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()
Exemple #2
0

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)
Exemple #3
0
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]) 
Exemple #5
0

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)