print "Loadnet:"
try:
headline("general kernel information")
funcnum = krui.getNoOfFunctions()
print "Available functions:"
for i in range(funcnum)[1:] :
params = krui.getFuncInfo(i)
print "Function:", params[0],
print " Type: ",
print util.func_types[params[1] & krui.FUNC_TYPE_MASK],
print " i/o params: ",
print krui.getFuncParamInfo(params[0],params[1])
headline("some fixed dimension example (encoder.*)")
net = krui.loadNet("encoder.net")
print "Net loaded: " + net
print krui.getMemoryManagerInfo()
print "Number of input units:", krui.getNoOfTTypeUnits(krui.INPUT)
print "Number of output units:", krui.getNoOfOutputUnits()
print "Total number of units:", krui.getNoOfUnits()
print "Number of special input units:", krui.getNoOfSpecialInputUnits()
print krui.getNetInfo()
print krui.getSymbolTable()
if krui.setFirstFTypeEntry() :
print "First prototype name:", krui.getFTypeName()
else :
print "No prototypes found."
krui.createFTypeEntry("myftype","Act_Identity","Out_Identity")
if krui.setFirstFTypeEntry() :
print "New prototype defined:", krui.getFTypeName()
oldnum = krui.getNoOfFunctions()
print "Builtin functions:", oldnum
util.registerFunction(outtestfunc, "outtestfunc", krui.OUT_FUNC, 0, 0)
util.registerFunction(outtestfunc2, "outtestfunc2", krui.OUT_FUNC, 0, 0)
util.registerFunction(acttest, "acttest", krui.ACT_FUNC, 0, 0)
util.registerFunction(actderivtest, "acttest", krui.ACT_DERIV_FUNC, 0, 0)
util.registerFunction(actderiv2test, "acttest", krui.ACT_2_DERIV_FUNC, 0, 0)
newnum = krui.getNoOfFunctions()
print "After adding:", newnum
for num in range(oldnum - 2, newnum + 1):
print "Function number", num, "Info:", krui.getFuncInfo(num)
print krui.loadNet('encoder.net')
for num in [1, 10, 19]:
krui.setUnitOutFunc(num, "outtestfunc")
for num in [2, 11, 18]:
krui.setUnitOutFunc(num, "outtestfunc2")
for num in [3, 9, 17]:
krui.setUnitActFunc(num, "acttest")
krui.loadNewPatterns('encoder.pat')
krui.DefTrainSubPat()
print "Learning one pattern"
krui.learnSinglePattern(1, (0.2, 0))
krui.setUnitDefaults(1.0, 0, krui.INPUT, 0, 1, "acttest", "outtestfunc")
newunit = krui.createDefaultUnit()
print "New unit:", newunit
print "Act func name:", krui.getUnitActFuncName(newunit)
print "Loadnet:"
try:
headline("general kernel information")
funcnum = krui.getNoOfFunctions()
print "Available functions:"
for i in range(funcnum)[1:]:
params = krui.getFuncInfo(i)
print "Function:", params[0],
print " Type: ",
print util.func_types[params[1] & krui.FUNC_TYPE_MASK],
print " i/o params: ",
print krui.getFuncParamInfo(params[0], params[1])
headline("some fixed dimension example (encoder.*)")
net = krui.loadNet("encoder.net")
print "Net loaded: " + net
print krui.getMemoryManagerInfo()
print "Number of input units:", krui.getNoOfTTypeUnits(krui.INPUT)
print "Number of output units:", krui.getNoOfOutputUnits()
print "Total number of units:", krui.getNoOfUnits()
print "Number of special input units:", krui.getNoOfSpecialInputUnits()
print krui.getNetInfo()
print krui.getSymbolTable()
if krui.setFirstFTypeEntry():
print "First prototype name:", krui.getFTypeName()
else:
print "No prototypes found."
krui.createFTypeEntry("myftype", "Act_Identity", "Out_Identity")
if krui.setFirstFTypeEntry():
print "New prototype defined:", krui.getFTypeName()
#!/usr/bin/python
# shows the coordinates of the winner neurons for the som_cube example
from snns import krui, util
krui.loadNet('som_cube.net')
krui.loadNewPatterns('som_cube.pat')
patnum = krui.getNoOfPatterns()
units = krui.getNoOfUnits()
for pat in range(1,patnum+1) :
krui.setPatternNo(pat)
krui.showPattern(krui.OUTPUT_NOTHING)
krui.updateNet(())
results = []
for unit in range(1,units+1) :
if krui.getUnitTType(unit) == krui.HIDDEN :
results.append((krui.getUnitActivation(unit),unit))
bestact, bestunit = min(results)
rawpos = krui.getUnitPosition(bestunit)[:2]
print "Pattern", pat, "Act", bestact, "Unit", bestunit,
print "Grid Position", (rawpos[0]-4, rawpos[1])
oldnum = krui.getNoOfFunctions()
print "Builtin functions:", oldnum
util.registerFunction(outtestfunc,"outtestfunc",krui.OUT_FUNC,0,0)
util.registerFunction(outtestfunc2,"outtestfunc2",krui.OUT_FUNC,0,0)
util.registerFunction(acttest,"acttest",krui.ACT_FUNC,0,0)
util.registerFunction(actderivtest,"acttest",krui.ACT_DERIV_FUNC,0,0)
util.registerFunction(actderiv2test,"acttest",krui.ACT_2_DERIV_FUNC,0,0)
newnum = krui.getNoOfFunctions()
print "After adding:", newnum
for num in range(oldnum - 2, newnum + 1) :
print "Function number", num, "Info:",krui.getFuncInfo(num)
print krui.loadNet('encoder.net')
for num in [1,10,19] :
krui.setUnitOutFunc(num,"outtestfunc")
for num in [2,11,18] :
krui.setUnitOutFunc(num,"outtestfunc2")
for num in [3,9,17] :
krui.setUnitActFunc(num,"acttest")
krui.loadNewPatterns('encoder.pat')
krui.DefTrainSubPat()
print "Learning one pattern"
krui.learnSinglePattern(1,(0.2,0))
krui.setUnitDefaults(1.0,0,krui.INPUT,0,1,"acttest","outtestfunc")
newunit = krui.createDefaultUnit()
print "New unit:", newunit
print "Act func name:", krui.getUnitActFuncName(newunit)