Search the Community
Showing results for tags 'ai'.
Found 3 results
-
the IDE complains over the rem's it is translated from c++ throu basic i can not test this let me know if you got it working ''' bluatigro 14 nov 2021 ann try based on : http://code.activestate.com/recipes/578148-simple-back-propagation-neural-network-in-python-s/ ''' import random , math # consts NI = 2 # number of inputs NH = 2 # number of hidden cels NO = 1 # number of outputs # cels ai = list() for i in range( NI ) : ai.append( 0.0 ) ah = list() for h in range( NH ) : ah.append( 0.0 ) ao = list() for o in range( NO ) : ao.append( 0.0 ) # wished output wish = list() for w in range( NO ) : wish.append( 0.0 ) # weights wih = list() ci = list() for i in range( NI ) : wih[ i ] = list() ci[ i ] = list() for h in range( NH ) : wih[ i ].append( random.random() ) ci[ i ].append( random.random() ) who = list() co = list() for h in range( NH ) : who[ h ] = list() co( h ) = list() for o in range( NO ) : who[ h ].append( random.random() ) co[ h ].append( random.random() ) od = list() hd = list() for h in range( NH ) : od.append( random.random() ) hd.append( random.random() ) # input output training data : XOR function paterns = 4 pin = {{0.0,0.0},{1.0,0.0},{0.0,1.0},{1.0,1.0}} pout = { 0.0 , 1.0 , 1.0 , 0.0 } def tanh( x ) : return ( 1 - math.exp( -2 * x ) ) \ / ( 1 + math.exp( -2 *x ) ) def dsignoid( x ) : return 1 - x * x def calc( p ) : for i in range( NI ) : ai[ i ] = pin[ i ][ p ] for h in range( NH ) : som = 0.0 for i in range( NI ) : som += ai[ i ] * wih[ i ][ h ] ah[ h ] = tanh( som / NI ) for o in range( NO ) : som = 0.0 for h in range( NH ) : som += ah[ h ] * who[ h ][ o ] ao[ o ] = tanh( som / NH ) def backprop( n , m ) : ''' http://www.youtube.com/watch?v=aVId8KMsdUU&feature=BFa&list=LLldMCkmXl4j9_v0HeKdNcRA calc output deltas we want to find the instantaneous rate of change of ( error with respect to weight from node j to node k) output_delta is defined as an attribute of each ouput node. It is not the final rate we need. To get the final rate we must multiply the delta by the activation of the hidden layer node in question. This multiplication is done according to the chain rule as we are taking the derivative of the activation function of the ouput node. dE/dw[j][k] = (t[k] - ao[k]) * s'( SUM( w[j][k]*ah[j] ) ) * ah[j] ''' totfout = 0 for k in range( NO ) : totfout += math.abs( ao( k ) - wish( k ) ) # update output weights for j in range( NH ) : for k in range( NO ) # output_deltas[k] * self.ah[j] # is the full derivative of # dError/dweight[j][k] c = od[ k ] * ah[ j ] wo[ j ][ k ] += n * c + m * co[ j ][ k ] co[ j ][ k ] = c # calc hidden deltas for j in range( NH ) : fout = 0 for k in range( NO ) : fout += od[ k ] * wo[ j ][ k ) hd[ j ] = fout * dsignoid( ah[ j ] ) # update input weights for i in range( NI ) : for j in range( NH ) : c = hd[ j ] * ai[ i ] wi[ i ][ j ] += n * c + m * ci[ i ][ j ] ci[ i ][ j ] = c return totfout / 2 for e in range( 10000 ) : fout = 0 # fill input cel's for p in range( paterns ) : for i in range( NH ) : ai[ i ] = pin[ i ][ p ] # fill output wish for o in range( NO ) : wish[ o ] = pout[ p ] fout += backprop( .5 , .5 ) print( 'generatie ' , e , ' error ' , fout ) -
This is a recreation of the robot character GLaDOS from the game 'Portal', using Technic and Mindstorms. Just to answer a question some of you may have: Why Mindstorms? Why not PF? Well for a start, I don't own that many PF motors (only got 2 medium motors and 1 XL motor), and secondly, I don't own any PF IR receivers or remotes. The mindstorms kit has its own IR sensor and remote, so I used that. Functions: 1. Head elevation controlled by EV3 large motor 2. Head rotation controlled by EV3 medium motor 3. Body elevation controlled by EV3 large motor 4. Body rotation controlled by EV3 medium motor These may not be very many functions, but they allow for most of GLaDOS's movement seen in the actual game. The range of movement is quite reasonable. Regarding aesthetics, I don't own a lot of LEGO pieces, so the model may look a bit incomplete or strange in some places. But I think I did an OK job with what I had to work with, especially the head. Some more images: Video: More info about the robot can be found here: https://www.us.lego.com/en-us/mindstorms/community/robot?projectid=d1208d08-db8f-421c-9aa7-5f618912d7b0
-
If you are in the UK in the middle of March the following might be worth attending, it is only 1 1/2 hours but it is free to attend. https://www.anglia.ac.uk/community-engagement/and-artificial-intelligence
