我在试着把时间序列聚在一起。我也想使用Sklearn光学。在文档中,它说输入向量X应该有维度(n_samples,n_features)。我的数组位于表单上(n_samples、n_time_stamps、n_features)。代码中的示例。
我的问题是如何利用光学中的拟合函数和时间序列。我知道人们使用光学和DBSCAN的时间序列。我只是不知道他们是怎么实现的。任何帮助都将不胜感激。
[[[t00, x0], [t01, x01], ... [t0_n_timestamps, x0_n_timestamps]],
[[t10, x10], [t11, x11], ... [t1_n_timestamps, x1_n_timestamps]],
.
.
.
[[t_n_samples_0, x_n_samples_0], [[t_n_samples_1, x_n_samples_1], ... [t_n_samples_n_timestamps, x_n_samples_n_timestamps]]]
发布于 2022-04-02 14:40:12
给定以下np.array作为输入:
data = np.array([
[["00:00", 7], ["00:01", 37], ["00:02", 3]],
[["00:00", 27], ["00:01", 137], ["00:02", 33]],
[["00:00", 14], ["00:01", 17], ["00:02", 12]],
[["00:00", 15], ["00:01", 123], ["00:02", 11]],
[["00:00", 16], ["00:01", 12], ["00:02", 92]],
[["00:00", 17], ["00:01", 23], ["00:02", 22]],
[["00:00", 18], ["00:01", 23], ["00:02", 112]],
[["00:00", 100], ["00:01", 200], ["00:02", 301]],
[["00:00", 101], ["00:01", 201], ["00:02", 302]],
[["00:00", 102], ["00:01", 203], ["00:02", 303]],
[["00:00", 104], ["00:01", 207], ["00:02", 304]]])
我将按以下方式进行:
# save shape info in three separate variables
x, y, z = data.shape
# idea from https://stackoverflow.com/a/36235454/5050691
output_arr = np.column_stack((np.repeat(np.arange(x), y), data.reshape(x * y, -1)))
# create a df out of the arr
df = pd.DataFrame(output_arr)
# rename for understandability
df = df.rename(columns={0: 'index', 1: 'time', 2: 'value'})
# Change the orientation between rows and columns so that rows
# that contain time info become columns
df = df.pivot(index="index", columns="time", values="value")
df.rename_axis(None, axis=1).reset_index()
# get columns that refer to specific interval of time series
temporal_accessors = ["00:00", "00:01", "00:02"]
# extract data that will be used to carry out clustering
data_for_clustering = df[temporal_accessors].to_numpy()
# a set of exemplary params
params = {
"xi": 0.05,
"metric": "euclidean",
"min_samples": 3
}
clusterer = OPTICS(**params)
fitted = clusterer.fit(data_for_clustering)
cluster_labels = fitted.labels_
df["cluster"] = cluster_labels
# Note: density based algortihms have a notion of the "noise-cluster", which is marked with
# -1 by sklearn algorithms. That's why starting index is -1 for density based clustering,
# and 0 otherwise.
对于给定的数据和参数的选择,您将得到以下集群:0 0 1 0 0 0 1 1 1
https://stackoverflow.com/questions/70955592
复制相似问题