def main2():
np.set_printoptions(suppress=True)
data = tool.my_data("D:/pythonWorkspace/data/iris.csv")[50:150]
dist = tool.cal_dist(np.array(data).reshape(100, 4))
#降维
V = np.real(mds.MDS(dist, 2)).tolist()
new_dist = tool.cal_dist(np.array(V).reshape(100, 2))
E = tool.k_edge(0.5, new_dist)
graph_data = (V, E)
graph = FruchtermannReingold('', graph_data[0], graph_data[1], 30, 5, 0.5,
4, 0.02)
#新的数据
value = graph.draw()
print(value)
def main2():
np.set_printoptions(suppress=True)
data = tool.my_data("D:/pythonWorkspace/data/iris.csv")[50:150]
dist = tool.cal_dist(np.array(data).reshape(100, 4))
#降维
V = np.real(mds.MDS(dist, 2)).tolist()
new_dist = tool.cal_dist(np.array(V).reshape(100, 2))
# print(new_dist[:9:, :9:]-dist[:9:, :9:])
E = tool.k_edge(0.5, new_dist)
graph_data = (V, E)
graph = FruchtermannReingold('', graph_data[0], graph_data[1], 30, 5, 0.5, 4, 0.02)
#新的数据
value = graph.draw().tolist()
# 计算各个特征的边界值
feature1 = [x[0] for x in value]
feature2 = [x[1] for x in value]
feature1_border = km.kmeans_border(km.kmeans_class(np.array(feature1).reshape(-1, 1), 2))
feature2_border = km.kmeans_border(km.kmeans_class(np.array(feature2).reshape(-1, 1), 2))
def fr(data):
size_of_data = len(data)
np.set_printoptions(suppress=True)
new_dist = tool.cal_dist(np.array(data).reshape(size_of_data, 2))
E = tool.k_edge(1, new_dist)
graph_data = (data, E)
graph = my_fr.FruchtermannReingold('', graph_data[0], graph_data[1], 30, 5, 10, 0.4, 0.02)
# 新的数据
value = graph.draw()
return value
def main_df(path, began):
np.set_printoptions(suppress=True)
data = tool.read_KEEL_data(path, began)
#标准化数据
data = tool.unitilize_data(data)
#计算距离矩阵
dist = tool.cal_df_dist(data)
#降维
V = np.real(mds.MDS(dist, 2)).tolist()
new_dist = tool.cal_dist(np.array(V).reshape(100, 2))
# print(new_dist[:9:, :9:]-dist[:9:, :9:])
E = tool.k_edge(0.5, new_dist)
graph_data = (V, E)
graph = FruchtermannReingold('', graph_data[0], graph_data[1], 30, 5, 0.5, 4, 0.02)
#新的数据
value = graph.draw().tolist()
# 计算各个特征的边界值
feature1 = [x[0] for x in value]
feature2 = [x[1] for x in value]
feature1_border = km.kmeans_border(km.kmeans_class(np.array(feature1).reshape(-1, 1), 2))
feature2_border = km.kmeans_border(km.kmeans_class(np.array(feature2).reshape(-1, 1), 2))