本文整理汇总了Python中minisom.MiniSom.train_batch方法的典型用法代码示例。如果您正苦于以下问题：Python MiniSom.train_batch方法的具体用法？Python MiniSom.train_batch怎么用？Python MiniSom.train_batch使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类minisom.MiniSom的用法示例。
在下文中一共展示了MiniSom.train_batch方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: SOM
# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import train_batch [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()示例2: SOM
# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import train_batch [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()示例3: _minisombatch
# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import train_batch [as 别名]
def _minisombatch(self):
"""Clusters sentence vectors using minisombatch algorithm
Returns
-------
numpy ndarray
codebook (weights) of the trained SOM
"""
H = int(self.opts['size'])
W = int(self.opts['size'])
N = self.X.shape[1]
som = MiniSom(H, W, N, sigma=1.0, random_seed=1)
if self.opts['initialization']:
som.random_weights_init(self.X)
som.train_batch(self.X, self.opts['niterations'])
return som.get_weights()示例4: test_train_batch
# 需要导入模块: from minisom import MiniSom [as 别名]
# 或者: from minisom.MiniSom import train_batch [as 别名]
def test_train_batch(self):
som = MiniSom(5, 5, 2, sigma=1.0, learning_rate=0.5, random_seed=1)
data = np.array([[4, 2], [3, 1]])
q1 = som.quantization_error(data)
som.train_batch(data, 10)
assert q1 > som.quantization_error(data)