Python MiniSom.distance_map方法代码示例

# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import distance_map [as 别名]
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()

# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import distance_map [as 别名]
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()

# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import distance_map [as 别名]
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

# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import distance_map [as 别名]
data = numpy.nan_to_num(data)
print (data)
data = apply_along_axis(lambda x: x/linalg.norm(x),1,data) # data normalization

### Initialization and training ###
som = MiniSom(40,40,136,sigma=1.0,learning_rate=0.5)
som.random_weights_init(data)
print("Training...")
som.train_random(data,10000) # 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()

target = genfromtxt('class5.csv',delimiter=',',usecols=(0),dtype=int) # loadingthe labels
t = zeros(len(target),dtype=int)
print (target)

t[target == 0] = 0
t[target == 1] = 1
t[target == 2] = 2
t[target == 3] = 3
t[target == 4] = 4
t[target == 5] = 5
t[target == 6] = 6
t[target == 7] = 7
t[target == 8] = 8

# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import distance_map [as 别名]
# Feature Scaling
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range = (0, 1))
X = sc.fit_transform(X)

# Training the SOM
from minisom import MiniSom
som = MiniSom(x = 10, y = 10, input_len = 15, sigma = 1.0, learning_rate = 0.5)
som.random_weights_init(X)
som.train_random(data = X, num_iteration = 100)

# Visualizing the results
from pylab import bone, pcolor, colorbar, plot, show
bone()
pcolor(som.distance_map().T)
colorbar()
markers = ['o', 's']
colors = ['r', 'g']
for i, x in enumerate(X):
    w = som.winner(x)
    plot(w[0] + 0.5,
         w[1] + 0.5,
         markers[y[i]],
         markeredgecolor = colors[y[i]],
         markerfacecolor = 'None',
         markersize = 10,
         markeredgewidth = 2)
show()

# Finding the frauds

# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import distance_map [as 别名]
class SomPage(tk.Frame):
    
    ## UI 생성
    def __init__(self, parent, controller):
     
        ## tk.Frame 초기화
        tk.Frame.__init__(self, parent)
        
        style.use("ggplot")

        self.figure = pl.figure(1)
        self.a = self.figure.add_subplot(111)

        self.canvas = FigureCanvasTkAgg(self.figure, self)
        self.canvas.get_tk_widget().grid(sticky="news")
        self.canvas._tkcanvas.grid(sticky="news")
        
        ## Initialization
        self.som = MiniSom(10,10,136,sigma=1.0,learning_rate=0.5)
    
    ####---------------------------------------------   
    ## THREAD 생성
    def callback_threaded(self, status):
        
        self.queue = Queue()    # 크기가 1인 버퍼
        
        self.thread = Thread(target=self.training, args=(status,))
        self.thread.daemon = True
        self.thread.start()
        
        self.queue.put(object()) ## 첫번째
        print("Start")
        self.queue.join() ## 네번째
        self.thread.join()
        self.plotting(status)
        
    
    ## size, learning_rate, training_count, target_count)
    def training(self, status):
        
        self.figure.clear()
        self.queue.get() ## 두번째
        
        ## reading the dataset in the csv format  
        self.data = genfromtxt('iris6.csv', delimiter=',',dtype = float)
        self.data = numpy.nan_to_num(self.data)
        ## data normalization
        self.data = apply_along_axis(lambda x: x/linalg.norm(x),1,self.data) 
        
        self.som.random_weights_init(self.data)
        
        print("Training...")
        
        self.som.train_random(self.data,100) # random training
        
        bone()
        ## plotting the distance map as background
        pcolor(self.som.distance_map().T)
        colorbar()
        
        ## loadingthe labels
        target = genfromtxt('iris4_2.csv', delimiter=',', usecols=(0), dtype=int)
        self.t = zeros(len(target),dtype=int)
        
        print("...ready to plot...")
        
        for i in range(len(target)):
            self.t[target == i] = i
        
        self.som.win_map(self.data)
        
        self.queue.task_done() ## 세번째
        
    def plotting(self, status):
        
        self.figure = pl.figure(1)
        self.a = self.figure.add_subplot(111)
        
        print("Plotting...")
        
        ## use differet colors and markers for each label
        ## markers = []
        colors = ['r','g','b','y','w','orange','black','pink','brown','purple']
        
        ## making bm file
        with open('bm.txt', 'w') as f:
             f.write(str(len(self.data))+'\n')
             for cnt,xx in enumerate(self.data):
                 win = self.som.winner(xx) # getting the winner
             # palce a marker on the winning position for the sample xx
                 self.a.plot(win[0]+.5,win[1]+.5,'.', markerfacecolor='None', markeredgecolor=colors[self.t[cnt]], markersize=1, markeredgewidth=1)
                 f.write(str(win[0])+'\t'+str(win[1])+'\t'+str(self.t[cnt])+'\n')
        
        ## making umx file
        with open('umx.txt', 'w') as f:
            for cnt,xx in enumerate(self.data):
             win = self.som.winner(xx) # getting the winner
             # palce a marker on the winning position for the sample xx
             self.a.plot(win[0]+.5,win[1]+.5,'.',markerfacecolor='None',
                   markeredgecolor=colors[self.t[cnt]], markersize=1, markeredgewidth=1)
#.........这里部分代码省略.........