def next_activity(self):
"""Return activity matrix for next time step"""
A = np.zeros((self.nyg_ex, self.nxg_ex), int)
rec = self.next_record()
for k in range(len(rec)):
kx = conv.kxPos(rec[k], self.nxg_ex, self.nyg_ex, self.nf)
ky = conv.kyPos(rec[k], self.nxg_ex, self.nyg_ex, self.nf)
A[ky, kx] = 1
return A
def next_activity(self):
"""Return activity matrix for next time step"""
A = np.zeros((self.nyg_ex, self.nxg_ex), int)
rec = self.next_record()
for k in range(len(rec)):
kx = conv.kxPos(rec[k], self.nxg_ex, self.nyg_ex, self.nf)
ky = conv.kyPos(rec[k], self.nxg_ex, self.nyg_ex, self.nf)
A[ky,kx] = 1
return A
row = int(y + 0.5)
x2 = (x / 16.0)
y2 = (y / 16.0)
x = (x / 4.0)
y = (y / 4.0)
if col >= 0 and col < numcols and row >= 0 and row < numrows:
z = P[row, col]
return 'x=%1.4f, y=%1.4f, z=%1.4f' % (x, y, z)
else:
return 'x=%1.4d, y=%1.4d, x2=%1.4d, y2=%1.4d' % (int(x), int(y),
int(x2), int(y2))
k = 16
for ko in range(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
p = w.next_patch()
poff = wOff.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
acount = acount + 1
if kxOn == margin + 1 and kyOn == margin + 1:
don = p
doff = poff
d = np.append(don, doff)
else:
don = p
doff = poff
e = np.append(don, doff)
d = np.vstack((d, e))
nf = w.nf
margin = 10
marginstart = margin
marginend = nx - margin
nx_im = nx * (nxp + space) + space
ny_im = ny * (nyp + space) + space
im = np.zeros((nx_im, ny_im))
im[:, :] = (w.max - w.min) / 2.
h = np.zeros(9, dtype=int)
g = np.zeros(9, dtype=int)
for k in range(numpat):
kxOn = conv.kxPos(k, nx, ny, nf)
kyOn = conv.kyPos(k, nx, ny, nf)
p = w.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
d = whatFeature(p)
e = d[0]
f = d[1]
e += 1
f += 1
h[e] += 1
g[f] += 1
fig = plt.figure()
ax = fig.add_subplot(211, axisbg='darkslategray')
ax.plot(np.arange(len(h)), h, 'o', color='y')
nf = w.nf
margin = 10
marginstart = margin
marginend = nx - margin
nx_im = nx * (nxp + space) + space
ny_im = ny * (nyp + space) + space
im = np.zeros((nx_im, ny_im))
im[:,:] = (w.max - w.min) / 2.
h = np.zeros(9, dtype=int)
g = np.zeros(9, dtype=int)
for k in range(numpat):
kxOn = conv.kxPos(k, nx, ny, nf)
kyOn = conv.kyPos(k, nx, ny, nf)
p = w.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
d = whatFeature(p)
e = d[0]
f = d[1]
e += 1
f += 1
h[e] += 1
g[f] += 1
fig = plt.figure()
ax = fig.add_subplot(211, axisbg='darkslategray')
ax.plot(np.arange(len(h)), h, 'o', color='y')
rheady = ry[2]
rtaily = ry[3]
nx_im = wnx * (wnxp + space) + space
ny_im = wny * (wnyp + space) + space
small = w.max / 2
im = np.zeros((nx_im, ny_im))
im[:, :] = (w.max - w.min) / 2.
count = 0
wheremp = []
for k in range(w.numPatches):
kx = conv.kxPos(k, wnx, wny, wnf)
ky = conv.kyPos(k, wnx, wny, wnf)
P = w.next_patch()
if len(P) != wnxp * wnyp:
continue
if kx == kxPre:
if ky == kyPre:
mp = P
for i in range(wnxp * wnxp):
if mp[i] <= small:
mp[i] = 0.0
else:
wheremp = np.append(wheremp, count)
count += 1
mp = np.reshape(mp, (wnxp, wnyp))
nx_im = wnx * (wnxp + space) + space
ny_im = wny * (wnyp + space) + space
small = w.max / 2
im = np.zeros((nx_im, ny_im))
im[:,:] = (w.max - w.min) / 2.
count = 0
wheremp = []
for k in range(w.numPatches):
kx = conv.kxPos(k, wnx, wny, wnf)
ky = conv.kyPos(k, wnx, wny, wnf)
P = w.next_patch()
if len(P) != wnxp * wnyp:
continue
if kx == kxPre:
if ky == kyPre:
mp = P
for i in range(wnxp*wnxp):
if mp[i] <= small:
mp[i] = 0.0
else:
wheremp = np.append(wheremp, count)
count += 1
mp = np.reshape(mp, (wnxp, wnyp))
def k_stability_analysis(k, forwardjump):
w = rw.PVReadWeights(sys.argv[1])
feature = k - 1
count = 0
d = np.zeros((nxp, nyp))
w.rewind()
for ko in np.arange(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
p = w.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
if cluster[count] == feature:
e = p
e = e.reshape(nxp, nyp)
numrows, numcols = e.shape
count = count + 1
patpos = w.file.tell()
patchposition.append(patpos)
else:
e = d
count = count + 1
else:
e = d
else:
e = d
x = (nxp) * (ko % nx)
y = (nyp) * (ko / nx)
im2[y:y + nyp, x:x + nxp] = e
##########
# Find Valuse of K-cluster[x] Patches
##########
w = rw.PVReadWeights(sys.argv[2])
w.rewind()
patpla = patchposition
lenpat = len(patpla)
number = w.numPatches
count = 0
exp = []
exppn = []
body = w.recSize + 4
hs = w.headerSize
filesize = os.path.getsize(sys.argv[2])
bint = filesize / body
bint = bint - forwardjump - 1
if forwardjump == 0:
4
else:
leap = ((body * forwardjump) + (100 * forwardjump))
w.file.seek(leap, os.SEEK_CUR)
for i in range(bint):
if i == 0:
for j in range(lenpat):
if j == 0:
go = patpla[0] - hs - patchblen
w.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
if len(p) == 0:
print "STOPPEP SUPER EARLY"
sys.exit()
d = p
allpat = 0
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn), (1, flatnxp))
p = np.reshape(np.matrix(p), (flatnxp, 1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp, pm)
else:
pospost = patpla[j - 1]
poscur = patpla[j]
jump = poscur - pospost - patchblen
w.file.seek(jump, os.SEEK_CUR)
p = w.next_patch()
if len(p) == 0:
print "STOPPED EARLY"
sys.exit()
d = p
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn), (1, flatnxp))
p = np.reshape(np.matrix(p), (flatnxp, 1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp, pm)
#print "Ch-Ch-Changes", exppn
else:
count = 0
prejump = body - patpla[lenpat - 1] + hs
w.file.seek(prejump, os.SEEK_CUR)
for j in range(lenpat):
if j == 0:
go = patpla[0] - 4 - patchblen
w.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
test = p
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
d = p
p = w.normalize(d)
p = np.reshape(np.matrix(p), (flatnxp, 1))
j1 = 0
j2 = flatnxp
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp, pm)
count += 1
else:
pospost = patpla[j - 1]
poscur = patpla[j]
jump = poscur - pospost - patchblen
w.file.seek(jump, os.SEEK_CUR)
p = w.next_patch()
test = p
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
d = p
p = w.normalize(d)
p = np.reshape(np.matrix(p), (flatnxp, 1))
j1 = flatnxp * j
j2 = flatnxp * (j + 1)
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp, pm)
count += 1
##########
# Find Average of K-cluster[x] Weights
##########
thenumber = lenpat
thenumberf = float(thenumber)
patpla = exp
lenpat = len(patpla)
howlong = lenpat / thenumber
total = []
logtotal = []
for i in range(thenumber):
subtotal = []
logsubtotal = []
for j in range(howlong):
if i == 0:
value = patpla[i + (thenumber * j)]
total = np.append(total, value)
logvalue = patpla[i + (thenumber * j)]
logvalue = math.log10(logvalue)
logtotal = np.append(logtotal, logvalue)
else:
value = patpla[i + (thenumber * j)]
subtotal = np.append(subtotal, value)
logvalue = patpla[i + (thenumber * j)]
logvalue = math.log10(logvalue)
logsubtotal = np.append(logsubtotal, logvalue)
if i > 0:
total = total + subtotal
if i > 0:
logtotal = logtotal + logsubtotal
total = total / thenumberf
logtotal = logtotal / thenumberf
global total1
global total2
global total3
global total4
global total5
global total6
global total7
global total8
global total9
global total10
global total11
global total12
global total13
global total14
global total15
global total16
global logtotal1
global logtotal2
global logtotal3
global logtotal4
global logtotal5
global logtotal6
global logtotal7
global logtotal8
global logtotal9
global logtotal10
global logtotal11
global logtotal12
global logtotal13
global logtotal14
global logtotal15
global logtotal16
if feature == 0:
total1 = [0.0]
total2 = [0.0]
total3 = [0.0]
total4 = [0.0]
total5 = [0.0]
total6 = [0.0]
total7 = [0.0]
total8 = [0.0]
total9 = [0.0]
total10 = [0.0]
total11 = [0.0]
total12 = [0.0]
total13 = [0.0]
total14 = [0.0]
total15 = [0.0]
total16 = [0.0]
logtotal1 = [0.0]
logtotal2 = [0.0]
logtotal3 = [0.0]
logtotal4 = [0.0]
logtotal5 = [0.0]
logtotal6 = [0.0]
logtotal7 = [0.0]
logtotal8 = [0.0]
logtotal9 = [0.0]
logtotal10 = [0.0]
logtotal11 = [0.0]
logtotal12 = [0.0]
logtotal13 = [0.0]
logtotal14 = [0.0]
logtotal15 = [0.0]
logtotal16 = [0.0]
if feature == 0:
total1 = total
logtotal1 = logtotal
if feature == 1:
total2 = total
logtotal2 = logtotal
if feature == 2:
total3 = total
logtotal3 = logtotal
if feature == 3:
total4 = total
logtotal4 = logtotal
if feature == 4:
total5 = total
logtotal5 = logtotal
if feature == 5:
total6 = total
logtotal6 = logtotal
if feature == 6:
total7 = total
logtotal7 = logtotal
if feature == 7:
total8 = total
logtotal8 = logtotal
if feature == 8:
total9 = total
logtotal9 = logtotal
if feature == 9:
total10 = total
logtotal10 = logtotal
if feature == 10:
total11 = total
logtotal11 = logtotal
if feature == 11:
total12 = total
logtotal12 = logtotal
if feature == 12:
total13 = total
logtotal13 = logtotal
if feature == 13:
total14 = total
logtotal14 = logtotal
if feature == 14:
total15 = total
logtotal15 = logtotal
if feature == 15:
total16 = total
logtotal16 = logtotal
return
#ax.plot(np.arange(len(A15q)), A15q, color=cm.Paired(0.90) , ls = '-')
#ax.plot(np.arange(len(A16q)), A16q, color=cm.Paired(0.96) , ls = '-')
plt.show()
sys.exit()
if 1 == 1:
kd = []
AW = AW.reshape(lenofb, 1)
AWO = AWO.reshape(lenofb, 1)
count = 0
for k in range(w.numPatches):
p = w.next_patch()
pO = wO.next_patch()
kx = conv.kyPos(k, nx, ny, nf)
ky = conv.kyPos(k, nx, ny, nf)
if len(p) != nxp * nyp:
continue
#print "p = ", p
count += 1
#print "count = ", count
if AW[k] == 1:
if len(kd) == 0:
don = p
doff = pO
kd = np.append(don, doff)
else:
don = p
for k in range(len(M)):
for b in range(numbins):
if (M[k] > bins[b]): dist[b] += 1
dist = dist / count
# print maximum projection
#
maxp = 0
maxk = 0
for k in range(len(M)):
if M[k] > maxp:
maxp = M[k]
maxk = k
kx = conv.kxPos(k, numcols, numrows, w.nf)
ky = conv.kyPos(k, numcols, numrows, w.nf)
print "maximum projected value = ", maxp, maxk, kx, ky
M = M.reshape((numrows, numcols))
# print averaged projection over column
#
s = np.zeros(numcols)
maxs = 0.0
maxcol = 0
for col in range(numcols):
s[col] = np.sum(M[:, col])
if s[col] > maxs:
maxs = s[col]
maxcol = col
s = s / numrows
def k_stability_analysis(k, forwardjump):
w = rw.PVReadWeights(sys.argv[1])
feature = k - 1
count = 0
d = np.zeros((nxp,nyp))
w.rewind()
for ko in np.arange(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
p = w.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
##########
# Find Valuse of K-cluster[x] Patches
##########
w = rw.PVReadWeights(sys.argv[3])
wOff = rw.PVReadWeights(sys.argv[4])
w.rewind()
wOff.rewind()
patpla = patchposition
lenpat = len(patpla)
number = w.numPatches
count = 0
exp = []
expOff = []
exppn = []
exppnOff = []
body = w.recSize + 4
hs = w.headerSize
filesize = os.path.getsize(sys.argv[3])
bint = filesize / body
bint = bint - forwardjump - 1
if forwardjump == 0:
print "43110"
else:
leap = (body * forwardjump)
w.file.seek(leap, os.SEEK_CUR)
for i in range(bint):
if i == 0:
for j in range(lenpat):
if j == 0:
p = w.next_patch()
pOff = wOff.next_patch()
if len(p) == 0:
print"STOPPEP SUPER EARLY"
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn),(1,32))
p = np.reshape(np.matrix(p),(32,1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp,pm)
else:
p = w.next_patch()
pOff = wOff.next_patch()
if len(pOff) == 0:
print"STOPPED EARLY"
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn),(1,32))
p = np.reshape(np.matrix(p),(32,1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp,pm)
else:
count = 0
prejump = body - patpla[lenpat-1] + hs
w.file.seek(prejump, os.SEEK_CUR)
wOff.file.seek(prejump, os.SEEK_CUR)
for j in range(lenpat):
if j == 0:
p = w.next_patch()
pOff = wOff.next_patch()
test = p
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
p = w.normalize(d)
p = np.reshape(np.matrix(p),(32,1))
j1 = 0
j2 = 32
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp,pm)
count += 1
else:
p = w.next_patch()
pOff = wOff.next_patch()
test = pOff
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
p = w.normalize(d)
p = np.reshape(np.matrix(p),(32,1))
j1 = 32 * j
j2 = 32 * (j +1)
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp,pm)
count += 1
##########
# Find Average of K-cluster[x] Weights
##########
thenumber = lenpat
thenumberf = float(thenumber)
patpla = exp
lenpat = len(patpla)
howlong = lenpat / thenumber
total = []
logtotal = []
for i in range(thenumber):
subtotal = []
logsubtotal = []
for j in range(howlong):
if i == 0:
value = patpla[i + (thenumber * j)]
total = np.append(total, value)
#logvalue = patpla[i + (thenumber * j)]
#logvalue = math.log10(logvalue)
#logtotal = np.append(logtotal, logvalue)
else:
value = patpla[i + (thenumber * j)]
subtotal = np.append(subtotal, value)
#logvalue = patpla[i + (thenumber * j)]
#logvalue = math.log10(logvalue)
#logsubtotal = np.append(logsubtotal, logvalue)
if i > 0:
total = total + subtotal
#if i > 0:
#logtotal = logtotal + logsubtotal
total = total / thenumberf
#logtotal = logtotal / thenumberf
global total1
global total2
global total3
global total4
global total5
global total6
global total7
global total8
global total9
global total10
global total11
global total12
global total13
global total14
global total15
global total16
#global logtotal1
#global logtotal2
#global logtotal3
#global logtotal4
#global logtotal5
#global logtotal6
#global logtotal7
#global logtotal8
#global logtotal9
#global logtotal10
#global logtotal11
#global logtotal12
#global logtotal13
#global logtotal14
#global logtotal15
#global logtotal16
if feature == 0:
total1 = total
if feature == 1:
total2 = total
if feature == 2:
total3 = total
if feature == 3:
total4 = total
if feature == 4:
total5 = total
if feature == 5:
total6 = total
if feature == 6:
total7 = total
if feature == 7:
total8 = total
if feature == 8:
total9 = total
if feature == 9:
total10 = total
if feature == 10:
total11 = total
if feature == 11:
total12 = total
if feature == 12:
total13 = total
if feature == 13:
total14 = total
if feature == 14:
total15 = total
if feature == 15:
total16 = total
return
count = 0
coord = 1
nx_im2 = nx * (nxp + space) + space
ny_im2 = ny * (nyp + space) + space
im2 = np.zeros((nx_im2, ny_im2))
im[:,:] = (w2.max - w2.min) / 2.
for i in range(numpat):
kx = conv.kxPos(i, nx, ny, nf)
ky = conv.kyPos(i, nx, ny, nf)
p = w2.next_patch()
if kx == whichpatx:
if ky == whichpaty:
e = p
e = e.reshape(nxp, nyp)
numrows, numcols = e.shape
count += 1
im3 = e
else:
e = d
count += 1
else:
e = d
count += 1
def patchover(x, huk, testnumber):
w = rw.PVReadWeights(sys.argv[x])
space = 1
d = np.zeros((4, 4))
nx = w.nx
ny = w.ny
nxp = w.nxp
nyp = w.nyp
coor = nxp * nyp
# print coor
patlen = nxp * nyp
numpat = w.numPatches
nf = w.nf
check = sys.argv[1]
a = check.find("w")
b = check[a].strip("w")
# print b
margin = 10
marginstart = margin
marginend = nx - margin
acount = 0
patchposition = []
nx = w.nx
ny = w.ny
nxp = w.nxp
nyp = w.nyp
test = 0
if testnumber == 0:
for i in range(1):
while test == 0:
testnumber = random.randint(0, numpat)
for i in range(128):
for j in range(128):
if marginstart < i < marginend:
if marginstart < j < marginend:
if testnumber == (j + (i * 128)):
test = 1
# testnumber = (69 + (62 * 128))
postpat = []
for ko in range(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
prepat = w.file.tell()
p = w.next_patch()
postpat = w.file.tell()
# print postpat
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
if testnumber == (kxOn + (kyOn * 128)):
patpos = w.file.tell()
# print
# print "kx", kxOn
# print "ky", kyOn
donepat = postpat - prepat
# print donepat
w.rewind()
exp = []
exppn = []
exp2 = []
exppn2 = []
body = w.recSize + 4
# print "body = ", body
body = 475140
hs = w.headerSize
# print "hs = ", hs
filesize = os.path.getsize(sys.argv[x])
bint = filesize / body
# print "bint = ", bint
# sys.exit()
# print
# print "Number of steps = ", bint
# forwardjump = input('How many steps forward:')
forwardjump = 0
bint = bint - forwardjump
sqnxynum = math.sqrt(bint)
nxynum = int(round(sqnxynum, 0))
nxynum += 1
if forwardjump == 0:
1
else:
leap = (body * forwardjump) + (100 * forwardjump)
w.file.seek(leap, os.SEEK_CUR)
global count
if huk == 0:
count = 0
allpat = []
count2 = 1
for i in range(bint):
if i == 0:
# print "count2 = ", count2
go = patpos - hs - donepat
w.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
# print count
# print w.file.tell()
if len(p) == 0:
print "STOPPEP SUPER EARLY"
sys.exit()
don = p
allpat = don
P = np.reshape(p, (nxp, nyp))
numrows, numcols = P.shape
x = space + (space + nxp) * (count % nx2)
y = space + (space + nyp) * (count / nx2)
im[y : y + nyp, x : x + nxp] = P
p = w.normalize(don)
pn = p
pn = np.reshape(np.matrix(pn), (1, coor))
p = np.reshape(np.matrix(p), (coor, 1))
pm = pn * p
exppn2 = np.append(exppn2, pn)
exp2 = np.append(exp2, pm)
count2 += 1
count += 1
else:
# print "count2 = ", count2
prejump = body - patpos + hs
w.file.seek(prejump, os.SEEK_CUR)
go = patpos - 4 - donepat
w.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
# print w.file.tell()
test = p
# print count
if len(test) == 0:
print "stop"
input("Press Enter to Continue")
sys.exit()
hh = bint / 16
yy = i % hh
# print "yy = ", yy
if yy == 0:
don = p
allpat = np.append(allpat, don)
P = np.reshape(p, (nxp, nyp))
numrows, numcols = P.shape
x = space + (space + nxp) * (count % nx2)
y = space + (space + nyp) * (count / nx2)
im[y : y + nyp, x : x + nxp] = P
p = w.normalize(don)
p = np.reshape(np.matrix(p), (coor, 1))
j1 = 0
j2 = coor
pm = np.matrix(exppn2[j1:j2]) * p
exp2 = np.append(exp2, pm)
count += 1
count2 += 1
# allpat = np.split(allpat, (count - 1))
# lenallpat = len(allpat)
# print np.shape(allpat)
# print "count", count
# print
# for i in range(lenallpat):
# if i == 0:
# other = -(i + 1)
# p = w.normalize(allpat[other])
# pn = p
# pn = np.reshape(np.matrix(pn),(1,patlen))
# p = np.reshape(np.matrix(p),(patlen,1))
# pm = pn * p
# exppn = np.append(exppn, pn)
# exp = np.append(exp,pm)
# else:
# other = -(i + 1)
# p = w.normalize(allpat[other])
# p = np.reshape(np.matrix(p),(patlen,1))
# j1 = 0
# j2 = patlen
# pm = np.matrix(exppn[j1:j2]) * p
# exp = np.append(exp,pm)
wir = [[im], [testnumber]]
return wir
def k_stability_analysis(k, forwardjump):
w = rw.PVReadWeights(sys.argv[1])
feature = k - 1
count = 0
d = np.zeros((nxp, nyp))
w.rewind()
for ko in np.arange(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
p = w.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
if cluster[count] == feature:
e = p
e = e.reshape(nxp, nyp)
numrows, numcols = e.shape
count = count + 1
patpos = w.file.tell()
patchposition.append(patpos)
else:
e = d
count = count + 1
else:
e = d
else:
e = d
x = (nxp) * (ko % nx)
y = (nyp) * (ko / nx)
im2[y : y + nyp, x : x + nxp] = e
##########
# Find Valuse of K-cluster[x] Patches
##########
w = rw.PVReadWeights(sys.argv[3])
wOff = rw.PVReadWeights(sys.argv[4])
w.rewind()
wOff.rewind()
patpla = patchposition
lenpat = len(patpla)
number = w.numPatches
count = 0
exp = []
expOff = []
exppn = []
exppnOff = []
body = w.recSize + 4
hs = w.headerSize
filesize = os.path.getsize(sys.argv[3])
bint = filesize / body
bint = bint - forwardjump
if forwardjump == 0:
4
else:
leap = (body * forwardjump) + (100 * forwardjump)
w.file.seek(leap, os.SEEK_CUR)
for i in range(bint):
if i == 0:
for j in range(lenpat):
if j == 0:
go = patpla[0] - hs - 20
w.file.seek(go, os.SEEK_CUR)
wOff.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
pOff = wOff.next_patch()
if len(p) == 0:
print "STOPPEP SUPER EARLY"
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
fallpat = d
# p = w.normalize(d)
# pn = p
# pn = np.reshape(np.matrix(pn),(1,32))
# p = np.reshape(np.matrix(p),(32,1))
# pm = pn * p
# exppn = np.append(exppn, pn)
# exp = np.append(exp,pm)
else:
pospost = patpla[j - 1]
poscur = patpla[j]
jump = poscur - pospost - 20
w.file.seek(jump, os.SEEK_CUR)
wOff.file.seek(jump, os.SEEK_CUR)
p = w.next_patch()
pOff = wOff.next_patch()
if len(pOff) == 0:
print "STOPPED EARLY"
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
nallpat = d
fallpat = np.vstack((fallpat, nallpat))
# p = w.normalize(d)
# pn = p
# pn = np.reshape(np.matrix(pn),(1,32))
# p = np.reshape(np.matrix(p),(32,1))
# pm = pn * p
# exppn = np.append(exppn, pn)
# exp = np.append(exp,pm)
else:
count = 0
prejump = body - patpla[lenpat - 1] + hs
w.file.seek(prejump, os.SEEK_CUR)
wOff.file.seek(prejump, os.SEEK_CUR)
for j in range(lenpat):
if j == 0:
go = patpla[0] - 4 - 20
w.file.seek(go, os.SEEK_CUR)
wOff.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
pOff = wOff.next_patch()
test = p
if len(test) == 0:
print "stop"
input("Press Enter to Continue")
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
nfallpat = d
# p = w.normalize(d)
# p = np.reshape(np.matrix(p),(32,1))
# j1 = 0
# j2 = 32
# pm = np.matrix(exppn[j1:j2]) * p
# exp = np.append(exp,pm)
# count += 1
else:
pospost = patpla[j - 1]
poscur = patpla[j]
jump = poscur - pospost - 20
w.file.seek(jump, os.SEEK_CUR)
wOff.file.seek(jump, os.SEEK_CUR)
p = w.next_patch()
pOff = wOff.next_patch()
test = pOff
if len(test) == 0:
print "stop"
input("Press Enter to Continue")
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
nfallpat = np.vstack((nfallpat, d))
# p = w.normalize(d)
# p = np.reshape(np.matrix(p),(32,1))
# j1 = 32 * j
# j2 = 32 * (j +1)
# pm = np.matrix(exppn[j1:j2]) * p
# exp = np.append(exp,pm)
# count += 1
fallpat = np.dstack((fallpat, nfallpat))
# print fallpat
# print np.shape(fallpat)
# print bint
exp = []
exppn = []
dsallpat = np.dsplit(fallpat, bint)
for i in range(bint):
postds = dsallpat[-(i + 1)]
sh = np.shape(postds)
sh = sh[0]
# print sh
if i == 0:
for j in range(sh):
if j == 0:
d = postds[j]
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn), (1, 32))
p = np.reshape(np.matrix(p), (32, 1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp, pm)
else:
d = postds[j]
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn), (1, 32))
p = np.reshape(np.matrix(p), (32, 1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp, pm)
else:
for j in range(sh):
if j == 0:
d = postds[j]
p = w.normalize(d)
p = np.reshape(np.matrix(p), (32, 1))
j1 = 0
j2 = 32
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp, pm)
count += 1
else:
d = postds[j]
p = w.normalize(d)
p = np.reshape(np.matrix(p), (32, 1))
j1 = 32 * j
j2 = 32 * (j + 1)
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp, pm)
count += 1
##########
# Find Average of K-cluster[x] Weights
##########
thenumber = lenpat
thenumberf = float(thenumber)
patpla = exp
lenpat = len(patpla)
howlong = lenpat / thenumber
total = []
logtotal = []
for i in range(thenumber):
subtotal = []
logsubtotal = []
for j in range(howlong):
if i == 0:
value = patpla[i + (thenumber * j)]
total = np.append(total, value)
logvalue = patpla[i + (thenumber * j)]
logvalue = math.log10(logvalue)
logtotal = np.append(logtotal, logvalue)
else:
value = patpla[i + (thenumber * j)]
subtotal = np.append(subtotal, value)
logvalue = patpla[i + (thenumber * j)]
logvalue = math.log10(logvalue)
logsubtotal = np.append(logsubtotal, logvalue)
if i > 0:
total = total + subtotal
if i > 0:
logtotal = logtotal + logsubtotal
total = total / thenumberf
logtotal = logtotal / thenumberf
global total1
global total2
global total3
global total4
global total5
global total6
global total7
global total8
global total9
global total10
global total11
global total12
global total13
global total14
global total15
global total16
global logtotal1
global logtotal2
global logtotal3
global logtotal4
global logtotal5
global logtotal6
global logtotal7
global logtotal8
global logtotal9
global logtotal10
global logtotal11
global logtotal12
global logtotal13
global logtotal14
global logtotal15
global logtotal16
if feature == 0:
total1 = [0]
total2 = [0]
total3 = [0]
total4 = [0]
total5 = [0]
total6 = [0]
total7 = [0]
total8 = [0]
total9 = [0]
total10 = [0]
total11 = [0]
total12 = [0]
total13 = [0]
total14 = [0]
total15 = [0]
total16 = [0]
logtotal1 = [0]
logtotal2 = [0]
logtotal3 = [0]
logtotal4 = [0]
logtotal5 = [0]
logtotal6 = [0]
logtotal7 = [0]
logtotal8 = [0]
logtotal9 = [0]
logtotal10 = [0]
logtotal11 = [0]
logtotal12 = [0]
logtotal13 = [0]
logtotal14 = [0]
logtotal15 = [0]
logtotal16 = [0]
# global logtotal1
# global logtotal2
# global logtotal3
# global logtotal4
# global logtotal5
# global logtotal6
# global logtotal7
# global logtotal8
# global logtotal9
# global logtotal10
# global logtotal11
# global logtotal12
# global logtotal13
# global logtotal14
# global logtotal15
# global logtotal16
if feature == 0:
total1 = total
logtotal1 = logtotal
if feature == 1:
total2 = total
logtotal2 = logtotal
if feature == 2:
total3 = total
logtotal3 = logtotal
if feature == 3:
total4 = total
logtotal4 = logtotal
if feature == 4:
total5 = total
logtotal5 = logtotal
if feature == 5:
total6 = total
logtotal6 = logtotal
if feature == 6:
total7 = total
logtotal7 = logtotal
if feature == 7:
total8 = total
logtotal8 = logtotal
if feature == 8:
total9 = total
logtotal9 = logtotal
if feature == 9:
total10 = total
logtotal10 = logtotal
if feature == 10:
total11 = total
logtotal11 = logtotal
if feature == 11:
total12 = total
logtotal12 = logtotal
if feature == 12:
total13 = total
logtotal13 = logtotal
if feature == 13:
total14 = total
logtotal14 = logtotal
if feature == 14:
total15 = total
logtotal15 = logtotal
if feature == 15:
total16 = total
logtotal16 = logtotal
return
def k_stability_analysis(k, forwardjump):
w = rw.PVReadWeights(sys.argv[1])
feature = k - 1
count = 0
d = np.zeros((nxp,nyp))
w.rewind()
for ko in np.arange(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
p = w.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
if cluster[count] == feature:
e = p
e = e.reshape(nxp, nyp)
numrows, numcols = e.shape
count = count + 1
patpos = w.file.tell()
patchposition.append(patpos)
else:
e = d
count = count + 1
else:
e = d
else:
e = d
x = (nxp) * (ko % nx)
y = ( nyp) * (ko / nx)
im2[y:y+nyp, x:x+nxp] = e
##########
# Find Valuse of K-cluster[x] Patches
##########
w = rw.PVReadWeights(sys.argv[2])
w.rewind()
patpla = patchposition
lenpat = len(patpla)
number = w.numPatches
count = 0
exp = []
exppn = []
body = w.recSize + 4
hs = w.headerSize
filesize = os.path.getsize(sys.argv[2])
bint = filesize / body
bint = bint - forwardjump - 1
if forwardjump == 0:
4
else:
leap = ((body * forwardjump) + (100 * forwardjump))
w.file.seek(leap, os.SEEK_CUR)
for i in range(bint):
if i == 0:
for j in range(lenpat):
if j == 0:
go = patpla[0] - hs - patchblen
w.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
if len(p) == 0:
print"STOPPEP SUPER EARLY"
sys.exit()
d = p
allpat = 0
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn),(1,flatnxp))
p = np.reshape(np.matrix(p),(flatnxp,1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp,pm)
else:
pospost = patpla[j - 1]
poscur = patpla[j]
jump = poscur - pospost - patchblen
w.file.seek(jump, os.SEEK_CUR)
p = w.next_patch()
if len(p) == 0:
print"STOPPED EARLY"
sys.exit()
d = p
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn),(1,flatnxp))
p = np.reshape(np.matrix(p),(flatnxp,1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp,pm)
#print "Ch-Ch-Changes", exppn
else:
count = 0
prejump = body - patpla[lenpat-1] + hs
w.file.seek(prejump, os.SEEK_CUR)
for j in range(lenpat):
if j == 0:
go = patpla[0] - 4 - patchblen
w.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
test = p
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
d = p
p = w.normalize(d)
p = np.reshape(np.matrix(p),(flatnxp,1))
j1 = 0
j2 = flatnxp
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp,pm)
count += 1
else:
pospost = patpla[j - 1]
poscur = patpla[j]
jump = poscur - pospost - patchblen
w.file.seek(jump, os.SEEK_CUR)
p = w.next_patch()
test = p
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
d = p
p = w.normalize(d)
p = np.reshape(np.matrix(p),(flatnxp,1))
j1 = flatnxp * j
j2 = flatnxp * (j +1)
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp,pm)
count += 1
##########
# Find Average of K-cluster[x] Weights
##########
thenumber = lenpat
thenumberf = float(thenumber)
patpla = exp
lenpat = len(patpla)
howlong = lenpat / thenumber
total = []
logtotal = []
for i in range(thenumber):
subtotal = []
logsubtotal = []
for j in range(howlong):
if i == 0:
value = patpla[i + (thenumber * j)]
total = np.append(total, value)
logvalue = patpla[i + (thenumber * j)]
logvalue = math.log10(logvalue)
logtotal = np.append(logtotal, logvalue)
else:
value = patpla[i + (thenumber * j)]
subtotal = np.append(subtotal, value)
logvalue = patpla[i + (thenumber * j)]
logvalue = math.log10(logvalue)
logsubtotal = np.append(logsubtotal, logvalue)
if i > 0:
total = total + subtotal
if i > 0:
logtotal = logtotal + logsubtotal
total = total / thenumberf
logtotal = logtotal / thenumberf
global total1
global total2
global total3
global total4
global total5
global total6
global total7
global total8
global total9
global total10
global total11
global total12
global total13
global total14
global total15
global total16
global logtotal1
global logtotal2
global logtotal3
global logtotal4
global logtotal5
global logtotal6
global logtotal7
global logtotal8
global logtotal9
global logtotal10
global logtotal11
global logtotal12
global logtotal13
global logtotal14
global logtotal15
global logtotal16
if feature == 0:
total1 = [0.0]
total2 = [0.0]
total3 = [0.0]
total4 = [0.0]
total5 = [0.0]
total6 = [0.0]
total7 = [0.0]
total8 = [0.0]
total9 = [0.0]
total10 = [0.0]
total11 = [0.0]
total12 = [0.0]
total13 = [0.0]
total14 = [0.0]
total15 = [0.0]
total16 = [0.0]
logtotal1 = [0.0]
logtotal2 = [0.0]
logtotal3 = [0.0]
logtotal4 = [0.0]
logtotal5 = [0.0]
logtotal6 = [0.0]
logtotal7 = [0.0]
logtotal8 = [0.0]
logtotal9 = [0.0]
logtotal10 = [0.0]
logtotal11 = [0.0]
logtotal12 = [0.0]
logtotal13 = [0.0]
logtotal14 = [0.0]
logtotal15 = [0.0]
logtotal16 = [0.0]
if feature == 0:
total1 = total
logtotal1 = logtotal
if feature == 1:
total2 = total
logtotal2 = logtotal
if feature == 2:
total3 = total
logtotal3 = logtotal
if feature == 3:
total4 = total
logtotal4 = logtotal
if feature == 4:
total5 = total
logtotal5 = logtotal
if feature == 5:
total6 = total
logtotal6 = logtotal
if feature == 6:
total7 = total
logtotal7 = logtotal
if feature == 7:
total8 = total
logtotal8 = logtotal
if feature == 8:
total9 = total
logtotal9 = logtotal
if feature == 9:
total10 = total
logtotal10 = logtotal
if feature == 10:
total11 = total
logtotal11 = logtotal
if feature == 11:
total12 = total
logtotal12 = logtotal
if feature == 12:
total13 = total
logtotal13 = logtotal
if feature == 13:
total14 = total
logtotal14 = logtotal
if feature == 14:
total15 = total
logtotal15 = logtotal
if feature == 15:
total16 = total
logtotal16 = logtotal
return
def patchover(x, huk, testnumber):
w = rw.PVReadWeights(sys.argv[x])
space = 1
d = np.zeros((4, 4))
nx = w.nx
ny = w.ny
nxp = w.nxp
nyp = w.nyp
coor = nxp * nyp
#print coor
patlen = nxp * nyp
numpat = w.numPatches
nf = w.nf
check = sys.argv[1]
a = check.find("w")
b = check[a].strip("w")
#print b
margin = 10
marginstart = margin
marginend = nx - margin
acount = 0
patchposition = []
nx = w.nx
ny = w.ny
nxp = w.nxp
nyp = w.nyp
test = 0
if testnumber == 0:
for i in range(1):
while test == 0:
testnumber = random.randint(0, numpat)
for i in range(128):
for j in range(128):
if marginstart < i < marginend:
if marginstart < j < marginend:
if testnumber == (j + (i * 128)):
test = 1
#testnumber = (69 + (62 * 128))
postpat = []
for ko in range(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
prepat = w.file.tell()
p = w.next_patch()
postpat = w.file.tell()
#print postpat
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
if testnumber == (kxOn + (kyOn * 128)):
patpos = w.file.tell()
#print
#print "kx", kxOn
#print "ky", kyOn
donepat = postpat - prepat
#print donepat
w.rewind()
exp = []
exppn = []
exp2 = []
exppn2 = []
body = w.recSize + 4
#print "body = ", body
body = 475140
hs = w.headerSize
#print "hs = ", hs
filesize = os.path.getsize(sys.argv[x])
bint = filesize / body
#print "bint = ", bint
#sys.exit()
#print
#print "Number of steps = ", bint
#forwardjump = input('How many steps forward:')
forwardjump = 0
bint = bint - forwardjump
sqnxynum = math.sqrt(bint)
nxynum = int(round(sqnxynum, 0))
nxynum += 1
if forwardjump == 0:
1
else:
leap = ((body * forwardjump) + (100 * forwardjump))
w.file.seek(leap, os.SEEK_CUR)
global count
if huk == 0:
count = 0
allpat = []
count2 = 1
for i in range(bint):
if i == 0:
#print "count2 = ", count2
go = patpos - hs - donepat
w.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
#print count
#print w.file.tell()
if len(p) == 0:
print "STOPPEP SUPER EARLY"
sys.exit()
don = p
allpat = don
P = np.reshape(p, (nxp, nyp))
numrows, numcols = P.shape
x = space + (space + nxp) * (count % nx2)
y = space + (space + nyp) * (count / nx2)
im[y:y + nyp, x:x + nxp] = P
p = w.normalize(don)
pn = p
pn = np.reshape(np.matrix(pn), (1, coor))
p = np.reshape(np.matrix(p), (coor, 1))
pm = pn * p
exppn2 = np.append(exppn2, pn)
exp2 = np.append(exp2, pm)
count2 += 1
count += 1
else:
#print "count2 = ", count2
prejump = body - patpos + hs
w.file.seek(prejump, os.SEEK_CUR)
go = patpos - 4 - donepat
w.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
#print w.file.tell()
test = p
#print count
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
hh = bint / 16
yy = i % hh
#print "yy = ", yy
if yy == 0:
don = p
allpat = np.append(allpat, don)
P = np.reshape(p, (nxp, nyp))
numrows, numcols = P.shape
x = space + (space + nxp) * (count % nx2)
y = space + (space + nyp) * (count / nx2)
im[y:y + nyp, x:x + nxp] = P
p = w.normalize(don)
p = np.reshape(np.matrix(p), (coor, 1))
j1 = 0
j2 = coor
pm = np.matrix(exppn2[j1:j2]) * p
exp2 = np.append(exp2, pm)
count += 1
count2 += 1
#allpat = np.split(allpat, (count - 1))
#lenallpat = len(allpat)
#print np.shape(allpat)
#print "count", count
#print
#for i in range(lenallpat):
# if i == 0:
# other = -(i + 1)
# p = w.normalize(allpat[other])
# pn = p
# pn = np.reshape(np.matrix(pn),(1,patlen))
# p = np.reshape(np.matrix(p),(patlen,1))
# pm = pn * p
# exppn = np.append(exppn, pn)
# exp = np.append(exp,pm)
# else:
# other = -(i + 1)
# p = w.normalize(allpat[other])
# p = np.reshape(np.matrix(p),(patlen,1))
# j1 = 0
# j2 = patlen
# pm = np.matrix(exppn[j1:j2]) * p
# exp = np.append(exp,pm)
wir = [[im], [testnumber]]
return wir
row = int(y+0.5)
x2 = (x / 20.0)
y2 = (y / 20.0)
x = (x / 5.0)
y = (y / 5.0)
if col>=0 and col<numcols and row>=0 and row<numrows:
z = P[row,col]
return 'x=%1.4f, y=%1.4f, z=%1.4f'%(x, y, z)
else:
return 'x=%1.4d, y=%1.4d, x2=%1.4d, y2=%1.4d'%(int(x), int(y), int(x2), int(y2))
k = 24
for ko in range(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
p = w.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
acount = acount + 1
if kxOn == margin + 1 and kyOn == margin + 1:
d = p
else:
d = np.vstack((d,p))
wd = sp.whiten(d)
result = sp.kmeans2(wd, k)
cluster = result[1]
k2 = k / 2
def k_stability_analysis(k, forwardjump):
w = rw.PVReadWeights(sys.argv[1])
feature = k - 1
count = 0
d = np.zeros((nxp,nyp))
w.rewind()
for ko in np.arange(numpat):
kxOn = conv.kxPos(ko, nx, ny, nf)
kyOn = conv.kyPos(ko, nx, ny, nf)
p = w.next_patch()
if marginstart < kxOn < marginend:
if marginstart < kyOn < marginend:
if cluster[count] == feature:
e = p
e = e.reshape(nxp, nyp)
numrows, numcols = e.shape
count = count + 1
patpos = w.file.tell()
patchposition.append(patpos)
else:
e = d
count = count + 1
else:
e = d
else:
e = d
x = (nxp) * (ko % nx)
y = ( nyp) * (ko / nx)
im2[y:y+nyp, x:x+nxp] = e
##########
# Find Valuse of K-cluster[x] Patches
##########
w = rw.PVReadWeights(sys.argv[3])
wOff = rw.PVReadWeights(sys.argv[4])
w.rewind()
wOff.rewind()
patpla = patchposition
lenpat = len(patpla)
number = w.numPatches
count = 0
exp = []
expOff = []
exppn = []
exppnOff = []
body = w.recSize + 4
hs = w.headerSize
filesize = os.path.getsize(sys.argv[3])
bint = filesize / body
bint = bint - forwardjump
if forwardjump == 0:
4
else:
leap = ((body * forwardjump) + (100 * forwardjump))
w.file.seek(leap, os.SEEK_CUR)
for i in range(bint):
if i == 0:
for j in range(lenpat):
if j == 0:
go = patpla[0] - hs - 20
w.file.seek(go, os.SEEK_CUR)
wOff.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
pOff = wOff.next_patch()
if len(p) == 0:
print"STOPPEP SUPER EARLY"
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
fallpat = d
#p = w.normalize(d)
#pn = p
#pn = np.reshape(np.matrix(pn),(1,32))
#p = np.reshape(np.matrix(p),(32,1))
#pm = pn * p
#exppn = np.append(exppn, pn)
#exp = np.append(exp,pm)
else:
pospost = patpla[j - 1]
poscur = patpla[j]
jump = poscur - pospost - 20
w.file.seek(jump, os.SEEK_CUR)
wOff.file.seek(jump, os.SEEK_CUR)
p = w.next_patch()
pOff = wOff.next_patch()
if len(pOff) == 0:
print"STOPPED EARLY"
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
nallpat = d
fallpat = np.vstack((fallpat, nallpat))
#p = w.normalize(d)
#pn = p
#pn = np.reshape(np.matrix(pn),(1,32))
#p = np.reshape(np.matrix(p),(32,1))
#pm = pn * p
#exppn = np.append(exppn, pn)
#exp = np.append(exp,pm)
else:
count = 0
prejump = body - patpla[lenpat-1] + hs
w.file.seek(prejump, os.SEEK_CUR)
wOff.file.seek(prejump, os.SEEK_CUR)
for j in range(lenpat):
if j == 0:
go = patpla[0] - 4 - 20
w.file.seek(go, os.SEEK_CUR)
wOff.file.seek(go, os.SEEK_CUR)
p = w.next_patch()
pOff = wOff.next_patch()
test = p
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
nfallpat = d
#p = w.normalize(d)
#p = np.reshape(np.matrix(p),(32,1))
#j1 = 0
#j2 = 32
#pm = np.matrix(exppn[j1:j2]) * p
#exp = np.append(exp,pm)
#count += 1
else:
pospost = patpla[j - 1]
poscur = patpla[j]
jump = poscur - pospost - 20
w.file.seek(jump, os.SEEK_CUR)
wOff.file.seek(jump, os.SEEK_CUR)
p = w.next_patch()
pOff = wOff.next_patch()
test = pOff
if len(test) == 0:
print "stop"
input('Press Enter to Continue')
sys.exit()
don = p
doff = pOff
d = np.append(don, doff)
nfallpat = np.vstack((nfallpat, d))
#p = w.normalize(d)
#p = np.reshape(np.matrix(p),(32,1))
#j1 = 32 * j
#j2 = 32 * (j +1)
#pm = np.matrix(exppn[j1:j2]) * p
#exp = np.append(exp,pm)
#count += 1
fallpat = np.dstack((fallpat, nfallpat))
#print fallpat
#print np.shape(fallpat)
#print bint
exp = []
exppn = []
dsallpat = np.dsplit(fallpat, bint)
for i in range(bint):
postds = dsallpat[-(i + 1)]
sh = np.shape(postds)
sh = sh[0]
#print sh
if i == 0:
for j in range(sh):
if j == 0:
d = postds[j]
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn), (1,32))
p = np.reshape(np.matrix(p), (32,1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp, pm)
else:
d = postds[j]
p = w.normalize(d)
pn = p
pn = np.reshape(np.matrix(pn),(1,32))
p = np.reshape(np.matrix(p),(32,1))
pm = pn * p
exppn = np.append(exppn, pn)
exp = np.append(exp, pm)
else:
for j in range(sh):
if j == 0:
d = postds[j]
p = w.normalize(d)
p = np.reshape(np.matrix(p),(32,1))
j1 = 0
j2 = 32
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp, pm)
count += 1
else:
d = postds[j]
p = w.normalize(d)
p = np.reshape(np.matrix(p),(32,1))
j1 = 32 * j
j2 = 32 * (j + 1)
pm = np.matrix(exppn[j1:j2]) * p
exp = np.append(exp, pm)
count += 1
##########
# Find Average of K-cluster[x] Weights
##########
thenumber = lenpat
thenumberf = float(thenumber)
patpla = exp
lenpat = len(patpla)
howlong = lenpat / thenumber
total = []
logtotal = []
for i in range(thenumber):
subtotal = []
logsubtotal = []
for j in range(howlong):
if i == 0:
value = patpla[i + (thenumber * j)]
total = np.append(total, value)
logvalue = patpla[i + (thenumber * j)]
logvalue = math.log10(logvalue)
logtotal = np.append(logtotal, logvalue)
else:
value = patpla[i + (thenumber * j)]
subtotal = np.append(subtotal, value)
logvalue = patpla[i + (thenumber * j)]
logvalue = math.log10(logvalue)
logsubtotal = np.append(logsubtotal, logvalue)
if i > 0:
total = total + subtotal
if i > 0:
logtotal = logtotal + logsubtotal
total = total / thenumberf
logtotal = logtotal / thenumberf
global total1
global total2
global total3
global total4
global total5
global total6
global total7
global total8
global total9
global total10
global total11
global total12
global total13
global total14
global total15
global total16
global logtotal1
global logtotal2
global logtotal3
global logtotal4
global logtotal5
global logtotal6
global logtotal7
global logtotal8
global logtotal9
global logtotal10
global logtotal11
global logtotal12
global logtotal13
global logtotal14
global logtotal15
global logtotal16
if feature == 0:
total1=[0]
total2=[0]
total3=[0]
total4=[0]
total5=[0]
total6=[0]
total7=[0]
total8=[0]
total9=[0]
total10=[0]
total11=[0]
total12=[0]
total13=[0]
total14=[0]
total15=[0]
total16=[0]
logtotal1=[0]
logtotal2=[0]
logtotal3=[0]
logtotal4=[0]
logtotal5=[0]
logtotal6=[0]
logtotal7=[0]
logtotal8=[0]
logtotal9=[0]
logtotal10=[0]
logtotal11=[0]
logtotal12=[0]
logtotal13=[0]
logtotal14=[0]
logtotal15=[0]
logtotal16=[0]
#global logtotal1
#global logtotal2
#global logtotal3
#global logtotal4
#global logtotal5
#global logtotal6
#global logtotal7
#global logtotal8
#global logtotal9
#global logtotal10
#global logtotal11
#global logtotal12
#global logtotal13
#global logtotal14
#global logtotal15
#global logtotal16
if feature == 0:
total1 = total
logtotal1 = logtotal
if feature == 1:
total2 = total
logtotal2 = logtotal
if feature == 2:
total3 = total
logtotal3 = logtotal
if feature == 3:
total4 = total
logtotal4 = logtotal
if feature == 4:
total5 = total
logtotal5 = logtotal
if feature == 5:
total6 = total
logtotal6 = logtotal
if feature == 6:
total7 = total
logtotal7 = logtotal
if feature == 7:
total8 = total
logtotal8 = logtotal
if feature == 8:
total9 = total
logtotal9 = logtotal
if feature == 9:
total10 = total
logtotal10 = logtotal
if feature == 10:
total11 = total
logtotal11 = logtotal
if feature == 11:
total12 = total
logtotal12 = logtotal
if feature == 12:
total13 = total
logtotal13 = logtotal
if feature == 13:
total14 = total
logtotal14 = logtotal
if feature == 14:
total15 = total
logtotal15 = logtotal
if feature == 15:
total16 = total
logtotal16 = logtotal
return
plt.show()
sys.exit()
if 1 == 1:
kd = []
AW = AW.reshape(lenofb, 1)
AWO = AWO.reshape(lenofb, 1)
count = 0
for k in range(w.numPatches):
p = w.next_patch()
pO = wO.next_patch()
kx = conv.kyPos(k, nx, ny, nf)
ky = conv.kyPos(k, nx, ny, nf)
if len(p) != nxp * nyp:
continue
#print "p = ", p
count += 1
#print "count = ", count
if AW[k] == 1:
if len(kd) == 0:
don = p
doff = pO
kd = np.append(don, doff)
else:
don = p
count = numrows * numcols
for k in range(numbins): bins[k] = k * 1.0/(numbins-1)
for k in range(len(M)):
for b in range(numbins):
if (M[k] > bins[b]): dist[b] += 1
dist = dist/count
# print maximum projection
#
maxp = 0
maxk = 0
for k in range(len(M)):
if M[k] > maxp: maxp = M[k]; maxk = k
kx = conv.kxPos(k, numcols, numrows, w.nf)
ky = conv.kyPos(k, numcols, numrows, w.nf)
print "maximum projected value = ", maxp, maxk, kx, ky
M = M.reshape( (numrows,numcols) )
# print averaged projection over column
#
s = np.zeros(numcols)
maxs = 0.0
maxcol = 0
for col in range(numcols):
s[col] = np.sum(M[:,col])
if s[col] > maxs: maxs = s[col]; maxcol = col
s = s/numrows
print "(maxcol, maxsum) = (", maxcol, ",", maxs/numrows, ")"