Example #1
0
	
	krui.saveNet('foo.net','Testnet')
	print krui.getLearnFunc()
	#krui.setLearnFunc('RBF-DDA')
	print krui.getInitialisationFunc()
	print krui.getUpdateFunc()
	patset = krui.loadNewPatterns('encoder.pat')
	print "Patternset", patset, "loaded."
	print "Old pattern number:", krui.getPatternNo()
	# fiddle around with some patterns
	krui.setPatternNo(8)
	krui.deletePattern()
	krui.setPatternNo(5)
	krui.modifyPattern()
	krui.setPatternNo(7)
	krui.showPattern(krui.OUTPUT_OUT)
	krui.newPattern()
	krui.shufflePatterns(1)
	krui.shuffleSubPatterns(1)
	krui.saveNewPatterns('foo.pat',patset)
	krui.setPatternNo(1)
	(setinfo, patinfo) = krui.GetPatInfo()
	print "Number of patterns: ", setinfo.number_of_pattern
	print "Input Dimensions:", len(patinfo.input_dim_sizes)
	print "Output Dimensions:", len(patinfo.output_dim_sizes)
	print "Number of subpatterns for each pattern:", krui.DefTrainSubPat((),(),(),())
	print "Total number of subpatterns:", krui.getTotalNoOfSubPatterns()
	krui.setRemapFunc("None",())
	print "Learning all patterns, result:"
	print krui.learnAllPatterns((1.2,0))
	print "Learning pattern 3, result:"
Example #2
0
    krui.saveNet('foo.net', 'Testnet')
    print krui.getLearnFunc()
    #krui.setLearnFunc('RBF-DDA')
    print krui.getInitialisationFunc()
    print krui.getUpdateFunc()
    patset = krui.loadNewPatterns('encoder.pat')
    print "Patternset", patset, "loaded."
    print "Old pattern number:", krui.getPatternNo()
    # fiddle around with some patterns
    krui.setPatternNo(8)
    krui.deletePattern()
    krui.setPatternNo(5)
    krui.modifyPattern()
    krui.setPatternNo(7)
    krui.showPattern(krui.OUTPUT_OUT)
    krui.newPattern()
    krui.shufflePatterns(1)
    krui.shuffleSubPatterns(1)
    krui.saveNewPatterns('foo.pat', patset)
    krui.setPatternNo(1)
    (setinfo, patinfo) = krui.GetPatInfo()
    print "Number of patterns: ", setinfo.number_of_pattern
    print "Input Dimensions:", len(patinfo.input_dim_sizes)
    print "Output Dimensions:", len(patinfo.output_dim_sizes)
    print "Number of subpatterns for each pattern:", krui.DefTrainSubPat(
        (), (), (), ())
    print "Total number of subpatterns:", krui.getTotalNoOfSubPatterns()
    krui.setRemapFunc("None", ())
    print "Learning all patterns, result:"
    print krui.learnAllPatterns((1.2, 0))
Example #3
0
#!/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]) 
Example #4
0
	i = i + 1

# druhá fáze (jemnìj¹í)
i=0
while i < pruch_2:
	res = krui.learnAllPatterns(0.03, 0.1)
	if not i % 100 : print "Fáze 2, chyba v cyklu %d:" % i, res[0]
	i = i + 1

print "Rekonstruuji pùvodní obrázek"

im.blank()

for p in xrange(krui.getNoOfPatterns()):
	krui.setPatternNo(p + 1)
	krui.showPattern(1);
	krui.updateNet()
	block = []
	for u in xrange(64 + 16 + 1, 64 * 2 + 16 + 1):
		block.append(real2pix(krui.getUnitActivation(u)))
	im.setBlock(p, block)

print "Zapisuji výsledný obrázek na disk"

im.store("compressed.rgb")			

print "Vytváøím soubor vzorkù pro SNNS"
krui.saveNewPatterns('image.pat', patset)
print "Vytváøím soubor sítì pro SNNS"
krui.saveNet('image.net','image')