self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'],
noS=True)
[xmin, ymin,
ierror] = self.pesobj.search_pes(-1, volper=self.para['volper'])
return [xmin, 0., [sp.NaN]]
def run_search(self):
print "begin PES:"
try:
del (self.pesobj)
except:
pass
self.pesobj = PES.PES(self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'])
[Qmin, ymin,
ierror] = self.pesobj.search_acq(self.para['cfn'],
self.para['logsl'],
self.para['logsu'],
volper=self.para['volper'])
return [Qmin[:-1], 10**Qmin[-1], [sp.NaN]]
def PESvsaq(optstate, persist, **para):
para = copy.deepcopy(para)
if persist == None:
persist = {'n': 0, 'd': len(para['ub'])}
n = persist['n']
d = persist['d']
if n < para['nrandinit']:
persist['n'] += 1
return randomaq(optstate, persist, **para)
logger.info('PESvsaq')
#logger.debug(sp.vstack([e[0] for e in optstate.ev]))
#raise
x = sp.vstack(optstate.x)
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
pesobj = PES.PES(x,
y,
s,
dx,
para['lb'],
para['ub'],
para['kindex'],
para['mprior'],
para['sprior'],
DH_SAMPLES=para['DH_SAMPLES'],
DM_SAMPLES=para['DM_SAMPLES'],
DM_SUPPORT=para['DM_SUPPORT'],
DM_SLICELCBPARA=para['DM_SLICELCBPARA'],
mode=para['SUPPORT_MODE'],
noS=para['noS'])
[xmin, ymin, ierror] = pesobj.search_acq(para['cfn'],
para['logsl'],
para['logsu'],
volper=para['volper'])
logger.debug([xmin, ymin, ierror])
para['ev']['s'] = 10**xmin[-1]
xout = [i for i in xmin[:-1]]
return xout, para['ev'], persist, {
'HYPdraws': [k.hyp for k in pesobj.G.kf],
'mindraws': pesobj.Z,
'DIRECTmessage': ierror,
'PESmin': ymin
}
return
def pcs(self):
self.pesobj = PES.PES(self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'])
xmin = self.reccomend()
plt.figure(1)
plt.plot(xmin[0], xmin[1], 'r.')
print xmin
plt.figure(2)
plt.subplot(4, 1, 1)
ns = 6000
sup = sp.linspace(-1, 1, ns)
for i in xrange(2):
X = sp.vstack([xmin for k in xrange(ns)])
print X.shape
for j in xrange(ns):
X[j, i] = sup[j]
[m, v] = self.pesobj.G.infer_diag_post(X, [[sp.NaN]] * ns)
s = sp.sqrt(v)
plt.subplot(4, 1, 2 * i + 1)
plt.fill_between(sup, (m - 2 * s).flatten(), (m + 2 * s).flatten(),
facecolor='lightblue',
edgecolor='lightblue')
plt.plot(sup, m.flatten())
[m, v] = self.pesobj.G.infer_diag_post(X, [[i]] * ns)
s = sp.sqrt(v)
plt.subplot(4, 1, 2 * i + 2)
plt.fill_between(sup, (m - 2 * s).flatten(), (m + 2 * s).flatten(),
facecolor='lightblue',
edgecolor='lightblue')
plt.plot(sup, m.flatten(), 'r')
p = sp.exp(-0.5 * (m**2) / v)
plt.twinx().plot(sup, p.flatten(), 'g')
return
def run_search(self):
print "begin PESFS:"
try:
del (self.pesobj)
except:
pass
self.pesobj = PES.PES(self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'])
[xmin, ymin,
ierror] = self.pesobj.search_pes(self.sdefault,
volper=self.para['volper'])
return [xmin, self.para['s'], [sp.NaN]]
black = [0, 0, 0]
red = [255, 0, 0]
green = [0, 255, 0]
blue = [0, 0, 255]
w = 600
h = 600
# display list of line segments from contouring routine
gr = racg.Graphics(w=600, h=600)
gr.Clear()
s = 0.5 * 10**8
k = 3
x0, y0 = 200, 100
gr.Clear()
G = lambda s: lambda x, y: F(x, y) - s
ix = 50
iy = 50
for y0 in range(1, 600, iy):
for x0 in range(1, 600, ix):
ctr.ShowLevelSet(gr, G(s), x0, y0, blue)
gr.Point([x0, y0], red)
ch = gr.Show("result", 15)
if ch == ord('e'):
break
gr.Show("result", -1)
fn = "ImplicitFunction-ContourVelocity-%02d.jpg" % k
#gr.Save(fn)
gr.Close()
def PESbsaq(optstate, persist, **para):
para = copy.deepcopy(para)
if persist == None:
persist = {'n': 0, 'd': len(para['ub'])}
n = persist['n']
d = persist['d']
if n < para['nrandinit']:
persist['n'] += 1
para['ev']['xa'] = sp.random.uniform(para['xal'], para['xau'])
return randomaq(optstate, persist, **para)
logger.info('PESssaq')
x = sp.hstack(
[sp.vstack([e['xa'] for e in optstate.ev]),
sp.vstack(optstate.x)])
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
print "\n at pesinplane x {} axis 0".format(x)
pesobj = PES.PES_inplane(x,
y,
s,
dx, [para['xal']] + para['lb'],
[para['xau']] + para['ub'],
para['kindex'],
para['mprior'],
para['sprior'],
0,
0,
DH_SAMPLES=para['DH_SAMPLES'],
DM_SAMPLES=para['DM_SAMPLES'],
DM_SUPPORT=para['DM_SUPPORT'],
DM_SLICELCBPARA=para['DM_SLICELCBPARA'],
mode=para['SUPPORT_MODE'])
if para['traincfn']: #
#print "XXXXXXXXXXXXXXx"
cx = sp.vstack([e['xa'] for e in optstate.ev])
cc = sp.vstack([e for e in optstate.c])
#print cx
#print cc
#print optstate.ev
#print optstate.x
cfn = objectives.traincfn(cx, cc)
"""
if len(cc)%5==0:
from matplotlib import pyplot as plt
f,a = plt.subplots(1)
xt = sp.linspace(0,1,100)
m = sp.empty(100)
for i in xrange(100):
m[i]=cfn(0.,**{'xa':xt[i]})
a.plot(xt,m,'b')
for i in xrange(len(optstate.c)):
a.plot(cx[i,0],cc[i,0],'ro')
plt.show()
"""
else:
cfn = para['cfn']
[xmin, ymin, ierror] = pesobj.search_acq(cfn,
lambda s: para['ev']['s'],
volper=para['volper'])
logger.debug([xmin, ymin, ierror])
para['ev']['xa'] = xmin[0]
xout = [i for i in xmin[1:]]
return xout, para['ev'], persist, {
'HYPdraws': [k.hyp for k in pesobj.G.kf],
'mindraws': pesobj.Z,
'DIRECTmessage': ierror,
'PESmin': ymin
}
# Creates stitch outlines for each shape
if args.noOutline is not True:
subshapeLineGroups = prependShapeTraces(paths, subshapeLineGroups, maxStitchDistance=maxStitchDistance)
#DEBUG lines: [[[Line(0+0j, 300+0j), Line(300+0j, 300+300j), Line(300+300j, 0+300j) , Line(0+300j, 0+0j)]]]
PECCommands = createPECStitchRoutines(subshapeLineGroups, fillColors, threadWidth, maxStitchDistance=maxStitchDistance)
left, right, bottom, top = shape.bbox()
pec = PEC(label="simple", colors=PECColors, commands=PECCommands, size=complex(right - left, top - bottom))
# Render the PEC commands
renderPEC(pec)
pes = PES(PEC=pec, shape=shape)
encodedPES = pes.encode()
with open(args.outputFile, "w") as f:
f.write(encodedPES)
print("Wrote {} to disk.".format(args.outputFile))
if args.debugRendering:
loadedPES = pyembroidery.read(args.outputFile)
if loadedPES is not None:
print("Generating debug image.")
debugImagePath = replaceFilenameAndExtensionFromPath(args.outputFile, "debugPicture" + getFilenameAndExtensionFromPath(args.outputFile)[0], "png")
pyembroidery.write_png(loadedPES, debugImagePath)
print("Image written to disk: {}".format(debugImagePath))
else:
from scipy import linalg as spl
from scipy import stats as sps
from matplotlib import pyplot as plt
import GPdc
import PES
import DIRECT
# -------------------------------------------------------------------------
# 2d
nt = 60
d = 2
lb = sp.array([-1.0] * d)
ub = sp.array([1.0] * d)
[X, Y, S, D] = ESutils.gen_dataset(nt, d, lb, ub, GPdc.SQUEXP, sp.array([1.5, 0.35, 0.30]))
G = PES.makeG(X, Y, S, D, GPdc.SQUEXP, sp.array([0.0, -1.0, -1.0]), sp.array([1.0, 1.0, 1.0]), 6)
nz = 8
Z = PES.drawmins_inplane(
G, nz, sp.array([-1.0] * d), sp.array([1.0] * d), axis=0, value=0.0, SUPPORT=500, SLICELCB_PARA=1.0
)
print Z
Ga = [
GPdc.GPcore(*PES.addmins_inplane(G, X, Y, S, D, Z[i, :], axis=0, value=0.0, MINPOLICY=PES.NOMIN) + [G.kf])
for i in xrange(nz)
]
np = 220
sup = sp.linspace(-1, 1, np)
Dp = [[sp.NaN]] * np
Xp0 = sp.vstack([sp.array([i, Z[0, 1]]) for i in sup])
def directwrap(Q, extra):
x = sp.array([Q])
s = sf
acq = PES.PESgain(G, Ga, Z, x, [[sp.NaN]], [s])
R = -acq / cost(x, s)
return (R, 0)
import ESutils
import scipy as sp
from scipy import linalg as spl
from scipy import stats as sps
from matplotlib import pyplot as plt
import GPdc
import PES
nt=20
d=1
lb = sp.array([-1.]*d)
ub = sp.array([1.]*d)
[X,Y,S,D] = ESutils.gen_dataset(nt,d,lb,ub,GPdc.SQUEXP,sp.array([1.5,0.15]))
G = PES.makeG(X,Y,S,D,GPdc.SQUEXP,sp.array([0.,-1.]),sp.array([1.,1.]),18)
H = sp.vstack([i.hyp for i in G.kf])
f,a = plt.subplots(1)
a.plot(H[:,0],H[:,1],'r.')
np=100
sup = sp.linspace(-1,1,np)
Dp = [[sp.NaN]]*np
Xp = sp.vstack([sp.array([i]) for i in sup])
[m,V] = G.infer_diag(Xp,Dp)
z=5
f,a = plt.subplots(z+2)
for e,i in enumerate(sp.random.choice(range(m.shape[0]),size=z,replace=False)):
s = sp.sqrt(V[i,:])
a[e].fill_between(sup,sp.array(m[i,:]-2.*s).flatten(),sp.array(m[i,:]+2.*s).flatten(),facecolor='lightblue',edgecolor='lightblue')
import ESutils import DIRECT import scipy as sp from scipy import linalg as spl from scipy import stats as sps from matplotlib import pyplot as plt import GPdc import PES nt=12 d=1 lb = sp.array([-1.]*d) ub = sp.array([1.]*d) [X,Y,S,D] = ESutils.gen_dataset(nt,d,lb,ub,GPdc.SQUEXP,sp.array([1.5,0.15])) G = PES.makeG(X,Y,S,D,GPdc.SQUEXP,sp.array([0.,-1.]),sp.array([1.,1.]),12) Z=PES.drawmins(G,8,sp.array([-1.]),sp.array([1.]),SUPPORT=400,SLICELCB_PARA=1.) Ga = GPdc.GPcore(*PES.addmins(G,X,Y,S,D,Z[0,:])+[G.kf]) np=100 sup = sp.linspace(-1,1,np) Dp = [[sp.NaN]]*np Xp = sp.vstack([sp.array([i]) for i in sup]) [m,V] = G.infer_diag_post(Xp,Dp) [mp,Vp] = Ga.infer_diag_post(Xp,Dp) f,a = plt.subplots(2) s = sp.sqrt(V[0,:]) a[0].fill_between(sup,sp.array(m[0,:]-2.*s).flatten(),sp.array(m[0,:]+2.*s).flatten(),facecolor='lightblue',edgecolor='lightblue')
import ESutils
import scipy as sp
from scipy import linalg as spl
from scipy import stats as sps
from matplotlib import pyplot as plt
import GPdc
import PES
nt = 20
d = 1
lb = sp.array([-1.] * d)
ub = sp.array([1.] * d)
[X, Y, S, D] = ESutils.gen_dataset(nt, d, lb, ub, GPdc.SQUEXP,
sp.array([1.5, 0.15]))
G = PES.makeG(X, Y, S, D, GPdc.SQUEXP, sp.array([0., -1.]), sp.array([1., 1.]),
18)
H = sp.vstack([i.hyp for i in G.kf])
f, a = plt.subplots(1)
a.plot(H[:, 0], H[:, 1], 'r.')
np = 100
sup = sp.linspace(-1, 1, np)
Dp = [[sp.NaN]] * np
Xp = sp.vstack([sp.array([i]) for i in sup])
[m, V] = G.infer_diag(Xp, Dp)
z = 5
f, a = plt.subplots(z + 2)
for e, i in enumerate(
sp.random.choice(range(m.shape[0]), size=z, replace=False)):
s = sp.sqrt(V[i, :])
return val
black = [0,0,0]
red = [255,0,0]
green = [0,255,0]
blue = [0,0,255]
w = 600
h = 600
# display list of line segments from contouring routine
gr = racg.Graphics(w=600,h=600)
gr.Clear()
s = 0.5*10**8
k = 4
x0,y0 = 200,100
gr.Clear()
G = lambda s: lambda x,y: F(x,y)-s
ix = 100
iy = 100
for y0 in range(1,600,iy):
for x0 in range(1,600,ix):
ctr.ShowLevelSet(gr,G(s), x0, y0, blue, tmax = 50)
gr.Point([x0,y0],red)
ch = gr.Show("result",15)
if ch == ord('e'):
break
gr.Show("result",-1)
fn = "ImplicitFunction-ContourVelocity-%02d.jpg" % k
#gr.Save(fn)
gr.Close()
from scipy import stats as sps
from matplotlib import pyplot as plt
import GPdc
import PES
import DIRECT
#-------------------------------------------------------------------------
#2d
nt = 60
d = 2
lb = sp.array([-1.] * d)
ub = sp.array([1.] * d)
[X, Y, S, D] = ESutils.gen_dataset(nt, d, lb, ub, GPdc.SQUEXP,
sp.array([1.5, 0.35, 0.30]))
G = PES.makeG(X, Y, S, D, GPdc.SQUEXP, sp.array([0., -1., -1.]),
sp.array([1., 1., 1.]), 6)
nz = 8
Z = PES.drawmins_inplane(G,
nz,
sp.array([-1.] * d),
sp.array([1.] * d),
axis=0,
value=0.,
SUPPORT=500,
SLICELCB_PARA=1.)
print Z
Ga = [
GPdc.GPcore(*PES.addmins_inplane(
G, X, Y, S, D, Z[i, :], axis=0, value=0., MINPOLICY=PES.NOMIN) +
[G.kf]) for i in xrange(nz)
lb = sp.array([-1.] * d)
ub = sp.array([1.] * d)
[X, Y, S, D] = ESutils.gen_dataset(nt, d, lb, ub, GPdc.SQUEXP,
sp.array([1.5, 0.35, 0.30]))
kindex = GPdc.SQUEXP
mprior = sp.array([0.] + [-1.] * d)
sprior = sp.array([1.] * (d + 1))
pesobj = PES.PES(X,
Y,
S,
D,
lb,
ub,
kindex,
mprior,
sprior,
DH_SAMPLES=8,
DM_SAMPLES=8,
DM_SUPPORT=400,
DM_SLICELCBPARA=1.,
mode=ESutils.SUPPORT_SLICEEI)
np = 150
sup = sp.linspace(-1, 1, np)
Dp = [[sp.NaN]] * np
Sp = sp.array([[1e-3] * np]).T
Xp0 = sp.vstack([sp.array([i, pesobj.Z[0, 1]]) for i in sup])
Xp1 = sp.vstack([sp.array([pesobj.Z[0, 0], i]) for i in sup])
f, a = plt.subplots(d)
sp.array([1.5, 0.55, 0.25]))
kindex = GPdc.SQUEXP
mprior = sp.array([0.] + [-1.] * d)
sprior = sp.array([1.] * (d + 1))
axis = 0
value = 0.
pesobj = PES.PES_inplane(X,
Y,
S,
D,
lb,
ub,
kindex,
mprior,
sprior,
axis,
value,
DH_SAMPLES=8,
DM_SAMPLES=8,
DM_SUPPORT=400,
DM_SLICELCBPARA=1.,
AM_POLICY=PES.NOMIN,
mode=ESutils.SUPPORT_SLICEEI)
def cfn(x):
return 1. - (0.6 * x[0])**0.1
def sfn(x):
