python实现SOM算法

import numpy as npimport pylab as plclass SOM(object):  def __init__(self, X, output, iteration, batch_size):    """    :param X: 形状是N*D， 输入样本有N个,每个D维    :param output: (n,m)一个元组，为输出层的形状是一个n*m的二维矩阵    :param iteration:迭代次数    :param batch_size:每次迭代时的样本数量    初始化一个权值矩阵，形状为D*(n*m)，即有n*m权值向量，每个D维    """    self.X = X    self.output = output    self.iteration = iteration    self.batch_size = batch_size    self.W = np.random.rand(X.shape[1], output[0] * output[1])    print (self.W.shape)  def GetN(self, t):    """    :param t:时间t, 这里用迭代次数来表示时间    :return: 返回一个整数，表示拓扑距离，时间越大，拓扑邻域越小    """    a = min(self.output)    return int(a-float(a)*t/self.iteration)  def Geteta(self, t, n):    """    :param t: 时间t, 这里用迭代次数来表示时间    :param n: 拓扑距离    :return: 返回学习率，    """    return np.power(np.e, -n)/(t+2)  def updata_W(self, X, t, winner):    N = self.GetN(t)    for x, i in enumerate(winner):      to_update = self.getneighbor(i[0], N)      for j in range(N+1):        e = self.Geteta(t, j)        for w in to_update[j]:          self.W[:, w] = np.add(self.W[:,w], e*(X[x,:] - self.W[:,w]))  def getneighbor(self, index, N):    """    :param index:获胜神经元的下标    :param N: 邻域半径    :return ans: 返回一个集合列表，分别是不同邻域半径内需要更新的神经元坐标    """    a, b = self.output    length = a*b    def distence(index1, index2):      i1_a, i1_b = index1 // a, index1 % b      i2_a, i2_b = index2 // a, index2 % b      return np.abs(i1_a - i2_a), np.abs(i1_b - i2_b)    ans = [set() for i in range(N+1)]    for i in range(length):      dist_a, dist_b = distence(i, index)      if dist_a <= N and dist_b <= N: ans[max(dist_a, dist_b)].add(i)    return ans  def train(self):    """    train_Y:训练样本与形状为batch_size*(n*m)    winner:一个一维向量，batch_size个获胜神经元的下标    :return:返回值是调整后的W    """    count = 0    while self.iteration > count:      train_X = self.X[np.random.choice(self.X.shape[0], self.batch_size)]      normal_W(self.W)      normal_X(train_X)      train_Y = train_X.dot(self.W)      winner = np.argmax(train_Y, axis=1).tolist()      self.updata_W(train_X, count, winner)      count += 1    return self.W  def train_result(self):    normal_X(self.X)    train_Y = self.X.dot(self.W)    winner = np.argmax(train_Y, axis=1).tolist()    print (winner)    return winnerdef normal_X(X):  """  :param X:二维矩阵，N*D，N个D维的数据  :return: 将X归一化的结果  """  N, D = X.shape  for i in range(N):    temp = np.sum(np.multiply(X[i], X[i]))    X[i] /= np.sqrt(temp)  return Xdef normal_W(W):  """  :param W:二维矩阵，D*(n*m)，D个n*m维的数据  :return: 将W归一化的结果  """  for i in range(W.shape[1]):    temp = np.sum(np.multiply(W[:,i], W[:,i]))    W[:, i] /= np.sqrt(temp)  return W#画图def draw(C):  colValue = ['r', 'y', 'g', 'b', 'c', 'k', 'm']  for i in range(len(C)):    coo_X = []  #x坐标列表    coo_Y = []  #y坐标列表    for j in range(len(C[i])):      coo_X.append(C[i][j][0])      coo_Y.append(C[i][j][1])    pl.scatter(coo_X, coo_Y, marker='x', color=colValue[i%len(colValue)], label=i)  pl.legend(loc='upper right')  pl.show()#数据集：每三个是一组分别是西瓜的编号，密度，含糖量data = """1,0.697,0.46,2,0.774,0.376,3,0.634,0.264,4,0.608,0.318,5,0.556,0.215,6,0.403,0.237,7,0.481,0.149,8,0.437,0.211,9,0.666,0.091,10,0.243,0.267,11,0.245,0.057,12,0.343,0.099,13,0.639,0.161,14,0.657,0.198,15,0.36,0.37,16,0.593,0.042,17,0.719,0.103,18,0.359,0.188,19,0.339,0.241,20,0.282,0.257,21,0.748,0.232,22,0.714,0.346,23,0.483,0.312,24,0.478,0.437,25,0.525,0.369,26,0.751,0.489,27,0.532,0.472,28,0.473,0.376,29,0.725,0.445,30,0.446,0.459"""a = data.split(',')dataset = np.mat([[float(a[i]), float(a[i+1])] for i in range(1, len(a)-1, 3)])dataset_old = dataset.copy()som = SOM(dataset, (5, 5), 1, 30)som.train()res = som.train_result()classify = {}for i, win in enumerate(res):  if not classify.get(win[0]):    classify.setdefault(win[0], [i])  else:    classify[win[0]].append(i)C = []#未归一化的数据分类结果D = []#归一化的数据分类结果for i in classify.values():  C.append(dataset_old[i].tolist())  D.append(dataset[i].tolist())draw(C)draw(D)