Usage

Examples

Colors

importnumpyasnpfromsomberimportSomX=np.array([[0.,0.,0.],              [0.,0.,1.],              [0.,0.,0.5],              [0.125,0.529,1.0],              [0.33,0.4,0.67],              [0.6,0.5,1.0],              [0.,1.,0.],              [1.,0.,0.],              [0.,1.,1.],              [1.,0.,1.],              [1.,1.,0.],              [1.,1.,1.],              [.33,.33,.33],              [.5,.5,.5],              [.66,.66,.66]])color_names= ['black','blue','darkblue','skyblue','greyblue','lilac','green','red','cyan','violet','yellow','white','darkgrey','mediumgrey','lightgrey']# initializes=Som((10,10),learning_rate=0.3)# train# 10 updates with 10 epochs = 100 updates to the parameters.s.fit(X,num_epochs=10,updates_epoch=10)# predict: get the index of each best matching unit.predictions=s.predict(X)# quantization error: how well do the best matching units fit?quantization_error=s.quantization_error(X)# inversion: associate each node with the exemplar that fits best.inverted=s.invert_projection(X,color_names)# Mapping: get weights, mapped to the grid points of the SOMmapped=s.map_weights()importmatplotlib.pyplotaspltplt.imshow(mapped)

Sequences

importnumpyasnpfromsomberimportRecursiveSomfromstringimportascii_lowercase# Dumb sequence generator.defseq_gen(num_to_gen,probas):symbols=ascii_lowercase[:probas.shape[0]]identities=np.eye(probas.shape[0])seq= []ids= []r=0choices=np.arange(probas.shape[0])forxinrange(num_to_gen):r=np.random.choice(choices,p=probas[r])ids.append(symbols[r])seq.append(identities[r])returnnp.array(seq),ids# Transfer probabilities.# after an A, we have a 50% chance of B or C# after B, we have a 100% chance of A# after C, we have a 50% chance of B or C# therefore, we will never expect sequential A or B, but we do expect# sequential C.probas=np.array(((0.0,0.5,0.5),                   (1.0,0.0,0.0),                   (0.0,0.5,0.5)))X,ids=seq_gen(10000,probas)# initialize# alpha = contribution of non-recurrent part to the activation.# beta = contribution of recurrent part to activation.# higher alpha to beta ratios=RecursiveSom((10,10),learning_rate=0.3,alpha=1.2,beta=.9)# train# show a progressbar.s.fit(X,num_epochs=100,updates_epoch=10,show_progressbar=True)# predict: get the index of each best matching unit.predictions=s.predict(X)# quantization error: how well do the best matching units fit?quantization_error=s.quantization_error(X)# inversion: associate each node with the exemplar that fits best.inverted=s.invert_projection(X,ids)# find which sequences are mapped to which neuron.receptive_field=s.receptive_field(X,ids)# generate some data by starting from some position.# the position can be anything, but must have a dimensionality# equal to the number of weights.starting_pos=np.ones(s.num_neurons)generated_indices=s.generate(50,starting_pos)# turn the generated indices into a sequence of symbols.generated_seq=inverted[generated_indices]

TODO

Contributors

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