177 lines
5.7 KiB
Python
177 lines
5.7 KiB
Python
import math
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import matplotlib.pyplot as plt
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import numpy as np
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MIN_DISTANCE = 0.000001 # mini error
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def load_data(path, feature_num=2):
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"""导入数据
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input: path(string)文件的存储位置
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feature_num(int)特征的维数
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output: data(array)特征
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"""
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f = open(path) # 打开文件
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data = []
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for line in f.readlines():
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lines = line.strip().split("\t")
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data_tmp = []
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if len(lines) != feature_num: # 判断特征的维数是否正确
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continue
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for i in range(feature_num):
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data_tmp.append(float(lines[i]))
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data.append(data_tmp)
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f.close() # 关闭文件
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return data
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def gaussian_kernel(distance, bandwidth):
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"""高斯核函数
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input: distance(mat):欧式距离
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bandwidth(int):核函数的带宽
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output: gaussian_val(mat):高斯函数值
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"""
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m = np.shape(distance)[0] # 样本个数
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right = np.asmatrix(np.zeros((m, 1))) # 声明mx1的矩阵
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for i in range(m):
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right[i, 0] = (-0.5 * distance[i] * distance[i].T) / (bandwidth * bandwidth)
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right[i, 0] = np.exp(right[i, 0])
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left = 1 / (bandwidth * math.sqrt(2 * math.pi))
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gaussian_val = left * right
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return gaussian_val
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def shift_point(point, points, kernel_bandwidth):
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"""计算均值漂移点，对样本点进行漂移
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input: point(mat)需要计算的点
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points(array)所有的样本点
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kernel_bandwidth(int)核函数的带宽s
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output: point_shifted(mat)漂移后的点
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"""
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points = np.asmatrix(points)
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m = np.shape(points)[0] # 样本的个数
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# 计算距离
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point_distances = np.asmatrix(np.zeros((m, 1)))
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for i in range(m):
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point_distances[i, 0] = euclidean_dist(point, points[i])
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# 计算高斯核
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point_weights = gaussian_kernel(point_distances, kernel_bandwidth) # mx1的矩阵
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# 计算分母
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all_sum = 0.0
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for i in range(m):
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all_sum += point_weights[i, 0]
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# 均值偏移
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point_shifted = point_weights.T * points / all_sum
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return point_shifted
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def euclidean_dist(pointA, pointB):
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"""计算欧式距离
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input: pointA(mat):A点的坐标
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pointB(mat):B点的坐标
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output: math.sqrt(total):两点之间的欧式距离
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"""
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# 计算pointA和pointB之间的欧式距离
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total = (pointA - pointB) * (pointA - pointB).T
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return math.sqrt(total) # 欧式距离
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def group_points(mean_shift_points):
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"""计算所属的类别
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input: mean_shift_points(mat):漂移向量
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output: group_assignment(array):所属类别
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"""
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group_assignment = []
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m, n = np.shape(mean_shift_points)
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index = 0
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index_dict = {}
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for i in range(m):
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item = []
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for j in range(n):
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item.append(str(("%5.2f" % mean_shift_points[i, j])))
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item_1 = "_".join(item)
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if item_1 not in index_dict:
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index_dict[item_1] = index
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index += 1
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for i in range(m):
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item = []
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for j in range(n):
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item.append(str(("%5.2f" % mean_shift_points[i, j])))
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item_1 = "_".join(item)
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group_assignment.append(index_dict[item_1])
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return group_assignment
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def train_mean_shift(points, kenel_bandwidth=2):
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"""训练Mean shift模型
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input: points(array):特征数据
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kenel_bandwidth(int):核函数的带宽
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output: points(mat):特征点
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mean_shift_points(mat):均值漂移点
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group(array):类别
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"""
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mean_shift_points = np.asmatrix(points) # 需要聚类的样本点
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max_min_dist = 1
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iteration = 0 # 训练的代数
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m = np.shape(mean_shift_points)[0] # 样本的个数
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need_shift = [True] * m # 标记是否需要漂移
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# 计算均值漂移向量
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while max_min_dist > MIN_DISTANCE:
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max_min_dist = 0
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iteration += 1
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print("\titeration : " + str(iteration))
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for i in range(0, m):
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# 判断每一个样本点是否需要计算偏移均值
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if not need_shift[i]:
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continue
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p_new = mean_shift_points[i]
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p_new_start = p_new
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p_new = shift_point(p_new, points, kenel_bandwidth) # 对样本点进行漂移
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dist = euclidean_dist(p_new, p_new_start) # 计算该点与漂移后的点之间的距离
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if dist > max_min_dist:
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max_min_dist = dist
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if dist < MIN_DISTANCE: # 不需要移动
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need_shift[i] = False
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mean_shift_points[i] = p_new
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# 计算最终的group
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group = group_points(mean_shift_points) # 计算所属的类别
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return np.asmatrix(points), mean_shift_points, group
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def save_result(file_name, data):
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"""保存最终的计算结果
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input: file_name(string):存储的文件名
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data(mat):需要保存的文件
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"""
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f = open(file_name, "w")
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m, n = np.shape(data)
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for i in range(m):
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tmp = []
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for j in range(n):
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tmp.append(str(data[i, j]))
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f.write("\t".join(tmp) + "\n")
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f.close()
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if __name__ == "__main__":
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color = [".r", ".g", ".b", ".y"] # 颜色种类
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# 导入数据集
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print("----------1.load data ------------")
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data = load_data("7/data.txt", 2)
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N = len(data)
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# 训练，h=2
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print("----------2.training ------------")
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points, shift_points, cluster = train_mean_shift(data, 2)
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# 保存所属的类别文件
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save_result("7/center.txt", shift_points)
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data = np.array(data)
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for i in range(N):
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if cluster[i] == 0:
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plt.plot(data[i, 0], data[i, 1], "ro")
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elif cluster[i] == 1:
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plt.plot(data[i, 0], data[i, 1], "go")
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elif cluster[i] == 2:
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plt.plot(data[i, 0], data[i, 1], "bo")
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plt.show()
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