def load_data():
"""加载数据
return: list, bolls
"""
l = []
code = MyCode.get()
codes = [code]
for code in codes:
datas = jbs.getData(code) # from local
upper, middle, lower, df, adx = datas
if len(df) < 100:
continue
for index in range(100, len(df) - jbs.g_scope_len):
l.append(getBolls(index, datas))
return l
def myhclust(datas, indexs):
"""尝试层次聚类, 因为大样本会造成计算速度过慢， 因此不能2000的切片
datas: list or np.ndarray 数据源
indexs: array 索引, 按顺序的索引列表，不支持打乱
return: df 中心点
"""
offset = indexs[0]
#l = load_data()
l = datas
print("num=%d, total_num = %d"%(len(indexs), len(l)))
l = l[indexs[0]:indexs[-1]]
indexs = range(indexs[-1]-indexs[0])
assert(len(indexs) == len(l))
#原始数据集
code = MyCode.get()
tick_period = jbs.g_scope_len
df_datas = jbs.getData(code)[3]
#写入本地img中
fname = 'img_labels/hclust_imgs'
#agl.removeDir(fname)
#agl.createDir(fname)
#imlist = []
##for i in range(len(indexs)):
#for i in indexs:
#fname1 =fname + '/img_%s.png'%(i)
##pl.figure
##draw(g_list[i])
##pl.savefig(fname1)
##pl.close()
#imlist.append(fname1)
#df = pd.DataFrame(g_report)
# 转换一下数据
features = [np.array([i]) for i in indexs]
#features = indexs
#把距离值放入一个id为key的字典
#dict_distance = {}
#for v in g_report:
#ni,nj = v[2:4]
#dict_distance[ni,nj] = (v[0]+v[1])/2
#在100样本下， 使用0.2比较合适
tree , distances = hcluster.hcluster(features, l, distfcn=distfn)
clusters = tree.extract_clusters(0.2 * tree.distance)
center_indexs = [] # 保存中心点
center_indexs_columns = ['datas_index', 'code', 'dt', 'tick_period', 'clust_id', 'label_id', 'label_desc']
print('clusters num=', len(clusters))
def ShowResult():
img_dir = 'html/'
agl.removeDir(img_dir)
for j,c in enumerate(clusters):
elements = c.get_cluster_elements()
nbr_elements = len(elements)
center_index = calc_center(elements, distances) + offset
#添加的字段
dt = df_datas.index[center_index]
clust_id = j
label_id = 0
label_desc = ''
center_indexs.append([center_index, code, dt, tick_period, clust_id, label_id, label_desc])
print(j, center_index, len(elements),elements)
#if nbr_elements >= 6:
#draw_multi(l, j, offset, elements, center_index)
#hcluster.draw_dendrogram(tree,imlist,filename='./sunset.png')
ShowResult()
df = pd.DataFrame(center_indexs, columns=center_indexs_columns)
print(df)
return df
def test():
code = '000001'
data = getData(code)
obj = boll_judge_score(data)
obj._show()