Python minisom.MiniSom类代码示例

class SomModel(Model):
      def __init__(self,input_length):
          from minisom import MiniSom
          self.som = MiniSom(10, 10, input_length,sigma=0.3,learning_rate=0.1,normalize=True)

      def run(self,inp):
          self.som.trian_single_instance(inp.flatten())

def SOM(data,leninput,lentarget):
    som = MiniSom(16,16,leninput,sigma=1.0,learning_rate=0.5)
    som.random_weights_init(data)
    print("Training...")
    som.train_random(data,10000) # training with 10000 iterations
    print("\n...ready!")
    
    numpy.save('weight_som',som.weights)

class Som:
    def init(self):
        self.core = MiniSom(50,50,6,sigma=.8,learning_rate=.5) # needs to match generating minisom command (specifically the load_map)
        self.core.load_map()
        self.callme = rospy.Service("mapping", Compute, self.callback)
	print "SOM setup complete"
    
    def callback(self, data):
        vector = np.array([data.fx, data.fy, data.fz, data.tx, data.ty, data.tz]) # format as needed
	print vector        
	w = self.core.winner(vector)
        return w[0],w[1]

def test_som():
    print "Clustering.."
    
    session_log_db = db.session_log
    allTopic = articles.distinct("topic")
    lentopic = len(allTopic)
    uniqueTopic = []
    for t in allTopic:
        uniqueTopic.append("Topik " + str(t).strip())
    
    lebarSOM = lentopic*lentopic + lentopic*2 + 1
    panjangSOM = session_log_db.find({"data_uji":no_uji}).count()
    #somInput = zeros((panjangSOM,lebarSOM),dtype=int16)
    somInput = []
    oriSess = []
    for s in session_log_db.find({"data_uji":no_uji}):
        somInput.append(getPresedenceMatrix(convertSession(s["session"],uniqueTopic),uniqueTopic,1))
        oriSess.append(s["session"])

    som = MiniSom(16,16,lentopic,sigma=1.0,learning_rate=0.5)
    som.weights = numpy.load('weight_som.npy')
    #print som.weights
    outfile = open('cluster-result.csv','w')
    seq_number = 0
    cluster_mongo = db.cluster_result
    cluster_mongo.remove({"data_uji":no_uji})
    for cnt,xx in enumerate(somInput):
        w = som.winner(xx) # getting the winner
        #print cnt
        #print xx
        #print w
        
        #for z in xx:
        #    outfile.write("%s " % str(z))
        outfile.write("%s " % str(("|".join(oriSess[seq_number]))))
        outfile.write("%s-%s \n" % (str(w[0]),str(w[1])))
        cluster_mongo.insert({"topik":"|".join(oriSess[seq_number]),"cluster":(str(w[0])+"-"+str(w[1])),"data_uji":no_uji})
        seq_number = seq_number + 1
        #outfile.write("%s %s\n" % str(xx),str(w))
        # palce a marker on the winning position for the sample xx
        #plot(w[0]+.5,w[1]+.5,markers[t[cnt]],markerfacecolor='None',
        #     markeredgecolor=colors[t[cnt]],markersize=12,markeredgewidth=2)
    outfile.close()
    #TopikCluster()
    
    html = '<div role="alert" class="alert alert-success alert-dismissible fade in">'
    html = html + ' <button aria-label="Close" data-dismiss="alert" class="close" type="button"><span aria-hidden="true">Close</span></button>'
    html = html + 'Berhasil Melakukan Clustering</div>'
    
    return html

def test_recommendation():
    uji_profil = db.uji_profil
    current_seq = []
    for t in uji_profil.find({}):
        current_seq.append("Topik " + str(t['topic']))

    '''
    APPLY SOM
    '''
    allTopic = articles.distinct("topic")
    lentopic = len(allTopic)
    uniqueTopic = []
    for t in allTopic:
        uniqueTopic.append("Topik " + str(t).strip())

    lebarSOM = lentopic*lentopic + lentopic*2 + 1
    
    somInput = []
    somInput.append(getPresedenceMatrix(convertSession(current_seq,uniqueTopic),uniqueTopic,1))
    som = MiniSom(16,16,lentopic,sigma=1.0,learning_rate=0.5)
    som.weights = numpy.load('weight_som.npy')
    cluster_winner = ""
    for cnt,xx in enumerate(somInput):
        w = som.winner(xx) # getting the winner
        cluster_winner = (str(w[0])+"-"+str(w[1]))

    '''
    SEARCH FOR THE PATTERN IN PARTICULAR CLUSTER
    '''

    print cluster_winner
    print current_seq

    prefix_result = db.prefix_result
    prefix_cluster = prefix_result.find({"cluster":cluster_winner,"data_uji":no_uji}).sort("min_sup",pymongo.DESCENDING)

    topik_rekomendasi = getTopikRekomendasi(current_seq,prefix_cluster)

    if topik_rekomendasi == "":
        prefix_cluster = prefix_result.find({"data_uji":no_uji}).sort("min_sup",pymongo.DESCENDING)
        topik_rekomendasi = getTopikRekomendasi(current_seq,prefix_cluster)
    
    html = "--tidak ada topik rekomendasi--"
    if(topik_rekomendasi!=""):
        the_topik = topik_rekomendasi.replace("Topik","").strip()
        html = getTestArticle(the_topik,"Rekomendasi 1","accordion_recommendation",'col_rek1',"")
        html += getTestArticle(the_topik,"Rekomendasi 2","accordion_recommendation",'col_rek2',"")
        html += getTestArticle(the_topik,"Rekomendasi 3","accordion_recommendation",'col_rek3',"")

    return html

class KuKuModel(Model):
    
      def __init__(self,proprioception_input_length,sensory_input_length,reservoir_size):
            # Build the Reservoir
            tau = .1 # execution timestep for the cortical rate model
            sigma = .001 # intra-reservoir weights
            eps = .1 # learning rate
            som_size = 10*10
            self.sensory_input_length = sensory_input_length
            self.proprioception_input_length = proprioception_input_length
            
            full_reservoir_input_length = proprioception_input_length+som_size
            # Nodes: units, tau, method
            self.reservoir_input = esn.Node((full_reservoir_input_length,), 0, esn._load )
            self.reservoir = esn.Node((reservoir_size,), tau, esn._reservoir )
            self.reservoir_output = esn.Node((som_size,), 0, esn._load )
            
            # Arcs: target, source, weight, eps
            # input from som
            self.d_P  = esn.Arc( self.reservoir, self.reservoir_input, sigma, 0 )
            self.d_P.initConnections( numpy.random.randn, self.reservoir.shape+self.reservoir_input.shape ) # type of init numpy func
            #print d_P.connections

            # recurrent connections intra node
            self.r_P  = esn.Arc( self.reservoir, self.reservoir, sigma, 0 )
            self.r_P.initConnections( numpy.random.randn, self.reservoir.shape+self.reservoir.shape ) # type of init numpy func
            #print r_P.connections

            # input from som
            self.d_out  = esn.Arc( self.reservoir_output, self.reservoir, 0, eps )
            self.d_out.initConnections( numpy.random.randn, self.reservoir_output.shape+self.reservoir.shape ) # type of init numpy func
            #print d_out.connections
                      
            from minisom import MiniSom
            self.som = MiniSom(10, 10, sensory_input_length,sigma=0.3,learning_rate=0.1,normalize=True)
            
            self.previous_som_activation = numpy.zeros((10,10))
          

      def run(self,inp):
          self.som.train_single_instance(inp[:self.sensory_input_length])
          
          self.reservoir_input.update(numpy.append(self.previous_som_activation.flatten().copy(),inp[-self.proprioception_input_length:])) # 3
          self.reservoir.update(self.d_P.read())
          self.reservoir_output.update(self.d_out.read())
          print "error:",self.som.activation_map.flatten()  - self.reservoir_output.state
          self.d_out.learn(self.som.activation_map.flatten()  - self.reservoir_output.state )
          
          self.previous_som_activation = self.som.activation_map.flatten().copy()

    def make_treeview(self, data, liststore):
	#i = 0
	cols = self.columns[self.combobox.get_active()]
	#print type(cols)
	#print len(cols)
	for d in data:
	  #i += 1

	  tmp = d.tolist()
	  #print 'tmp', tmp
	  #while len(tmp) < cols:
	    #tmp.append(False)
	    #print 'tmp', tmp
	    #cols = cols - 1
	  Qe = MiniSom.quantization_error_subset(self.som,d,len(cols))
	  #print tmp
	  tmp.append(Qe)
	  tmp.append(4 * Qe ** 0.5)
	  liststore.append(tmp)

	treeview = gtk.TreeView(model=liststore)
	#i = 0
	for d in range(len(self.test_data[0])):
	  #print i
	  #i += 1
	  renderer_text = gtk.CellRendererText()
	  column_text = gtk.TreeViewColumn(self.pattern_labels[d], renderer_text, text=d)
	  treeview.append_column(column_text)
	column_text = gtk.TreeViewColumn('Qe', renderer_text, text=d+1)
	treeview.append_column(column_text)
	column_text = gtk.TreeViewColumn('NLT', renderer_text, text=d+2)
	treeview.append_column(column_text)

	return treeview

 def setUp(self):
     self.som = MiniSom(5, 5, 1)
     for w in self.som.weights:  # checking weights normalization
         assert_almost_equal(1.0, np.linalg.norm(w))
     self.som.weights = np.zeros((5, 5))  # fake weights
     self.som.weights[2, 3] = 5.0
     self.som.weights[1, 1] = 2.0

      def __init__(self,proprioception_input_length,sensory_input_length,reservoir_size):
            # Build the Reservoir
            tau = .1 # execution timestep for the cortical rate model
            sigma = .001 # intra-reservoir weights
            eps = .1 # learning rate
            som_size = 10*10
            self.sensory_input_length = sensory_input_length
            self.proprioception_input_length = proprioception_input_length
            
            full_reservoir_input_length = proprioception_input_length+som_size
            # Nodes: units, tau, method
            self.reservoir_input = esn.Node((full_reservoir_input_length,), 0, esn._load )
            self.reservoir = esn.Node((reservoir_size,), tau, esn._reservoir )
            self.reservoir_output = esn.Node((som_size,), 0, esn._load )
            
            # Arcs: target, source, weight, eps
            # input from som
            self.d_P  = esn.Arc( self.reservoir, self.reservoir_input, sigma, 0 )
            self.d_P.initConnections( numpy.random.randn, self.reservoir.shape+self.reservoir_input.shape ) # type of init numpy func
            #print d_P.connections

            # recurrent connections intra node
            self.r_P  = esn.Arc( self.reservoir, self.reservoir, sigma, 0 )
            self.r_P.initConnections( numpy.random.randn, self.reservoir.shape+self.reservoir.shape ) # type of init numpy func
            #print r_P.connections

            # input from som
            self.d_out  = esn.Arc( self.reservoir_output, self.reservoir, 0, eps )
            self.d_out.initConnections( numpy.random.randn, self.reservoir_output.shape+self.reservoir.shape ) # type of init numpy func
            #print d_out.connections
                      
            from minisom import MiniSom
            self.som = MiniSom(10, 10, sensory_input_length,sigma=0.3,learning_rate=0.1,normalize=True)
            
            self.previous_som_activation = numpy.zeros((10,10))

def SOM(data,leninput,lentarget):
    som = MiniSom(5,5,leninput,sigma=1.0,learning_rate=0.5)
    som.random_weights_init(data)
    print("Training...")
    som.train_batch(data,10000) # training with 10000 iterations
    print("\n...ready!")
    
    numpy.save('weight_som.txt',som.weights)
   
    bone()
    pcolor(som.distance_map().T) # distance map as background
    colorbar()
    
    t = zeros(lentarget,dtype=int)
    
    # use different colors and markers for each label
    markers = ['o','s','D']
    colors = ['r','g','b']
    outfile = open('cluster-result.csv','w')
    for cnt,xx in enumerate(data):
        w = som.winner(xx) # getting the winner
        #print cnt
        #print xx
        #print w
        
        for z in xx:
            outfile.write("%s " % str(z))
        outfile.write("%s-%s \n" % (str(w[0]),str(w[1])))
        
        #outfile.write("%s %s\n" % str(xx),str(w))
        # palce a marker on the winning position for the sample xx
        #plot(w[0]+.5,w[1]+.5,markers[t[cnt]],markerfacecolor='None',
        #     markeredgecolor=colors[t[cnt]],markersize=12,markeredgewidth=2)
    outfile.close()

def testSOMs():
    from sklearn import datasets
    from minisom import MiniSom

    d = datasets.load_iris()
    data = np.apply_along_axis(lambda x: x/np.linalg.norm(x), 1, d['data']) # data normalization

    som = MiniSom(7, 7, 4, sigma=1.0, learning_rate=0.5)

    som.random_weights_init(data)
    print("Training...")
    som.train_random(data, 1000) # random training
    print("\n...ready!")

    ### Plotting the response for each pattern in the iris dataset ###
    from pylab import plot,axis,show,pcolor,colorbar,bone
    bone()
    pcolor(som.distance_map().T) # plotting the distance map as background
    colorbar()
    t = d['target']
    # use different colors and markers for each label
    markers = ['o','s','D']
    colors = ['r','g','b']
    for cnt,xx in enumerate(data):
     w = som.winner(xx) # getting the winner
     # palce a marker on the winning position for the sample xx
     plot(w[0]+.5,w[1]+.5,markers[t[cnt]],markerfacecolor='None',
        markeredgecolor=colors[t[cnt]],markersize=12,markeredgewidth=2)
    axis([0,som.weights.shape[0],0,som.weights.shape[1]])
    show() # show the figure

def SOM(data,leninput,lentarget,alpha_som,omega_som):
    som = MiniSom(16,16,leninput,sigma=omega_som,learning_rate=alpha_som)
    som.random_weights_init(data)
    print("Training...")
    som.train_batch(data,20000) # training with 10000 iterations
    print("\n...ready!")
    
    numpy.save('weight_som',som.weights)
   
    bone()
    pcolor(som.distance_map().T) # distance map as background
    colorbar()
    
    t = zeros(lentarget,dtype=int)
    
    # use different colors and markers for each label
    markers = ['o','s','D']
    colors = ['r','g','b']
    outfile = open('cluster-result.csv','w')
    for cnt,xx in enumerate(data):
        w = som.winner(xx) # getting the winner
        
        
        for z in xx:
            outfile.write("%s " % str(z))
        outfile.write("%s-%s \n" % (str(w[0]),str(w[1])))
        
        
    outfile.close()

    def train_som(self):

        training_data = [v[0] for v in self.vectors]
        from minisom import MiniSom
        size = len(training_data[0])
        self.som = MiniSom(10, 10, size, sigma=0.3, learning_rate=0.5)
        print "Training SOM..."
        self.som.train_random(training_data, 100)
        print "...ready!"

    def init_som(self, widget=None, data=None):
      ##print self.data
      ### Initialization and training ###
      cols = self.columns[self.combobox.get_active()]
      data = self.data[:, 0:len(cols)]

      #print len(cols)
      self.som = MiniSom(self.width_spin_button.get_value_as_int(), self.height_spin_button.get_value_as_int(), len(cols),sigma=1.2,learning_rate=0.5)
#      self.som.weights_init_gliozzi(data)
      self.som.random_weights_init(data)

def train_som(data, offset=None):
    """
    offset: offset between points used for training
    """
    
    if offset:
        data = data[::offset, :]
    
    som = MiniSom(
        param['nr_rows'],
        param['nr_cols'], 
        data.shape[1], 
        data, 
        sigma=param['sigma'], 
        learning_rate=param['learning_rate'], 
        norm='minmax')
        
    #som.random_weights_init() # choose initial nodes from data points
    som.train_random(param['nr_epochs']) # random training
    
    return som